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Momi Et Al. 2023

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Building patient-personalized brain simulations to improve interpretation of neuroimaging and neurostimulation data


Neuroscience as a field is very good at measuring things and poking things. It has an impressive armoury of experimental techniques using electricity, light, acoustics, biochemistry, and more to measure and modulate neural activity in everything from a blob of cells on a dish to a conscious human in a surgical theatre or an MRI scanner.

What neuroscience is less good at, for the most part, is theory; i.e. understanding the sources of the patterns we see in our measurements, and what is actually happening in the brain when we poke it with electrical stimulation and the like. The sub-field that deals with developing theoretical mechanistic understanding of brain processes is known as computational neuroscience. It does this by building mathematical models that allow us to simulate and study brain activity “in-silico”.

In our research we bring the tools of computational neuroscience to the domains of neuroimaging and brain stimulation. The paradigm we use for this, whole-brain modelling, blends elements from neurophysiology, cognitive neuroscience, neuroanatomy, connectomics, complex networks, dynamical systems, machine learning, and biophysics.

We combine whole-brain computational modelling of network dynamics, oscillations, plasticity, and cognitive function with experimental and data analytic work across multiple neuroimaging and neurostimulation modalities (EEG, MEG, s/d/fMRI, fNIRS, TMS, TES). This unique theoretical-experimental approach is applied to questions on both basic principles of brain organization, as well as how it is affected in neuropsychiatric and neurological disease.

Schematic of whole-brain modelling approach (Griffiths et al. 2022)

Research Areas

The core research activites of the group consist of applying whole-brain modelling techniques to a variety of research questions relating to brain stimulation, brain rhythms, and brain networks. Most projects involve intersections of 2 or 3 of these, and have both basic science and clinical application components, such as therapeutic brain stimulation for depression. We also have a number of additional ongoing projects involving mobile EEG, fNIRS, sleep, neuroinformatics, and computational modelling methodology. See below for more info on each of these, as well as links to relevant publications and reproducible code.

.js-id-primaryarea
All Primary Areas -Additional Projects

Brain Stimulation

Computational and theoretical models of brain dynamics

Brain Networks

Brain Rhythms

Modelling brain damage

Selected Publications

More publications here

A major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. …
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Brain Stimulation

