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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.

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Multi-task R2 changes.Normalized R2 changes with respect to hypergraph cardinality are shown for individuals in the multi-task study. R2 changes are calculated from the regression procedure outlined in Methods, with five distinct categories common to the multi-task and age-memory studies. The largest normalized R2 change is from the demographics factor, but no factors exhibit a signficant correspondence with hypergraph cardinality.
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pcbi.1005178.g006: Multi-task R2 changes.Normalized R2 changes with respect to hypergraph cardinality are shown for individuals in the multi-task study. R2 changes are calculated from the regression procedure outlined in Methods, with five distinct categories common to the multi-task and age-memory studies. The largest normalized R2 change is from the demographics factor, but no factors exhibit a signficant correspondence with hypergraph cardinality.

Mentions: To identify possible drivers of this individual variation, we perform another regression analysis, using the individual difference measures from Table 1 as independent variables and overall hypergraph cardinality as the dependent variable. Fig 6 depicts the R2 changes from this analysis for each category of factors. The t-test identifies no factors with significant correspondence to hypergraph cardinality, but we observe that the demographics category has the largest R2 change. The t-test p-value for one of the factors in the demographics category is p < 0.05 and is by far the lowest p-value in this stage of the analysis. However, due to our stringent requirements for correcting for multiple comparisons and the number of tests we performed, this correlation is not statistically significant. The marginally significant demographics factor has a loading of −0.95 for the age measure and −0.31 for the years of education measure; the loading for sex and handedness demographic measures are comparatively negligible, with magnitudes less than 0.02.


Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan
Multi-task R2 changes.Normalized R2 changes with respect to hypergraph cardinality are shown for individuals in the multi-task study. R2 changes are calculated from the regression procedure outlined in Methods, with five distinct categories common to the multi-task and age-memory studies. The largest normalized R2 change is from the demographics factor, but no factors exhibit a signficant correspondence with hypergraph cardinality.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1005178.g006: Multi-task R2 changes.Normalized R2 changes with respect to hypergraph cardinality are shown for individuals in the multi-task study. R2 changes are calculated from the regression procedure outlined in Methods, with five distinct categories common to the multi-task and age-memory studies. The largest normalized R2 change is from the demographics factor, but no factors exhibit a signficant correspondence with hypergraph cardinality.
Mentions: To identify possible drivers of this individual variation, we perform another regression analysis, using the individual difference measures from Table 1 as independent variables and overall hypergraph cardinality as the dependent variable. Fig 6 depicts the R2 changes from this analysis for each category of factors. The t-test identifies no factors with significant correspondence to hypergraph cardinality, but we observe that the demographics category has the largest R2 change. The t-test p-value for one of the factors in the demographics category is p < 0.05 and is by far the lowest p-value in this stage of the analysis. However, due to our stringent requirements for correcting for multiple comparisons and the number of tests we performed, this correlation is not statistically significant. The marginally significant demographics factor has a loading of −0.95 for the age measure and −0.31 for the years of education measure; the loading for sex and handedness demographic measures are comparatively negligible, with magnitudes less than 0.02.

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