Dynamic Mode Decomposition of time-series fMRI
Project description
Dynamic Mode Decomposition
Based on Casorso et al., 2019 the dynamic mode decomposition (DMD) algorithm allows for a dynamic analysis of cortical neurological activation. Here, a Python module is developed facilitating both analysis and visualization aspects of the DMD.
Installation
To install the package, simply run the following command::
pip install nidmd
Usage
Dashboard
In parallel to this Python module, a dashboard called nidmd-dashboard has been developed to facilitate analysis, comparison, and mode matching of the DMD of time-series fMRI data.
Input data
This dashboard handles preprocessed data as described in Casorso et al., 2019 - Methods.
The input needed for a successful visualization is one or multiple files containing time-series data. Each file corresponds to an fMRI run and should contain one matrix of size N x T
, with N
being the number of ROIs in the cortical parcellation and T
being the observational timepoints.
In the current version, two parcellations are supported:
- Glasser et al., 2016, containing
N = 360
regions. - Schaefer et al., 2018, containing
N = 400
regions.
References
1 M. F. Glasser et al., “A multi-modal parcellation of human cerebral cortex,” Nature, vol. 536, no. 7615, pp. 171–178, 11 2016, doi: 10.1038/nature18933.
2 J. Casorso, X. Kong, W. Chi, D. Van De Ville, B. T. T. Yeo, and R. Liégeois, “Dynamic mode decomposition of resting-state and task fMRI,” NeuroImage, vol. 194, pp. 42–54, Jul. 2019, doi: 10.1016/j.neuroimage.2019.03.019.
3 A. Schaefer et al., “Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI,” Cerebral Cortex, vol. 28, no. 9, pp. 3095–3114, Sep. 2018, doi: 10.1093/cercor/bhx179.
Project details
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