Computational and theoretical models of brain dynamics

Brain Networks

Brain Rhythms

Modelling brain damage

Selected Publications

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A major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. …
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Connectome-based neural mass modelling is the emerging computational neuroscience paradigm for simulating large-scale network dynamics …
Preprint PDF diff --git a/index.json b/index.json index a29a745..4f749d3 100644 --- a/index.json +++ b/index.json @@ -1 +1 @@ -[{"authors":null,"categories":null,"content":"This feature can be used for publishing content such as:\n Project or software documentation Online courses Tutorials The parent folder may be renamed, for example, to docs for project documentation or course for creating an online course.\nTo disable this feature, either delete the parent folder, or set draft = true in the front matter of all its pages.\nAfter renaming or deleting the parent folder, you may wish to update any [[menu.main]] menu links to it in the config.toml.\n","date":1536451200,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1536451200,"objectID":"c3224f3a64174f08aaf31e1f1d16ffd3","permalink":"/tutorial/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/tutorial/","section":"tutorial","summary":"This feature can be used for publishing content such as:\n Project or software documentation Online courses Tutorials The parent folder may be renamed, for example, to docs for project documentation or course for creating an online course.\nTo disable this feature, either delete the parent folder, or set draft = true in the front matter of all its pages.\nAfter renaming or deleting the parent folder, you may wish to update any [[menu.","tags":null,"title":"Overview","type":"docs"},{"authors":null,"categories":null,"content":"","date":1546387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546387200,"objectID":"2b941b45cee10983ad62283cc69ac730","permalink":"/project/primary_areas/brain_stimulation/","publishdate":"2019-01-02T00:00:00Z","relpermalink":"/project/primary_areas/brain_stimulation/","section":"project","summary":"","tags":["primaryarea","brain stimulation"],"title":"Brain Stimulation","type":"project"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"210e4d6ae5ca6a4a87d11dba5e09a084","permalink":"/code_and_software/fooof-unit/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/fooof-unit/","section":"code_and_software","summary":"","tags":["Misc"],"title":"FOOOF-Unit","type":"code_and_software"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"8714aec8034b6f2cc24ab3bc0d897919","permalink":"/code_and_software/griffithsetal2020/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/griffithsetal2020/","section":"code_and_software","summary":"","tags":["Papers"],"title":"Griffiths Et Al. 2020","type":"code_and_software"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"579ab4e7c4fa375bc5a096a18eec3d54","permalink":"/code_and_software/griffithsetal2022/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/griffithsetal2022/","section":"code_and_software","summary":"","tags":["Papers"],"title":"Griffiths Et Al. 2022","type":"code_and_software"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"c0abd358e556a428b94aad6a4a801ca0","permalink":"/code_and_software/haritaetal2022/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/haritaetal2022/","section":"code_and_software","summary":"","tags":["Papers"],"title":"Harita et al. 2022","type":"code_and_software"},{"authors":null,"categories":null,"content":" \n \n ","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"8d421e9c8c9a8076117af72c31e0f8ac","permalink":"/talks_and_teaching/2023-01-07__kcnischool2022_wbm_lectures/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2023-01-07__kcnischool2022_wbm_lectures/","section":"talks_and_teaching","summary":"JG's lectures at the 2021 / 2022 KCNI Summer School","tags":["Pinned","ComputationalNeuroscience","Neuroimaging","Neuroinformatics"],"title":"KCNI School Whole-Brain Modelling Lectures","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":"See here\n","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"d5abf9fd77cadceac0e4d43f39f5aac5","permalink":"/code_and_software/momietal2023/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/momietal2023/","section":"code_and_software","summary":"See here","tags":["Papers","Featured"],"title":"Momi Et Al. 2023","type":"code_and_software"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"3667df62a32dad53417419d4a54ae99f","permalink":"/code_and_software/ohbm_wbm_course/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/ohbm_wbm_course/","section":"code_and_software","summary":"","tags":["Education","Featured"],"title":"OHBM2023 Brain Modelling Course","type":"code_and_software"},{"authors":["Davide Momi","Zheng Wang","John D. Griffiths"],"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"36726f87a94d799bc4c800bd11310873","permalink":"/publication/2023-momi-et-al-tmsevokedresponses/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/publication/2023-momi-et-al-tmsevokedresponses/","section":"publication","summary":"A major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. This question finds sharp relief in the case of TMS-EEG evoked potentials (TEPs). TEPs are spatiotemporal waveform patterns with characteristic inflections at ∼50ms, ∼100ms, and ∼150-200ms following a single TMS pulse that disperse from, and later reconverge to, the primary stimulated regions. What parts of the TEP are due to recurrent activity? And what light might this shed on more general principles of brain organization? We studied this using source-localized TMS-EEG analyses and whole-brain connectome-based computational modelling. Results indicated that recurrent network feedback begins to drive TEP responses from ∼100ms post-stimulation, with earlier TEP components being attributable to local reverberatory activity within the stimulated region. Subject-specific estimation of neurophysiological parameters additionally indicated an important role for inhibitory GABAergic neural populations in scaling cortical excitability levels, as reflected in TEP waveform characteristics.","tags":["brain stimulation","brain networks"],"title":"TMS-Evoked Responses Are Driven by Recurrent Large-Scale Network Dynamics","type":"publication"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"5180828ca91b213cdba831677fe44b1f","permalink":"/code_and_software/whobpyt/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/whobpyt/","section":"code_and_software","summary":"","tags":["Software_Libraries","Featured"],"title":"WhoBPyT","type":"code_and_software"},{"authors":["John D. Griffiths","Zheng Wang","Andrew Clappison","Syed Hussain Ather","Davide Momi","Scott Rich","Andreea Diaconescu","Anthony Randal McIntosh","Kelly Shen"],"categories":null,"content":"","date":1664582400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664582400,"objectID":"8357cf53ba185b16084a55596f8191ac","permalink":"/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/","publishdate":"2022-10-01T00:00:00Z","relpermalink":"/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/","section":"publication","summary":"Connectome-based neural mass modelling is the emerging computational neuroscience paradigm for simulating large-scale network dynamics observed in whole-brain activity measurements such as fMRI, M/EEG, and related techniques. Estimating physiological parameters by fitting these models to empirical data is challenging however, due to large network sizes, often physiologically detailed fast-timescale system equations, and the need for long (e.g. tens of minutes) simulation runs. Here we introduce a novel approach to connectome-based neural mass model parameter estimation by employing optimization tools developed for deep learning. We cast the system of differential equations representing both neural and haemodynamic activity dynamics as a deep neural network, implemented within a widely used machine learning programming environment (PyTorch). This allows us to use robust industry-standard optimization algorithms, automatic differentiation for computation of gradients, and other useful functionality. The approach is demonstrated using a connectome-based network with nodal dynamics specified by the two-state RWW mean-field neural mass model equations, which we use here as a model of fMRI-measured activity and correlation fluctuations. Additional optimization constraints are explored and prove fruitful, including restricting the model to domains of parameter space near a bifurcation point that yield metastable dynamics. Using these techniques, we first show robust recovery of physiological model parameters in synthetic data and then, as a proof-of-principle, apply the framework to modelling of empirical resting-state fMRI data from the Human Connectome Project database. For resting state activity, the system can be understood as a deep net that receives uncorrelated noise on its input layer, which is transformed into network-wide modelled functional connectivity on its output layer. This is consistent with the prevailing conception in theoretical neuroscience of resting-state functional connectivity patterns as an emergent phenomenon that is driven by (effectively) random activity fluctuations, which are then in turn spatiotemporally filtered by anatomical connectivity and local neural dynamics.","tags":null,"title":"Deep-learning based parameter estimation for neurophysiological models of neuroimaging data","type":"publication"},{"authors":["Shreyas Harita","Davide Momi","Frank Mazza","John D. Griffiths"],"categories":null,"content":"","date":1655942400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1655942400,"objectID":"f5ee61730f2093f08827b0bc04e93c43","permalink":"/publication/2022-harita-et-al-mapping-interindividual-fc-tms/","publishdate":"2022-06-23T00:00:00Z","relpermalink":"/publication/2022-harita-et-al-mapping-interindividual-fc-tms/","section":"publication","summary":"Transcranial magnetic stimulation (TMS) is an emerging alternative to existing treatments for major depressive disorder (MDD). The effects of TMS on both brain physiology and therapeutic outcomes are known to be highly variable from subject to subject, however. Proposed reasons for this variability include individual differences in neurophysiology, in cortical geometry, and in brain connectivity. Standard approaches to TMS target site definition tend to focus on coordinates or landmarks within the individual brain regions implicated in MDD, such as the dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC). Additionally considering the network connectivity of these sites (i.e., the wider set of brain regions that may be mono- or poly-synaptically activated by TMS stimulation) has the potential to improve subject-specificity of TMS targeting and, in turn, improve treatment outcomes. In this study, we looked at the functional connectivity (FC) of dlPFC and OFC TMS targets, based on induced electrical field (E-field) maps, estimated using the SimNIBS library. We hypothesized that individual differences in spontaneous functional brain dynamics would contribute more to downstream network engagement than individual differences in cortical geometry (i.e., E-field variability). We generated individualized E-field maps on the cortical surface for 121 subjects (67 female) from the Human Connectome Project database using tetrahedral head models generated from T1- and T2-weighted MR images. F3 and Fp1 electrode positions were used to target the left dlPFC and left OFC, respectively. We analyzed inter-subject variability in the shape and location of these TMS target E-field patterns, their FC, and the major functional networks to which they belong. Our results revealed the key differences in TMS target FC between the dlPFC and OFC, and also how this connectivity varies across subjects. Three major functional networks were targeted across the dlPFC and OFC: the ventral attention, fronto-parietal and default-mode networks in the dlPFC, and the fronto-parietal and default mode networks in the OFC. Inter-subject variability in cortical geometry and in FC was high. Our analyses showed that the use of normative neuroimaging reference data (group-average or representative FC and subject E-field) allows prediction of which networks are targeted, but fails to accurately quantify the relative loading of TMS targeting on each of the principal networks. Our results characterize the FC patterns of canonical therapeutic TMS targets, and the key dimensions of their variability across subjects. The high inter-individual variability in cortical geometry and FC, leading to high variability in distributions of targeted brain networks, may account for the high levels of variability in physiological and therapeutic TMS outcomes. These insights should, we hope, prove useful as part of the broader effort by the psychiatry, neurology, and neuroimaging communities to help improve and refine TMS therapy, through a better understanding of the technology and its neurophysiological effects.","tags":[],"title":"Mapping inter-individual functional connectivity variability in TMS targets for major depressive disorder","type":"publication"},{"authors":["John D. Griffiths","Sorenza Bastiaens","Neda Kaboodvand"],"categories":null,"content":" ","date":1643673600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1643673600,"objectID":"e2da99a312236451b325ce43d7081ea5","permalink":"/publication/2022-griffiths-et-al-wbm-pastpresentfuture/","publishdate":"2022-02-01T00:00:00Z","relpermalink":"/publication/2022-griffiths-et-al-wbm-pastpresentfuture/","section":"publication","summary":"Whole-Brain Modelling is a scientific field with a short history and a long past. Its various disciplinary roots and conceptual ingredients extend back to as early as the 1940s. It was not until the late 2000s, however, that a nascent paradigm emerged in roughly its current form—concurrently, and in many ways joined at the hip, with its sister field of macro-connectomics. This period saw a handful of seminal papers authored by a certain motley crew of notable theoretical and cognitive neuroscientists, which have served to define much of the landscape of whole-brain modelling as it stands at the start of the 2020s. At the same time, the field has over the past decade expanded in a dozen or more fascinating new methodological, theoretical, and clinical directions. In this chapter we offer a potted Past, Present, and Future of whole-brain modelling, noting what we take to be some of its greatest successes, hardest challenges, and most exciting opportunities.","tags":["brain stimulation","brain networks","brain rhythms"],"title":"Whole-Brain Modelling: Past, Present, and Future","type":"publication"},{"authors":["Frank Mazza","John D. Griffiths","Etay Hay"],"categories":null,"content":"","date":1611964800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611964800,"objectID":"de3da3e3e599d8647aff1e32580ffe56","permalink":"/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/","publishdate":"2021-01-30T00:00:00Z","relpermalink":"/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/","section":"publication","summary":"","tags":[],"title":"EEG Biomarkers of reduced inhibition in human cortical microcircuits in depression","type":"publication"},{"authors":null,"categories":null,"content":" \n","date":1611014400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611014400,"objectID":"fd53de43d6d08f9da9141cfe4d4c806c","permalink":"/talks_and_teaching/2021-01-01__nbn_thebraininsideout/","publishdate":"2021-01-19T00:00:00Z","relpermalink":"/talks_and_teaching/2021-01-01__nbn_thebraininsideout/","section":"talks_and_teaching","summary":"JG's New Books in Neuroscience podcast interview with GB in his new book","tags":["Pinned","ComputationalNeuroscience","Podcast"],"title":"Interview w/ Gyorgi Buzsaki on 'The Brain From Inside Out'","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" \n","date":1611014400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611014400,"objectID":"e1bef046ccbda311f3030d0386e1ab8b","permalink":"/talks_and_teaching/2021-01-01__nbn_themindisflat/","publishdate":"2021-01-19T00:00:00Z","relpermalink":"/talks_and_teaching/2021-01-01__nbn_themindisflat/","section":"talks_and_teaching","summary":"JG's New Books in Neuroscience podcast interview with NC in his new book","tags":["Pinned","ComputationalNeuroscience","Philosophy","Podcast"],"title":"Interview w/ Nick Chater on 'The Mind is Flat'","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" \n","date":1611014400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611014400,"objectID":"52f2a2dc4bdf2e5c0a2c456d631061e0","permalink":"/talks_and_teaching/2021-01-01__nbn_electric_brain/","publishdate":"2021-01-19T00:00:00Z","relpermalink":"/talks_and_teaching/2021-01-01__nbn_electric_brain/","section":"talks_and_teaching","summary":"JG's New Books in Neuroscience podcast interview with RDF in his new book","tags":["Pinned","ComputationalNeuroscience","Neuroimaging","Podcast"],"title":"Interview w/ R. Douglas Fields on 'Electric Brain'","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" \n","date":1611014400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611014400,"objectID":"d74c3ea2855f2c2a3368b1c0fc445531","permalink":"/talks_and_teaching/2021-01-01__nbn_neurosciencefiction/","publishdate":"2021-01-19T00:00:00Z","relpermalink":"/talks_and_teaching/2021-01-01__nbn_neurosciencefiction/","section":"talks_and_teaching","summary":"JG's New Books in Neuroscience podcast interview with RQQ in his new book","tags":["Pinned","ComputationalNeuroscience","Philosophy","Podcast"],"title":"Interview w/ Rodrigo Quian Quiroga on 'Neuroscience Fiction'","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" \n","date":1611014400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611014400,"objectID":"ab536001a3fb6a2d4f00fa33e9e62a46","permalink":"/talks_and_teaching/2021-01-01__nbn_teachingneuroscience/","publishdate":"2021-01-19T00:00:00Z","relpermalink":"/talks_and_teaching/2021-01-01__nbn_teachingneuroscience/","section":"talks_and_teaching","summary":"JG's New Books in Neuroscience podcast interview with CH in his new book","tags":["Pinned","ComputationalNeuroscience","Neuroimaging","Podcast"],"title":"Interview. Chris Harris on 'Teaching Neuroscience'","type":"talks_and_teaching"},{"authors":["John D. Griffiths","Anthony R. McIntosh","Jérémie Lefebvre"],"categories":null,"content":"","date":1580342400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1580342400,"objectID":"89f61c64304934bd8e07bcd0840178b6","permalink":"/publication/2020-griffiths-et-al-corticothalamic-model/","publishdate":"2020-01-30T00:00:00Z","relpermalink":"/publication/2020-griffiths-et-al-corticothalamic-model/","section":"publication","summary":"Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.","tags":[],"title":"A connectome-based, corticothalamic model of state- and stimulation-dependent modulation of rhythmic neural activity and connectivity","type":"publication"},{"authors":["Jennifer D Ryan","Kelly Shen","Arber Kacollja","Heather Tian","John D. Griffiths","Randy McIntosh"],"categories":null,"content":"","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1577836800,"objectID":"8f01319b44b630c89b8ffa1c550e0e03","permalink":"/publication/2020-ryan-et-al-modeling/","publishdate":"2020-01-01T00:00:00Z","relpermalink":"/publication/2020-ryan-et-al-modeling/","section":"publication","summary":"","tags":[],"title":"The functional reach of the hippocampal memory system to the oculomotor system","type":"publication"},{"authors":["John"],"categories":null,"content":"Last week we ran another successful brainhack workshop at Ryerson University\u0026rsquo;s Biomedical Zone, as a satellite event for the Canadian Neuroscience Association (CAN-ACN) conference, which was held in Toronto this year.\nI\u0026rsquo;ve written previously about previous Toronto brainhacks and why I like them so much, so I won\u0026rsquo;t repeat all of that here. But here\u0026rsquo;s a very brief recap:\nThis figure from Craddock et al. 2016 conveys the key ideas nicely:\nIn short, the purpose of brainhacks is to help people collaborate (locally, and also \u0026lsquo;virtually\u0026rsquo; with people at other sites) on both new and established projects ( -\u0026gt; \u0026lsquo;hackathon\u0026rsquo; ); to learn new technical skills ( -\u0026gt; \u0026lsquo;educational\u0026rsquo;); and to communicate with people in their field(s), by hearing about people\u0026rsquo;s research, general questions, miscellaneous interesting ideas, etc. ( -\u0026gt; \u0026lsquo;unconference\u0026rsquo; ). Also I would like to highlight perhaps the most important intersection in this figure ( -\u0026gt; \u0026lsquo;meet and greet\u0026rsquo;).\nLast week\u0026rsquo;s event was our third Toronto Brainhack. As far as we are able to discern the event was a roaring success, with over 80 attendees from all over Canada, and glowing reports all round. Hats off to the BrainhackTO working group, who put several months of moderately hard work into making last week\u0026rsquo;s workshop happen. As always (well, since the first BrainhackTO in 2016) the charge was led by the working group\u0026rsquo;s benevolent dictator - breaker of bugs, queen of the container and mother of surfaces, Dr. Erin Dickie. The very strong supporting cast included Gabi Herman, Jerry Jaychandra, Natalie Ford, Chris Hammill, Nathan Chan, Derek Beaton, Michael Joseph, Amanda Aesson, Shreejoy Tripathy, Ben Darwin, and Myself.\nThe #BrainHackTO organizers are working hard to organize the best educational opportunities (and snacks) for the big day. Sign up now! https://t.co/eldg7Dulut pic.twitter.com/Jpu3tFGeFI\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 3, 2019 The working group\u0026rsquo;s work included the usual essentials of securing funding/sponsors (thank you OBI, InteraXon, Cognixion), stash (!), venues, food, attendance, advertising, social media, neurotech gear for hacking, etc (thank you @choosemuse).\nPlenty of @ChooseMuse EEGs for @BrainHackTO hackers to go crazy with next week 😎 https://t.co/k9L0DNjpae pic.twitter.com/UVf1cZEcTz\n\u0026mdash; John Griffiths (@neurodidact) May 17, 2019 Here\u0026rsquo;s what we did. The full program is shown on Jerry\u0026rsquo;s snazzy event website. The workshop opened up with \u0026lsquo;ignite\u0026rsquo; talks by Amanpreet Badwhar (McGill) and Paul Pavlidis (UBC). This was followed by project pitches, where around 8 individuals proposed hacking/research projects to work on for the next 2 days.\nOpening remarks from @ErinWDickie! There are so many exciting #BrainHackTO workshops ahead. pic.twitter.com/briiVmmc6i\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 21, 2019 Projects included: multivariate statistical analysis and visualiation in R,\nJenny Rieck\u0026#39;s team applies various norming methods in an interactive Shiny app. They used a practise dataset for now, but intend to use it to explore brain connectivity. #BrainHackTO pic.twitter.com/gkkkHPMset\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 22, 2019 Human-mouse gene expression analysis\nAmazing work from team Human2Mouse - entering an MNI coordinate finds the nearest (human) location with gene expression data, the finds a corresponding mouse brain coordinate. #BrainHackTO pic.twitter.com/ntbxGnKWBO\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 22, 2019 computing and analyzing resting state and visually evoked brain responses with EEG,\nWithin only a short time, Team EEG (led by @neurodidact and @AmandaKEasson) acquired hours of data with a @ChooseMuse headset. Then they started to analyze it! A great learning project at #BrainHackTO pic.twitter.com/9CHiSDisW6\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 22, 2019 and MRI surface-based analysis for the hippocampus.\nTo wrap things up, we also gave out two prizes of a muse portable EEG headband, generously donated by InteraXon.\nCongratulations @Sridevi292 on your @ChooseMuse headset!!!! Get in touch with our team to claim your prize. pic.twitter.com/rihR49IrTa\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 30, 2019 As always, the awesome scientific discussions and hack expertise exchanges continued into the late hours with a well-chosen change of location at the splendid Duke\u0026rsquo;s Refresher bar on Gerrard St.\nHuge thanks and congrats again to the rest of the working group for a successful event. Next one is expected to be late fall 2019. If you\u0026rsquo;d like to learn more and/or get involved, do get in touch!\n","date":1559520000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1559520000,"objectID":"0de7bffcbdfa921c95c2f670f8557be9","permalink":"/post/brainhack_can/","publishdate":"2019-06-03T00:00:00Z","relpermalink":"/post/brainhack_can/","section":"post","summary":"Brainhack CAN Toronto","tags":null,"title":"BrainHack CAN Toronto","type":"post"},{"authors":["John"],"categories":null,"content":"Last week I spent a few days in Princeton at a small invited workshop aimed at developing a BIDS specification for computational models. Organizer Russ Poldrack succeeded in securing a very smart and capable list of attendees, including Petra Ritter, Tal Yarkoni, Randy McIntosh, Brad Love, Jonathan Cohen, Marmaduke Woodman, Tim Rogers, Rosalyn Moran, Chris Markiewicz, Padraig Gleeson, and many others.\nThe basic objectives were twofold. First, to identify the types of neuroimaging data commonly used in computational modelling of various flavours, and start work in developing a common vocabulary and file/folder nomenclature for them. This was largely based on several of the existing BIDS derivatives specs, such as connectivity, electrophysiology, bold fmri, etc. The second aim of the discussions was a little more ambitious - namely to find a generic model description that encompasses as many currently used computational modelling approaches as possible.\nIt was a fairly productive few days, with concrete products including a 15 page BIDS extension proposal, outline BIDS apps for neural mass modelling with The Virtual Brain and Dynamic Causal Modelling, and for Bayesian visual fMRI DDM models.\nMost of the work for these projects is of course still ahead, but all in all I think this was an excellent start. Hats off to Russ et al for and pulling together the contributors and pushing forward these important developments in the neuroinformatics of neuroimaging and computational neuroscience.