step_by_step_validating_VWE.md

Validating the Voxelwiseencoding Model

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This step-by-step guide provides guidance for running the by for the Modulation Power Spectrum and different parameters for stimulus length used and offset of the fMRI data. The Voxelwissencoding app allows to train voxelwiseendocing models on naturalistic stimuli.The output after this validation are Correlation Scores (Pearson Correlation) corrected for multiple correlation and thresholded to > 0 (to account for over-correction of the model).

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Installing the Voxelwiseencoing app (install in Console, not in sypder/anaconda)

pip install -e /your_datapath/voxelwiseencoding

Requirements: Naturalistic auditory data (speech) in BIDS compliant format.

Load Packages

import json
from nilearn.masking import unmask
import nilearn
from nilearn.masking import apply_mask
from nilearn.plotting import plot_stat_map
from nilearn.image import mean_img
from nilearn import plotting 
import nibabel
import numpy as np
from nilearn.image import threshold_stats_img
from nilearn.input_data import MultiNiftiMasker
from nilearn.masking import compute_brain_mask

Defining the paramters used in extracting the MPS

mel_params = {'n_mels': 64, 'sr': 44100, 'hop_length': 882, 'n_fft': 882, 'fmax': 8000,'mps_hop_length': 100, 'mps_n_fft':100}
with open('config.json', 'w+') as fl:
    json.dump(mel_params, fl)

Extracting Modulation Power Spectrum

!python audio2bidsstim/wav_files_to_bids_tsv_2.py /data/fg_audio/*.wav -c config.json

Importing the model validation from the voxelwissencoding app

from voxelwiseencoding.process_bids import run_model_for_subject

Define parameters which should be applied to BOLD data (these are the parameters used for preprocessing the BOLD fMRI files)

bold_prep_params = {'standardize': 'zscore', 'detrend': True}

Important:

This is the part where you can get "creative" and change the lag_time (stimulus length used to predict BOLD data in seconds) and offset_stim (offset of the fMRI data in relation to the stimulus presentation)

lagging_params = {'lag_time': 6, 'offset_stim': 2}

Defining the CV parameters(these are the parameters for sklearn's Ridge estimator, cv = number of folds used for CV)

ridge_params = {'alphas': [1e-1, 1, 100, 1000],'cv': 2, 'normalize': True}

Run the validation and define the subject (here '01') and where it can be found (here '/data/your_path/aligned_mps/')

ridges, scores, computed_mask = run_model_for_subject('01', '/data/your_path/aligned_mps/',
                                                      task='aomovie', mask='epi', bold_prep_kwargs=bold_prep_params,
                                                      preprocess_kwargs=lagging_params, encoding_kwargs=ridge_params)
                                                      

(Optional) The output will be the Raw Correlation Scores which can be saved for later analyses

mps_scores01_6lag_2off = scores

Correcting Scores for multiple correlation using bonferroni method