From 41ab2193c6904546e11961880c47d8cc36b7788a Mon Sep 17 00:00:00 2001 From: ljchang Date: Wed, 3 Apr 2024 20:29:02 +0000 Subject: [PATCH] deploy: 917e4d28a16d5ad68f3e9e7f791cc0012be4c4ff --- .../content/GLM_Single_Subject_Model.ipynb | 6 ++--- content/Download_Data.html | 2 +- content/GLM_Single_Subject_Model.html | 14 ++++------- content/Instructors.html | 2 +- content/Introduction_to_Discovery.html | 24 +++++++++---------- content/Introduction_to_Pandas.html | 2 +- content/Parcellations.html | 4 ++-- content/Preprocessing.html | 2 +- content/Syllabus.html | 8 +++---- content/Thresholding_Group_Analyses.html | 2 +- content/fmriprep_on_discovery.html | 6 ++--- content/intro.html | 2 +- searchindex.js | 2 +- 13 files changed, 34 insertions(+), 42 deletions(-) diff --git a/_sources/content/GLM_Single_Subject_Model.ipynb b/_sources/content/GLM_Single_Subject_Model.ipynb index 68d951e..30a4f9a 100644 --- a/_sources/content/GLM_Single_Subject_Model.ipynb +++ b/_sources/content/GLM_Single_Subject_Model.ipynb @@ -1112,13 +1112,11 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "```{note}\n", - "Multicollineary as discussed above can lead to unstable estimates of the regression coefficients, particularly for the regressors of interest. Multicollinearity between covariates of no interest tends to be less of a problem because we generally are just interested in explaining noise in our model and are rarely interested in interpreting the individual covariate beta coefficients. However, in cases of extreme collinearity, the model may become rank deficient, which can lead to difficulty with even fitting the model. \n", + "As discussed above, multicollineary can lead to unstable estimates of the regression coefficients, which is particularly important to keep in mind for regressors of interest. Multicollinearity between covariates of no interest tends to be less of a problem because we generally are just interested in explaining noise in our model and are rarely interested in interpreting the individual covariate beta coefficients. However, in cases of extreme collinearity, the model may become rank deficient, which can lead to difficulty with even fitting the model. \n", "\n", "A simple fix to this problem is to use the `.clean()` method. This method will remove any columns that are perfectly collinear with other columns in the design matrix.\n", "```" diff --git a/content/Download_Data.html b/content/Download_Data.html index b0ef6d2..e11877c 100644 --- a/content/Download_Data.html +++ b/content/Download_Data.html @@ -740,7 +740,7 @@

Windows Path Separators

Download Data with DataLad#

-

The Pinel localizer dataset can be accessed at the following location https://gin.g-node.org/ljchang/Localizer/. To download the Localizer dataset run datalad install https://gin.g-node.org/ljchang/Localizer in a terminal in the location where you would like to install the dataset. Don’t forget to change the directory to a folder on your local computer. The full dataset is approximately 42gb.

+

The Pinel localizer dataset can be accessed at the following location https://gin.g-node.org/ljchang/Localizer/. To download the Localizer dataset run datalad install https://gin.g-node.org/ljchang/Localizer in a terminal in the location where you would like to install the dataset. Don’t forget to change the directory to a folder on your local computer. The full dataset is approximately 42gb.

You can run this from the notebook using the ! cell magic.

diff --git a/content/GLM_Single_Subject_Model.html b/content/GLM_Single_Subject_Model.html index d4b7dd1..1e559a4 100644 --- a/content/GLM_Single_Subject_Model.html +++ b/content/GLM_Single_Subject_Model.html @@ -1077,16 +1077,10 @@

Noise Covariates

-
-
-
```{note}
-Multicollineary as discussed above can lead to unstable estimates of the regression coefficients, particularly for the regressors of interest. Multicollinearity between covariates of no interest tends to be less of a problem because we generally are just interested in explaining noise in our model and are rarely interested in interpreting the individual covariate beta coefficients. However, in cases of extreme collinearity, the model may become rank deficient, which can lead to difficulty with even fitting the model. 
