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Decoding task-specific cognitive states with slow, directed functional networks in the human brain

Ajmera, S and Jain, H and Sundaresan, M and Sridharan, D (2020) Decoding task-specific cognitive states with slow, directed functional networks in the human brain. In: eNeuro, 7 (4). pp. 1-23.

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Official URL: https://doi.org/10.1523/ENEURO.0512-19.2019


Flexible functional interactions among brain regions mediate critical cognitive functions. Such interactions can be measured using functional magnetic resonance imaging (fMRI) data either with instantaneous (zero-lag) or lag-based (time-lagged) functional connectivity. Because the fMRI hemodynamic response is slow, and is sampled at a timescale (seconds) several orders of magnitude slower than the underlying neural dynamics (milli-seconds), simulation studies have shown that lag-based fMRI functional connectivity, measured with approaches like Granger–Geweke causality (GC), provides spurious and unreliable estimates of underlying neural interactions. Experimental verification of this claim is challenging because neural ground truth connectivity is often unavailable concurrently with fMRI recordings. Here we demonstrate that, despite these widely held caveats, GC networks estimated from fMRI recordings contain useful information for classifying task-specific cognitive states. We estimated instantaneous and lag-based GC functional connectivity networks using fMRI data from 1000 participants (Human Connectome Project database). A linear classifier, trained on either instantaneous or lag-based GC, reli-ably discriminated among seven different task and resting brain states, with >80% cross-validation accuracy. With network simulations, we demonstrate that instantaneous and lag-based GC exploited interactions at fast and slow timescales, respectively, to achieve robust classification. With human fMRI data, instantaneous and lag-based GC identified complementary, task–core networks. Finally, variations in GC connectivity explained inter-individual variations in a variety of cognitive scores. Our findings show that instantaneous and lag-based methods reveal complementary aspects of functional connectivity in the brain, and suggest that slow, directed functional interactions, estimated with fMRI, may provide useful markers of behaviorally relevant cognitive states.

Item Type: Journal Article
Publication: eNeuro
Publisher: Society for Neuroscience
Additional Information: The copyright for this article belongs to the Author(s).
Keywords: adult; article; connectome; cross validation; female; functional connectivity; functional magnetic resonance imaging; human; human experiment; major clinical study; male; prediction; simulation; support vector machine; brain; brain mapping; cognition; computer simulation; connectome; diagnostic imaging; nerve cell network; nuclear magnetic resonance imaging, Brain; Brain Mapping; Cognition; Computer Simulation; Connectome; Humans; Magnetic Resonance Imaging; Nerve Net
Department/Centre: Division of Biological Sciences > Centre for Neuroscience
Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 12 Jan 2023 11:25
Last Modified: 12 Jan 2023 11:25
URI: https://eprints.iisc.ac.in/id/eprint/79298

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