\n","date":1554681600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1554681600,"objectID":"ffdae5b58b940b20942c0bca0821c15c","permalink":"/post/bids_compmodels_mtg/","publishdate":"2019-04-08T00:00:00Z","relpermalink":"/post/bids_compmodels_mtg/","section":"post","summary":"BIDS-Compmodels worshop - Princeton","tags":null,"title":"Towards standardization of computational models in neuroimaging","type":"post"},{"authors":["John D. Griffiths","Jérémie Lefebvre"],"categories":null,"content":"","date":1548806400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1548806400,"objectID":"341d59502de2a11b21727bd5bd08bacf","permalink":"/publication/2019-griffiths-et-al-shapingbrainrhythms/","publishdate":"2019-01-30T00:00:00Z","relpermalink":"/publication/2019-griffiths-et-al-shapingbrainrhythms/","section":"publication","summary":"Rhythmic, collective activity is a fundamental feature of neural systems. As a result of this, many of the challenges and opportunities involved in developing clinical tools from basic neuroscience knowledge come down to questions about control of dynamic, oscillatory networks. In this chapter we review a range of experimental and theoretical work on control of neural oscillations, in healthy brains and in relation to various clinical conditions. We highlight the main types of qualitative system behaviour that can result from application of periodic stimulation and present a simple case study on this using a mathematical model of rhythmogenesis in thalamocortical circuits. The concepts discussed here may, we hope, help provide some guidelines and principles for the development of future generations of more physiologically and dynamically informed brain stimulation techniques, paradigms,and researchers.","tags":[],"title":"Shaping brain rhythms: dynamic and control-theoretic perspectives on periodic brain stimulation for treatment of neurological disorders","type":"publication"},{"authors":["John"],"categories":null,"content":"After several months\u0026rsquo; lead-in and build-up, I\u0026rsquo;m delighted to announce that the KCNI WBMG / GriffLab is now officially \u0026lsquo;open for business\u0026rsquo;. Recruitment is now getting underway for post-docs, graduate students, and undergrads (see here for more details). This ramp-up phase is being done in parallel with the KCNI computational genomics (triplab) and microcircuit modelling (haylab) groups, and there will be a lots of ideas, skills, and research exchange between our three teams in the coming months and years. A big buzz right now amongst the various KCNI teams (which currently is numbering around 20 people, and will probably double in the next year or two) is our transition in a few weeks over to the fancy new open-plan space on the top floor of 250 college street. In addition to the stunning view of Toronto this will feature a lot of cool tech, and a very modern \u0026lsquo;team-science\u0026rsquo; office culture. I\u0026rsquo;ll be writing more about this here soon. Apart from the inevitable admin barrage of starting a new job, there\u0026rsquo;s plenty of exciting things happening at the moment. I outlined some of the plans and progress to date for the KCNI WBMG in my talk at the CAMH Campbell symposium 2 weeks ago. Much of this is given in summary form in the sections of this site, and will of course be expanded on in due course. With my colleagues at NeurotechTO we are co-organizing a workshop in March with OBI and INCF, aimed at developing exchange between academic research and private companies in the neurotech sector. With KCNI colleagues we are organizing a BrainHack in April as a satellite event ahead of the Canadian Neuroscience Association annual meeting, which is in Toronto this year. A few invitations have come in to exciting meetings on neuroinformatics and computational neuroscience later in the year in cool places like Princeton and Barcelona, which I\u0026rsquo;m sure will be very productive. Again, watch this space for updates and summaries of these and other happenings ;) ","date":1548720000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1548720000,"objectID":"f79c2ee477bf45e3f48d1ecf6afff2a0","permalink":"/post/grifflab_opens/","publishdate":"2019-01-29T00:00:00Z","relpermalink":"/post/grifflab_opens/","section":"post","summary":"Exciting times. ","tags":null,"title":"GriffLab opens for business","type":"post"},{"authors":null,"categories":null,"content":"The Virtual Brain (TVB) is a modelling and neuroinformatics platform for simulating meso-scale whole-brain neural activity. TVB is the brainchild of our colleagues Randy McIntosh Viktor Jirsa, and is one of the principal tools we use in our computational modelling research work.\nAs members of the \u0026lsquo;extended\u0026rsquo; TVB team, we provide technical support on the Forum, teach regularly at workshops, and develop new technical features and applications.\nNewbies to TVB: check out the main website and the github organization; and particularly the jupyter notebook demos and tutorials. There are also a variety of handy nuggets from us in the TVB entries on the JDG LabNotebook.\nAnd of course everyone should take a minute to watch the awesome TVB short movie\u0026hellip;\n ","date":1546560000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546560000,"objectID":"f636914b1ca3394abbd355173a3d3761","permalink":"/code_and_software/tvb/","publishdate":"2019-01-04T00:00:00Z","relpermalink":"/code_and_software/tvb/","section":"code_and_software","summary":"The Virtual Brain (TVB) is a modelling and neuroinformatics platform for simulating meso-scale whole-brain neural activity. TVB is the brainchild of our colleagues Randy McIntosh Viktor Jirsa, and is one of the principal tools we use in our computational modelling research work.\nAs members of the \u0026lsquo;extended\u0026rsquo; TVB team, we provide technical support on the Forum, teach regularly at workshops, and develop new technical features and applications.\nNewbies to TVB: check out the main website and the github organization; and particularly the jupyter notebook demos and tutorials.","tags":["Software_Libraries"],"title":"The Virtual Brain (TVB) Platform","type":"code_and_software"},{"authors":null,"categories":null,"content":"eeg-notebooks is a python-based software library that provides a simple end-to-end platform for conducting cognitive neuroscience experiments using consumer-grade EEG technology. It emerged out of the close relationship between our good friends at NeuroTechX and other other good friends at InterAxon (just down the road from CAMH). Using eeg-notebooks together with a standard laptop computer and ~$200 EEG headset such as the Interaxon MUSE, one can conduct high-quality event-related potential (ERP) experiments that traditionally require research-grade equipment costing tens of thousands of dollars. This opens up the door to a myriad of exciting neuroscience research and education possibilities.\n(1/2) Special feature at this year\u0026#39;s #brainhack global Toronto: \u0026#39;MUSE corner\u0026#39;, where we be piloting #NeuroBRITE EEG experiments on willing attendees in ~20 min slots...\n\u0026mdash; John Griffiths (@neurodidact) April 23, 2018 One of these, which we have been closely involved in, is using eeg-notebooks as a vehicle for STEM (Science, Technology, Engineering, Mathematics) education in high-schools. This has been spearheaded in Toronto by the Rotman-Baycrest based initiative NeuroBRITE, and in New York by the brainwaves project.\n","date":1546560000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546560000,"objectID":"32fc5172f39969c51ba46305bdf5b176","permalink":"/code_and_software/eeg-notebooks/","publishdate":"2019-01-04T00:00:00Z","relpermalink":"/code_and_software/eeg-notebooks/","section":"code_and_software","summary":"eeg-notebooks is a python-based software library that provides a simple end-to-end platform for conducting cognitive neuroscience experiments using consumer-grade EEG technology. It emerged out of the close relationship between our good friends at NeuroTechX and other other good friends at InterAxon (just down the road from CAMH). Using eeg-notebooks together with a standard laptop computer and ~$200 EEG headset such as the Interaxon MUSE, one can conduct high-quality event-related potential (ERP) experiments that traditionally require research-grade equipment costing tens of thousands of dollars.","tags":["Software_Libraries","Featured"],"title":"eeg-notebooks","type":"code_and_software"},{"authors":null,"categories":null,"content":"A main overarching theme of most of the work we do in the WBMG is meso/macroscopic computational models of neural dynamics. We use mathematical models describing neural population activity on the scale of millimetres to centimetres, together with network structure from macaque histology or diffusion MRI fibre tracking, to simulate (approximately) whole-brain activity measurements of the kind measured by fMRI and M/EEG. These techniques are gradually allowing us to obtain more mechanistic insights into observed patterns in neuroimaging data relating to the resting state, oscillations, and the impact of neurological and neurodegenerative disease. Much of our work in this area is done collaboratively with colleagues in the Virtual Brain, Human Brain, and Blue Brain Projects.\nA specific area on which we are focusing is the generation and modulation of fast-timescale brain rhythms, as measured noninvasively by MEG and EEG.\nTogether with Dr. Jeremie Lefebvre of the Sync Lab at the \u0026lsquo;other\u0026rsquo; Krembil Research Institute (UHN Toronto Western Hospital), we have studied how low-frequency rhythms generated by delayed recurrent inhibition in thalamocortical loops interact with higher-frequency rhythms generated by intracolumnar excitatory-inhibitory interactions.\nWe find that a thalamocortical loop motif consisting of excitatory and inhibitory cortical neuronal populations, and excitatory (relay) and inhibitory (reticular) thalamic nuclei, can reproduce a large amount of the empirical pheonomena measured with resting state M/EEG. We are now investigating how the topological structure of the cortex interacts with the rhythmogenic properties of this base circuit motif.\nA different line of work, done together with Prof. Peter Robinson and colleagues at the University of Sydney, focuses on macroscopic neural field models for large-scale brain dynamics. These are generally formulated in terms of second-order partial differential (wave) equations that are substantially simpler to work with analytically than the stochastic delay-differential equation numerically integrated in TVB simulations. In particular, we have studied the role of the spherical topology and physical embedding of the cerebral cortex on anatomical structure and dynamics.\nThis, together with the steady-state eigenmode solutions to the Robinson neural field equations, predicts the presence of spherical harmonic-like patterns in fast-timescale activity measurements. Consistent with this, we have recently shown that this spherical harmonic-like spatial eigenmode structure is observed in cortical surface Laplacians, anatomical connectivity (tractography) networks, (MEG) functional connectivity networks, and numerical simulations. These results provide new insight into the role of the network topology and geometric embedding of the cerebral hemispheres in shaping brain dynamics, as well as the theoretical and practical utility of macroscopic neural field models with spherical boundary conditions.\n","date":1546473600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546473600,"objectID":"526d41857bbcfbc10b5139b48d5bb459","permalink":"/project/secondary_areas/models_of_brain_dynamics/","publishdate":"2019-01-03T00:00:00Z","relpermalink":"/project/secondary_areas/models_of_brain_dynamics/","section":"project","summary":"A main overarching theme of most of the work we do in the WBMG is meso/macroscopic computational models of neural dynamics. We use mathematical models describing neural population activity on the scale of millimetres to centimetres, together with network structure from macaque histology or diffusion MRI fibre tracking, to simulate (approximately) whole-brain activity measurements of the kind measured by fMRI and M/EEG. These techniques are gradually allowing us to obtain more mechanistic insights into observed patterns in neuroimaging data relating to the resting state, oscillations, and the impact of neurological and neurodegenerative disease.","tags":["modelling","theory"],"title":"Computational and theoretical models of brain dynamics","type":"project"},{"authors":null,"categories":null,"content":"ConWhAt (for Connectome-based White matter Atlas Analyses) is a tool for studying the effects of white matter damage on brain networks.\nIts primary functionality is connectome-based white matter atlas analysis, for which it uses a set of connectome-based white matter atlases developed by our research group for this purpose, as well as (optionally) similar atlases developed by other groups. ConWhAt can also be used for more conventional tract-based white matter atlas analysis, and more generally for querying how spatial locations in the white matter tissue relate to different grey matter regions and brain networks.\nOur main goal in developing ConWhAt is to support patient-specific neurocomputational modelling using The Virtual Brain (TVB) modelling and neuroinformatics platform. Examples of how to set up TVB models from ConWhAt analysis outputs are given in the documentation.\n","date":1546473600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546473600,"objectID":"9f20a26285b8755c8c86c64e445c30cb","permalink":"/code_and_software/conwhat/","publishdate":"2019-01-03T00:00:00Z","relpermalink":"/code_and_software/conwhat/","section":"code_and_software","summary":"ConWhAt (for Connectome-based White matter Atlas Analyses) is a tool for studying the effects of white matter damage on brain networks.\nIts primary functionality is connectome-based white matter atlas analysis, for which it uses a set of connectome-based white matter atlases developed by our research group for this purpose, as well as (optionally) similar atlases developed by other groups. ConWhAt can also be used for more conventional tract-based white matter atlas analysis, and more generally for querying how spatial locations in the white matter tissue relate to different grey matter regions and brain networks.","tags":["Software_Libraries"],"title":"ConWhAt","type":"code_and_software"},{"authors":null,"categories":null,"content":"","date":1546387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546387200,"objectID":"41b5718717e6104cec8842a539a73a13","permalink":"/project/primary_areas/brain_networks/","publishdate":"2019-01-02T00:00:00Z","relpermalink":"/project/primary_areas/brain_networks/","section":"project","summary":"","tags":["primaryarea"],"title":"Brain Networks","type":"project"},{"authors":null,"categories":null,"content":"","date":1546387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546387200,"objectID":"ed1f8ad962211225b62c450c55dc3a36","permalink":"/project/primary_areas/brain_rhythms/","publishdate":"2019-01-02T00:00:00Z","relpermalink":"/project/primary_areas/brain_rhythms/","section":"project","summary":"","tags":["primaryarea","brain rhythms"],"title":"Brain Rhythms","type":"project"},{"authors":null,"categories":null,"content":"Link here\nThe JDG LabNotebook is an online, digital lab notebook that has been used over the years as part technical blog, part project management tool, and part collaboration facilitator. Entries are generally a mix of text, code, and rich media.It has been used for both general notes and for research project documentation and collaboration, and so the site contains both open-access and password-protected + encrypted entries. Use the tags on the right hand side to list open access entries; all are welcome to peruse these and use any code goodies they find useful. If you are interested in seeing the content of any password-protected entries, get in touch.\nNote that a re-vamped version of the LabNotebook is currently underway, as a lot has happened in the jupyter, open science, and open publishing world since the LabNotebook and ipynb-workdocs (the library used to generate entries from jupyter notebooks using cell tag filtering, AWS S3 bucket manipulation through boto, reveal.js, and various other jazzy bits and bobs) were first put together. The content of the entries is entirely independent of this however, and I\u0026rsquo;m sure remain useful and relevant for a long time to come.\n","date":1546387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546387200,"objectID":"7260d0282f65e205069ea0e674680c40","permalink":"/code_and_software/jdg_lab_notebook/","publishdate":"2019-01-02T00:00:00Z","relpermalink":"/code_and_software/jdg_lab_notebook/","section":"code_and_software","summary":"Link here\nThe JDG LabNotebook is an online, digital lab notebook that has been used over the years as part technical blog, part project management tool, and part collaboration facilitator. Entries are generally a mix of text, code, and rich media.It has been used for both general notes and for research project documentation and collaboration, and so the site contains both open-access and password-protected + encrypted entries. Use the tags on the right hand side to list open access entries; all are welcome to peruse these and use any code goodies they find useful.","tags":["Education"],"title":"JDG Lab Notebook","type":"code_and_software"},{"authors":null,"categories":null,"content":"The effects of white matter damage on cognitive and neural function is a subject of major interest for the group. Earlier work studied how different spatial profiles of damage following stroke resulted in differential types of linguistic impairments. A large part of our current work asks similar questions from a more systems level perspective: what is the impact of a given spatial profile of damage on the topological structure and dynamic behaviour in connectome-based neural mass models?\n\nIn order to help address questions like this, we are developing a new software library, ConWhAt, for \u0026lsquo;connectome-based white matter atlas analysis\u0026rsquo;. This allows the construction of \u0026lsquo;damaged\u0026rsquo; brain networks for a given spatial profile of damage, obviating the need to run complicated tractography analyses in individual patients.\n\nTogether with Jed Meltzer\u0026rsquo;s team from the Rotman Research Institute at Baycrest hospital, we are also beginning to investigate physiological changes in perilesional tissue following stroke, and how brain stimulation (informed by computational modelling) can be used to target and renormalize abberant activity in these regions.\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"9c57c1ef5b6e0d8199cb2ba5e64e7a2b","permalink":"/project/secondary_areas/brain_damage/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/project/secondary_areas/brain_damage/","section":"project","summary":"The effects of white matter damage on cognitive and neural function is a subject of major interest for the group. Earlier work studied how different spatial profiles of damage following stroke resulted in differential types of linguistic impairments. A large part of our current work asks similar questions from a more systems level perspective: what is the impact of a given spatial profile of damage on the topological structure and dynamic behaviour in connectome-based neural mass models?","tags":["modelling"],"title":"Modelling brain damage","type":"project"},{"authors":null,"categories":null,"content":" In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 \u0026hellip;\nTip 2 \u0026hellip;\n","date":1536451200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1536451200,"objectID":"6a451186c775f5f0adb3a0416d0cb711","permalink":"/tutorial/example/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/tutorial/example/","section":"tutorial","summary":"In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 \u0026hellip;\nTip 2 \u0026hellip;","tags":null,"title":"Example Page","type":"docs"},{"authors":["Seong Hyun Park","John D. Griffiths","André Longtin","Jérémie Lefebvre"],"categories":null,"content":"","date":1534982400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1534982400,"objectID":"1311e5db3f431b4a802635a0de0b220c","permalink":"/publication/2018-park-et-al-persistententrainment/","publishdate":"2018-08-23T00:00:00Z","relpermalink":"/publication/2018-park-et-al-persistententrainment/","section":"publication","summary":"We investigate the dynamics of a non-linear network with noise, periodic forcing and delayed feedback. Our model reveals that there exist forcing regimes—called persistent entrainment regimes—in which the system displays oscillatory responses that outlast the termination of the forcing. Our analysis shows that in presence of delays, periodic forcing can selectively excite components of an infinite reservoir of intrinsic modes and hence display a wide range of damped frequencies. Mean-field and linear stability analysis allows a characterization of the magnitude and duration of these persistent oscillations, as well as their dependence on noise intensity and time delay. These results provide new perspectives on the control of non-linear delayed system using periodic forcing.","tags":[],"title":"Persistent entrainment in nonlinear neural networks with noise","type":"publication"},{"authors":null,"categories":null,"content":" ","date":1517443200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1517443200,"objectID":"611c348d10b3efc733190c163bfd8760","permalink":"/talks_and_teaching/2018-03-01__reproducible_research_workflows/","publishdate":"2018-02-01T00:00:00Z","relpermalink":"/talks_and_teaching/2018-03-01__reproducible_research_workflows/","section":"talks_and_teaching","summary":"Hands-on tutorial talk at University of Toronto","tags":["OpenScience"],"title":"Reproducible Research Workflows and Hosting a Technical Blog","type":"talks_and_teaching"},{"authors":["Axel Hutt","John D. Griffiths","Christoph S Herrmann","Jereremie Lefebvre"],"categories":null,"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"b3019fed06d57b6bc003375383e12a0a","permalink":"/publication/2018-hutt-et-al-effectofstimwaveform/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/2018-hutt-et-al-effectofstimwaveform/","section":"publication","summary":"","tags":[],"title":"Effect of stimulation waveform on the non-linear entrainment of cortical alpha oscillations","type":"publication"},{"authors":["Joelle Zimmerman","John D. Griffiths","Michael Schirner","Petra Ritter","Anthony R. McIntosh"],"categories":null,"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"0584fd766f0fbeb2796e95e5f2b8cf8c","permalink":"/publication/2018-zimmerman-et-al-subject-specificity/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/2018-zimmerman-et-al-subject-specificity/","section":"publication","summary":"","tags":[],"title":"Subject-specificity of the correlation between large-scale structural and functional connectivity","type":"publication"},{"authors":["Jennifer D Ryan","Kelly Shen","Arber Kacollja","Heather Tian","John D. Griffiths","Randy McIntosh"],"categories":null,"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"3ca8dc458f514f1d9849a53935221edc","permalink":"/publication/2018-ryan-et-al-functionalorganization/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/2018-ryan-et-al-functionalorganization/","section":"publication","summary":"","tags":[],"title":"The functional reach of the hippocampal memory system to the oculomotor system","type":"publication"},{"authors":null,"categories":null,"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"d3ce0694f66f0c9a37f69df83db7d3c3","permalink":"/talks_and_teaching/2018-01-01__using_hcp_data/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2018-01-01__using_hcp_data/","section":"talks_and_teaching","summary":"Tutorial session given at Rotman Research Institute, Baycrest (jointly given with Dr. Erin Dickie)","tags":["Neuroimaging"],"title":"Using human connectome project (HCP) data","type":"talks_and_teaching"},{"authors":[],"categories":null,"content":"","date":1512086400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1512086400,"objectID":"2f6fa7c950391b9a35023c3698dafd77","permalink":"/talks_and_teaching/2017-12-01__spherical_harmonics/","publishdate":"2017-12-01T00:00:00Z","relpermalink":"/talks_and_teaching/2017-12-01__spherical_harmonics/","section":"talks_and_teaching","summary":"Invited talk at the 'Large scale trends in cortical organization' workshop, Leipzig","tags":["Modelling"],"title":"The spherical harmonic structure of the human connectome","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":"","date":1491004800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1491004800,"objectID":"00547f21e2715025f6276d6a17c0cb16","permalink":"/talks_and_teaching/2017-04-01__modelling_brain_dynamics_at_rest/","publishdate":"2017-04-01T00:00:00Z","relpermalink":"/talks_and_teaching/2017-04-01__modelling_brain_dynamics_at_rest/","section":"talks_and_teaching","summary":"Guest (inaugral) lecture given for the Krembil Computational Neuroscience (KCN) Events series","tags":["Modelling"],"title":"Modelling brain dynamics at rest: practical tools and theoretical perspectives","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" ","date":1488326400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1488326400,"objectID":"5be8d17e26288cca1da4ac4f74b3c961","permalink":"/talks_and_teaching/2017-03-01__intro_to_neuroimaging/","publishdate":"2017-03-01T00:00:00Z","relpermalink":"/talks_and_teaching/2017-03-01__intro_to_neuroimaging/","section":"talks_and_teaching","summary":"First of the two lectures given at the Banff International Research Station (BIRS) workshop on Topological Methods in Brain Network Analysis","tags":["Pinned","Neuroimaging"],"title":"Intro to Neuroimaging for Algebraic Topologists","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" ","date":1488326400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1488326400,"objectID":"081a7374d618e0b685625e65e222f637","permalink":"/talks_and_teaching/2017-03-01__working_with_neuro_data/","publishdate":"2017-03-01T00:00:00Z","relpermalink":"/talks_and_teaching/2017-03-01__working_with_neuro_data/","section":"talks_and_teaching","summary":"Second of the two lectures given at the Banff International Research Station (BIRS) workshop on Topological Methods in Brain Network Analysis","tags":["Pinned","Neuroimaging"],"title":"Working with Neuroimaging data (for Algebraic Topologists)","type":"talks_and_teaching"},{"authors":["Robinson, Peter