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Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain.

Xia M, Lin Q, Bi Y, He Y - Front Hum Neurosci (2016)

Bottom Line: We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions.Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges.Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China.

ABSTRACT
White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

No MeSH data available.


Path motifs of the whole-brain WM network and of different brain systems. (A) Examples of the six types of path motifs in the whole-brain WM network. Path motifs were defined as the ordered sequence of the pivotal or the non-pivotal edges on the routes of each shortest path. N, non-pivotal edge; P, pivotal edge. (B) The frequency percentage and normalized distribution of path motifs derived by comparing the actual frequency of each path motif to that of 1000 equivalent random networks. The “non-pivotal to pivotal to non-pivotal” (N-P-N) path motif was the most frequent path motif in the brain network (Z = 23.1). (C) The bottom matrix shows the proportions of path motifs between each pair of the brain systems. The following hierarchical clustering analysis revealed four path-motif distribution patterns, of which the path motif “N” decreased gradually while the “P” related path motifs constantly accumulated. Notably, most within-system and intra hemispheric paths communicated with the style “N,” while the inter-hemispheric paths, especially paths between heterotopic systems, utilized the pivotal edges more often. Fro, frontal; L, left; Occ, occipital; R, right; Par, parietal; Tem, temporal; Sub, subcortical; IntraHemi, intra hemispheric; InterHemi, inter hemispheric.
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Figure 5: Path motifs of the whole-brain WM network and of different brain systems. (A) Examples of the six types of path motifs in the whole-brain WM network. Path motifs were defined as the ordered sequence of the pivotal or the non-pivotal edges on the routes of each shortest path. N, non-pivotal edge; P, pivotal edge. (B) The frequency percentage and normalized distribution of path motifs derived by comparing the actual frequency of each path motif to that of 1000 equivalent random networks. The “non-pivotal to pivotal to non-pivotal” (N-P-N) path motif was the most frequent path motif in the brain network (Z = 23.1). (C) The bottom matrix shows the proportions of path motifs between each pair of the brain systems. The following hierarchical clustering analysis revealed four path-motif distribution patterns, of which the path motif “N” decreased gradually while the “P” related path motifs constantly accumulated. Notably, most within-system and intra hemispheric paths communicated with the style “N,” while the inter-hemispheric paths, especially paths between heterotopic systems, utilized the pivotal edges more often. Fro, frontal; L, left; Occ, occipital; R, right; Par, parietal; Tem, temporal; Sub, subcortical; IntraHemi, intra hemispheric; InterHemi, inter hemispheric.

Mentions: Every shortest path between nodes walks through a series of edges, of which the ordered sequence of the pivotal or the ordinary edges on the routes was referred to as the “path motifs.” Six types of path motif were identified in the whole-brain WM network (Figure 5A), and their appearing frequencies were statistically compared to those of 1000 equivalent random networks. The comparison revealed that the paths in the brain network exhibited significantly greater percentages of several pivotal-edge related connection types (e.g., “P,” “N-P,” “N-P-N,” and “N-P-N-P,” all Zs > 4.4, Figure 5B), especially the non-pivotal to pivotal to non-pivotal (N-P-N) path motif (Z = 23.1). Such a path-motif distribution profile indicates the central role of pivotal edges in the communicational organization of brain circuits.


Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain.

Xia M, Lin Q, Bi Y, He Y - Front Hum Neurosci (2016)

Path motifs of the whole-brain WM network and of different brain systems. (A) Examples of the six types of path motifs in the whole-brain WM network. Path motifs were defined as the ordered sequence of the pivotal or the non-pivotal edges on the routes of each shortest path. N, non-pivotal edge; P, pivotal edge. (B) The frequency percentage and normalized distribution of path motifs derived by comparing the actual frequency of each path motif to that of 1000 equivalent random networks. The “non-pivotal to pivotal to non-pivotal” (N-P-N) path motif was the most frequent path motif in the brain network (Z = 23.1). (C) The bottom matrix shows the proportions of path motifs between each pair of the brain systems. The following hierarchical clustering analysis revealed four path-motif distribution patterns, of which the path motif “N” decreased gradually while the “P” related path motifs constantly accumulated. Notably, most within-system and intra hemispheric paths communicated with the style “N,” while the inter-hemispheric paths, especially paths between heterotopic systems, utilized the pivotal edges more often. Fro, frontal; L, left; Occ, occipital; R, right; Par, parietal; Tem, temporal; Sub, subcortical; IntraHemi, intra hemispheric; InterHemi, inter hemispheric.
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Figure 5: Path motifs of the whole-brain WM network and of different brain systems. (A) Examples of the six types of path motifs in the whole-brain WM network. Path motifs were defined as the ordered sequence of the pivotal or the non-pivotal edges on the routes of each shortest path. N, non-pivotal edge; P, pivotal edge. (B) The frequency percentage and normalized distribution of path motifs derived by comparing the actual frequency of each path motif to that of 1000 equivalent random networks. The “non-pivotal to pivotal to non-pivotal” (N-P-N) path motif was the most frequent path motif in the brain network (Z = 23.1). (C) The bottom matrix shows the proportions of path motifs between each pair of the brain systems. The following hierarchical clustering analysis revealed four path-motif distribution patterns, of which the path motif “N” decreased gradually while the “P” related path motifs constantly accumulated. Notably, most within-system and intra hemispheric paths communicated with the style “N,” while the inter-hemispheric paths, especially paths between heterotopic systems, utilized the pivotal edges more often. Fro, frontal; L, left; Occ, occipital; R, right; Par, parietal; Tem, temporal; Sub, subcortical; IntraHemi, intra hemispheric; InterHemi, inter hemispheric.
Mentions: Every shortest path between nodes walks through a series of edges, of which the ordered sequence of the pivotal or the ordinary edges on the routes was referred to as the “path motifs.” Six types of path motif were identified in the whole-brain WM network (Figure 5A), and their appearing frequencies were statistically compared to those of 1000 equivalent random networks. The comparison revealed that the paths in the brain network exhibited significantly greater percentages of several pivotal-edge related connection types (e.g., “P,” “N-P,” “N-P-N,” and “N-P-N-P,” all Zs > 4.4, Figure 5B), especially the non-pivotal to pivotal to non-pivotal (N-P-N) path motif (Z = 23.1). Such a path-motif distribution profile indicates the central role of pivotal edges in the communicational organization of brain circuits.

Bottom Line: We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions.Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges.Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China.

ABSTRACT
White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

No MeSH data available.