-
-A simple fix to this problem is to use the `.clean()` method. This method will remove any columns that are perfectly collinear with other columns in the design matrix.
-```
-
-
-
+
+

Note

+

As discussed above, multicollineary can lead to unstable estimates of the regression coefficients, which is particularly important to keep in mind for regressors of interest. Multicollinearity between covariates of no interest tends to be less of a problem because we generally are just interested in explaining noise in our model and are rarely interested in interpreting the individual covariate beta coefficients. However, in cases of extreme collinearity, the model may become rank deficient, which can lead to difficulty with even fitting the model.

+

A simple fix to this problem is to use the .clean() method. This method will remove any columns that are perfectly collinear with other columns in the design matrix.

diff --git a/content/Instructors.html b/content/Instructors.html index 0a69489..624ec94 100644 --- a/content/Instructors.html +++ b/content/Instructors.html @@ -455,7 +455,7 @@

Professors../_images/finn.jpg

Emily Finn, PhD (Winter 2021) is an Assistant Professor of Psychological and Brain Sciences at Dartmouth College and directs the Functional Imaging & Naturalistic Neuroscience (FINN) Lab. She completed a BA in linguistics at Yale University and a PhD in neuroscience, also at Yale. She then did her postdoctoral training in the Section on Functional Imaging Methods in the Laboratory of Brain and Cognition and the National Institute of Mental Health. Her research is focused on individual variability in brain activity and behavior, especially as it relates to appraisal of ambiguous information under naturalistic conditions. Professor Finn is committed to the ideals of open science, including data and code sharing (see examples here, here, and here, and to helping train other scientists in innovative new methods for neuroimaging data acquisition and analysis.

../_images/wager.jpg -

Tor Wager, PhD (Spring 2021) is the Diana L. Taylor Distinguished Professor in Neuroscience at Dartmouth College. He received his Ph.D. from the University of Michigan in Cognitive Psychology in 2003, and served as an Assistant (2004-2008) and Associate Professor (2009) at Columbia University, and as Associate (2010-2014) and Full Professor (2014-2019) at the University of Colorado, Boulder. Since 2004, he has directed the Cognitive and Affective Neuroscience laboratory, a research lab devoted to work on the neurophysiology of affective processes—pain, emotion, stress, and empathy—and how they are shaped by cognitive and social influences. Dr. Wager and his lab are also dedicated to developing analysis methods for functional neuroimaging and sharing ideas, tools, and scientific data with the scientific community and public. See https://canlab.github.io for papers, data, tools, and code.

+

Tor Wager, PhD (Spring 2021) is the Diana L. Taylor Distinguished Professor in Neuroscience at Dartmouth College. He received his Ph.D. from the University of Michigan in Cognitive Psychology in 2003, and served as an Assistant (2004-2008) and Associate Professor (2009) at Columbia University, and as Associate (2010-2014) and Full Professor (2014-2019) at the University of Colorado, Boulder. Since 2004, he has directed the Cognitive and Affective Neuroscience laboratory, a research lab devoted to work on the neurophysiology of affective processes—pain, emotion, stress, and empathy—and how they are shaped by cognitive and social influences. Dr. Wager and his lab are also dedicated to developing analysis methods for functional neuroimaging and sharing ideas, tools, and scientific data with the scientific community and public. See https://canlab.github.io for papers, data, tools, and code.

../_images/huckins.jpg

Jeremy Huckins, PhD (Fall 2019) is a Lecturer and Post-Doctoral researcher in the department of Psychological and Brain Sciences at Dartmouth College. He completed a BA in Neuroscience at Bowdoin College, worked with as a researcher with the King Lab at Harvard Medical School then completed a PhD in Experimental and Molecular Medicine at Dartmouth College. His current research program is focused on gaining insights into mental health using fMRI and mobile smartphone sensing.

