Static and dynamic connectomics differentiate between depressed patients with and without suicidal ideation

Abstract

Neural circuit dysfunction underlies the biological mechanisms of suicidal ideation (SI). However, little is known about how the brain's "dynome" differentiate between depressed patients with and without SI. This study included depressed patients (n = 48) with SI, without SI (NSI), and healthy controls (HC, n = 30). All participants underwent resting-state functional magnetic resonance imaging. We constructed dynamic and static connectomics on 200 nodes using a sliding window and full-length time-series correlations, respectively. Specifically, the temporal variability of dynamic connectomic was quantified using the variance of topological properties across sliding window. The overall topological properties of both static and dynamic connectomics further differentiated between SI and NSI, and also predicted the severity of SI. The SI showed decreased overall topological properties of static connectomic relative to the HC. The SI exhibited increases in overall topological properties with regard to the dynamic connectomic when compared with the HC and the NSI. Importantly, combining the overall topological properties of dynamic and static connectomics yielded mean 75% accuracy (all p < .001) with mean 71% sensitivity and mean 75% specificity in differentiating between SI and NSI. Moreover, these features may predict the severity of SI (mean r = .55, all p < .05). The findings revealed that combining static and dynamic connectomics could differentiate between SI and NSI, offering new insight into the physiopathological mechanisms underlying SI. Furthermore, combining the brain's connectome and dynome may be considered a neuromarker for diagnostic and predictive models in the study of SI.

Exploring the functional connectome in white matter

“…We decreased the r thr from 1 to 0 (from maximum to minimum) till the existing number of edges satisfies a sparsity threshold. Specifically, where expresses the existing number of edges generated by thresholding at r thr , and N ( N − 1)/2 represents the maximum possible number of edges existing in a given network of N nodes (Bullmore & Bassett, ; Liao et al, ). In this case, when r thr = 0, sparsity = 1; when r thr = 1, sparsity = 0.…”

“…The minimum sparsity was defined across all subjects as follows: (a) the mean degree of a node (the number of connections to the node) over all nodes in thresholded weighted matrices was greater than 2 × log( N ) ≈ 9.7 (Achard & Bullmore, ; Ji et al, ; Liao et al, , ); where N expresses the number of nodes, N = 128. In this case, total number of edges ≈882, which was equivalent to sparsity = 0.11 or 11% of the maximum number of edges possible () in a network of 128 nodes.…”

“…We counted the remaining edges after thresholding ( p < .05) for the weighted matrix of each subject, which results in maximum sparsity (the remaining edges divided by the maximum possible edges) for that subject. This procedure minimizes the number of spurious edges (Ji et al, ; Liao et al, ). As such, a range of sparsity thresholds (WM functional network: 0.17–0.42, interval = 0.01; GM functional network: 0.11–0.48, interval = 0.01) could be pre‐selected.…”

“…This AUC provides a summarized scalar for topological properties independent of single threshold selection, as any single threshold admits that the topological properties would be uniquely dependent on the precise threshold (Achard & Bullmore, ). The integrated AUC of topological properties has been used in previous brain network studies, and is sensitive to topological comparison (Ji et al, ; Liao et al, ).…”

“…In the present study, we explored the resting‐state WM functional connectome in a large cohort of healthy controls ( n = 488). We utilized graph theoretical analysis, which had been successfully applied to an association matrix of functional connectivity measures (Liao et al, ). We investigated the following: (a) whether the WM functional connectome has features of complex networks, such as a small‐world topology, densely connected hubs, or modular organization; (b) in the case that the WM functional connectome showed typical network architectures, whether any differences of brain architectures or mechanisms were found between WM and GM functional connectomes; and (c) whether these brain architectures were reliable over a long‐term (over 10 months) period in healthy controls (Time 1 and Time 2: n = 175).…”