Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan
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...
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| Format: | Online |
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Public Library of Science
2016
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5120784/ |
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pubmed-51207842016-12-15 Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan Davison, Elizabeth N. Turner, Benjamin O. Schlesinger, Kimberly J. Miller, Michael B. Grafton, Scott T. Bassett, Danielle S. Carlson, Jean M. Research Article 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. Public Library of Science 2016-11-23 /pmc/articles/PMC5120784/ /pubmed/27880785 http://dx.doi.org/10.1371/journal.pcbi.1005178 Text en © 2016 Davison et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Davison, Elizabeth N. Turner, Benjamin O. Schlesinger, Kimberly J. Miller, Michael B. Grafton, Scott T. Bassett, Danielle S. Carlson, Jean M. |
| spellingShingle |
Davison, Elizabeth N. Turner, Benjamin O. Schlesinger, Kimberly J. Miller, Michael B. Grafton, Scott T. Bassett, Danielle S. Carlson, Jean M. Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan |
| author_facet |
Davison, Elizabeth N. Turner, Benjamin O. Schlesinger, Kimberly J. Miller, Michael B. Grafton, Scott T. Bassett, Danielle S. Carlson, Jean M. |
| author_sort |
Davison, Elizabeth N. |
| title |
Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan |
| title_short |
Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan |
| title_full |
Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan |
| title_fullStr |
Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan |
| title_full_unstemmed |
Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan |
| title_sort |
individual differences in dynamic functional brain connectivity across the human lifespan |
| description |
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. |
| publisher |
Public Library of Science |
| publishDate |
2016 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5120784/ |
| _version_ |
1613738532338139136 |