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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.

No MeSH data available.


Spatial distribution of hyperedges for three age groups.Average hyperedge node degree for three discrete age groups in the age-memory study. Regions of relative high node degree are consistent across the three groups, but the overall node degree is about five times larger in the group with ages from 60–75. This corresponds to previous observations of increasing cardinality with age and illustrates how the increase in cardinality is spread across the brain.
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pcbi.1005178.g011: Spatial distribution of hyperedges for three age groups.Average hyperedge node degree for three discrete age groups in the age-memory study. Regions of relative high node degree are consistent across the three groups, but the overall node degree is about five times larger in the group with ages from 60–75. This corresponds to previous observations of increasing cardinality with age and illustrates how the increase in cardinality is spread across the brain.

Mentions: Given the positive relationship between age and hypergraph cardinality, we next identify how the spatial organization of hyperedges reflect the increase in cardinality. We group subjects from the age-memory study into three age ranges based on the age-memory task data distribution: 18 years old (39 subjects), 25–33 years old (34 subjects), and 60–75 years old (35 subjects). For each set of subjects, we calculate the average hyperedge node degree for each region and depict them on the brain in Fig 11. The plots for the two younger populations exhibit few differences, although there is a slight increase in degree for the middle population. Hypergraphs in the oldest population exhibit higher hyperedge node degree across the brain, although regions of relatively high hyperedge node degree are consistent with those in the other populations.


Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan
Spatial distribution of hyperedges for three age groups.Average hyperedge node degree for three discrete age groups in the age-memory study. Regions of relative high node degree are consistent across the three groups, but the overall node degree is about five times larger in the group with ages from 60–75. This corresponds to previous observations of increasing cardinality with age and illustrates how the increase in cardinality is spread across the brain.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1005178.g011: Spatial distribution of hyperedges for three age groups.Average hyperedge node degree for three discrete age groups in the age-memory study. Regions of relative high node degree are consistent across the three groups, but the overall node degree is about five times larger in the group with ages from 60–75. This corresponds to previous observations of increasing cardinality with age and illustrates how the increase in cardinality is spread across the brain.
Mentions: Given the positive relationship between age and hypergraph cardinality, we next identify how the spatial organization of hyperedges reflect the increase in cardinality. We group subjects from the age-memory study into three age ranges based on the age-memory task data distribution: 18 years old (39 subjects), 25–33 years old (34 subjects), and 60–75 years old (35 subjects). For each set of subjects, we calculate the average hyperedge node degree for each region and depict them on the brain in Fig 11. The plots for the two younger populations exhibit few differences, although there is a slight increase in degree for the middle population. Hypergraphs in the oldest population exhibit higher hyperedge node degree across the brain, although regions of relatively high hyperedge node degree are consistent with those in the other populations.

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.