Mutual Connectivity Analysis with Local Models
This software was developed using MATLAB* 2016a and contains all the functions required to perform mutual connectivity analysis with local models (MCA-LM) . This method can be used to extract directed influence flow between every pair of time-series in a system. This toolbox demonstrates the applicability of MCA-LM on realistic fMRI simulations generated using the NetSim software . Performance is evaluated by comparing the recovered networks with the true network structure (adjacency matrix) of the simulations. Effects of reducing repetition time (TR) are demonstrated in the demo.
 DSouza, Adora M., Anas Z. Abidin, Udaysankar Chockanathan, Giovanni Schifitto, and Axel Wismüller. "Mutual connectivity analysis of resting-state functional MRI data with local models." NeuroImage 178 (2018): 210-223.
 Smith, Stephen M., Karla L. Miller, Gholamreza Salimi-Khorshidi, Matthew Webster, Christian F. Beckmann, Thomas E. Nichols, Joseph D. Ramsey, and Mark W. Woolrich. "Network modelling methods for FMRI." Neuroimage 54, no. 2 (2011): 875-891.
This software is free for academic non-commercial usage. If you use it, please cite above reference .
Large-Scale Granger Causality Toolbox
This software was developed using MATLAB* 2014a and contains all the functions required to perform large-scale Granger causality (lsGC) Analysis [1,2]. This method can be used to extract directed influence flow between every pair of time-series in a system. It is an extension to multivariate Granger Causality analysis for very large systems, such as functional MRI data sets. The principle of Granger causality is based on the concept of cross-predictability where the improvement in prediction quality of a time-series in the presence of another time-series is evaluated and quantified, revealing influence direction between the two series.
The flow of influence between individual time-series is obtained using predictive models such as vector auto-regressive (VAR) modelling. For systems with very large number of time-series N as compared to number of time points T (N>>T), calculating a multivariate Granger causality index is not possible as the parameter estimation in multivariate predictive modelling scheme is limited by T resulting in an underdetermined problem. To counter this problem, we introduce a linear dimension reduction step prior to performing VAR. Predictions are performed in the low-dimensional space which are transformed back to the original space using the inverse of the transformation function. This permits us to compare the prediction with the true time-series in the original space, resulting in an lsGC index for each pair of time-series in the system.
 DSouza, Adora M., Anas Z. Abidin, Lutz Leistritz, and Axel Wismueller. "Exploring connectivity with large-scale Granger causality on resting-state functional MRI." Journal of neuroscience methods 287 (2017): 68-79.
 D'Souza, Adora M., Anas Zainul Abidin, Lutz Leistritz, and Axel Wismüller. "Large-scale Granger causality analysis on resting-state functional MRI." In Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 9788, p. 97880L. International Society for Optics and Photonics, 2016.
This software is free for academic non-commercial usage. If you use it, please cite above references [1,2].
Copyright Wismüller Computational Radiology Laboratory, November 2015 University of Rochester, Adora D’Souza (firstname.lastname@example.org)
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