A","Zhao, Xuelong","Aquino, Kevin M","Griffiths, John D","Sarkar, Somwrita","Mehta-Pandejee, Grishma"],"categories":null,"content":"","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"68e82f6b8eac82b63bbcc54515efaf55","permalink":"/publication/2016-robinson-et-al-eigenmodestructure/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/2016-robinson-et-al-eigenmodestructure/","section":"publication","summary":"","tags":[],"title":"Eigenmodes of brain activity: Neural field theory predictions and comparison with experiment","type":"publication"},{"authors":["John D. Griffiths"],"categories":null,"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1420070400,"objectID":"017edc06e0107dc1fbbfb38c84137765","permalink":"/publication/2015-griffiths-causal/","publishdate":"2015-01-01T00:00:00Z","relpermalink":"/publication/2015-griffiths-causal/","section":"publication","summary":"","tags":[],"title":"Causal influence in neural systems: reconciling mechanistic-reductionist and statistical perspectives","type":"publication"},{"authors":["John D. Griffiths","William D. Marslen-Wilson","Emmanuel A. Stamatakis","Lorraine K. Tyler"],"categories":null,"content":"","date":1356998400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1356998400,"objectID":"8d1b249f9abf807f381c848e9a8f5728","permalink":"/publication/2013-grifffiths-et-al-functionalorganizationoflanguage/","publishdate":"2013-01-01T00:00:00Z","relpermalink":"/publication/2013-grifffiths-et-al-functionalorganizationoflanguage/","section":"publication","summary":"","tags":[],"title":"Functional organization of the neural language system: dorsal and ventral pathways are critical for syntax","type":"publication"},{"authors":["Marina Papoutsi","Emmanuel A. Stamtakis","John D. Griffiths","William D. Marslen-Wilson","Lorraine K. Tyler"],"categories":null,"content":"","date":1293840000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1293840000,"objectID":"71450039c0c7848c9f7bb23a2e0b33e1","permalink":"/publication/2011-paoutsi-et-al-istheleftdlpfc/","publishdate":"2011-01-01T00:00:00Z","relpermalink":"/publication/2011-paoutsi-et-al-istheleftdlpfc/","section":"publication","summary":"","tags":[],"title":"Is left fronto-temporal connectivity essential for syntax? Effective connectivity, tractography and performance in left-hemisphere damaged patients.","type":"publication"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"cc5853c14d94f3ab99a0ac7568930252","permalink":"/publication/2024-cabral-and-griffiths-book-chapter/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/publication/2024-cabral-and-griffiths-book-chapter/","section":"publication","summary":"","tags":null,"title":"","type":"publication"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"8ffc3b5ee68f1940aa3951de66424d5d","permalink":"/talks_and_teaching/2017-01-01__analysis_of_fmri_data/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2017-01-01__analysis_of_fmri_data/","section":"talks_and_teaching","summary":"Lecture given by JG at the Canadian Association for Neuroscience Meeting Satellite workshop: *Neural signal and image processing: quantitative analysis of neural activity*","tags":["Neuroimaging"],"title":"Analysis of fMRI data: principles \u0026 techniques","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"804d49a4bd3b74834221a542cd15ec61","permalink":"/talks_and_teaching/2018-02-01__intro_to_dwi/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2018-02-01__intro_to_dwi/","section":"talks_and_teaching","summary":"Workshop given by JG at Rotman Research Institute","tags":["Neuroimaging"],"title":"Introduction to diffusion-weighted MRI","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" Lab Members Dr. John David Griffiths Group Lead\n CV\nDr. John D. Griffiths is a cognitive and computational neuroscientist based at the Toronto Centre for Addiction and Mental Health (CAMH)’s Krembil Centre for Neuroinformatics (KCNI; krembilneuroinformatics.ca), where he leads a research team focused on computational modelling of brain network dynamics, neurostimulation, and multimodal neuroimaging data (www.grifflab.com). He is an Assistant Professor in the University of Toronto Departments of Psychiatry, Medical Sciences, and Biomedical Engineering. Originally hailing from North-West England, Dr. Griffiths completed his PhD at the University of Cambridge, where he worked on structural and functional neuroimaging of language comprehension and its impairment in ageing and stroke. Prior to this, he completed his undergraduate training in psychology, philosophy, and cognitive neuroscience at the Universities of Warwick and York, UK. Subsequently, he worked as a post-doctoral fellow in the groups of Peter Robinson (University of Sydney School of Physics), Randy McIntosh (Rotman Research Institute) and Jeremie Lefebvre (Toronto Western Hospital). During this time he conducted research and developed core expertise in multimodal neuroimaging and macro connectomics, neurophysiological modelling and numerical simulations, neural field theory, and noninvasive neuromodulation. He also was an early contributor to the The Virtual Brain (TVB) software library and research consortium. Dr. Griffiths joined the CAMH KCNI as a PI and head of Whole-Brain Modelling in 2019, where he leads a multidisciplinary team of engineers, physicists, mathematicians, biologists, and psychologists, working on endogenous oscillations and neurophysiology of noninvasive brain stimulation. The theory-based work of the group is complemented by novel EEG, fNIRS, TMS, and MRI experiments in healthy and patient populations.\nDr. Davide Momi Post-Doctoral Fellow\n Research Interests:\nDavide completed his Ph.D. at the Department of Neuroscience, Imaging and Clinical Sciences at the University G. d\u0026rsquo;Annunzio of Chieti. As part of his PhD, Davide attended a period abroad as a visiting PhD student at the Martinos Center for Biomedical Imaging in Boston. Prior to his doctoral studies, he obtained a Master’s degree in Neurosciences and Neuro-Psychological Rehabilitation from the University of Bologna and a Bachelor’s in psychology from the University of Perugia. Davide has experience with multimodal neuroimaging data (e.g. DWI, fMRI, ASL) analysis, TMS applications, EEG data collection and analysis, quantitative structural MRI assessment (e.g. brain morphometry, cortical thickness, etc.), machine learning, cognitive tasks development with a growing passion for simulations of network-level macroscale brain dynamics. His main interest is focused on predicting the TMS signal propagation at the network-level, based on neuroimaging and electrophysiological data. At the WBMG, Davide will be working on several projects which will involve multimodal neuroimaging, non-invasive brain stimulation (TMS, TES) and whole-brain modelling. The primary aim of his project is to predict TMS outcomes by combining computational, neuroimaging and electrophysiological approaches.\nTrivia about Davide:\nAs a proud Italian, Davide never forgets to renew his membership for the “fighting against pineapple on pizza” (a.k.a. Hawaiian pizza) movement. He conducts a healthy lifestyle. He goes to the gym; he likes to hike and go for walks surrounded by nature. Davide is an enthusiastic basketball fan and he used to play semi-pro in the past. He is 6 ft 6 in. tall and is gradually getting used to his height.\nShreyas Harita Ph.D. Student\n Research Interests:\nShreyas is a Ph.D. student who joined the lab as a Research Trainee in May, 2019. His research interests lie in the use of connectome based neural mass modeling to study neural population activity, and how it changes in response to brain stimulation and in neuropathological conditions. The primary aims of his research project are as follows:\n- To investigate the stimulation-induced network propagation and connectivity patterns in the human brain. He hopes to achieve a better understanding of this using brain stimulation techniques like TMS and multimodal brain imaging techniques such as structural MRI, fMRI, DTI, and EEG.\n- To study how individual variability in brain geometry affects responses to brain stimulation, and how this information can inform neuro-navigation techniques to improve the identification of potential TMS targets.\nTrivia about Shreyas:\nShreyas claims to be the \u0026lsquo;funny\u0026rsquo; one. No one else sees it. Shreyas\u0026rsquo; favourite cuisines include Indian, Mexican, Arabic and Italian. He is the unofficial social director for the students at the lab and while no one really appointed him, they like the fact that he tries to bring people together. Shreyas leads an active lifestyle. He plays football (soccer), swims and enjoys biking (even in the winter!) within the city and on natural trails. Shreyas is an avid chess player and enjoys playing blitz and bullet chess.\nSorenza Bastiaens Ph.D. Student\n Research Interests:\nSorenza officially joined the lab in January, 2021. She completed her M.Eng. in Biomedical Engineering at Imperial College London, with a final year project at the Neural Coding department. She first worked with Dr. John Griffiths during Google Summer of Code 2020 where she developed a python package for running validation tests on models generating a neural power spectrum. Sorenza\u0026rsquo;s research in the lab is focused on mathematical modelling of large-scale brain network dynamics, with a focus on measured alpha oscillations observed in EEG. The aim of her project is to use and extend these computational models to better understand the role of brain rhythms in large-scale brain networks. She is also interested in understanding how brain stimulation affects these networks, which is crucial to finding treatments for neurological diseases. Sorenza is currently applying her skills in computational neuroscience, neuroimaging and signal processing to compare and analyze different cortico-cortical and corticothalamic models such as the Jansen-Rit, Wilson-Cowan and Robinson models.\nTrivia about Sorenza:\nSorenza is the \u0026lsquo;sporty\u0026rsquo; one. She is always down for pretty much any physical activity and particularly loves to play football (aka soccer), tennis, and loves to go running and hiking. She claims to have a mild coffee dependency. Her day has to start with a cup of coffee, it is non-negotiable apparently. The rest of her day involves some form of working out and meditation routine, followed by a session of reading. Sorenza is no stranger to the world of video games and enjoys playing action-adventure and battle royale games. She is a huge fan of Harry Potter (#Gryffindor) and sci-fi shows. Her secret talent is juggling and bad singing.\nKevin Kadak Ph.D. Student\n Research Interests:\nKevin is a Ph.D. student who joined the lab in September, 2020. His research focuses on modelling synaptic plasticity dynamics in the brain to better optimize TMS stimulation parameters for the treatment of major depressive disorder. Within the theories of calcium-dependent plasticity and metaplasticity, he will be expanding current synaptic simulation models to encompass E-fields estimates, white matter connectivity, cortical geometry, and corticothalamic oscillations to project patient-specific stimulation protocols. This may aid in more robust, long-term neural rewiring and treatment. Both structural and functional imaging techniques, including fMRI and EEG, will be utilized to inform these models.\nHe looks forward to leveraging his previous experience in modelling zebrafish decision patterns towards further refining his computational skillset and, most ideally, constructing novel frameworks for applied clinical use.\nTrivia about Kevin:\nWhen the world isn’t collapsing in on itself, Kevin enjoys Brazilian Jiu-Jitsu, thrift shopping, bothering others with puns, and practicing guitar despite never actually managing to improve at it. Kevin is a firm believer in carbonated water, pineapple on pizza (DO NOT put him on a group project with Davide!), and cutting sandwiches diagonally. He also spends his spare time convincing the world that Sky High (2005) is a cinematic masterpiece. When not involved with one of these activities, Kevin is probably squinting at code documentation, confused.\nTaha Morshedzadeh M.Sc. Student\n Research Interests:\nTaha initially joined the lab as an undergraduate research student in January 2021. He started his M.Sc. here in September 2021. He is working on data aquired from market-available electroencephalography headsets such as Muse S, and will be fitting thalamocortical neural field models to this data.\nHe studied Dentistry in Iran for 3 years before dropping out in 2016 to do Neuroscience in Canada. During this time in Iran, he worked with Prof. Mohammad Nami on EEG data. He completed his BSc in Neuroscience at the University of Toronto in 2020. His undergraduate supervisor was Dr. Milad Lankarany with whom he worked on modelling basal ganglia neurons in Parkinson\u0026rsquo;s Disease. He also co-leads NeurotechUofT (neurotechuoft.com), a NeuroTechX student club working on open-source BCI projects.\nTrivia about Taha:\nTaha enjoys running in the hills of Toronto suburbs and city biking. You can always find him up to some shenanigans on Strava. He also redecorates his home almost every two weeks. He says he doesn\u0026rsquo;t need to see a therapist about this and promises that he\u0026rsquo;s good. Taha enjoys listening to and singing Persian traditional music and is trying to get into composing as well! Taha would like to use this tribune to support Davide in his fight against all evil (i.e., pineapple pizza).\nParsa Oveisi M.A.Sc. Student\n Research Interests:\nParsa joined Grifflab in September 2021 as an undergraduate research student. Parsa’s overarching interest in learning about new frameworks in which the functioning of the brain can be described. Currently, Parsa is working on data acquisition of concurrent EEG and functional near-infrared spectroscopy (fNIRS) recordings using the Kernel Flow ™ headset. He is then using this data, as well as other EEG and fMRI/fNIRS datasets, to develop a deep learning-based whole brain model that can be fitted to individual brains. The model can then be used to predict empirical EEG and hemodynamic responses, as well as shedding light on how patterns from these different modalities arise and interact. Parsa officially joined the lab as a Master\u0026rsquo;s student in September 2022.\nTrivia about Parsa:\nParsa has a spur-of-the-moment attitude in picking up new hobbies and sports. Sources say he picked up archery by fantasizing about being a steppe horse archer of old, though it cannot be confirmed. You will usually find him at his desk (yes, his. So don’t book it!) taking advantage of the extra monitor. If you look closely, however, you will find that where he actually is, is in a state of utter confusion. The keyboard might be being pressed but no actual progress is being made. If you ever need to talk to him, just follow the scent of black tea, or alternatively, start criticizing Lord of the Rings. He will find you.\nAndrew Clappison M.Sc. Student\n Research Interests:\nAndrew joined the WBMG in September 2021, as a graduate student in the School of Electrical Engineering and Computer Science at the University of Ottawa. Previously, he had completed a Bachelor of Mathematics degree at the University of Waterloo with minors in Computational Mathematics and Cognitive Science. Andrew is interested in discovering how the brain works, particularly how it produces consciousness. During his time in the lab, he will be working with multimodal whole brain models and using machine learning to fit them to concurrent resting-state EEG and fMRI/fNIRS datasets. In doing so, he will try to reproduce and explain phenomena observed in neuroimaging studies such as the Alpha Power - BOLD anticorrelation.\nLab Alumni Minarose Ismail Summer research student\n Minoarose was a summer research student during the summer of 2019. She was a first year undergraduate student at McMaster pursuing a bachelor in Neuroscience with a background in Python coding. Her work was in the field of computational neuroscience and her project dealt with fitting oscillations and one over frequency on MEG data using the FOOOF toolbox to parametrize the power spectra and perform summary analysis.\nSurya Pandiaraju Co-op Student\n Research Interests:\nSurya is a biomedical engineering student at the University of Waterloo. He is co-supervised by Dr. Andreea Diaconescu. He is interested in EEG data analysis and brain-computer interfaces and their applications in both clinical and consumer-based settings. Currently, he is working on a project focused on designing a pipeline for clinically obtained EEG data that would generate first-level and second-level statistical analyses and is doing so by modifying currently existing tools in MATLAB.\nTrivia:\nSurya enjoys partaking in a number of hobbies including but not limited to drawing (even when he is not supposed to be), digital arts, collectible card games, video games, anime \u0026amp; manga, working out, exploring new restaurants, singing/whistling/beatboxing/humming in the car whilst pretending no one can hear him, and is trying to get his hand in producing after compulsively purchasing a mini MIDI keyboard after watching a video or two on his YouTube recommended list at 12am. If Surya could have any superpower, it would be universal time-control, which he could use to get back the time he spent trying to make this funny.\nDr. Neda Kaboodvand Post-Doctoral Fellow\n Research Interests:\nNeda completed her Ph.D. at the department of clinical neuroscience at Karolinska Institutet, Stockholm, Sweden. In addition, she has master’s in biomedical engineering from Tehran Polytechnic (aka Amirkabir University of Technology), Tehran, Iran. Neda has a strong analytical and computational background, with a growing passion for applying quantitative problem solving and innovative data analysis for solving tangible real-world problems. She has experience in modeling healthy and diseased brains, linear/non-linear time-series and neuroimaging data analysis, statistical and computational modeling of the data, network science and machine learning. She enjoys working in dynamic cross-functional teams on challenging tasks, with measurable real-world outcomes. She joined the lab in July, 2020. She is currently working on whole-brain modelling as well as the analysis and modelling of TMS-evoked responses. The primary aim of her project is to undertake key computational and experimental groundwork for the development and iterative improvement of a novel computational tool for predicting TMS responses and induced plasticity effects. Her long-term aim is to develop computational models of repetitive TMS responses that can be used by clinicians and researchers for optimal patient-specific design of brain stimulation therapies.\nFrank Mazza M.Sc. Student\n Research Interests:\nFrank is a M.Sc. student who joined the lab in September, 2019. He is co-supervised by Dr. Etay Hay (https://www.haybrainlab.com/). Frank is interested in identifying electrophysiological signals for diagnosis of depression and transcranial magnetic stimulation (TMS) protocols for treatment of depression, using computer simulations of detailed brain circuits and whole-brain networks. With previous work in identifying EEG correlates of response in a novel rTMS stimulation paradigm (OFC-rTMS), he now focuses on identifying biomarkers in brain signals for diagnosis of depression by linking cellular, circuit, and connectivity changes to signals relevant for clinical diagnosis. In addition, he is working to optimize TMS protocols using computer simulations at the local and global brain scales by simulating protocol effects on local circuits, such as circuit inhibition, and the propagation of activity to other regions in the brain.\nTrivia about Frank:\nFrank \u0026lsquo;The Grank\u0026rsquo; Mazza. Frank is quite and likes to go about his business, often finishing his work before deadlines, be it grants, abstracts, or manuscripts. Frank does intermittent fasting. No one knows why. Frank likes to read and write about all things art and architecture. He likes to paint, ink, sketch, sculpt and lino-print. Frank has a humble garden where he likes to experiment with growing different plants. He likes to work out and is avid music fan, trying to appreciate everything that isn\u0026rsquo;t country.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"c1d17ff2b20dca0ad6653a3161942b64","permalink":"/people/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/people/","section":"","summary":"Lab Members Dr. John David Griffiths Group Lead\n CV\nDr. John D. Griffiths is a cognitive and computational neuroscientist based at the Toronto Centre for Addiction and Mental Health (CAMH)’s Krembil Centre for Neuroinformatics (KCNI; krembilneuroinformatics.ca), where he leads a research team focused on computational modelling of brain network dynamics, neurostimulation, and multimodal neuroimaging data (www.grifflab.com). He is an Assistant Professor in the University of Toronto Departments of Psychiatry, Medical Sciences, and Biomedical Engineering.","tags":null,"title":"People","type":"docs"},{"authors":null,"categories":null,"content":" Welcome to Slides Academic\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;) Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = \\;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne Two Three \nA fragment can accept two optional parameters:\n class: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\n Only the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/img/boards.jpg\u0026quot; \u0026gt;}} {{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}} {{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}} Custom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; } Questions? Ask\nDocumentation\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"c2915ec5da95791851caafdcba9664af","permalink":"/slides/example-slides/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/slides/example-slides/","section":"slides","summary":"Welcome to Slides Academic\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;) Math In-line math: $x + y = z$","tags":null,"title":"Slides","type":"slides"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"825ce41f7da94d7247fa74f4dc225962","permalink":"/talks_and_teaching/2016-01-01__wiredminds_allenatlas/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2016-01-01__wiredminds_allenatlas/","section":"talks_and_teaching","summary":"JG \u0026 CH discuss the seminal paper by Oh et al.","tags":["Philosophy"],"title":"Wired Minds Episode: Allen Atlas","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"2c2d30b57e58592695efacacf023eb65","permalink":"/talks_and_teaching/2016-01-01__wiredminds_philosophyofminduploading/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2016-01-01__wiredminds_philosophyofminduploading/","section":"talks_and_teaching","summary":"John Griffiths \u0026 Christopher Harris discuss an interesting but troubled corner of modern philosophy of mind","tags":["Philosophy"],"title":"Wired Minds Episode: Philosophy \u0026 Neuroscience of Mind Uploading","type":"talks_and_teaching"}] \ No newline at end of file +[{"authors":null,"categories":null,"content":"This feature can be used for publishing content such as:\n Project or software documentation Online courses Tutorials The parent folder may be renamed, for example, to docs for project documentation or course for creating an online course.\nTo disable this feature, either delete the parent folder, or set draft = true in the front matter of all its pages.\nAfter renaming or deleting the parent folder, you may wish to update any [[menu.main]] menu links to it in the config.toml.\n","date":1536451200,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1536451200,"objectID":"c3224f3a64174f08aaf31e1f1d16ffd3","permalink":"/tutorial/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/tutorial/","section":"tutorial","summary":"This feature can be used for publishing content such as:\n Project or software documentation Online courses Tutorials The parent folder may be renamed, for example, to docs for project documentation or course for creating an online course.\nTo disable this feature, either delete the parent folder, or set draft = true in the front matter of all its pages.