Aberrant brain structural–functional connectivity coupling in euthymic bipolar disorder

Abstract

Aberrant structural (diffusion tensor imaging [DTI]) and resting‐state functional magnetic resonance imagining connectivity are core features of bipolar disorder. However, few studies have explored the integrity agreement between structural and functional connectivity (SC–FC) in bipolar disorder. We examine SC connectivity coupling index whether could potentially provide additional clinical predictive value for bipolar disorder spectrum disorders besides the intramodality network measures. By examining the structural (DTI) and resting‐state functional network properties, as well as their coupling index, among 57 euthymic bipolar disorder patients (age 13–28 years, 18 females) and 42 age‐ and gender‐matched healthy controls (age 13–28 years, 16 females), we found that compared to controls, bipolar disorder patients showed increased structural rich‐club connectivity as well as decreased functional modularity. Importantly, the coupling strength between structural and functional connectome was decreased in patients compared to controls, which emerged as the most powerful feature discriminating the two groups. Our findings suggest that structural–functional coupling strength could serve as a valuable biological trait‐like feature for bipolar disorder over and above the intramodality network measures. Such measure can have important clinical implications for early identification of bipolar disorder individuals, and inform strategies for prevention of bipolar disorder onset and relapse.

Frontolimbic, Frontoparietal, and Default Mode Involvement in Functional Dysconnectivity in Psychotic Bipolar Disorder

“…To the best of our knowledge, this is the first study to explore structural-functional connectome coalescence in an adult sample of euthymic individuals with BD. Previous investigations include a young euthymic BD group (38) and offspring (39) reporting decreased structural-functional coupling in BD (38) and increased coupling in offspring (39) with structure-function breakdown involving intrahemispheric and whole-brain connectivity (38) or long-range connections (39). We failed to detect any difference in structure-function associations in BD relative to controls globally and within connection classes.…”

Brain Structural Connectivity Predicts Brain Functional Complexity: Diffusion Tensor Imaging Derived Centrality Accounts for Variance in Fractal Properties of Functional Magnetic Resonance Imaging Signal

“…Furthermore, resting state fMRI studies have revealed interesting baseline patterns of activation (i.e., resting state networks), and ushered in an age of network science in brain research. With the rise in resting state fMRI research, predicting resting state fMRI functional connectivity from diffusion tensor imaging (DTI) structural connectivity has been an important recent endeavor for researchers, and the use of graph theory analyses of structural networks have been integral to these investigations (e.g., Honey, Thivierge, & Sporns, 2010;Goñi et al, 2014;Bettinardi et al, 2017;Zhang et al, 2019;Chen, Hu, Chen, & Feng , 2019). Furthermore, there has been recent interest in predicting taskbased fMRI activation from DTI structural connectivity (Osher et al, 2016;.…”

Bipolar Disorder and Gender Are Associated with Frontolimbic and Basal Ganglia Dysconnectivity: A Study of Topological Variance Using Network Analysis

“…A plausible explanation for this may be the increased specificity of our connectome approach relative to other observations. Furthermore, one study has shown marginal reductions in rich-club connectivity in BD (O'Donoghue et al, 2017b), whereas two have described an increase in NOS-weighted rich-club coefficients compared to controls (Zhang et al, 2018;O'Donoghue, et al, 2016). Comparisons of rich-club findings is limited by the different rich-club network mapping employed across studies, specifically if these have confined their rich-club observations to cortical connections (Collin et al, 2016) or have not defined rich-club nodes in a subject-specific manner (Roberts et al, 2018;Wang et al, 2018 ;O'Donoghue et al, 2017b).…”