diff --git a/content/Introduction_to_Discovery.html b/content/Introduction_to_Discovery.html index 7b10701..904f4a7 100644 --- a/content/Introduction_to_Discovery.html +++ b/content/Introduction_to_Discovery.html @@ -566,11 +566,11 @@

High Performance Computing at Dartmouth

What is High Performance Computing?#

-

https://www.youtube.com/watch?v=nIBu1EFYmBU 00:13 - 01:21

+

https://www.youtube.com/watch?v=nIBu1EFYmBU 00:13 - 01:21

HPCs at Dartmouth#

-

https://rc.dartmouth.edu/index.php/discoveryhpc/

+

https://rc.dartmouth.edu/index.php/discoveryhpc/

Discovery Cluster#

Where is Discovery located? In the basement of Baker Berry! In a huge room full of servers. Racks of computers. Each computer is a node - newer nodes have 32 cores with 8GB per core. Used by all Departments (PBS, CS, Physics & Astronomy, etc.)

@@ -584,16 +584,16 @@

System Layout

Research Computing Team#

Centralized high-performance computing system group.

-

services.dartmouth.edu - where help/request forms are for research computing (try searching for Discovery, SLURM, etc.) Locally written documentation for things that people have problems with.

+

services.dartmouth.edu - where help/request forms are for research computing (try searching for Discovery, SLURM, etc.) Locally written documentation for things that people have problems with.

Simple, basic examples of what you can do with the scheduler.

-

If you have a Discovery question, should go to services.dartmouth.edu, search for a key word, see if it’s already written up. If you have that question, likely a bunch of others did as well, and RC has tried to answer it within the Dartmouth context.

-

research.computing@dartmouth.edu with questions.

-

https://services.dartmouth.edu

+

If you have a Discovery question, should go to services.dartmouth.edu, search for a key word, see if it’s already written up. If you have that question, likely a bunch of others did as well, and RC has tried to answer it within the Dartmouth context.

+

research.computing@dartmouth.edu with questions.

+

https://services.dartmouth.edu

DBIC Resources on Discovery#

DBIC owns 15 nodes (16 cores?) on Discovery. Acess to ~1200 CPUs (Luke, what is this x5 multiplier? How do we go from 15x16=240 to 1200 CPUs?)

-

DBIC Node (Ndoli): high-RAM node (1.5 TB), 20-24 CPUs. Can only access it by directly logging in. UserID@ndoli.dartmouth.edu (not available on scheduler).

+

DBIC Node (Ndoli): high-RAM node (1.5 TB), 20-24 CPUs. Can only access it by directly logging in. UserID@ndoli.dartmouth.edu (not available on scheduler).

PBS HPCs#

@@ -639,7 +639,7 @@

ACL Permissionshttps://services.dartmouth.edu/TDClient/1806/Portal/KB/ArticleDet?ID=88459

@@ -736,8 +736,8 @@

Running Batch Jobsresearch.computing@dartmouth.edu. You can use the SBATCH qnotify directive to let you know a certain amont of time before your job terminates.

+

Other SBATCH directives: check out this link. https://slurm.schedmd.com/sbatch.html

Arrays: Running Multiple Batch Jobs at Once#

@@ -764,7 +764,7 @@

Scheduler Etiquette

Can I use Jupyter Notebook on Discovery?#

-

AskPBS.org link

+

AskPBS.org link

Have Additional Discovery Questions?#

@@ -773,7 +773,7 @@

AskPBS

DBIC Handbook#

-

https://dbic-handbook.readthedocs.io/en/latest/discovery.html

+

https://dbic-handbook.readthedocs.io/en/latest/discovery.html

diff --git a/content/Introduction_to_Pandas.html b/content/Introduction_to_Pandas.html index 1e9c5aa..05a226f 100644 --- a/content/Introduction_to_Pandas.html +++ b/content/Introduction_to_Pandas.html @@ -2243,7 +2243,7 @@

Create New Columns

Indexing and slicing Data#

-

Indexing in Pandas can be tricky. There are many ways to index in pandas, for this tutorial we will focus on four: loc, iloc, boolean, and indexing numpy values. For a more in depth overview see Jake Vanderplas’s tutorial](https://github.com/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.02-Data-Indexing-and-Selection.ipynb), where he also covers more advanced topics, such as hierarchical indexing.