\nAfter renaming or deleting the parent folder, you may wish to update any [[menu.","tags":null,"title":"Overview","type":"docs"},{"authors":null,"categories":null,"content":"","date":1546387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546387200,"objectID":"2b941b45cee10983ad62283cc69ac730","permalink":"/project/primary_areas/brain_stimulation/","publishdate":"2019-01-02T00:00:00Z","relpermalink":"/project/primary_areas/brain_stimulation/","section":"project","summary":"","tags":["primaryarea","brain stimulation"],"title":"Brain Stimulation","type":"project"},{"authors":["Joana Cabral","John D. Griffiths"],"categories":null,"content":"","date":1704067200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1704067200,"objectID":"cc5853c14d94f3ab99a0ac7568930252","permalink":"/publication/2024-cabral-and-griffiths-book-chapter/","publishdate":"2024-01-01T00:00:00Z","relpermalink":"/publication/2024-cabral-and-griffiths-book-chapter/","section":"publication","summary":"","tags":[],"title":"Dynamic brain network models: How interactions in the structural connectome shape brain dynamics","type":"publication"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"210e4d6ae5ca6a4a87d11dba5e09a084","permalink":"/code_and_software/fooof-unit/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/fooof-unit/","section":"code_and_software","summary":"","tags":["Misc"],"title":"FOOOF-Unit","type":"code_and_software"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"8714aec8034b6f2cc24ab3bc0d897919","permalink":"/code_and_software/griffithsetal2020/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/griffithsetal2020/","section":"code_and_software","summary":"","tags":["Papers"],"title":"Griffiths Et Al. 2020","type":"code_and_software"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"579ab4e7c4fa375bc5a096a18eec3d54","permalink":"/code_and_software/griffithsetal2022/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/griffithsetal2022/","section":"code_and_software","summary":"","tags":["Papers"],"title":"Griffiths Et Al. 2022","type":"code_and_software"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"c0abd358e556a428b94aad6a4a801ca0","permalink":"/code_and_software/haritaetal2022/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/haritaetal2022/","section":"code_and_software","summary":"","tags":["Papers"],"title":"Harita et al. 2022","type":"code_and_software"},{"authors":null,"categories":null,"content":" \n \n ","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"8d421e9c8c9a8076117af72c31e0f8ac","permalink":"/talks_and_teaching/2023-01-07__kcnischool2022_wbm_lectures/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2023-01-07__kcnischool2022_wbm_lectures/","section":"talks_and_teaching","summary":"JG's lectures at the 2021 / 2022 KCNI Summer School","tags":["Pinned","ComputationalNeuroscience","Neuroimaging","Neuroinformatics"],"title":"KCNI School Whole-Brain Modelling Lectures","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":"See here\n","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"d5abf9fd77cadceac0e4d43f39f5aac5","permalink":"/code_and_software/momietal2023/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/momietal2023/","section":"code_and_software","summary":"See here","tags":["Papers","Featured"],"title":"Momi Et Al. 2023","type":"code_and_software"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"3667df62a32dad53417419d4a54ae99f","permalink":"/code_and_software/ohbm_wbm_course/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/ohbm_wbm_course/","section":"code_and_software","summary":"","tags":["Education","Featured"],"title":"OHBM2023 Brain Modelling Course","type":"code_and_software"},{"authors":["Davide Momi","Zheng Wang","John D. Griffiths"],"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"36726f87a94d799bc4c800bd11310873","permalink":"/publication/2023-momi-et-al-tmsevokedresponses/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/publication/2023-momi-et-al-tmsevokedresponses/","section":"publication","summary":"A major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. This question finds sharp relief in the case of TMS-EEG evoked potentials (TEPs). TEPs are spatiotemporal waveform patterns with characteristic inflections at ∼50ms, ∼100ms, and ∼150-200ms following a single TMS pulse that disperse from, and later reconverge to, the primary stimulated regions. What parts of the TEP are due to recurrent activity? And what light might this shed on more general principles of brain organization? We studied this using source-localized TMS-EEG analyses and whole-brain connectome-based computational modelling. Results indicated that recurrent network feedback begins to drive TEP responses from ∼100ms post-stimulation, with earlier TEP components being attributable to local reverberatory activity within the stimulated region. Subject-specific estimation of neurophysiological parameters additionally indicated an important role for inhibitory GABAergic neural populations in scaling cortical excitability levels, as reflected in TEP waveform characteristics.","tags":["brain stimulation","brain networks"],"title":"TMS-Evoked Responses Are Driven by Recurrent Large-Scale Network Dynamics","type":"publication"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"5180828ca91b213cdba831677fe44b1f","permalink":"/code_and_software/whobpyt/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/code_and_software/whobpyt/","section":"code_and_software","summary":"","tags":["Software_Libraries","Featured"],"title":"WhoBPyT","type":"code_and_software"},{"authors":["John D. Griffiths","Zheng Wang","Andrew Clappison","Syed Hussain Ather","Davide Momi","Scott Rich","Andreea Diaconescu","Anthony Randal McIntosh","Kelly Shen"],"categories":null,"content":"","date":1664582400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664582400,"objectID":"8357cf53ba185b16084a55596f8191ac","permalink":"/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/","publishdate":"2022-10-01T00:00:00Z","relpermalink":"/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/","section":"publication","summary":"Connectome-based neural mass modelling is the emerging computational neuroscience paradigm for simulating large-scale network dynamics observed in whole-brain activity measurements such as fMRI, M/EEG, and related techniques. Estimating physiological parameters by fitting these models to empirical data is challenging however, due to large network sizes, often physiologically detailed fast-timescale system equations, and the need for long (e.g. tens of minutes) simulation runs. Here we introduce a novel approach to connectome-based neural mass model parameter estimation by employing optimization tools developed for deep learning. We cast the system of differential equations representing both neural and haemodynamic activity dynamics as a deep neural network, implemented within a widely used machine learning programming environment (PyTorch). This allows us to use robust industry-standard optimization algorithms, automatic differentiation for computation of gradients, and other useful functionality. The approach is demonstrated using a connectome-based network with nodal dynamics specified by the two-state RWW mean-field neural mass model equations, which we use here as a model of fMRI-measured activity and correlation fluctuations. Additional optimization constraints are explored and prove fruitful, including restricting the model to domains of parameter space near a bifurcation point that yield metastable dynamics. Using these techniques, we first show robust recovery of physiological model parameters in synthetic data and then, as a proof-of-principle, apply the framework to modelling of empirical resting-state fMRI data from the Human Connectome Project database. For resting state activity, the system can be understood as a deep net that receives uncorrelated noise on its input layer, which is transformed into network-wide modelled functional connectivity on its output layer. This is consistent with the prevailing conception in theoretical neuroscience of resting-state functional connectivity patterns as an emergent phenomenon that is driven by (effectively) random activity fluctuations, which are then in turn spatiotemporally filtered by anatomical connectivity and local neural dynamics.","tags":null,"title":"Deep-learning based parameter estimation for neurophysiological models of neuroimaging data","type":"publication"},{"authors":["Shreyas Harita","Davide Momi","Frank Mazza","John D. Griffiths"],"categories":null,"content":"","date":1655942400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1655942400,"objectID":"f5ee61730f2093f08827b0bc04e93c43","permalink":"/publication/2022-harita-et-al-mapping-interindividual-fc-tms/","publishdate":"2022-06-23T00:00:00Z","relpermalink":"/publication/2022-harita-et-al-mapping-interindividual-fc-tms/","section":"publication","summary":"Transcranial magnetic stimulation (TMS) is an emerging alternative to existing treatments for major depressive disorder (MDD). The effects of TMS on both brain physiology and therapeutic outcomes are known to be highly variable from subject to subject, however. Proposed reasons for this variability include individual differences in neurophysiology, in cortical geometry, and in brain connectivity. Standard approaches to TMS target site definition tend to focus on coordinates or landmarks within the individual brain regions implicated in MDD, such as the dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC). Additionally considering the network connectivity of these sites (i.e., the wider set of brain regions that may be mono- or poly-synaptically activated by TMS stimulation) has the potential to improve subject-specificity of TMS targeting and, in turn, improve treatment outcomes. In this study, we looked at the functional connectivity (FC) of dlPFC and OFC TMS targets, based on induced electrical field (E-field) maps, estimated using the SimNIBS library. We hypothesized that individual differences in spontaneous functional brain dynamics would contribute more to downstream network engagement than individual differences in cortical geometry (i.e., E-field variability). We generated individualized E-field maps on the cortical surface for 121 subjects (67 female) from the Human Connectome Project database using tetrahedral head models generated from T1- and T2-weighted MR images. F3 and Fp1 electrode positions were used to target the left dlPFC and left OFC, respectively. We analyzed inter-subject variability in the shape and location of these TMS target E-field patterns, their FC, and the major functional networks to which they belong. Our results revealed the key differences in TMS target FC between the dlPFC and OFC, and also how this connectivity varies across subjects. Three major functional networks were targeted across the dlPFC and OFC: the ventral attention, fronto-parietal and default-mode networks in the dlPFC, and the fronto-parietal and default mode networks in the OFC. Inter-subject variability in cortical geometry and in FC was high. Our analyses showed that the use of normative neuroimaging reference data (group-average or representative FC and subject E-field) allows prediction of which networks are targeted, but fails to accurately quantify the relative loading of TMS targeting on each of the principal networks. Our results characterize the FC patterns of canonical therapeutic TMS targets, and the key dimensions of their variability across subjects. The high inter-individual variability in cortical geometry and FC, leading to high variability in distributions of targeted brain networks, may account for the high levels of variability in physiological and therapeutic TMS outcomes. These insights should, we hope, prove useful as part of the broader effort by the psychiatry, neurology, and neuroimaging communities to help improve and refine TMS therapy, through a better understanding of the technology and its neurophysiological effects.","tags":[],"title":"Mapping inter-individual functional connectivity variability in TMS targets for major depressive disorder","type":"publication"},{"authors":["John D. Griffiths","Sorenza Bastiaens","Neda Kaboodvand"],"categories":null,"content":" ","date":1643673600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1643673600,"objectID":"e2da99a312236451b325ce43d7081ea5","permalink":"/publication/2022-griffiths-et-al-wbm-pastpresentfuture/","publishdate":"2022-02-01T00:00:00Z","relpermalink":"/publication/2022-griffiths-et-al-wbm-pastpresentfuture/","section":"publication","summary":"Whole-Brain Modelling is a scientific field with a short history and a long past. Its various disciplinary roots and conceptual ingredients extend back to as early as the 1940s. It was not until the late 2000s, however, that a nascent paradigm emerged in roughly its current form—concurrently, and in many ways joined at the hip, with its sister field of macro-connectomics. This period saw a handful of seminal papers authored by a certain motley crew of notable theoretical and cognitive neuroscientists, which have served to define much of the landscape of whole-brain modelling as it stands at the start of the 2020s. At the same time, the field has over the past decade expanded in a dozen or more fascinating new methodological, theoretical, and clinical directions. In this chapter we offer a potted Past, Present, and Future of whole-brain modelling, noting what we take to be some of its greatest successes, hardest challenges, and most exciting opportunities.","tags":["brain stimulation","brain networks","brain rhythms"],"title":"Whole-Brain Modelling: Past, Present, and Future","type":"publication"},{"authors":["Frank Mazza","John D. Griffiths","Etay Hay"],"categories":null,"content":"","date":1611964800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611964800,"objectID":"de3da3e3e599d8647aff1e32580ffe56","permalink":"/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/","publishdate":"2021-01-30T00:00:00Z","relpermalink":"/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/","section":"publication","summary":"","tags":[],"title":"EEG Biomarkers of reduced inhibition in human cortical microcircuits in depression","type":"publication"},{"authors":null,"categories":null,"content":" \n","date":1611014400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611014400,"objectID":"fd53de43d6d08f9da9141cfe4d4c806c","permalink":"/talks_and_teaching/2021-01-01__nbn_thebraininsideout/","publishdate":"2021-01-19T00:00:00Z","relpermalink":"/talks_and_teaching/2021-01-01__nbn_thebraininsideout/","section":"talks_and_teaching","summary":"JG's New Books in Neuroscience podcast interview with GB in his new book","tags":["Pinned","ComputationalNeuroscience","Podcast"],"title":"Interview w/ Gyorgi Buzsaki on 'The Brain From Inside Out'","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" \n","date":1611014400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611014400,"objectID":"e1bef046ccbda311f3030d0386e1ab8b","permalink":"/talks_and_teaching/2021-01-01__nbn_themindisflat/","publishdate":"2021-01-19T00:00:00Z","relpermalink":"/talks_and_teaching/2021-01-01__nbn_themindisflat/","section":"talks_and_teaching","summary":"JG's New Books in Neuroscience podcast interview with NC in his new book","tags":["Pinned","ComputationalNeuroscience","Philosophy","Podcast"],"title":"Interview w/ Nick Chater on 'The Mind is Flat'","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" \n","date":1611014400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611014400,"objectID":"52f2a2dc4bdf2e5c0a2c456d631061e0","permalink":"/talks_and_teaching/2021-01-01__nbn_electric_brain/","publishdate":"2021-01-19T00:00:00Z","relpermalink":"/talks_and_teaching/2021-01-01__nbn_electric_brain/","section":"talks_and_teaching","summary":"JG's New Books in Neuroscience podcast interview with RDF in his new book","tags":["Pinned","ComputationalNeuroscience","Neuroimaging","Podcast"],"title":"Interview w/ R. Douglas Fields on 'Electric Brain'","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" \n","date":1611014400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611014400,"objectID":"d74c3ea2855f2c2a3368b1c0fc445531","permalink":"/talks_and_teaching/2021-01-01__nbn_neurosciencefiction/","publishdate":"2021-01-19T00:00:00Z","relpermalink":"/talks_and_teaching/2021-01-01__nbn_neurosciencefiction/","section":"talks_and_teaching","summary":"JG's New Books in Neuroscience podcast interview with RQQ in his new book","tags":["Pinned","ComputationalNeuroscience","Philosophy","Podcast"],"title":"Interview w/ Rodrigo Quian Quiroga on 'Neuroscience Fiction'","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" \n","date":1611014400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611014400,"objectID":"ab536001a3fb6a2d4f00fa33e9e62a46","permalink":"/talks_and_teaching/2021-01-01__nbn_teachingneuroscience/","publishdate":"2021-01-19T00:00:00Z","relpermalink":"/talks_and_teaching/2021-01-01__nbn_teachingneuroscience/","section":"talks_and_teaching","summary":"JG's New Books in Neuroscience podcast interview with CH in his new book","tags":["Pinned","ComputationalNeuroscience","Neuroimaging","Podcast"],"title":"Interview. Chris Harris on 'Teaching Neuroscience'","type":"talks_and_teaching"},{"authors":["John D. Griffiths","Anthony R. McIntosh","Jérémie Lefebvre"],"categories":null,"content":"","date":1580342400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1580342400,"objectID":"89f61c64304934bd8e07bcd0840178b6","permalink":"/publication/2020-griffiths-et-al-corticothalamic-model/","publishdate":"2020-01-30T00:00:00Z","relpermalink":"/publication/2020-griffiths-et-al-corticothalamic-model/","section":"publication","summary":"Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.","tags":[],"title":"A connectome-based, corticothalamic model of state- and stimulation-dependent modulation of rhythmic neural activity and connectivity","type":"publication"},{"authors":["Jennifer D Ryan","Kelly Shen","Arber Kacollja","Heather Tian","John D. Griffiths","Randy McIntosh"],"categories":null,"content":"","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1577836800,"objectID":"8f01319b44b630c89b8ffa1c550e0e03","permalink":"/publication/2020-ryan-et-al-modeling/","publishdate":"2020-01-01T00:00:00Z","relpermalink":"/publication/2020-ryan-et-al-modeling/","section":"publication","summary":"","tags":[],"title":"The functional reach of the hippocampal memory system to the oculomotor system","type":"publication"},{"authors":["John"],"categories":null,"content":"Last week we ran another successful brainhack workshop at Ryerson University\u0026rsquo;s Biomedical Zone, as a satellite event for the Canadian Neuroscience Association (CAN-ACN) conference, which was held in Toronto this year.\nI\u0026rsquo;ve written previously about previous Toronto brainhacks and why I like them so much, so I won\u0026rsquo;t repeat all of that here. But here\u0026rsquo;s a very brief recap:\nThis figure from Craddock et al. 2016 conveys the key ideas nicely:\nIn short, the purpose of brainhacks is to help people collaborate (locally, and also \u0026lsquo;virtually\u0026rsquo; with people at other sites) on both new and established projects ( -\u0026gt; \u0026lsquo;hackathon\u0026rsquo; ); to learn new technical skills ( -\u0026gt; \u0026lsquo;educational\u0026rsquo;); and to communicate with people in their field(s), by hearing about people\u0026rsquo;s research, general questions, miscellaneous interesting ideas, etc. ( -\u0026gt; \u0026lsquo;unconference\u0026rsquo; ). Also I would like to highlight perhaps the most important intersection in this figure ( -\u0026gt; \u0026lsquo;meet and greet\u0026rsquo;).\nLast week\u0026rsquo;s event was our third Toronto Brainhack. As far as we are able to discern the event was a roaring success, with over 80 attendees from all over Canada, and glowing reports all round. Hats off to the BrainhackTO working group, who put several months of moderately hard work into making last week\u0026rsquo;s workshop happen. As always (well, since the first BrainhackTO in 2016) the charge was led by the working group\u0026rsquo;s benevolent dictator - breaker of bugs, queen of the container and mother of surfaces, Dr. Erin Dickie. The very strong supporting cast included Gabi Herman, Jerry Jaychandra, Natalie Ford, Chris Hammill, Nathan Chan, Derek Beaton, Michael Joseph, Amanda Aesson, Shreejoy Tripathy, Ben Darwin, and Myself.\nThe #BrainHackTO organizers are working hard to organize the best educational opportunities (and snacks) for the big day. Sign up now! https://t.co/eldg7Dulut pic.twitter.com/Jpu3tFGeFI\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 3, 2019 The working group\u0026rsquo;s work included the usual essentials of securing funding/sponsors (thank you OBI, InteraXon, Cognixion), stash (!), venues, food, attendance, advertising, social media, neurotech gear for hacking, etc (thank you @choosemuse).\nPlenty of @ChooseMuse EEGs for @BrainHackTO hackers to go crazy with next week 😎 https://t.co/k9L0DNjpae pic.twitter.com/UVf1cZEcTz\n\u0026mdash; John Griffiths (@neurodidact) May 17, 2019 Here\u0026rsquo;s what we did. The full program is shown on Jerry\u0026rsquo;s snazzy event website. The workshop opened up with \u0026lsquo;ignite\u0026rsquo; talks by Amanpreet Badwhar (McGill) and Paul Pavlidis (UBC). This was followed by project pitches, where around 8 individuals proposed hacking/research projects to work on for the next 2 days.\nOpening remarks from @ErinWDickie! There are so many exciting #BrainHackTO workshops ahead. pic.twitter.com/briiVmmc6i\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 21, 2019 Projects included: multivariate statistical analysis and visualiation in R,\nJenny Rieck\u0026#39;s team applies various norming methods in an interactive Shiny app. They used a practise dataset for now, but intend to use it to explore brain connectivity. #BrainHackTO pic.twitter.com/gkkkHPMset\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 22, 2019 Human-mouse gene expression analysis\nAmazing work from team Human2Mouse - entering an MNI coordinate finds the nearest (human) location with gene expression data, the finds a corresponding mouse brain coordinate. #BrainHackTO pic.twitter.com/ntbxGnKWBO\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 22, 2019 computing and analyzing resting state and visually evoked brain responses with EEG,\nWithin only a short time, Team EEG (led by @neurodidact and @AmandaKEasson) acquired hours of data with a @ChooseMuse headset. Then they started to analyze it! A great learning project at #BrainHackTO pic.twitter.com/9CHiSDisW6\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 22, 2019 and MRI surface-based analysis for the hippocampus.\nTo wrap things up, we also gave out two prizes of a muse portable EEG headband, generously donated by InteraXon.\nCongratulations @Sridevi292 on your @ChooseMuse headset!!!! Get in touch with our team to claim your prize. pic.twitter.com/rihR49IrTa\n\u0026mdash; Brain Hack Toronto (@BrainHackTO) May 30, 2019 As always, the awesome scientific discussions and hack expertise exchanges continued into the late hours with a well-chosen change of location at the splendid Duke\u0026rsquo;s Refresher bar on Gerrard St.\nHuge thanks and congrats again to the rest of the working group for a successful event. Next one is expected to be late fall 2019. If you\u0026rsquo;d like to learn more and/or get involved, do get in touch!\n","date":1559520000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1559520000,"objectID":"0de7bffcbdfa921c95c2f670f8557be9","permalink":"/post/brainhack_can/","publishdate":"2019-06-03T00:00:00Z","relpermalink":"/post/brainhack_can/","section":"post","summary":"Brainhack CAN Toronto","tags":null,"title":"BrainHack CAN Toronto","type":"post"},{"authors":["John"],"categories":null,"content":"Last week I spent a few days in Princeton at a small invited workshop aimed at developing a BIDS specification for computational models. Organizer Russ Poldrack succeeded in securing a very smart and capable list of attendees, including Petra Ritter, Tal Yarkoni, Randy McIntosh, Brad Love, Jonathan Cohen, Marmaduke Woodman, Tim Rogers, Rosalyn Moran, Chris Markiewicz, Padraig Gleeson, and many others.\nThe basic objectives were twofold. First, to identify the types of neuroimaging data commonly used in computational modelling of various flavours, and start work in developing a common vocabulary and file/folder nomenclature for them. This was largely based on several of the existing BIDS derivatives specs, such as connectivity, electrophysiology, bold fmri, etc. The second aim of the discussions was a little more ambitious - namely to find a generic model description that encompasses as many currently used computational modelling approaches as possible.\nIt was a fairly productive few days, with concrete products including a 15 page BIDS extension proposal, outline BIDS apps for neural mass modelling with The Virtual Brain and Dynamic Causal Modelling, and for Bayesian visual fMRI DDM models.\nMost of the work for these projects is of course still ahead, but all in all I think this was an excellent start. Hats off to Russ et al for and pulling together the contributors and pushing forward these important developments in the neuroinformatics of neuroimaging and computational neuroscience.\n","date":1554681600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1554681600,"objectID":"ffdae5b58b940b20942c0bca0821c15c","permalink":"/post/bids_compmodels_mtg/","publishdate":"2019-04-08T00:00:00Z","relpermalink":"/post/bids_compmodels_mtg/","section":"post","summary":"BIDS-Compmodels worshop - Princeton","tags":null,"title":"Towards standardization of computational models in neuroimaging","type":"post"},{"authors":["John D. Griffiths","Jérémie Lefebvre"],"categories":null,"content":"","date":1548806400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1548806400,"objectID":"341d59502de2a11b21727bd5bd08bacf","permalink":"/publication/2019-griffiths-et-al-shapingbrainrhythms/","publishdate":"2019-01-30T00:00:00Z","relpermalink":"/publication/2019-griffiths-et-al-shapingbrainrhythms/","section":"publication","summary":"Rhythmic, collective activity is a fundamental feature of neural systems. As a result of this, many of the challenges and opportunities involved in developing clinical tools from basic neuroscience knowledge come down to questions about control of dynamic, oscillatory networks. In this chapter we review a range of experimental and theoretical work on control of neural oscillations, in healthy brains and in relation to various clinical conditions. We highlight the main types of qualitative system behaviour that can result from application of periodic stimulation and present a simple case study on this using a mathematical model of rhythmogenesis in thalamocortical circuits. The concepts discussed here may, we hope, help provide some guidelines and principles for the development of future generations of more physiologically and dynamically informed brain stimulation techniques, paradigms,and researchers.","tags":[],"title":"Shaping brain rhythms: dynamic and control-theoretic perspectives on periodic brain stimulation for treatment of neurological disorders","type":"publication"},{"authors":["John"],"categories":null,"content":"After several months\u0026rsquo; lead-in and build-up, I\u0026rsquo;m delighted to announce that the KCNI WBMG / GriffLab is now officially \u0026lsquo;open for business\u0026rsquo;. Recruitment is now getting underway for post-docs, graduate students, and undergrads (see here for more details). This ramp-up phase is being done in parallel with the KCNI computational genomics (triplab) and microcircuit modelling (haylab) groups, and there will be a lots of ideas, skills, and research exchange between our three teams in the coming months and years. A big buzz right now amongst the various KCNI teams (which currently is numbering around 20 people, and will probably double in the next year or two) is our transition in a few weeks over to the fancy new open-plan space on the top floor of 250 college street. In addition to the stunning view of Toronto this will feature a lot of cool tech, and a very modern \u0026lsquo;team-science\u0026rsquo; office culture. I\u0026rsquo;ll be writing more about this here soon. Apart from the inevitable admin barrage of starting a new job, there\u0026rsquo;s plenty of exciting things happening at the moment. I outlined some of the plans and progress to date for the KCNI WBMG in my talk at the CAMH Campbell symposium 2 weeks ago. Much of this is given in summary form in the sections of this site, and will of course be expanded on in due course. With my colleagues at NeurotechTO we are co-organizing a workshop in March with OBI and INCF, aimed at developing exchange between academic research and private companies in the neurotech sector. With KCNI colleagues we are organizing a BrainHack in April as a satellite event ahead of the Canadian Neuroscience Association annual meeting, which is in Toronto this year. A few invitations have come in to exciting meetings on neuroinformatics and computational neuroscience later in the year in cool places like Princeton and Barcelona, which I\u0026rsquo;m sure will be very productive. Again, watch this space for updates and summaries of these and other happenings ;) ","date":1548720000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1548720000,"objectID":"f79c2ee477bf45e3f48d1ecf6afff2a0","permalink":"/post/grifflab_opens/","publishdate":"2019-01-29T00:00:00Z","relpermalink":"/post/grifflab_opens/","section":"post","summary":"Exciting times. ","tags":null,"title":"GriffLab opens for business","type":"post"},{"authors":null,"categories":null,"content":"The Virtual Brain (TVB) is a modelling and neuroinformatics platform for simulating meso-scale whole-brain neural activity. TVB is the brainchild of our colleagues Randy McIntosh Viktor Jirsa, and is one of the principal tools we use in our computational modelling research work.