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Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan

View Article: PubMed Central - PubMed

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.

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Multi-task cumulative size distribution.The empirical cumulative distribution function of hyperedge sizes for all subjects in the multi-task study. Also shown are traces for the empirical cumulative distribution functions of hyperedge sizes over all subjects for each of the four task-specific hypergraphs. The distributions for both word and face memory tasks tend to have more large hyperedges, while the attention and rest tasks have similar hypergraph cardinality to the memory tasks over all subjects, but exhibit far fewer large hyperedges.
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pcbi.1005178.g003: Multi-task cumulative size distribution.The empirical cumulative distribution function of hyperedge sizes for all subjects in the multi-task study. Also shown are traces for the empirical cumulative distribution functions of hyperedge sizes over all subjects for each of the four task-specific hypergraphs. The distributions for both word and face memory tasks tend to have more large hyperedges, while the attention and rest tasks have similar hypergraph cardinality to the memory tasks over all subjects, but exhibit far fewer large hyperedges.

Mentions: Fig 3 depicts the empirical cumulative hyperedge size distributions for all hyperedges found across all subjects in the multi-task data set. As a test, we shuffle the data over time and find no hyperedges of size greater than one. There is a rough power law for the smaller sizes (s < 100), followed by a gap in the distribution from about 100 to 1000 and a sharp drop at the system size (). The shape of the distribution is due to the consistent hypergraph structure across individuals; the majority of subjects in this study have a hypergraph composed of one large hyperedge and many small hyperedges. While this characteristic structure is common to most subjects in the study, the size of the largest hyperedge varies across individuals. This size is closely related to the hypergraph cardinality, defined as the number of hyperedges in a hypergraph, a measure which also exhibits large variation.


Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan
Multi-task cumulative size distribution.The empirical cumulative distribution function of hyperedge sizes for all subjects in the multi-task study. Also shown are traces for the empirical cumulative distribution functions of hyperedge sizes over all subjects for each of the four task-specific hypergraphs. The distributions for both word and face memory tasks tend to have more large hyperedges, while the attention and rest tasks have similar hypergraph cardinality to the memory tasks over all subjects, but exhibit far fewer large hyperedges.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120784&req=5

pcbi.1005178.g003: Multi-task cumulative size distribution.The empirical cumulative distribution function of hyperedge sizes for all subjects in the multi-task study. Also shown are traces for the empirical cumulative distribution functions of hyperedge sizes over all subjects for each of the four task-specific hypergraphs. The distributions for both word and face memory tasks tend to have more large hyperedges, while the attention and rest tasks have similar hypergraph cardinality to the memory tasks over all subjects, but exhibit far fewer large hyperedges.
Mentions: Fig 3 depicts the empirical cumulative hyperedge size distributions for all hyperedges found across all subjects in the multi-task data set. As a test, we shuffle the data over time and find no hyperedges of size greater than one. There is a rough power law for the smaller sizes (s < 100), followed by a gap in the distribution from about 100 to 1000 and a sharp drop at the system size (). The shape of the distribution is due to the consistent hypergraph structure across individuals; the majority of subjects in this study have a hypergraph composed of one large hyperedge and many small hyperedges. While this characteristic structure is common to most subjects in the study, the size of the largest hyperedge varies across individuals. This size is closely related to the hypergraph cardinality, defined as the number of hyperedges in a hypergraph, a measure which also exhibits large variation.

View Article: PubMed Central - PubMed

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&mdash;hypergraph cardinality&mdash;we investigate individual variations in two separate, complementary data sets. The first data set (&ldquo;multi-task&rdquo;) 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 (&ldquo;age-memory&rdquo;), in which 95 individuals, aged 18&ndash;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.

No MeSH data available.


Related in: MedlinePlus