Metadata-Version: 2.1
Name: nidmd
Version: 0.2.4
Summary: Dynamic Mode Decomposition of time-series fMRI
Home-page: https://github.com/arnauddhaene/nidmd
Author: Arnaud Dhaene (EPFL)
Author-email: arnaud.dhaene@epfl.ch
License: UNKNOWN
Description: # Dynamic Mode Decomposition
        
        Based on [Casorso et al., 2019][2], 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](https://github.com/arnauddhaene/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][2].
        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][1], containing `N = 360` regions.
        * [Schaefer et al., 2018][2], containing `N = 400` regions.
        
        ### Examples
        
        A Jupyter Notebook can be found in the `examples` directory. It complements the [documentation](arnauddhaene.github.io/nidmd).
        
        ## 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.
        
        
        [2]: http://www.sciencedirect.com/science/article/pii/S1053811919301922
        [1]: https://pubmed.ncbi.nlm.nih.gov/27437579/
        [3]: https://academic.oup.com/cercor/article/28/9/3095/3978804
        [4]: https://build-system.fman.io/
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