\nAs members of the \u0026lsquo;extended\u0026rsquo; TVB team, we provide technical support on the Forum, teach regularly at workshops, and develop new technical features and applications.\nNewbies to TVB: check out the main website and the github organization; and particularly the jupyter notebook demos and tutorials. There are also a variety of handy nuggets from us in the TVB entries on the JDG LabNotebook.\nAnd of course everyone should take a minute to watch the awesome TVB short movie\u0026hellip;\n ","date":1546560000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546560000,"objectID":"f636914b1ca3394abbd355173a3d3761","permalink":"/code_and_software/tvb/","publishdate":"2019-01-04T00:00:00Z","relpermalink":"/code_and_software/tvb/","section":"code_and_software","summary":"The Virtual Brain (TVB) is a modelling and neuroinformatics platform for simulating meso-scale whole-brain neural activity. TVB is the brainchild of our colleagues Randy McIntosh Viktor Jirsa, and is one of the principal tools we use in our computational modelling research work.\nAs members of the \u0026lsquo;extended\u0026rsquo; TVB team, we provide technical support on the Forum, teach regularly at workshops, and develop new technical features and applications.\nNewbies to TVB: check out the main website and the github organization; and particularly the jupyter notebook demos and tutorials.","tags":["Software_Libraries"],"title":"The Virtual Brain (TVB) Platform","type":"code_and_software"},{"authors":null,"categories":null,"content":"eeg-notebooks is a python-based software library that provides a simple end-to-end platform for conducting cognitive neuroscience experiments using consumer-grade EEG technology. It emerged out of the close relationship between our good friends at NeuroTechX and other other good friends at InterAxon (just down the road from CAMH). Using eeg-notebooks together with a standard laptop computer and ~$200 EEG headset such as the Interaxon MUSE, one can conduct high-quality event-related potential (ERP) experiments that traditionally require research-grade equipment costing tens of thousands of dollars. This opens up the door to a myriad of exciting neuroscience research and education possibilities.\n(1/2) Special feature at this year\u0026#39;s #brainhack global Toronto: \u0026#39;MUSE corner\u0026#39;, where we be piloting #NeuroBRITE EEG experiments on willing attendees in ~20 min slots...\n\u0026mdash; John Griffiths (@neurodidact) April 23, 2018 One of these, which we have been closely involved in, is using eeg-notebooks as a vehicle for STEM (Science, Technology, Engineering, Mathematics) education in high-schools. This has been spearheaded in Toronto by the Rotman-Baycrest based initiative NeuroBRITE, and in New York by the brainwaves project.\n","date":1546560000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546560000,"objectID":"32fc5172f39969c51ba46305bdf5b176","permalink":"/code_and_software/eeg-notebooks/","publishdate":"2019-01-04T00:00:00Z","relpermalink":"/code_and_software/eeg-notebooks/","section":"code_and_software","summary":"eeg-notebooks is a python-based software library that provides a simple end-to-end platform for conducting cognitive neuroscience experiments using consumer-grade EEG technology. It emerged out of the close relationship between our good friends at NeuroTechX and other other good friends at InterAxon (just down the road from CAMH). Using eeg-notebooks together with a standard laptop computer and ~$200 EEG headset such as the Interaxon MUSE, one can conduct high-quality event-related potential (ERP) experiments that traditionally require research-grade equipment costing tens of thousands of dollars.","tags":["Software_Libraries","Featured"],"title":"eeg-notebooks","type":"code_and_software"},{"authors":null,"categories":null,"content":"A main overarching theme of most of the work we do in the WBMG is meso/macroscopic computational models of neural dynamics. We use mathematical models describing neural population activity on the scale of millimetres to centimetres, together with network structure from macaque histology or diffusion MRI fibre tracking, to simulate (approximately) whole-brain activity measurements of the kind measured by fMRI and M/EEG. These techniques are gradually allowing us to obtain more mechanistic insights into observed patterns in neuroimaging data relating to the resting state, oscillations, and the impact of neurological and neurodegenerative disease. Much of our work in this area is done collaboratively with colleagues in the Virtual Brain, Human Brain, and Blue Brain Projects.\nA specific area on which we are focusing is the generation and modulation of fast-timescale brain rhythms, as measured noninvasively by MEG and EEG.\nTogether with Dr. Jeremie Lefebvre of the Sync Lab at the \u0026lsquo;other\u0026rsquo; Krembil Research Institute (UHN Toronto Western Hospital), we have studied how low-frequency rhythms generated by delayed recurrent inhibition in thalamocortical loops interact with higher-frequency rhythms generated by intracolumnar excitatory-inhibitory interactions.\nWe find that a thalamocortical loop motif consisting of excitatory and inhibitory cortical neuronal populations, and excitatory (relay) and inhibitory (reticular) thalamic nuclei, can reproduce a large amount of the empirical pheonomena measured with resting state M/EEG. We are now investigating how the topological structure of the cortex interacts with the rhythmogenic properties of this base circuit motif.\nA different line of work, done together with Prof. Peter Robinson and colleagues at the University of Sydney, focuses on macroscopic neural field models for large-scale brain dynamics. These are generally formulated in terms of second-order partial differential (wave) equations that are substantially simpler to work with analytically than the stochastic delay-differential equation numerically integrated in TVB simulations. In particular, we have studied the role of the spherical topology and physical embedding of the cerebral cortex on anatomical structure and dynamics.\nThis, together with the steady-state eigenmode solutions to the Robinson neural field equations, predicts the presence of spherical harmonic-like patterns in fast-timescale activity measurements. Consistent with this, we have recently shown that this spherical harmonic-like spatial eigenmode structure is observed in cortical surface Laplacians, anatomical connectivity (tractography) networks, (MEG) functional connectivity networks, and numerical simulations. These results provide new insight into the role of the network topology and geometric embedding of the cerebral hemispheres in shaping brain dynamics, as well as the theoretical and practical utility of macroscopic neural field models with spherical boundary conditions.\n","date":1546473600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546473600,"objectID":"526d41857bbcfbc10b5139b48d5bb459","permalink":"/project/secondary_areas/models_of_brain_dynamics/","publishdate":"2019-01-03T00:00:00Z","relpermalink":"/project/secondary_areas/models_of_brain_dynamics/","section":"project","summary":"A main overarching theme of most of the work we do in the WBMG is meso/macroscopic computational models of neural dynamics. We use mathematical models describing neural population activity on the scale of millimetres to centimetres, together with network structure from macaque histology or diffusion MRI fibre tracking, to simulate (approximately) whole-brain activity measurements of the kind measured by fMRI and M/EEG. These techniques are gradually allowing us to obtain more mechanistic insights into observed patterns in neuroimaging data relating to the resting state, oscillations, and the impact of neurological and neurodegenerative disease.","tags":["modelling","theory"],"title":"Computational and theoretical models of brain dynamics","type":"project"},{"authors":null,"categories":null,"content":"ConWhAt (for Connectome-based White matter Atlas Analyses) is a tool for studying the effects of white matter damage on brain networks.\nIts primary functionality is connectome-based white matter atlas analysis, for which it uses a set of connectome-based white matter atlases developed by our research group for this purpose, as well as (optionally) similar atlases developed by other groups. ConWhAt can also be used for more conventional tract-based white matter atlas analysis, and more generally for querying how spatial locations in the white matter tissue relate to different grey matter regions and brain networks.\nOur main goal in developing ConWhAt is to support patient-specific neurocomputational modelling using The Virtual Brain (TVB) modelling and neuroinformatics platform. Examples of how to set up TVB models from ConWhAt analysis outputs are given in the documentation.\n","date":1546473600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546473600,"objectID":"9f20a26285b8755c8c86c64e445c30cb","permalink":"/code_and_software/conwhat/","publishdate":"2019-01-03T00:00:00Z","relpermalink":"/code_and_software/conwhat/","section":"code_and_software","summary":"ConWhAt (for Connectome-based White matter Atlas Analyses) is a tool for studying the effects of white matter damage on brain networks.\nIts primary functionality is connectome-based white matter atlas analysis, for which it uses a set of connectome-based white matter atlases developed by our research group for this purpose, as well as (optionally) similar atlases developed by other groups. ConWhAt can also be used for more conventional tract-based white matter atlas analysis, and more generally for querying how spatial locations in the white matter tissue relate to different grey matter regions and brain networks.","tags":["Software_Libraries"],"title":"ConWhAt","type":"code_and_software"},{"authors":null,"categories":null,"content":"","date":1546387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546387200,"objectID":"41b5718717e6104cec8842a539a73a13","permalink":"/project/primary_areas/brain_networks/","publishdate":"2019-01-02T00:00:00Z","relpermalink":"/project/primary_areas/brain_networks/","section":"project","summary":"","tags":["primaryarea"],"title":"Brain Networks","type":"project"},{"authors":null,"categories":null,"content":"","date":1546387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546387200,"objectID":"ed1f8ad962211225b62c450c55dc3a36","permalink":"/project/primary_areas/brain_rhythms/","publishdate":"2019-01-02T00:00:00Z","relpermalink":"/project/primary_areas/brain_rhythms/","section":"project","summary":"","tags":["primaryarea","brain rhythms"],"title":"Brain Rhythms","type":"project"},{"authors":null,"categories":null,"content":"Link here\nThe JDG LabNotebook is an online, digital lab notebook that has been used over the years as part technical blog, part project management tool, and part collaboration facilitator. Entries are generally a mix of text, code, and rich media.It has been used for both general notes and for research project documentation and collaboration, and so the site contains both open-access and password-protected + encrypted entries. Use the tags on the right hand side to list open access entries; all are welcome to peruse these and use any code goodies they find useful. If you are interested in seeing the content of any password-protected entries, get in touch.\nNote that a re-vamped version of the LabNotebook is currently underway, as a lot has happened in the jupyter, open science, and open publishing world since the LabNotebook and ipynb-workdocs (the library used to generate entries from jupyter notebooks using cell tag filtering, AWS S3 bucket manipulation through boto, reveal.js, and various other jazzy bits and bobs) were first put together. The content of the entries is entirely independent of this however, and I\u0026rsquo;m sure remain useful and relevant for a long time to come.\n","date":1546387200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546387200,"objectID":"7260d0282f65e205069ea0e674680c40","permalink":"/code_and_software/jdg_lab_notebook/","publishdate":"2019-01-02T00:00:00Z","relpermalink":"/code_and_software/jdg_lab_notebook/","section":"code_and_software","summary":"Link here\nThe JDG LabNotebook is an online, digital lab notebook that has been used over the years as part technical blog, part project management tool, and part collaboration facilitator. Entries are generally a mix of text, code, and rich media.It has been used for both general notes and for research project documentation and collaboration, and so the site contains both open-access and password-protected + encrypted entries. Use the tags on the right hand side to list open access entries; all are welcome to peruse these and use any code goodies they find useful.","tags":["Education"],"title":"JDG Lab Notebook","type":"code_and_software"},{"authors":null,"categories":null,"content":"The effects of white matter damage on cognitive and neural function is a subject of major interest for the group. Earlier work studied how different spatial profiles of damage following stroke resulted in differential types of linguistic impairments. A large part of our current work asks similar questions from a more systems level perspective: what is the impact of a given spatial profile of damage on the topological structure and dynamic behaviour in connectome-based neural mass models?\n\nIn order to help address questions like this, we are developing a new software library, ConWhAt, for \u0026lsquo;connectome-based white matter atlas analysis\u0026rsquo;. This allows the construction of \u0026lsquo;damaged\u0026rsquo; brain networks for a given spatial profile of damage, obviating the need to run complicated tractography analyses in individual patients.\n\nTogether with Jed Meltzer\u0026rsquo;s team from the Rotman Research Institute at Baycrest hospital, we are also beginning to investigate physiological changes in perilesional tissue following stroke, and how brain stimulation (informed by computational modelling) can be used to target and renormalize abberant activity in these regions.\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"9c57c1ef5b6e0d8199cb2ba5e64e7a2b","permalink":"/project/secondary_areas/brain_damage/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/project/secondary_areas/brain_damage/","section":"project","summary":"The effects of white matter damage on cognitive and neural function is a subject of major interest for the group. Earlier work studied how different spatial profiles of damage following stroke resulted in differential types of linguistic impairments. A large part of our current work asks similar questions from a more systems level perspective: what is the impact of a given spatial profile of damage on the topological structure and dynamic behaviour in connectome-based neural mass models?","tags":["modelling"],"title":"Modelling brain damage","type":"project"},{"authors":null,"categories":null,"content":" In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 \u0026hellip;\nTip 2 \u0026hellip;\n","date":1536451200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1536451200,"objectID":"6a451186c775f5f0adb3a0416d0cb711","permalink":"/tutorial/example/","publishdate":"2018-09-09T00:00:00Z","relpermalink":"/tutorial/example/","section":"tutorial","summary":"In this tutorial, I\u0026rsquo;ll share my top 10 tips for getting started with Academic:\nTip 1 \u0026hellip;\nTip 2 \u0026hellip;","tags":null,"title":"Example Page","type":"docs"},{"authors":["Seong Hyun Park","John D. Griffiths","André Longtin","Jérémie Lefebvre"],"categories":null,"content":"","date":1534982400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1534982400,"objectID":"1311e5db3f431b4a802635a0de0b220c","permalink":"/publication/2018-park-et-al-persistententrainment/","publishdate":"2018-08-23T00:00:00Z","relpermalink":"/publication/2018-park-et-al-persistententrainment/","section":"publication","summary":"We investigate the dynamics of a non-linear network with noise, periodic forcing and delayed feedback. Our model reveals that there exist forcing regimes—called persistent entrainment regimes—in which the system displays oscillatory responses that outlast the termination of the forcing. Our analysis shows that in presence of delays, periodic forcing can selectively excite components of an infinite reservoir of intrinsic modes and hence display a wide range of damped frequencies. Mean-field and linear stability analysis allows a characterization of the magnitude and duration of these persistent oscillations, as well as their dependence on noise intensity and time delay. These results provide new perspectives on the control of non-linear delayed system using periodic forcing.","tags":[],"title":"Persistent entrainment in nonlinear neural networks with noise","type":"publication"},{"authors":null,"categories":null,"content":" ","date":1517443200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1517443200,"objectID":"611c348d10b3efc733190c163bfd8760","permalink":"/talks_and_teaching/2018-03-01__reproducible_research_workflows/","publishdate":"2018-02-01T00:00:00Z","relpermalink":"/talks_and_teaching/2018-03-01__reproducible_research_workflows/","section":"talks_and_teaching","summary":"Hands-on tutorial talk at University of Toronto","tags":["OpenScience"],"title":"Reproducible Research Workflows and Hosting a Technical Blog","type":"talks_and_teaching"},{"authors":["Axel Hutt","John D. Griffiths","Christoph S Herrmann","Jereremie Lefebvre"],"categories":null,"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"b3019fed06d57b6bc003375383e12a0a","permalink":"/publication/2018-hutt-et-al-effectofstimwaveform/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/2018-hutt-et-al-effectofstimwaveform/","section":"publication","summary":"","tags":[],"title":"Effect of stimulation waveform on the non-linear entrainment of cortical alpha oscillations","type":"publication"},{"authors":["Joelle Zimmerman","John D. Griffiths","Michael Schirner","Petra Ritter","Anthony R. McIntosh"],"categories":null,"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"0584fd766f0fbeb2796e95e5f2b8cf8c","permalink":"/publication/2018-zimmerman-et-al-subject-specificity/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/2018-zimmerman-et-al-subject-specificity/","section":"publication","summary":"","tags":[],"title":"Subject-specificity of the correlation between large-scale structural and functional connectivity","type":"publication"},{"authors":["Jennifer D Ryan","Kelly Shen","Arber Kacollja","Heather Tian","John D. Griffiths","Randy McIntosh"],"categories":null,"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"3ca8dc458f514f1d9849a53935221edc","permalink":"/publication/2018-ryan-et-al-functionalorganization/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/2018-ryan-et-al-functionalorganization/","section":"publication","summary":"","tags":[],"title":"The functional reach of the hippocampal memory system to the oculomotor system","type":"publication"},{"authors":null,"categories":null,"content":"","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"d3ce0694f66f0c9a37f69df83db7d3c3","permalink":"/talks_and_teaching/2018-01-01__using_hcp_data/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2018-01-01__using_hcp_data/","section":"talks_and_teaching","summary":"Tutorial session given at Rotman Research Institute, Baycrest (jointly given with Dr. Erin Dickie)","tags":["Neuroimaging"],"title":"Using human connectome project (HCP) data","type":"talks_and_teaching"},{"authors":[],"categories":null,"content":"","date":1512086400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1512086400,"objectID":"2f6fa7c950391b9a35023c3698dafd77","permalink":"/talks_and_teaching/2017-12-01__spherical_harmonics/","publishdate":"2017-12-01T00:00:00Z","relpermalink":"/talks_and_teaching/2017-12-01__spherical_harmonics/","section":"talks_and_teaching","summary":"Invited talk at the 'Large scale trends in cortical organization' workshop, Leipzig","tags":["Modelling"],"title":"The spherical harmonic structure of the human connectome","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":"","date":1491004800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1491004800,"objectID":"00547f21e2715025f6276d6a17c0cb16","permalink":"/talks_and_teaching/2017-04-01__modelling_brain_dynamics_at_rest/","publishdate":"2017-04-01T00:00:00Z","relpermalink":"/talks_and_teaching/2017-04-01__modelling_brain_dynamics_at_rest/","section":"talks_and_teaching","summary":"Guest (inaugral) lecture given for the Krembil Computational Neuroscience (KCN) Events series","tags":["Modelling"],"title":"Modelling brain dynamics at rest: practical tools and theoretical perspectives","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" ","date":1488326400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1488326400,"objectID":"5be8d17e26288cca1da4ac4f74b3c961","permalink":"/talks_and_teaching/2017-03-01__intro_to_neuroimaging/","publishdate":"2017-03-01T00:00:00Z","relpermalink":"/talks_and_teaching/2017-03-01__intro_to_neuroimaging/","section":"talks_and_teaching","summary":"First of the two lectures given at the Banff International Research Station (BIRS) workshop on Topological Methods in Brain Network Analysis","tags":["Pinned","Neuroimaging"],"title":"Intro to Neuroimaging for Algebraic Topologists","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" ","date":1488326400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1488326400,"objectID":"081a7374d618e0b685625e65e222f637","permalink":"/talks_and_teaching/2017-03-01__working_with_neuro_data/","publishdate":"2017-03-01T00:00:00Z","relpermalink":"/talks_and_teaching/2017-03-01__working_with_neuro_data/","section":"talks_and_teaching","summary":"Second of the two lectures given at the Banff International Research Station (BIRS) workshop on Topological Methods in Brain Network Analysis","tags":["Pinned","Neuroimaging"],"title":"Working with Neuroimaging data (for Algebraic Topologists)","type":"talks_and_teaching"},{"authors":["Robinson, Peter A","Zhao, Xuelong","Aquino, Kevin M","Griffiths, John D","Sarkar, Somwrita","Mehta-Pandejee, Grishma"],"categories":null,"content":"","date":1451606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1451606400,"objectID":"68e82f6b8eac82b63bbcc54515efaf55","permalink":"/publication/2016-robinson-et-al-eigenmodestructure/","publishdate":"2016-01-01T00:00:00Z","relpermalink":"/publication/2016-robinson-et-al-eigenmodestructure/","section":"publication","summary":"","tags":[],"title":"Eigenmodes of brain activity: Neural field theory predictions and comparison with experiment","type":"publication"},{"authors":["John D. Griffiths"],"categories":null,"content":"","date":1420070400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1420070400,"objectID":"017edc06e0107dc1fbbfb38c84137765","permalink":"/publication/2015-griffiths-causal/","publishdate":"2015-01-01T00:00:00Z","relpermalink":"/publication/2015-griffiths-causal/","section":"publication","summary":"","tags":[],"title":"Causal influence in neural systems: reconciling mechanistic-reductionist and statistical perspectives","type":"publication"},{"authors":["John D. Griffiths","William D. Marslen-Wilson","Emmanuel A. Stamatakis","Lorraine K. Tyler"],"categories":null,"content":"","date":1356998400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1356998400,"objectID":"8d1b249f9abf807f381c848e9a8f5728","permalink":"/publication/2013-grifffiths-et-al-functionalorganizationoflanguage/","publishdate":"2013-01-01T00:00:00Z","relpermalink":"/publication/2013-grifffiths-et-al-functionalorganizationoflanguage/","section":"publication","summary":"","tags":[],"title":"Functional organization of the neural language system: dorsal and ventral pathways are critical for syntax","type":"publication"},{"authors":["Marina Papoutsi","Emmanuel A. Stamtakis","John D. Griffiths","William D. Marslen-Wilson","Lorraine K. Tyler"],"categories":null,"content":"","date":1293840000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1293840000,"objectID":"71450039c0c7848c9f7bb23a2e0b33e1","permalink":"/publication/2011-paoutsi-et-al-istheleftdlpfc/","publishdate":"2011-01-01T00:00:00Z","relpermalink":"/publication/2011-paoutsi-et-al-istheleftdlpfc/","section":"publication","summary":"","tags":[],"title":"Is left fronto-temporal connectivity essential for syntax? Effective connectivity, tractography and performance in left-hemisphere damaged patients.","type":"publication"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"8ffc3b5ee68f1940aa3951de66424d5d","permalink":"/talks_and_teaching/2017-01-01__analysis_of_fmri_data/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2017-01-01__analysis_of_fmri_data/","section":"talks_and_teaching","summary":"Lecture given by JG at the Canadian Association for Neuroscience Meeting Satellite workshop: *Neural signal and image processing: quantitative analysis of neural activity*","tags":["Neuroimaging"],"title":"Analysis of fMRI data: principles \u0026 techniques","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"804d49a4bd3b74834221a542cd15ec61","permalink":"/talks_and_teaching/2018-02-01__intro_to_dwi/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2018-02-01__intro_to_dwi/","section":"talks_and_teaching","summary":"Workshop given by JG at Rotman Research Institute","tags":["Neuroimaging"],"title":"Introduction to diffusion-weighted MRI","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":" Lab Members Dr. John David Griffiths Group Lead\n CV\nDr. John D. Griffiths is a cognitive and computational neuroscientist based at the Toronto Centre for Addiction and Mental Health (CAMH)’s Krembil Centre for Neuroinformatics (KCNI; krembilneuroinformatics.ca), where he leads a research team focused on computational modelling of brain network dynamics, neurostimulation, and multimodal neuroimaging data (www.grifflab.com). He is an Assistant Professor in the University of Toronto Departments of Psychiatry, Medical Sciences, and Biomedical Engineering. Originally hailing from North-West England, Dr. Griffiths completed his PhD at the University of Cambridge, where he worked on structural and functional neuroimaging of language comprehension and its impairment in ageing and stroke. Prior to this, he completed his undergraduate training in psychology, philosophy, and cognitive neuroscience at the Universities of Warwick and York, UK. Subsequently, he worked as a post-doctoral fellow in the groups of Peter Robinson (University of Sydney School of Physics), Randy McIntosh (Rotman Research Institute) and Jeremie Lefebvre (Toronto Western Hospital). During this time he conducted research and developed core expertise in multimodal neuroimaging and macro connectomics, neurophysiological modelling and numerical simulations, neural field theory, and noninvasive neuromodulation. He also was an early contributor to the The Virtual Brain (TVB) software library and research consortium. Dr. Griffiths joined the CAMH KCNI as a PI and head of Whole-Brain Modelling in 2019, where he leads a multidisciplinary team of engineers, physicists, mathematicians, biologists, and psychologists, working on endogenous oscillations and neurophysiology of noninvasive brain stimulation. The theory-based work of the group is complemented by novel EEG, fNIRS, TMS, and MRI experiments in healthy and patient populations.