ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Mapping distinct timescales of functional interactions among brain networks

Sundaresan, M and Nabeel, A and Sridharan, D (2017) Mapping distinct timescales of functional interactions among brain networks. In: 31st Annual Conference on Neural Information Processing Systems, NIPS 2017, 4 - 9 December 2017, Long Beach, pp. 4110-4119.

[img] PDF
NIPS_2017.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
Official URL: https://doi.org/10.1016/j.engfailanal.2022.106442


Abstract Brain processes occur at various timescales, ranging from milliseconds (neurons) to minutes and hours (behavior). Characterizing functional coupling among brain regions at these diverse timescales is key to understanding how the brain produces behavior. Here, we apply instantaneous and lag-based measures of conditional linear dependence, based on Granger-Geweke causality (GC), to infer network connections at distinct timescales from functional magnetic resonance imaging (fMRI) data. Due to the slow sampling rate of fMRI, it is widely held that GC produces spurious and unreliable estimates of functional connectivity when applied to fMRI data. We challenge this claim with simulations and a novel machine learning approach. First, we show, with simulated fMRI data, that instantaneous and lag-based GC identify distinct timescales and complementary patterns of functional connectivity. Next, we analyze fMRI scans from 500 subjects and show that a linear classifier trained on either instantaneous or lag-based GC connectivity reliably distinguishes task versus rest brain states, with ∼80-85% cross-validation accuracy. Importantly, instantaneous and lag-based GC exploit markedly different spatial and temporal patterns of connectivity to achieve robust classification. Our approach enables identifying functionally connected networks that operate at distinct timescales in the brain.

Item Type: Conference Paper
Publication: Advances in Neural Information Processing Systems
Publisher: Neural information processing systems foundation
Additional Information: The copyright for this article belongs to Neural information processing systems foundation.
Keywords: Brain; Learning systems; Magnetic levitation vehicles; Magnetic resonance imaging, Connected networks; Functional connectivity; Functional interaction; Functional magnetic resonance imaging; Linear classifiers; Machine learning approaches; Robust classification; Spatial and temporal patterns, Brain mapping
Department/Centre: Division of Biological Sciences > Centre for Neuroscience
Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 27 Jul 2022 09:56
Last Modified: 27 Jul 2022 09:56
URI: https://eprints.iisc.ac.in/id/eprint/74679

Actions (login required)

View Item View Item