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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.


Hypergraph cardinality and age.Hypergraph cardinality is shown as a function of age for the age-memory data set (blue) and word memory task of the multi-task data set (pink). Three distinct age groups are present for the age-memory data, while the multi-task ages overlap with the middle age-memory group. The correspondence between increasing age and larger hypergraph cardinality can be observed, where few older subjects have low hypergraph cardinalities, but the majority of the youngest subjects have cardinalities lower than 500.
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pcbi.1005178.g010: Hypergraph cardinality and age.Hypergraph cardinality is shown as a function of age for the age-memory data set (blue) and word memory task of the multi-task data set (pink). Three distinct age groups are present for the age-memory data, while the multi-task ages overlap with the middle age-memory group. The correspondence between increasing age and larger hypergraph cardinality can be observed, where few older subjects have low hypergraph cardinalities, but the majority of the youngest subjects have cardinalities lower than 500.

Mentions: This is a positive relationship, indicating that older individuals tend to have higher hypergraph cardinality, while younger participants tend towards lower hypergraph cardinality. An illustration of this correspondence between hypergraph cardinality and age is presented in Fig 10. As age increases, the number of hyperedges in a participant’s hypergraph increases as well. We verify that this relationship holds beyond this particular study by reintroducing the word-memory data from the multi-task study and performing a correlation between hypergraph cardinality and age over both studies. Age and hypergraph cardinality have a Spearman correlation coefficient of ρ = 0.32, and the p-value for this correlation, p < 10−5, is significant when we use the Bonferroni correction over all analyses presented in this paper.


Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan
Hypergraph cardinality and age.Hypergraph cardinality is shown as a function of age for the age-memory data set (blue) and word memory task of the multi-task data set (pink). Three distinct age groups are present for the age-memory data, while the multi-task ages overlap with the middle age-memory group. The correspondence between increasing age and larger hypergraph cardinality can be observed, where few older subjects have low hypergraph cardinalities, but the majority of the youngest subjects have cardinalities lower than 500.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1005178.g010: Hypergraph cardinality and age.Hypergraph cardinality is shown as a function of age for the age-memory data set (blue) and word memory task of the multi-task data set (pink). Three distinct age groups are present for the age-memory data, while the multi-task ages overlap with the middle age-memory group. The correspondence between increasing age and larger hypergraph cardinality can be observed, where few older subjects have low hypergraph cardinalities, but the majority of the youngest subjects have cardinalities lower than 500.
Mentions: This is a positive relationship, indicating that older individuals tend to have higher hypergraph cardinality, while younger participants tend towards lower hypergraph cardinality. An illustration of this correspondence between hypergraph cardinality and age is presented in Fig 10. As age increases, the number of hyperedges in a participant’s hypergraph increases as well. We verify that this relationship holds beyond this particular study by reintroducing the word-memory data from the multi-task study and performing a correlation between hypergraph cardinality and age over both studies. Age and hypergraph cardinality have a Spearman correlation coefficient of ρ = 0.32, and the p-value for this correlation, p < 10−5, is significant when we use the Bonferroni correction over all analyses presented in this paper.

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.