Functional hypergraph uncovers novel covariant structures over neurodevelopment

Abstract

Brain development during adolescence is marked by substantial changes in brain structure and function, leading to a stable network topology in adulthood. However, most prior work has examined the data through the lens of brain areas connected to one another in large-scale functional networks. Here, we apply a recently developed hypergraph approach that treats network connections (edges) rather than brain regions as the unit of interest, allowing us to describe functional network topology from a fundamentally different perspective. Capitalizing on a sample of 780 youth imaged as part of the Philadelphia Neurodevelopmental Cohort, this hypergraph representation of resting-state functional MRI data reveals three distinct classes of subnetworks (hyperedges): clusters, bridges, and stars, which respectively represent homogeneously connected, bipartite, and focal architectures. Cluster hyperedges show a strong resemblance to previously-described functional modules of the brain including somatomotor, visual, default mode, and salience systems. In contrast, star hyperedges represent highly localized subnetworks centered on a small set of regions, and are distributed across the entire cortex. Finally, bridge hyperedges link clusters and stars in a core-periphery organization. Notably, developmental changes within hyperedges are ordered in a similar core-periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. Taken together, these results reveal a novel decomposition of the network organization of human brain, and

Functional brain imaging in neuropsychology over the past 25 years.

“…Notably, developmental changes within hyperedges were ordered in a similar core–periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. These results reveal a novel decomposition of the network organization of human brain, and suggest that dynamic maturation of network modules in youth may be a critical driver for the development of cognition (Gu et al, 2016). …”

Resting-state network topology and planning ability in healthy adults

“…To measure FC and construct connectivity matrices we applied wavelet coherence on the time-series of each possible pair of the 194 brain regions within the frequency range 0.06 and 0.12 Hz (Chang and Glover 2010). Wavelet coherence has several advantages over Pearson's correlations, including denoising properties and robustness to outliers (Gu et al 2017;Fadili and Bullmore 2004;Achard et al 2006). The 0.06-0.12Hz frequency range was chosen because it has been suggested to be a reliable and robust range that is associated with cognitive performance Bassett et al 2013).…”

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

“…A promising avenue for studying relations among edges defined either on the same or different graphs is to build on the notion of a hypergraph, a mathematical object that can be used to formalize the idea that groups of edges -rather than single edges alone -can form a fundamental unit of interest Davison et al, 2015;Gu et al, 2017). This approach is partially motivated by evidence suggesting that edges can develop differentially in a coordinated fashion over the lifespan (Davison et al, 2016), leading to architectural features that cannot simply be defined by graphs composed of dyads .…”

“…Such developmental coordination of functional connections might be driven by intrinsic computations , and subsequently have mutually trophic effects on underlying structural connectivity (Bassett et al, 2008). Co-varying functional connections in early life could support the emergence of cognitive systems observed in adulthood (Gu et al, 2017). Hypergraphs can formalize these relationships, and thereby offer a unique perspective on brain graph architecture.…”

“…where Q min (k) = min i Q(C r(i) k |C r(i) ) and Q max (k) = max i Q(C r(i) k |C r(i) ). The quantity S(C k |C ) is a normalized measure that provides information about how strong the community is in comparison to what is expected under a permutation-based null model (Gu et al, 2017).…”