README
This is a repository for computing fidelity-weighted inverse operators with Python 3 to be used with cortical parcellations (see e.g. [1–3]) in neurophysiological research. Fidelity: how well simulated source activity is replicated after forward then inverse modeling the source activity.
For the minimal version:
- NumPy
- SciPy
MNE-Python is also supported, and requires:
- MNE-Python https://martinos.org/mne/stable/index.html.
- PySurfer https://pysurfer.github.io/
[1] Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006): An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31:968–980
[2] Destrieux C, Fischl B, Dale A, Halgren E (2010): Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53(1):1–15.
[3] Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo XN, Holmes AJ, Eickhoff SB, Yeo BTT (2018): Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex 28(9):3095–3114.