My to-do list

My to-do list

  • [X] preprocess the fMRI data with fmriprep

  • [X] run GLMs using SPM for the first level and FSL Randomise for the second level

  • [X] replicate GLM in Nilearn

    • [X] replicate a GLM in Nilearn

    • [X] compute the correlation between first-level beta maps in the SPM and Nilearn analysis

    • [ ] replicate the Nilearn GLM with the exact same parameters as in SPM

      • fd_threshold = 1 (default is 0.5mm)

      • change from load_confound_strategy to load_confoundas it’s more custamizable and allows to match parameters of SPM.

      • check if I used a high pass filter in SPM, if not deactivate it in Nilearn.

      • check if I standardized in SPM, if not deactivte it in Nilearn.

    • [ ] Cluster correct the Nilearn second-level contrasts using cluster_level-inference.

    • [ ] re-run the GLMs including a brain mask that excludes the cerebellum and brain stem. As we are not interested in the analysis of these regions their exclusion is legit. I can use a mask from brainvolt that I can download here.

  • [X] connectivity analysis

    • [X] compute connectome for all conditions and save as .npy

    • [ ] compute graph theory metrics to characterise and compare the connectomes. François Paugam recommended to look into the Networks of the Brain as an introduction.

  • [X] compute seed-to-voxel correlations

  • [X] ML classifier

    • [X] ML classifier on connectomes

    • [ ] ML classifier on seed-to-voxel maps

    • [ ] ML classifier on the average activity for each of the 10 trials (not entire runs) cropped to an ROI in the SMA

  • [X] Deep Neural Network Decoder using PyTorch

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