BrainBoard Percentage Count {{(computedThreshold * 100).toFixed(1)}}%

Examples #2 and #3 included in BrainBoard are generated using subject data from the Alzheimer's Disease Neurimaging Initiative 3 (ADNI3) dataset1. The fMRI image for the baseline scan of subject 168_S_6320 is minimally preprocessed using fMRIPrep2. The resulting data is reconstructed, parcellated into the Schaefer100x7 and Harvard-Oxford atlases and then exported using a custom Python script utilising functionality provided by the Nilearn3 Python package.

References
[1] Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., ... & Beckett, L. (2005). The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clinics of North America, 15(4), 869.
[2] Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., ... & Gorgolewski, K. J. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature methods, 16(1), 111-116.
[3] Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., ... & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in neuroinformatics, 8, 14.
Example #1 (Simple) Example #2 (Harvard Oxford) Example #3 (Schaefer100x7) View Source
Orthographic Projection
Connectivity Wheel
Region Timeseries