Enhanced Network Efficiency of Functional Brain Networks in Primary Insomnia Patients

Abstract

Accumulating evidence from neuroimaging studies suggests that primary insomnia (PI) affects interregional neural coordination of multiple interacting functional brain networks. However, a complete understanding of the whole-brain network organization from a system-level perspective in PI is still lacking. To this end, we investigated in topological organization changes in brain functional networks in PI. 36 PI patients and 38 age-, sex-, and education-matched healthy controls were recruited. All participants underwent a series of neuropsychological assessments and resting-state functional magnetic resonance imaging scans. Individual whole-brain functional network were constructed and analyzed using graph theory-based network approaches. There were no significant differences with respect to age, sex, or education between groups (P > 0.05). Graph-based analyses revealed that participants with PI had a significantly higher total number of edges (P = 0.022), global efficiency (P = 0.014), and normalized global efficiency (P = 0.002), and a significantly lower normalized local efficiency (P = 0.042) compared with controls. Locally, several prefrontal and parietal regions, the superior temporal gyrus, and the thalamus exhibited higher nodal efficiency in participants with PI (P < 0.05, false discovery rate corrected). In addition, most of these regions showed increased functional connectivity in PI patients (P < 0.05, corrected). Finally, altered network efficiency was correlated with neuropsychological variables of the Epworth Sleepiness Scale and Insomnia Severity Index in patients with PI. PI is associated with abnormal organization of large-scale functional brain networks, which may account for memory and emotional dysfunction in people with PI. These findings provide novel implications for neural substrates associated with PI.

Abnormal Topology of the Structural Connectome in the Limbic Cortico-Basal-Ganglia Circuit and Default-Mode Network Among Primary Insomnia Patients

“…In our study, anatomical topology analysis revealed that the two study groups had characteristic small-world organization, which was consistent with the finding of previous studies ( Lu et al, 2017b ; Ma et al, 2018 ). Eg and Eloc were used as measures of functional integration and segregation, respectively ( Rubinov and Sporns, 2010 ).…”

Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory

“…Graph analyses revealed diurnal differences in the CRB associated with higher measures of network centrality such as nodal efficiency and degree and betweenness centrality in the evening compared with the morning. These results indicate the high ability of bilateral Crus I and II but also left lobules VIIB, VIII, and X of the cerebellar hemisphere to transmit the information to other regions included in the CRB [ 95 ]. Dynamic interaction is related to greater efficiency and thereby better functioning of the whole cerebellum, which, apart from basic motor control such as voluntary limb movements, balance, and maintaining posture [ 96 ], is associated with the visual attention process and working memory [ 97 ].…”

Modifications in the Topological Structure of EEG Functional Connectivity Networks during Listening Tonal and Atonal Concert Music in Musicians and Non-Musicians

“…For a graph (G), GE is defined as the average of the inverse of the shortest path length from each node to all other nodes [ 62 , 63 ] which is mathematically expressed by: where d(i,j) is the shortest path length between node i and node j in graph G and is calculated as the smallest sum of edge/connections lengths throughout all possible paths from node i and node j. The length of a connection or edge was considered as the reciprocal of the connection weight (here, their PLV or FC), under the assumption that the distance between two nodes is inversely proportional to their FC [ 64 ]. Local efficiency (LE), which for a node is defined as the GE of the node, calculated in the subgraph created by its neighbors.…”

“…Taking both measures (LE and GE) into consideration, a regular network or lattice is defined as having high LE and low GE, a random network has low LE and high GE, and a small-world (SW) network would lie somewhere between a regular and random network, with a high LE and GE. To check whether a real network has a small-world structure, one of the proposed methods is to normalize LE and GE in the real network, in relation to those computed in matched random networks e.g., dividing LE and GE by the corresponding mean [LE(r) or GE(r)] obtained from 100 random networks that preserved the same number of nodes, connections, and degree distributions as the real brain networks [ 64 , 65 , 66 ]. In this way, a SW network would be characterized by having LE > LE(r), and a comparable GE ~ GE(r) or having the normalized versions NLE = LE/LE(r) > 1 and NGE = GE/GE(r) ~ 1.…”