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Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan

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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.

No MeSH data available.


Sorted hypergraph cardinality.Increasing hyperedge cardinality for individual multi-task word-only and age-memory hypergraphs. The variability for both studies is similar to the variability in multi-task overall hypergraph cardinality, depicted in Panel (A) of Fig 5. The range of hypergraph cardinalities for subjects in the word-only data is smaller than either the overall multi-task study or age-memory study.
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pcbi.1005178.g008: Sorted hypergraph cardinality.Increasing hyperedge cardinality for individual multi-task word-only and age-memory hypergraphs. The variability for both studies is similar to the variability in multi-task overall hypergraph cardinality, depicted in Panel (A) of Fig 5. The range of hypergraph cardinalities for subjects in the word-only data is smaller than either the overall multi-task study or age-memory study.

Mentions: The inter-subject variability in multi-task hypergraph cardinality spanned several orders of magnitude and followed consistent patterns within subjects for differing cognitive states. We compare the individual hypergraph cardinality for the age-memory and multi-task word-only studies in Fig 8. In the age-memory data, hypergraph cardinality ranges from 0 to 1817, which is a similar range of variability as that observed for the complete overall multi-task data set in Fig 5. There are 79 subjects with nonzero hyperedge cardinality, indicating that significant non-singleton hyperedges are present in less than two thirds of the subjects. For the remaining analyses, we only consider the 79 subjects with nonzero hypergraph cardinality. For the overall hypergraphs, hypergraph cardinality ranges from 0 to 1832. The maximum hypergraph cardinality for the multi-task word-only data is 1408, which is markedly less than that observed for the age-memory data and may be a result of the shorter time series for the multi-task word task. The presence of near-system size hyperedges, which may also be due to the shorter multi-task word time series, affects hypergraph cardinality by resulting in hypergraphs with cardinality near one.


Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan
Sorted hypergraph cardinality.Increasing hyperedge cardinality for individual multi-task word-only and age-memory hypergraphs. The variability for both studies is similar to the variability in multi-task overall hypergraph cardinality, depicted in Panel (A) of Fig 5. The range of hypergraph cardinalities for subjects in the word-only data is smaller than either the overall multi-task study or age-memory study.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1005178.g008: Sorted hypergraph cardinality.Increasing hyperedge cardinality for individual multi-task word-only and age-memory hypergraphs. The variability for both studies is similar to the variability in multi-task overall hypergraph cardinality, depicted in Panel (A) of Fig 5. The range of hypergraph cardinalities for subjects in the word-only data is smaller than either the overall multi-task study or age-memory study.
Mentions: The inter-subject variability in multi-task hypergraph cardinality spanned several orders of magnitude and followed consistent patterns within subjects for differing cognitive states. We compare the individual hypergraph cardinality for the age-memory and multi-task word-only studies in Fig 8. In the age-memory data, hypergraph cardinality ranges from 0 to 1817, which is a similar range of variability as that observed for the complete overall multi-task data set in Fig 5. There are 79 subjects with nonzero hyperedge cardinality, indicating that significant non-singleton hyperedges are present in less than two thirds of the subjects. For the remaining analyses, we only consider the 79 subjects with nonzero hypergraph cardinality. For the overall hypergraphs, hypergraph cardinality ranges from 0 to 1832. The maximum hypergraph cardinality for the multi-task word-only data is 1408, which is markedly less than that observed for the age-memory data and may be a result of the shorter time series for the multi-task word task. The presence of near-system size hyperedges, which may also be due to the shorter multi-task word time series, affects hypergraph cardinality by resulting in hypergraphs with cardinality near one.

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.

No MeSH data available.