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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 construction.An illustration of hyperedge identification on a representational set of edges. Edge weights are computed separately for each time window (A) and joined together to form edge weight time series (B). Significantly correlated edge time series are cross-linked to form a hyperedge, a group of nodes that are linked by correlated edges (C). The group of hyperedges for an individual, with singletons removed, forms a hypergraph (D).
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pcbi.1005178.g001: Hypergraph construction.An illustration of hyperedge identification on a representational set of edges. Edge weights are computed separately for each time window (A) and joined together to form edge weight time series (B). Significantly correlated edge time series are cross-linked to form a hyperedge, a group of nodes that are linked by correlated edges (C). The group of hyperedges for an individual, with singletons removed, forms a hypergraph (D).

Mentions: Given the duration of each scan, this windowing yields four rest, 18 attention, 18 word memory, and 18 face memory node-node adjacency matrices. The set of node-node adjacency matrices, one for each one-minute segment, represents the dynamic functional connectivity graph; each edge, or pairwise connection between nodes, has an edge weight time series describing its temporal evolution across time windows, as depicted in Fig 1(B).


Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan
Hypergraph construction.An illustration of hyperedge identification on a representational set of edges. Edge weights are computed separately for each time window (A) and joined together to form edge weight time series (B). Significantly correlated edge time series are cross-linked to form a hyperedge, a group of nodes that are linked by correlated edges (C). The group of hyperedges for an individual, with singletons removed, forms a hypergraph (D).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1005178.g001: Hypergraph construction.An illustration of hyperedge identification on a representational set of edges. Edge weights are computed separately for each time window (A) and joined together to form edge weight time series (B). Significantly correlated edge time series are cross-linked to form a hyperedge, a group of nodes that are linked by correlated edges (C). The group of hyperedges for an individual, with singletons removed, forms a hypergraph (D).
Mentions: Given the duration of each scan, this windowing yields four rest, 18 attention, 18 word memory, and 18 face memory node-node adjacency matrices. The set of node-node adjacency matrices, one for each one-minute segment, represents the dynamic functional connectivity graph; each edge, or pairwise connection between nodes, has an edge weight time series describing its temporal evolution across time windows, as depicted in Fig 1(B).

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.