Skip to main content

Full correlation matrix analysis (FCMA): An unbiased method for task-related functional connectivity

Author(s): Wang, Y; Cohen, Jonathan D.; Li, Kai; Turk-Browne, Nicholas B.

To refer to this page use:
Abstract: Background The analysis of brain imaging data often requires simplifying assumptions because exhaustive analyses are computationally intractable. Standard univariate and multivariate analyses of brain activity ignore interactions between regions and analyses of interactions (functional connectivity) reduce the computational challenge by using seed regions of interest or brain parcellations. New methodTo meet this challenge, we developed full correlation matrix analysis (FCMA), which leverages and optimizes algorithms from parallel computing and machine learning to efficiently analyze the pairwise correlations of all voxels in the brain during different cognitive tasks, with the goal of identifying task-related interactions in an unbiased manner. ResultsWhen applied to a localizer dataset on a small compute cluster, FCMA accelerated a naive, serial approach by four orders of magnitude, reducing running time from two years to one hour. In addition to this performance gain, FCMA emphasized different brain areas than existing methods. In particular, beyond replicating known category selectivity in visual cortex, FCMA also revealed a region of medial prefrontal cortex whose selectivity derived from differential patterns of functional connectivity across categories. Comparison with existing method(s)For benchmarking, we started with a naive approach and progressively built up to the complete FCMA procedure by adding optimized classifier algorithms, multi-threaded parallelism, and multi-node parallelism. To evaluate what can be learned with FCMA, we compared it against multivariate pattern analysis of activity and seed-based analysis of functional connectivity. ConclusionsFCMA demonstrates how advances in computer science can alleviate computational bottlenecks in neuroscience. We have released a software toolbox to help others evaluate FCMA.
Publication Date: 14-May-2015
Electronic Publication Date: 22-May-2015
Citation: Wang, Y, Cohen, JD, Li, K, Turk-Browne, NB. (2015). Full correlation matrix analysis (FCMA): An unbiased method for task-related functional connectivity. Journal of Neuroscience Methods, 251 (108 - 119. doi:10.1016/j.jneumeth.2015.05.012
DOI: doi:10.1016/j.jneumeth.2015.05.012
Pages: 108 - 119
Type of Material: Journal Article
Journal/Proceeding Title: Journal of Neuroscience Methods
Version: Author's manuscript

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.