+

Indexing in Pandas can be tricky. There are many ways to index in pandas, for this tutorial we will focus on four: loc, iloc, boolean, and indexing numpy values. For a more in depth overview see Jake Vanderplas’s tutorial](jakevdp/PythonDataScienceHandbook), where he also covers more advanced topics, such as hierarchical indexing.

Indexing with Keys#

First, we will cover indexing with keys using the .loc method. This method references the explicit index with a key name. It works for both index names and also column names. Note that often the keys for rows are integers by default.

diff --git a/content/Parcellations.html b/content/Parcellations.html index aee02f0..40ea617 100644 --- a/content/Parcellations.html +++ b/content/Parcellations.html @@ -662,7 +662,7 @@

Anatomical Atlasesfetch_atlas-<name> requires that you load in the ‘name of the atlas’ that you wish to load as ‘atlas-name’.

-

  • There are numerous Harvard-Oxford atlases available that can be called through this function. For ease of viewing and deciding which one you are interested in, we recommend looking here: https://neurovault.org/collections/262/. We provide code to load in two sample atlases below - one cortical and one subcortical:

    • +

  • There are numerous Harvard-Oxford atlases available that can be called through this function. For ease of viewing and deciding which one you are interested in, we recommend looking here: https://neurovault.org/collections/262/. We provide code to load in two sample atlases below - one cortical and one subcortical:

    • @@ -933,7 +933,7 @@

    Structural parcellations -

  • Rosen & Halgren: https://pubmed.ncbi.nlm.nih.gov/33483325/

    • +

  • Rosen & Halgren: https://pubmed.ncbi.nlm.nih.gov/33483325/

    • diff --git a/content/Preprocessing.html b/content/Preprocessing.html index 6837416..2a6fde2 100644 --- a/content/Preprocessing.html +++ b/content/Preprocessing.html @@ -1202,7 +1202,7 @@

    Spatial Smoothinghttps://fmriprep.readthedocs.io/

    image.png

    diff --git a/content/Syllabus.html b/content/Syllabus.html index 397a0d5..679f9d8 100644 --- a/content/Syllabus.html +++ b/content/Syllabus.html @@ -489,7 +489,7 @@

    SPACE AND TIME

    CLASS FORMAT#

    -

    The format of Psych60 will be slightly different than the typical in-person version of the course. All course materials will be made available online in the format of a Jupyter book, https://dartbrains.org/. Lectures will primarily be delivered via publicly available pre-recorded lectures available on youtube. Each course module will have a Jupyter book tutorial and an accompanying assignment that will require completing some type of programming task or analysis. Students will work within small groups throughout the term on their assignments. Scheduled classes will primarily take the form of a Q&A where solutions to the assignments will be discussed and specific topics can be discussed in more detail. There will be a single exam, in which students will have to replicate an analysis of a published study using real data. For the final project, students will analyze an existing publicly available dataset to answer a new research question. Unfortunately, we will not be able to collect a new fMRI dataset as the Dartmouth Brain Imaging Center is closed to non-PBS users this term.

    +

    The format of Psych60 will be slightly different than the typical in-person version of the course. All course materials will be made available online in the format of a Jupyter book, https://dartbrains.org/. Lectures will primarily be delivered via publicly available pre-recorded lectures available on youtube. Each course module will have a Jupyter book tutorial and an accompanying assignment that will require completing some type of programming task or analysis. Students will work within small groups throughout the term on their assignments. Scheduled classes will primarily take the form of a Q&A where solutions to the assignments will be discussed and specific topics can be discussed in more detail. There will be a single exam, in which students will have to replicate an analysis of a published study using real data. For the final project, students will analyze an existing publicly available dataset to answer a new research question. Unfortunately, we will not be able to collect a new fMRI dataset as the Dartmouth Brain Imaging Center is closed to non-PBS users this term.

