This assignment aims to provide hands-on experience in analyzing functional Magnetic Resonance Imaging (fMRI) data using advanced tools like SPM-12, AAL3, and MATLAB. By working with a real auditory task dataset, we have gain insights into the functional connectivity networks of the brain and their underlying dynamics.
- Install and configure the necessary software tools (MATLAB, SPM-12, AAL3, and MarsBar) for fMRI data analysis.
- Preprocess and analyze auditory fMRI data using SPM-12.
- Explore brain regions and label activated clusters using the AAL3 atlas.
- Extract time-series data from regions of interest (ROIs) using MarsBar.
- Construct functional connectivity networks based on the extracted time-series data.
- Perform graph theoretical analysis on the binary functional connectivity network.
- Set up the working environment by installing MATLAB, SPM-12, AAL3, and MarsBar.
- Download and preprocess the auditory fMRI dataset using SPM-12.
- Label activated clusters using the AAL3 atlas and visualize brain regions with MarsBar.
- Extract time-series data from ROIs and construct a functional connectivity network.
- Analyze the functional connectivity network using graph theoretical measures (clustering coefficient, transitivity, and characteristic path length).
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All the processed images can be found in dir MoAEpilot.
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To replicate results the job files in '.mat' format can be found in auditory/jobs.
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Matlab figures for each analysis steps are saved in auditory/jobs.
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Model file saved as spm.mat can be found in auditory/classical/spm.mat.
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Extracted ROI timeseries are also saved in auditory/classical directory.
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Part3.ipynb contains the functional connectivity analysis of the extracted ROI's using Pearson Correlation coefficient.
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part3_analysis.pdf contains the graph theoretical analysis and graph formed due to the same.
