Year: 2016
Type: article
Abstract: Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set ("multi-task") consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set ("age-memory"), in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.
Source: PLoS Computational Biology
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: National Science Foundation, David and Lucile Packard Foundation, John D. and Catherine T. MacArthur Foundation, Princeton University, National Institutes of Health, University of California, Santa Barbara, Office of Naval Research, National Institute of Mental Health, National Institute on Deafness and Other Communication Disorders, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Army Research Office, Army Research Laboratory, Institute for Collaborative Biotechnologies
Awards: G1024130705, G1041393086, G1099660080, G1115769823, Graduate Research Fellowship Program (GRFP), G1778482337, G2066955304, G212431111, G2130352337, G2236980202, G2252567680, G226397104, G235261508, G2425642435, G2864901048, G2887601406, G2901702611, G3137615481, G3330175468, G3424441068, G3564833335, G3611952402, G3690479056, G3824545241, G4069802742, Graduate Research Fellowship Program (GRFP), G4802959984, G5172716836, G5253651465, G5259331294, G572628577, G5884259427, CAREER: Linking Graph Topology of Learned Information to Behavioral Variability via Dynamics of Functional Brain Networks, CRCNS: Collaborative Research: Mapping and Control of Large-Scale Neural Dynamics, G6689184838, G6752185412, Linguistic and Nonlinguistic Functions of Frontal Cortex, G6981911394, G709691613, G7159985717, G7425413337, G7452299184, WORKSHOP: Quantitative Theories of Learning, Memory, and Prediction, G7530591242, G7747382488, G793584043, G7989609580, G8454816522, CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic Networks, G8554419822, G916315028, G91658921, G95210315
OpenAlex ID: W2472671480