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Fidelity-weighting

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. The code can also be used to estimate how well your source modeling performs.

Dependencies

For the minimal version:

  • NumPy
  • SciPy

MNE-Python is also supported, and requires:

Dataset

https://doi.org/10.5281/zenodo.5291628 has subject files that are used in the development of the code and in the coming article.

References

Fidelity weighting publication

In works.

Cortical parcellations

[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.

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Improve inverse operators used with parcellations

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