\nDr. Davide Momi Post-Doctoral Fellow\n Research Interests:\nDavide completed his Ph.D. at the Department of Neuroscience, Imaging and Clinical Sciences at the University G. d\u0026rsquo;Annunzio of Chieti. As part of his PhD, Davide attended a period abroad as a visiting PhD student at the Martinos Center for Biomedical Imaging in Boston. Prior to his doctoral studies, he obtained a Master’s degree in Neurosciences and Neuro-Psychological Rehabilitation from the University of Bologna and a Bachelor’s in psychology from the University of Perugia. Davide has experience with multimodal neuroimaging data (e.g. DWI, fMRI, ASL) analysis, TMS applications, EEG data collection and analysis, quantitative structural MRI assessment (e.g. brain morphometry, cortical thickness, etc.), machine learning, cognitive tasks development with a growing passion for simulations of network-level macroscale brain dynamics. His main interest is focused on predicting the TMS signal propagation at the network-level, based on neuroimaging and electrophysiological data. At the WBMG, Davide will be working on several projects which will involve multimodal neuroimaging, non-invasive brain stimulation (TMS, TES) and whole-brain modelling. The primary aim of his project is to predict TMS outcomes by combining computational, neuroimaging and electrophysiological approaches.\nTrivia about Davide:\nAs a proud Italian, Davide never forgets to renew his membership for the “fighting against pineapple on pizza” (a.k.a. Hawaiian pizza) movement. He conducts a healthy lifestyle. He goes to the gym; he likes to hike and go for walks surrounded by nature. Davide is an enthusiastic basketball fan and he used to play semi-pro in the past. He is 6 ft 6 in. tall and is gradually getting used to his height.\nShreyas Harita Ph.D. Student\n Research Interests:\nShreyas is a Ph.D. student who joined the lab as a Research Trainee in May, 2019. His research interests lie in the use of connectome based neural mass modeling to study neural population activity, and how it changes in response to brain stimulation and in neuropathological conditions. The primary aims of his research project are as follows:\n- To investigate the stimulation-induced network propagation and connectivity patterns in the human brain. He hopes to achieve a better understanding of this using brain stimulation techniques like TMS and multimodal brain imaging techniques such as structural MRI, fMRI, DTI, and EEG.\n- To study how individual variability in brain geometry affects responses to brain stimulation, and how this information can inform neuro-navigation techniques to improve the identification of potential TMS targets.\nTrivia about Shreyas:\nShreyas claims to be the \u0026lsquo;funny\u0026rsquo; one. No one else sees it. Shreyas\u0026rsquo; favourite cuisines include Indian, Mexican, Arabic and Italian. He is the unofficial social director for the students at the lab and while no one really appointed him, they like the fact that he tries to bring people together. Shreyas leads an active lifestyle. He plays football (soccer), swims and enjoys biking (even in the winter!) within the city and on natural trails. Shreyas is an avid chess player and enjoys playing blitz and bullet chess.\nSorenza Bastiaens Ph.D. Student\n Research Interests:\nSorenza officially joined the lab in January, 2021. She completed her M.Eng. in Biomedical Engineering at Imperial College London, with a final year project at the Neural Coding department. She first worked with Dr. John Griffiths during Google Summer of Code 2020 where she developed a python package for running validation tests on models generating a neural power spectrum. Sorenza\u0026rsquo;s research in the lab is focused on mathematical modelling of large-scale brain network dynamics, with a focus on measured alpha oscillations observed in EEG. The aim of her project is to use and extend these computational models to better understand the role of brain rhythms in large-scale brain networks. She is also interested in understanding how brain stimulation affects these networks, which is crucial to finding treatments for neurological diseases. Sorenza is currently applying her skills in computational neuroscience, neuroimaging and signal processing to compare and analyze different cortico-cortical and corticothalamic models such as the Jansen-Rit, Wilson-Cowan and Robinson models.\nTrivia about Sorenza:\nSorenza is the \u0026lsquo;sporty\u0026rsquo; one. She is always down for pretty much any physical activity and particularly loves to play football (aka soccer), tennis, and loves to go running and hiking. She claims to have a mild coffee dependency. Her day has to start with a cup of coffee, it is non-negotiable apparently. The rest of her day involves some form of working out and meditation routine, followed by a session of reading. Sorenza is no stranger to the world of video games and enjoys playing action-adventure and battle royale games. She is a huge fan of Harry Potter (#Gryffindor) and sci-fi shows. Her secret talent is juggling and bad singing.\nKevin Kadak Ph.D. Student\n Research Interests:\nKevin is a Ph.D. student who joined the lab in September, 2020. His research focuses on modelling synaptic plasticity dynamics in the brain to better optimize TMS stimulation parameters for the treatment of major depressive disorder. Within the theories of calcium-dependent plasticity and metaplasticity, he will be expanding current synaptic simulation models to encompass E-fields estimates, white matter connectivity, cortical geometry, and corticothalamic oscillations to project patient-specific stimulation protocols. This may aid in more robust, long-term neural rewiring and treatment. Both structural and functional imaging techniques, including fMRI and EEG, will be utilized to inform these models.\nHe looks forward to leveraging his previous experience in modelling zebrafish decision patterns towards further refining his computational skillset and, most ideally, constructing novel frameworks for applied clinical use.\nTrivia about Kevin:\nWhen the world isn’t collapsing in on itself, Kevin enjoys Brazilian Jiu-Jitsu, thrift shopping, bothering others with puns, and practicing guitar despite never actually managing to improve at it. Kevin is a firm believer in carbonated water, pineapple on pizza (DO NOT put him on a group project with Davide!), and cutting sandwiches diagonally. He also spends his spare time convincing the world that Sky High (2005) is a cinematic masterpiece. When not involved with one of these activities, Kevin is probably squinting at code documentation, confused.\nTaha Morshedzadeh M.Sc. Student\n Research Interests:\nTaha initially joined the lab as an undergraduate research student in January 2021. He started his M.Sc. here in September 2021. He is working on data aquired from market-available electroencephalography headsets such as Muse S, and will be fitting thalamocortical neural field models to this data.\nHe studied Dentistry in Iran for 3 years before dropping out in 2016 to do Neuroscience in Canada. During this time in Iran, he worked with Prof. Mohammad Nami on EEG data. He completed his BSc in Neuroscience at the University of Toronto in 2020. His undergraduate supervisor was Dr. Milad Lankarany with whom he worked on modelling basal ganglia neurons in Parkinson\u0026rsquo;s Disease. He also co-leads NeurotechUofT (neurotechuoft.com), a NeuroTechX student club working on open-source BCI projects.\nTrivia about Taha:\nTaha enjoys running in the hills of Toronto suburbs and city biking. You can always find him up to some shenanigans on Strava. He also redecorates his home almost every two weeks. He says he doesn\u0026rsquo;t need to see a therapist about this and promises that he\u0026rsquo;s good. Taha enjoys listening to and singing Persian traditional music and is trying to get into composing as well! Taha would like to use this tribune to support Davide in his fight against all evil (i.e., pineapple pizza).\nParsa Oveisi M.A.Sc. Student\n Research Interests:\nParsa joined Grifflab in September 2021 as an undergraduate research student. Parsa’s overarching interest in learning about new frameworks in which the functioning of the brain can be described. Currently, Parsa is working on data acquisition of concurrent EEG and functional near-infrared spectroscopy (fNIRS) recordings using the Kernel Flow ™ headset. He is then using this data, as well as other EEG and fMRI/fNIRS datasets, to develop a deep learning-based whole brain model that can be fitted to individual brains. The model can then be used to predict empirical EEG and hemodynamic responses, as well as shedding light on how patterns from these different modalities arise and interact. Parsa officially joined the lab as a Master\u0026rsquo;s student in September 2022.\nTrivia about Parsa:\nParsa has a spur-of-the-moment attitude in picking up new hobbies and sports. Sources say he picked up archery by fantasizing about being a steppe horse archer of old, though it cannot be confirmed. You will usually find him at his desk (yes, his. So don’t book it!) taking advantage of the extra monitor. If you look closely, however, you will find that where he actually is, is in a state of utter confusion. The keyboard might be being pressed but no actual progress is being made. If you ever need to talk to him, just follow the scent of black tea, or alternatively, start criticizing Lord of the Rings. He will find you.\nAndrew Clappison M.Sc. Student\n Research Interests:\nAndrew joined the WBMG in September 2021, as a graduate student in the School of Electrical Engineering and Computer Science at the University of Ottawa. Previously, he had completed a Bachelor of Mathematics degree at the University of Waterloo with minors in Computational Mathematics and Cognitive Science. Andrew is interested in discovering how the brain works, particularly how it produces consciousness. During his time in the lab, he will be working with multimodal whole brain models and using machine learning to fit them to concurrent resting-state EEG and fMRI/fNIRS datasets. In doing so, he will try to reproduce and explain phenomena observed in neuroimaging studies such as the Alpha Power - BOLD anticorrelation.\nLab Alumni Minarose Ismail Summer research student\n Minoarose was a summer research student during the summer of 2019. She was a first year undergraduate student at McMaster pursuing a bachelor in Neuroscience with a background in Python coding. Her work was in the field of computational neuroscience and her project dealt with fitting oscillations and one over frequency on MEG data using the FOOOF toolbox to parametrize the power spectra and perform summary analysis.\nSurya Pandiaraju Co-op Student\n Research Interests:\nSurya is a biomedical engineering student at the University of Waterloo. He is co-supervised by Dr. Andreea Diaconescu. He is interested in EEG data analysis and brain-computer interfaces and their applications in both clinical and consumer-based settings. Currently, he is working on a project focused on designing a pipeline for clinically obtained EEG data that would generate first-level and second-level statistical analyses and is doing so by modifying currently existing tools in MATLAB.\nTrivia:\nSurya enjoys partaking in a number of hobbies including but not limited to drawing (even when he is not supposed to be), digital arts, collectible card games, video games, anime \u0026amp; manga, working out, exploring new restaurants, singing/whistling/beatboxing/humming in the car whilst pretending no one can hear him, and is trying to get his hand in producing after compulsively purchasing a mini MIDI keyboard after watching a video or two on his YouTube recommended list at 12am. If Surya could have any superpower, it would be universal time-control, which he could use to get back the time he spent trying to make this funny.\nDr. Neda Kaboodvand Post-Doctoral Fellow\n Research Interests:\nNeda completed her Ph.D. at the department of clinical neuroscience at Karolinska Institutet, Stockholm, Sweden. In addition, she has master’s in biomedical engineering from Tehran Polytechnic (aka Amirkabir University of Technology), Tehran, Iran. Neda has a strong analytical and computational background, with a growing passion for applying quantitative problem solving and innovative data analysis for solving tangible real-world problems. She has experience in modeling healthy and diseased brains, linear/non-linear time-series and neuroimaging data analysis, statistical and computational modeling of the data, network science and machine learning. She enjoys working in dynamic cross-functional teams on challenging tasks, with measurable real-world outcomes. She joined the lab in July, 2020. She is currently working on whole-brain modelling as well as the analysis and modelling of TMS-evoked responses. The primary aim of her project is to undertake key computational and experimental groundwork for the development and iterative improvement of a novel computational tool for predicting TMS responses and induced plasticity effects. Her long-term aim is to develop computational models of repetitive TMS responses that can be used by clinicians and researchers for optimal patient-specific design of brain stimulation therapies.\nFrank Mazza M.Sc. Student\n Research Interests:\nFrank is a M.Sc. student who joined the lab in September, 2019. He is co-supervised by Dr. Etay Hay (https://www.haybrainlab.com/). Frank is interested in identifying electrophysiological signals for diagnosis of depression and transcranial magnetic stimulation (TMS) protocols for treatment of depression, using computer simulations of detailed brain circuits and whole-brain networks. With previous work in identifying EEG correlates of response in a novel rTMS stimulation paradigm (OFC-rTMS), he now focuses on identifying biomarkers in brain signals for diagnosis of depression by linking cellular, circuit, and connectivity changes to signals relevant for clinical diagnosis. In addition, he is working to optimize TMS protocols using computer simulations at the local and global brain scales by simulating protocol effects on local circuits, such as circuit inhibition, and the propagation of activity to other regions in the brain.\nTrivia about Frank:\nFrank \u0026lsquo;The Grank\u0026rsquo; Mazza. Frank is quite and likes to go about his business, often finishing his work before deadlines, be it grants, abstracts, or manuscripts. Frank does intermittent fasting. No one knows why. Frank likes to read and write about all things art and architecture. He likes to paint, ink, sketch, sculpt and lino-print. Frank has a humble garden where he likes to experiment with growing different plants. He likes to work out and is avid music fan, trying to appreciate everything that isn\u0026rsquo;t country.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"c1d17ff2b20dca0ad6653a3161942b64","permalink":"/people/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/people/","section":"","summary":"Lab Members Dr. John David Griffiths Group Lead\n CV\nDr. John D. Griffiths is a cognitive and computational neuroscientist based at the Toronto Centre for Addiction and Mental Health (CAMH)’s Krembil Centre for Neuroinformatics (KCNI; krembilneuroinformatics.ca), where he leads a research team focused on computational modelling of brain network dynamics, neurostimulation, and multimodal neuroimaging data (www.grifflab.com). He is an Assistant Professor in the University of Toronto Departments of Psychiatry, Medical Sciences, and Biomedical Engineering.","tags":null,"title":"People","type":"docs"},{"authors":null,"categories":null,"content":" Welcome to Slides Academic\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;) Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = \\;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne Two Three \nA fragment can accept two optional parameters:\n class: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\n Only the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/img/boards.jpg\u0026quot; \u0026gt;}} {{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}} {{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}} Custom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; } Questions? Ask\nDocumentation\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"c2915ec5da95791851caafdcba9664af","permalink":"/slides/example-slides/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/slides/example-slides/","section":"slides","summary":"Welcome to Slides Academic\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;) Math In-line math: $x + y = z$","tags":null,"title":"Slides","type":"slides"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"825ce41f7da94d7247fa74f4dc225962","permalink":"/talks_and_teaching/2016-01-01__wiredminds_allenatlas/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2016-01-01__wiredminds_allenatlas/","section":"talks_and_teaching","summary":"JG \u0026 CH discuss the seminal paper by Oh et al.","tags":["Philosophy"],"title":"Wired Minds Episode: Allen Atlas","type":"talks_and_teaching"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"2c2d30b57e58592695efacacf023eb65","permalink":"/talks_and_teaching/2016-01-01__wiredminds_philosophyofminduploading/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/talks_and_teaching/2016-01-01__wiredminds_philosophyofminduploading/","section":"talks_and_teaching","summary":"John Griffiths \u0026 Christopher Harris discuss an interesting but troubled corner of modern philosophy of mind","tags":["Philosophy"],"title":"Wired Minds Episode: Philosophy \u0026 Neuroscience of Mind Uploading","type":"talks_and_teaching"}] \ No newline at end of file diff --git a/index.xml b/index.xml index 51dfb96..2c1d494 100644 --- a/index.xml +++ b/index.xml @@ -1,4 +1,4 @@ -KCNI WBMG on KCNI WBMG/Recent content in KCNI WBMG on KCNI WBMGHugo -- gohugo.ioen-us&copy; 2018Wed, 02 Jan 2019 00:00:00 +0000Brain Stimulation/project/primary_areas/brain_stimulation/Wed, 02 Jan 2019 00:00:00 +0000/project/primary_areas/brain_stimulation/FOOOF-Unit/code_and_software/fooof-unit/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/fooof-unit/Griffiths Et Al. 2020/code_and_software/griffithsetal2020/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/griffithsetal2020/Griffiths Et Al. 2022/code_and_software/griffithsetal2022/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/griffithsetal2022/Harita et al. 2022/code_and_software/haritaetal2022/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/haritaetal2022/KCNI School Whole-Brain Modelling Lectures/talks_and_teaching/2023-01-07__kcnischool2022_wbm_lectures/Sun, 01 Jan 2023 00:00:00 +0000/talks_and_teaching/2023-01-07__kcnischool2022_wbm_lectures/<div align="center"> <img src="featured.png" margin="15px 15px 15px 15px" width="1800" /> </div> +KCNI WBMG on KCNI WBMG/Recent content in KCNI WBMG on KCNI WBMGHugo -- gohugo.ioen-us&copy; 2018Wed, 02 Jan 2019 00:00:00 +0000Brain Stimulation/project/primary_areas/brain_stimulation/Wed, 02 Jan 2019 00:00:00 +0000/project/primary_areas/brain_stimulation/Dynamic brain network models: How interactions in the structural connectome shape brain dynamics/publication/2024-cabral-and-griffiths-book-chapter/Mon, 01 Jan 2024 00:00:00 +0000/publication/2024-cabral-and-griffiths-book-chapter/FOOOF-Unit/code_and_software/fooof-unit/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/fooof-unit/Griffiths Et Al. 2020/code_and_software/griffithsetal2020/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/griffithsetal2020/Griffiths Et Al. 2022/code_and_software/griffithsetal2022/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/griffithsetal2022/Harita et al. 2022/code_and_software/haritaetal2022/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/haritaetal2022/KCNI School Whole-Brain Modelling Lectures/talks_and_teaching/2023-01-07__kcnischool2022_wbm_lectures/Sun, 01 Jan 2023 00:00:00 +0000/talks_and_teaching/2023-01-07__kcnischool2022_wbm_lectures/<div align="center"> <img src="featured.png" margin="15px 15px 15px 15px" width="1800" /> </div> <p><br></p> <div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden;"> <iframe src="//www.youtube.com/embed/EapY9R_yHbU?autoplay=1" style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; border:0;" allowfullscreen title="YouTube Video"></iframe> @@ -6,5 +6,4 @@ <p><br></p> <div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden;"> <iframe src="//www.youtube.com/embed/1LpH3gQ-UX0?autoplay=1" style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; border:0;" allowfullscreen title="YouTube Video"></iframe> -</div>Momi Et Al. 2023/code_and_software/momietal2023/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/momietal2023/<p>See <a href="../../publication/2023-momi-et-al-tmsevokedresponses/">here</a></p>OHBM2023 Brain Modelling Course/code_and_software/ohbm_wbm_course/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/ohbm_wbm_course/TMS-Evoked Responses Are Driven by Recurrent Large-Scale Network Dynamics/publication/2023-momi-et-al-tmsevokedresponses/Sun, 01 Jan 2023 00:00:00 +0000/publication/2023-momi-et-al-tmsevokedresponses/WhoBPyT/code_and_software/whobpyt/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/whobpyt/Deep-learning based parameter estimation for neurophysiological models of neuroimaging data/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/Sat, 01 Oct 2022 00:00:00 +0000/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/Mapping inter-individual functional connectivity variability in TMS targets for major depressive disorder/publication/2022-harita-et-al-mapping-interindividual-fc-tms/Thu, 23 Jun 2022 00:00:00 +0000/publication/2022-harita-et-al-mapping-interindividual-fc-tms/Whole-Brain Modelling: Past, Present, and Future/publication/2022-griffiths-et-al-wbm-pastpresentfuture/Tue, 01 Feb 2022 00:00:00 +0000/publication/2022-griffiths-et-al-wbm-pastpresentfuture/<div align="center"> <img src="/img/wbm_diagram_inverted.png" margin="15px 15px 15px 15px" width="800" /> </div>EEG Biomarkers of reduced inhibition in human cortical microcircuits in depression/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/Sat, 30 Jan 2021 00:00:00 +0000/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/Interview w/ Gyorgi Buzsaki on 'The Brain From Inside Out'/talks_and_teaching/2021-01-01__nbn_thebraininsideout/Tue, 19 Jan 2021 00:00:00 +0000/talks_and_teaching/2021-01-01__nbn_thebraininsideout/<div align="center"> <img src="featured.jpg" margin="15px 15px 15px 15px" width="1800" /> </div> -<p><br></p> \ No newline at end of file +</div>Momi Et Al. 2023/code_and_software/momietal2023/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/momietal2023/<p>See <a href="../../publication/2023-momi-et-al-tmsevokedresponses/">here</a></p>OHBM2023 Brain Modelling Course/code_and_software/ohbm_wbm_course/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/ohbm_wbm_course/TMS-Evoked Responses Are Driven by Recurrent Large-Scale Network Dynamics/publication/2023-momi-et-al-tmsevokedresponses/Sun, 01 Jan 2023 00:00:00 +0000/publication/2023-momi-et-al-tmsevokedresponses/WhoBPyT/code_and_software/whobpyt/Sun, 01 Jan 2023 00:00:00 +0000/code_and_software/whobpyt/Deep-learning based parameter estimation for neurophysiological models of neuroimaging data/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/Sat, 01 Oct 2022 00:00:00 +0000/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/Mapping inter-individual functional connectivity variability in TMS targets for major depressive disorder/publication/2022-harita-et-al-mapping-interindividual-fc-tms/Thu, 23 Jun 2022 00:00:00 +0000/publication/2022-harita-et-al-mapping-interindividual-fc-tms/Whole-Brain Modelling: Past, Present, and Future/publication/2022-griffiths-et-al-wbm-pastpresentfuture/Tue, 01 Feb 2022 00:00:00 +0000/publication/2022-griffiths-et-al-wbm-pastpresentfuture/<div align="center"> <img src="/img/wbm_diagram_inverted.png" margin="15px 15px 15px 15px" width="800" /> </div>EEG Biomarkers of reduced inhibition in human cortical microcircuits in depression/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/Sat, 30 Jan 2021 00:00:00 +0000/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/ \ No newline at end of file diff --git a/project/primary_areas/brain_networks/index.html b/project/primary_areas/brain_networks/index.html index dff3748..17963fa 100644 --- a/project/primary_areas/brain_networks/index.html +++ b/project/primary_areas/brain_networks/index.html @@ -1,7 +1,10 @@ Brain Networks | KCNI WBMG