    ZOOM MEETINGS#

    @@ -512,7 +512,7 @@

    COMMUNICATION

    TEXTBOOK#

    -

    The course website (https://dartbrains.org/) and embedded videos will serve as the textbook for this course. Students may find the optional book (Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2011). Handbook of functional MRI data analysis. Cambridge University Press) to be helpful.

    +

    The course website (https://dartbrains.org/) and embedded videos will serve as the textbook for this course. Students may find the optional book (Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2011). Handbook of functional MRI data analysis. Cambridge University Press) to be helpful.

    LECTURES#

    @@ -524,7 +524,7 @@

    READINGS

    CLASS PARTICIPATION (15% of grade)#

    -

    You will be expected to participate in class discussions. This might include asking clarifying questions or helping another student. You will be expected to post at least one question each week on the DartBrains Discourse page https://www.askpbs.org/c/dartbrains/9 . Students can receive extra credit by helping answering their peers’ questions.

    +

    You will be expected to participate in class discussions. This might include asking clarifying questions or helping another student. You will be expected to post at least one question each week on the DartBrains Discourse page https://www.askpbs.org/c/dartbrains/9 . Students can receive extra credit by helping answering their peers’ questions.

    HOMEWORK (30% of grade)#

    @@ -540,7 +540,7 @@

    EXAM (20% of grade)

    ANALYSIS PROJECT (30% of grade)#

    -

    A key component of this course is learning how to process and analyze imaging data. Students will be introduced to key concepts during the laboratory assignments. Students will be expected to apply what they learn to analyzing an fMRI dataset. Most students will likely analyze the data collected in class, but are free to analyze any publicly available dataset (e.g., https://openfmri.org/, https://www.datalad.org/datasets.html). Students may work in small groups (~2-3 people), but each will independently write a final report of the research. The final written paper should be in journal format using APA style with an abstract, intro, methods, results, discussion, and references. Format 12-20 typed double-space pages, 11pt Ariel or Times font. (bibliography not included in the page limit). Each person is expected to write their own intro and conclusion, but the group can collaborate on the methods and results sections if they want. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the last day of class. +

    A key component of this course is learning how to process and analyze imaging data. Students will be introduced to key concepts during the laboratory assignments. Students will be expected to apply what they learn to analyzing an fMRI dataset. Most students will likely analyze the data collected in class, but are free to analyze any publicly available dataset (e.g., https://openfmri.org/, https://www.datalad.org/datasets.html). Students may work in small groups (~2-3 people), but each will independently write a final report of the research. The final written paper should be in journal format using APA style with an abstract, intro, methods, results, discussion, and references. Format 12-20 typed double-space pages, 11pt Ariel or Times font. (bibliography not included in the page limit). Each person is expected to write their own intro and conclusion, but the group can collaborate on the methods and results sections if they want. At the end of the class each group will give a ~10 minute presentation on their project (background, hypothesis, experimental design, results, analyses, conclusions). Paper Due at Midnight on the last day of class. • Paper: 15% • Presentation: 10% • Group participation: 5%

    diff --git a/content/Thresholding_Group_Analyses.html b/content/Thresholding_Group_Analyses.html index 99aba93..c0124b3 100644 --- a/content/Thresholding_Group_Analyses.html +++ b/content/Thresholding_Group_Analyses.html @@ -999,7 +999,7 @@

    Thresholding Brain Maps

    Looks like there are some significant differences that survive in early visual cortex and also in other regions of the brain.

    This concludes are very quick overview to performing univariate analyses in fMRI data analysis.

    -

    We will continue to add more advanced tutorials to the dartbrains.org website. Stay tuned!

    +

    We will continue to add more advanced tutorials to the dartbrains.org website. Stay tuned!

