Computer Vision News - December 2021

26 Introduction to Neuroscience Image Processing Setup Check if you have not installed the following. If not, you can safely skip them. !pip install numpy !pip install nipy !pip install nilearn !pip install git +https://github.com/KamitaniLab/bdpy.git Download data !mkdir data # Subject 2 !curl https://ndownloader.figshare.com/files/28089525 \?private_link=3bd9a1c29f- 19649c8c0d -o data/sub-02_task-localizer_bold_preproc_native.h5 !curl https://ndownloader.figshare.com/files/28089570 \?private_link=3bd9a1c29f- 19649c8c0d -o data/sub-02_anatomy_t1 .nii.gz !curl https://ndownloader.figshare.com/files/28089528 \?private_link=3bd9a1c29f- 19649c8c0d -o data/sub-02_template_native .nii.gz # Subject 3 !curl https://ndownloader.figshare.com/files/28089534 \?private_link=3bd9a1c29f- 19649c8c0d -o data/sub-03_task-localizer_bold_preproc_native.h5 !curl https://ndownloader.figshare.com/files/28089582 \?private_link=3bd9a1c29f- 19649c8c0d -o data/sub-03_anatomy_t1 .nii.gz !curl https://ndownloader.figshare.com/files/28089537 \?private_link=3bd9a1c29f- 19649c8c0d -o data/sub-03_template_native .nii.gz Import modules import os from itertools import product import bdpy from bdpy.mri import export_brain_image import matplotlib.pyplot as plt import numpy as np from nipy.modalities.fmri.glm import GeneralLinearModel from nipy.modalities.fmri.experimental_paradigm import BlockParadigm from nipy.modalities.fmri.design_matrix import make_dmtx import nibabel from nilearn import plotting fMRI data In this example, we run GLM analysis on fMRI data collected in the higher visual areas’ localizer experiment . The aim of the experiment is to identify visual areas related to processing of complex visual information such as objects, faces, or scenes. During the experiment, a subject was required to look at an image presented in the scanner. The image was either of them.