Brain Networks

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primaryarea

Publications

A major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. …
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primaryarea

Publications

PDF +Code +DOI +url

A major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. …
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Connectome-based neural mass modelling is the emerging computational neuroscience paradigm for simulating large-scale network dynamics …
Preprint PDF diff --git a/project/primary_areas/brain_stimulation/index.html b/project/primary_areas/brain_stimulation/index.html index 6707525..a945355 100644 --- a/project/primary_areas/brain_stimulation/index.html +++ b/project/primary_areas/brain_stimulation/index.html @@ -2,7 +2,10 @@

Brain Stimulation

primaryarea -brain stimulation

Publications

A major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. …
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Publications

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A major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. …
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Transcranial magnetic stimulation (TMS) is an emerging alternative to existing treatments for major depressive disorder (MDD). The …
PDF Code diff --git a/publication/2011-paoutsi-et-al-istheleftdlpfc/index.html b/publication/2011-paoutsi-et-al-istheleftdlpfc/index.html index 9a49c1f..9846114 100644 --- a/publication/2011-paoutsi-et-al-istheleftdlpfc/index.html +++ b/publication/2011-paoutsi-et-al-istheleftdlpfc/index.html @@ -10,7 +10,7 @@ ,
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© 2018 · +Cite

Type
Journal article
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Neuroimage
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© 2018 · Powered by the Academic theme for Hugo. diff --git a/publication/2023-momi-et-al-tmsevokedresponses/index.html b/publication/2023-momi-et-al-tmsevokedresponses/index.html index 699a77c..60d4832 100644 --- a/publication/2023-momi-et-al-tmsevokedresponses/index.html +++ b/publication/2023-momi-et-al-tmsevokedresponses/index.html @@ -15,7 +15,7 @@

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Abstract

A major question in systems and cognitive neuroscience is to what extent neurostimulation responses are driven by recurrent activity. This question finds sharp relief in the case of TMS-EEG evoked potentials (TEPs). TEPs are spatiotemporal waveform patterns with characteristic inflections at ∼50ms, ∼100ms, and ∼150-200ms following a single TMS pulse that disperse from, and later reconverge to, the primary stimulated regions. What parts of the TEP are due to recurrent activity? And what light might this shed on more general principles of brain organization? We studied this using source-localized TMS-EEG analyses and whole-brain connectome-based computational modelling. Results indicated that recurrent network feedback begins to drive TEP responses from ∼100ms post-stimulation, with earlier TEP components being attributable to local reverberatory activity within the stimulated region. Subject-specific estimation of neurophysiological parameters additionally indicated an important role for inhibitory GABAergic neural populations in scaling cortical excitability levels, as reflected in TEP waveform characteristics.

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brain stimulation -brain networks
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© 2018 · Powered by the Academic theme for Hugo. diff --git a/publication/index.xml b/publication/index.xml index 672a0fc..90ed867 100644 --- a/publication/index.xml +++ b/publication/index.xml @@ -1 +1 @@ -Publications on KCNI WBMG/publication/Recent content in Publications on KCNI WBMGHugo -- gohugo.ioen-us&copy; 2018Sun, 01 Jan 2017 00:00:00 +0000TMS-Evoked Responses Are Driven by Recurrent Large-Scale Network Dynamics/publication/2023-momi-et-al-tmsevokedresponses/Sun, 01 Jan 2023 00:00:00 +0000/publication/2023-momi-et-al-tmsevokedresponses/Deep-learning based parameter estimation for neurophysiological models of neuroimaging data/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/Sat, 01 Oct 2022 00:00:00 +0000/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/Mapping inter-individual functional connectivity variability in TMS targets for major depressive disorder/publication/2022-harita-et-al-mapping-interindividual-fc-tms/Thu, 23 Jun 2022 00:00:00 +0000/publication/2022-harita-et-al-mapping-interindividual-fc-tms/Whole-Brain Modelling: Past, Present, and Future/publication/2022-griffiths-et-al-wbm-pastpresentfuture/Tue, 01 Feb 2022 00:00:00 +0000/publication/2022-griffiths-et-al-wbm-pastpresentfuture/EEG Biomarkers of reduced inhibition in human cortical microcircuits in depression/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/Sat, 30 Jan 2021 00:00:00 +0000/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/A connectome-based, corticothalamic model of state- and stimulation-dependent modulation of rhythmic neural activity and connectivity/publication/2020-griffiths-et-al-corticothalamic-model/Thu, 30 Jan 2020 00:00:00 +0000/publication/2020-griffiths-et-al-corticothalamic-model/The functional reach of the hippocampal memory system to the oculomotor system/publication/2020-ryan-et-al-modeling/Wed, 01 Jan 2020 00:00:00 +0000/publication/2020-ryan-et-al-modeling/Shaping brain rhythms: dynamic and control-theoretic perspectives on periodic brain stimulation for treatment of neurological disorders/publication/2019-griffiths-et-al-shapingbrainrhythms/Wed, 30 Jan 2019 00:00:00 +0000/publication/2019-griffiths-et-al-shapingbrainrhythms/Persistent entrainment in nonlinear neural networks with noise/publication/2018-park-et-al-persistententrainment/Thu, 23 Aug 2018 00:00:00 +0000/publication/2018-park-et-al-persistententrainment/Effect of stimulation waveform on the non-linear entrainment of cortical alpha oscillations/publication/2018-hutt-et-al-effectofstimwaveform/Mon, 01 Jan 2018 00:00:00 +0000/publication/2018-hutt-et-al-effectofstimwaveform/Subject-specificity of the correlation between large-scale structural and functional connectivity/publication/2018-zimmerman-et-al-subject-specificity/Mon, 01 Jan 2018 00:00:00 +0000/publication/2018-zimmerman-et-al-subject-specificity/The functional reach of the hippocampal memory system to the oculomotor system/publication/2018-ryan-et-al-functionalorganization/Mon, 01 Jan 2018 00:00:00 +0000/publication/2018-ryan-et-al-functionalorganization/Eigenmodes of brain activity: Neural field theory predictions and comparison with experiment/publication/2016-robinson-et-al-eigenmodestructure/Fri, 01 Jan 2016 00:00:00 +0000/publication/2016-robinson-et-al-eigenmodestructure/Causal influence in neural systems: reconciling mechanistic-reductionist and statistical perspectives/publication/2015-griffiths-causal/Thu, 01 Jan 2015 00:00:00 +0000/publication/2015-griffiths-causal/Functional organization of the neural language system: dorsal and ventral pathways are critical for syntax/publication/2013-grifffiths-et-al-functionalorganizationoflanguage/Tue, 01 Jan 2013 00:00:00 +0000/publication/2013-grifffiths-et-al-functionalorganizationoflanguage/Is left fronto-temporal connectivity essential for syntax? Effective connectivity, tractography and performance in left-hemisphere damaged patients./publication/2011-paoutsi-et-al-istheleftdlpfc/Sat, 01 Jan 2011 00:00:00 +0000/publication/2011-paoutsi-et-al-istheleftdlpfc/<link>/publication/2024-cabral-and-griffiths-book-chapter/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/publication/2024-cabral-and-griffiths-book-chapter/</guid><description/></item></channel></rss> \ No newline at end of file +<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Publications on KCNI WBMG/publication/Recent content in Publications on KCNI WBMGHugo -- gohugo.ioen-us&copy; 2018Sun, 01 Jan 2017 00:00:00 +0000Dynamic brain network models: How interactions in the structural connectome shape brain dynamics/publication/2024-cabral-and-griffiths-book-chapter/Mon, 01 Jan 2024 00:00:00 +0000/publication/2024-cabral-and-griffiths-book-chapter/TMS-Evoked Responses Are Driven by Recurrent Large-Scale Network Dynamics/publication/2023-momi-et-al-tmsevokedresponses/Sun, 01 Jan 2023 00:00:00 +0000/publication/2023-momi-et-al-tmsevokedresponses/Deep-learning based parameter estimation for neurophysiological models of neuroimaging data/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/Sat, 01 Oct 2022 00:00:00 +0000/publication/2022-griffiths-et-al-deeplearningbasedparameterestimation/Mapping inter-individual functional connectivity variability in TMS targets for major depressive disorder/publication/2022-harita-et-al-mapping-interindividual-fc-tms/Thu, 23 Jun 2022 00:00:00 +0000/publication/2022-harita-et-al-mapping-interindividual-fc-tms/Whole-Brain Modelling: Past, Present, and Future/publication/2022-griffiths-et-al-wbm-pastpresentfuture/Tue, 01 Feb 2022 00:00:00 +0000/publication/2022-griffiths-et-al-wbm-pastpresentfuture/EEG Biomarkers of reduced inhibition in human cortical microcircuits in depression/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/Sat, 30 Jan 2021 00:00:00 +0000/publication/2021-mazza-et-al-eeg-biomarkers-of-depression/A connectome-based, corticothalamic model of state- and stimulation-dependent modulation of rhythmic neural activity and connectivity/publication/2020-griffiths-et-al-corticothalamic-model/Thu, 30 Jan 2020 00:00:00 +0000/publication/2020-griffiths-et-al-corticothalamic-model/The functional reach of the hippocampal memory system to the oculomotor system/publication/2020-ryan-et-al-modeling/Wed, 01 Jan 2020 00:00:00 +0000/publication/2020-ryan-et-al-modeling/Shaping brain rhythms: dynamic and control-theoretic perspectives on periodic brain stimulation for treatment of neurological disorders/publication/2019-griffiths-et-al-shapingbrainrhythms/Wed, 30 Jan 2019 00:00:00 +0000/publication/2019-griffiths-et-al-shapingbrainrhythms/Persistent entrainment in nonlinear neural networks with noise/publication/2018-park-et-al-persistententrainment/Thu, 23 Aug 2018 00:00:00 +0000/publication/2018-park-et-al-persistententrainment/Effect of stimulation waveform on the non-linear entrainment of cortical alpha oscillations/publication/2018-hutt-et-al-effectofstimwaveform/Mon, 01 Jan 2018 00:00:00 +0000/publication/2018-hutt-et-al-effectofstimwaveform/Subject-specificity of the correlation between large-scale structural and functional connectivity/publication/2018-zimmerman-et-al-subject-specificity/Mon, 01 Jan 2018 00:00:00 +0000/publication/2018-zimmerman-et-al-subject-specificity/The functional reach of the hippocampal memory system to the oculomotor system/publication/2018-ryan-et-al-functionalorganization/Mon, 01 Jan 2018 00:00:00 +0000/publication/2018-ryan-et-al-functionalorganization/Eigenmodes of brain activity: Neural field theory predictions and comparison with experiment/publication/2016-robinson-et-al-eigenmodestructure/Fri, 01 Jan 2016 00:00:00 +0000/publication/2016-robinson-et-al-eigenmodestructure/Causal influence in neural systems: reconciling mechanistic-reductionist and statistical perspectives/publication/2015-griffiths-causal/Thu, 01 Jan 2015 00:00:00 +0000/publication/2015-griffiths-causal/Functional organization of the neural language system: dorsal and ventral pathways are critical for syntax/publication/2013-grifffiths-et-al-functionalorganizationoflanguage/Tue, 01 Jan 2013 00:00:00 +0000/publication/2013-grifffiths-et-al-functionalorganizationoflanguage/Is left fronto-temporal connectivity essential for syntax? Effective connectivity, tractography and performance in left-hemisphere damaged patients./publication/2011-paoutsi-et-al-istheleftdlpfc/Sat, 01 Jan 2011 00:00:00 +0000/publication/2011-paoutsi-et-al-istheleftdlpfc/ \ No newline at end of file diff --git a/publication_types/6/index.html b/publication_types/6/index.html index 5b0a8ca..3aa5078 100644 --- a/publication_types/6/index.html +++ b/publication_types/6/index.html @@ -1,6 +1,6 @@ -6 | KCNI WBMG

6

Whole-Brain Modelling: Past, Present, and Future

Whole-Brain Modelling is a scientific field with a short history and a long past. Its various disciplinary roots and conceptual ingredients extend back to as early as the 1940s. It was not until the late 2000s, however, that a nascent paradigm …

Shaping brain rhythms: dynamic and control-theoretic perspectives on periodic brain stimulation for treatment of neurological disorders

Rhythmic, collective activity is a fundamental feature of neural systems. As a result of this, many of the challenges and opportunities involved in developing clinical tools from basic neuroscience knowledge come down to questions about control of …

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+0000Dynamic brain network models: How interactions in the structural connectome shape brain dynamics/publication/2024-cabral-and-griffiths-book-chapter/Mon, 01 Jan 2024 00:00:00 +0000/publication/2024-cabral-and-griffiths-book-chapter/Whole-Brain Modelling: Past, Present, and Future/publication/2022-griffiths-et-al-wbm-pastpresentfuture/Tue, 01 Feb 2022 00:00:00 +0000/publication/2022-griffiths-et-al-wbm-pastpresentfuture/Shaping brain rhythms: dynamic and control-theoretic perspectives on periodic brain stimulation for treatment of neurological disorders/publication/2019-griffiths-et-al-shapingbrainrhythms/Wed, 30 Jan 2019 00:00:00 +0000/publication/2019-griffiths-et-al-shapingbrainrhythms/ \ No newline at end of file diff --git a/publication_types/index.xml b/publication_types/index.xml index 5ac4373..f47c984 100644 --- a/publication_types/index.xml +++ b/publication_types/index.xml @@ -1 +1 @@ -Publication_types on KCNI WBMG/publication_types/Recent content in Publication_types on 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0:000/tags/theory/2019-01-03T00:00:00+00:000 \ No newline at end of file diff --git a/talks_and_teaching/2016-01-01__wiredminds_philosophyofminduploading/index.html b/talks_and_teaching/2016-01-01__wiredminds_philosophyofminduploading/index.html index 3726452..b001ab2 100644 --- a/talks_and_teaching/2016-01-01__wiredminds_philosophyofminduploading/index.html +++ b/talks_and_teaching/2016-01-01__wiredminds_philosophyofminduploading/index.html @@ -1,7 +1,7 @@ Wired Minds Episode: Philosophy & Neuroscience of Mind Uploading | KCNI WBMG

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