Time-varying functional network information extracted from brief instances of spontaneous brain activity

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 110; no. 11; pp. 4392 - 4397
• Main Authors: Liu, Xiao, Duyn, Jeff H.
• Format: Journal Article
• Language: English
• Published: Washington, DC National Academy of Sciences 12.03.2013; National Acad Sciences
• Subjects: Adolescent; Adult; Biological and medical sciences; Biological Sciences; Brain; Brain - diagnostic imaging; Brain - physiology; Connectivity; Correlation analysis; Correlations; Data analysis; Databases, Factual; Datasets; Female; Fundamental and applied biological sciences. Psychology; Humans; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Models, Neurological; Neuropsychology; NMR; Nuclear magnetic resonance; Radiography; Signal reflection; Sleep; Spatial distribution; Vertebrates: nervous system and sense organs; instantaneous reactivation; Central nervous system; resting-state network; nonstationary connectivity; network dynamics; clustering analysis; Encephalon
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1216856110

Recent functional magnetic resonance imaging studies have shown that the brain is remarkably active even in the absence of overt behavior, and this activity occurs in spatial patterns that are reproducible across subjects and follow the brain’s established functional subdivision. Investigating the distribution of these spatial patterns is an active area of research with the goal of obtaining a better understanding of the neural networks underlying brain function. One intriguing aspect of spontaneous activity is an apparent nonstationarity, or variability of interaction between brain regions. It was recently proposed that spontaneous brain activity may be dominated by brief traces of activity, possibly originating from a neuronal avalanching phenomenon. Such traces may involve different subregions in a network at different times, potentially reflecting functionally relevant relationships that are not captured with conventional data analysis. To investigate this, we examined publicly available functional magnetic resonance imaging data with a dedicated analysis method and found indications that functional networks inferred from conventional correlation analysis may indeed be driven by activity at only a few critical time points. Subsequent analysis of the activity at these critical time points revealed multiple spatial patterns, each distinctly different from the established functional networks. The spatial distribution of these patterns suggests a potential functional relevance.