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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.


Age-memory R2 changes.Normalized R2 changes with respect to hypergraph cardinality across individuals in the age-memory study. The largest normalized R2 changes are from the demographics factor and head motion measure, but the demographics factor is the only significant predictor of hypergraph cardinality. In this figure, prediction significance is denoted with a bold outline. The composition of R2 changes for the age-memory task is consistent with that seen for the multi-task data in Fig 6, in that the normalized R2 change is largely due to the demographics factor.
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pcbi.1005178.g009: Age-memory R2 changes.Normalized R2 changes with respect to hypergraph cardinality across individuals in the age-memory study. The largest normalized R2 changes are from the demographics factor and head motion measure, but the demographics factor is the only significant predictor of hypergraph cardinality. In this figure, prediction significance is denoted with a bold outline. The composition of R2 changes for the age-memory task is consistent with that seen for the multi-task data in Fig 6, in that the normalized R2 change is largely due to the demographics factor.

Mentions: The overall R2 value for the multiple regression analysis was 0.3452, indicating that the predictors explain about a third of the variance in the overall data. After a Bonferroni correction for multiple comparisons across all regression studies included in this paper [53], the demographics factor is the only significant predictor of hyperedge cardinality. The normalized R2 changes for hypergraph cardinality can be seen in Fig 9; the demographics factor has the largest normalized R2 change and the only significant p-value (p < 0.005) in the regression. These results correspond with the marginal result from the multi-task data set, where the demographics factor is a marginally significant predictor.


Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan
Age-memory R2 changes.Normalized R2 changes with respect to hypergraph cardinality across individuals in the age-memory study. The largest normalized R2 changes are from the demographics factor and head motion measure, but the demographics factor is the only significant predictor of hypergraph cardinality. In this figure, prediction significance is denoted with a bold outline. The composition of R2 changes for the age-memory task is consistent with that seen for the multi-task data in Fig 6, in that the normalized R2 change is largely due to the demographics factor.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1005178.g009: Age-memory R2 changes.Normalized R2 changes with respect to hypergraph cardinality across individuals in the age-memory study. The largest normalized R2 changes are from the demographics factor and head motion measure, but the demographics factor is the only significant predictor of hypergraph cardinality. In this figure, prediction significance is denoted with a bold outline. The composition of R2 changes for the age-memory task is consistent with that seen for the multi-task data in Fig 6, in that the normalized R2 change is largely due to the demographics factor.
Mentions: The overall R2 value for the multiple regression analysis was 0.3452, indicating that the predictors explain about a third of the variance in the overall data. After a Bonferroni correction for multiple comparisons across all regression studies included in this paper [53], the demographics factor is the only significant predictor of hyperedge cardinality. The normalized R2 changes for hypergraph cardinality can be seen in Fig 9; the demographics factor has the largest normalized R2 change and the only significant p-value (p < 0.005) in the regression. These results correspond with the marginal result from the multi-task data set, where the demographics factor is a marginally significant predictor.

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.