nidmd 0.2.4

Dynamic Mode Decomposition of time-series fMRI

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.

Examples

A Jupyter Notebook can be found in the examples directory. It complements the documentation.

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.