    diff --git a/content/fmriprep_on_discovery.html b/content/fmriprep_on_discovery.html index ef39216..09cd188 100644 --- a/content/fmriprep_on_discovery.html +++ b/content/fmriprep_on_discovery.html @@ -477,7 +477,7 @@

    Running fMRIPrep on HPCBasics#

    Accessing Data in Rolando#

    -

    At the Dartmouth Brain Imaging Center (DBIC), all data collected on the scanner is copied to Rolando (rolando.cns.dartmouth.edu). To gain access to rolando, you will need to request an account from the DBIC systems administrator (Andrew Connolly andrew.c.connolly@dartmouth.edu). More details can be found here: https://dbic-handbook.readthedocs.io/en/latest/mri/dataaccess.html. Converting your dataset into the BIDS format can be done using heudiconv after speaking with Yarik Halchenko. This is still in the process of being fully automated.

    +

    At the Dartmouth Brain Imaging Center (DBIC), all data collected on the scanner is copied to Rolando (rolando.cns.dartmouth.edu). To gain access to rolando, you will need to request an account from the DBIC systems administrator (Andrew Connolly andrew.c.connolly@dartmouth.edu). More details can be found here: https://dbic-handbook.readthedocs.io/en/latest/mri/dataaccess.html. Converting your dataset into the BIDS format can be done using heudiconv after speaking with Yarik Halchenko. This is still in the process of being fully automated.

    Download Data with DataLad#

    @@ -490,8 +490,8 @@

    Using Discovery

    Logging on to Discovery#

    The discovery system can be accessed via ssh using a terminal. There are a number of options depending on what you intend to do. For example, the -X or -Y flag can can enable X11 forwarding in case you would like to have graphics enabled. This can be useful for using GUIs such as FSL or matlab. You can also tunnel into a Jupyter server launched on an interactive job.

    -

    ● Linux: ssh -X username@discovery.dartmouth.edu

    -

    ● Mac: ssh -Y username@discovery.dartmouth.edu
    +

    ● Linux: ssh -X username@discovery.dartmouth.edu

    +

    ● Mac: ssh -Y username@discovery.dartmouth.edu
    - Install Xquartz for graphical interface

    ● Windows: - MobaXterm built in Xserver and sftp (free and recommended) diff --git a/content/intro.html b/content/intro.html index 090c0b6..5f7285f 100644 --- a/content/intro.html +++ b/content/intro.html @@ -465,7 +465,7 @@

    Goals#

    Overview#

    This course is designed primarily around learning the basics of fMRI data analysis using the Python programming language. We will cover a lot of ground from introducing the Python programming language, to signal processing, to working with opensource packages from the Python Scientific Computing community. The format will be slightly different than the typical in-person version of the course.

      -

    • All course materials will be made available online in the format of a jupyter book, https://dartbrains.org/. Lectures will primarily be delivered via publicly available pre-recorded lectures available on youtube.

      • +

    • All course materials will be made available online in the format of a jupyter book, https://dartbrains.org/. Lectures will primarily be delivered via publicly available pre-recorded lectures available on youtube.

    • Each course module will have a jupyter book tutorial and an accompanying assignment that will require completing some type of programming task or analysis. Students will work within small groups throughout the term on their assignments.

    • Scheduled classes will primarily take the form of a Q&A where solutions to the assignments will be discussed and specific topics can be discussed in more detail.

    • There will be a single exam, in which students will have to replicate an analysis of a published study using real data.

      • diff --git a/searchindex.js b/searchindex.js index 2373f23..07c1bba 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["content/2019_Spring", "content/2020_Fall", "content/2020_Spring", "content/2021_Fall", "content/2022_Fall", "content/Connectivity", "content/Contributing", "content/Download_Data", "content/GLM", "content/GLM_Single_Subject_Model", "content/Glossary", "content/Group_Analysis", "content/ICA", "content/Instructors", "content/Intro_to_Neuroimaging", "content/Introduction_to_Discovery", "content/Introduction_to_ICA", "content/Introduction_to_JupyterHub", "content/Introduction_to_Neuroimaging_Data", "content/Introduction_to_Pandas", "content/Introduction_to_Plotting", "content/Introduction_to_Programming", "content/Multivariate_Prediction", "content/Parcellations", "content/Preprocessing", "content/RSA", "content/Resampling_Statistics", "content/Schedule", "content/Signal_Measurement", "content/Signal_Processing", 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