Connectome analysis of functional and structural hemispheric brain networks in major depressive disorder

Abstract

Neuroimaging studies have shown topological disruptions of both functional and structural whole-brain networks in major depressive disorder (MDD). This study examined common and specific alterations between these two types of networks and whether the alterations were differentially involved in the two hemispheres. Multimodal MRI data were collected from 35 MDD patients and 35 healthy controls, whose functional and structural hemispheric networks were constructed, characterized, and compared. We found that functional brain networks were profoundly altered at multiple levels, while structural brain networks were largely intact in patients with MDD. Specifically, the functional alterations included decreases in intra-hemispheric (left and right) and inter-hemispheric (heterotopic) functional connectivity; decreases in local, global and normalized global efficiency for both hemispheric networks; increases in normalized local efficiency for the left hemispheric networks; and decreases in intra-hemispheric integration and inter-hemispheric communication in the dorsolateral superior frontal gyrus, anterior cingulate gyrus and hippocampus. Regarding hemispheric asymmetry, largely similar patterns were observed between the functional and structural networks: the right hemisphere was over-connected and more efficient than the left hemisphere globally; the occipital and partial regions exhibited leftward asymmetry, and the frontal and temporal sites showed rightward lateralization with regard to regional connectivity profiles locally. Finally, the functional–structural coupling of intra-hemispheric connections was significantly decreased and correlated with the disease severity in the patients. Overall, this study demonstrates modality- and hemisphere-dependent and invariant network alterations in MDD, which are helpful for understanding elaborate and characteristic patterns of integrative dysfunction in this disease.

Significant Decrease in Hippocampus and Amygdala Mean Diffusivity in Treatment-Resistant Depression Patients Who Respond to Electroconvulsive Therapy

“…Jiang et al indeed highlight that similar patterns are observed between functional and structural networks: overall, the right hemisphere is over-connected and more efficient than the left hemisphere; the occipital and parietal regions exhibit leftward asymmetry; and the frontal and temporal sites show local rightward lateralization with regard to regional connectivity profiles. The Jiang et al study also shows that the functional–structural coupling of intra-hemispheric connections is significantly decreased and correlated to disease severity (39). However, to our knowledge, there are no available data focusing on hemispheric asymmetry in relation to MD of the hippocampus and amygdala in an MDD population.…”

“…Assessing differences in hemispheric asymmetry is difficult, as such differences exist not only in the healthy population (36–38) but also in MDD patients (39). Jiang et al indeed highlight that similar patterns are observed between functional and structural networks: overall, the right hemisphere is over-connected and more efficient than the left hemisphere; the occipital and parietal regions exhibit leftward asymmetry; and the frontal and temporal sites show local rightward lateralization with regard to regional connectivity profiles.…”

Surface-Based Connectivity Integration

“…Using predefined parcellations to calculate discrete adjacency matrices to study structural-functional coupling is a standard choice in the literature [Honey et al, 2009;Chamberland et al, 2017;Honey et al, 2010;Buckner et al, 2013;Ghumman et al, 2016;Baum et al, 2020;Cocchi et al, 2014;Jiang et al, 2019], with almost no exceptions. However, from our results in Figure 7 and Table 2, we see that the discrete SFC (derived from Desikan-Killiany atlas-based SC and FC) has several drawbacks: (1) it has low resolution (the number of ROIs in the Desikan-Killiany atlas); (2) it is less reproducible compared with our novel continuous local and global SFC features; and (3) it is less powerful in distinguishing groups.…”

“…Discrete Parcellation-based SC, FC and SC-FC Coupling: At last, following existing literature [Baum et al, 2020;Cocchi et al, 2014;Wang et al, 2018;Jiang et al, 2019;Zhang et al, 2011], we define discrete SFC for a given parcellation. We first convert our continuous SC and FC to finite adjacency matrices based on the given parcellation.…”

Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering

“…Connectome markers ranging from the simple graph descriptors such as edge weights and nodal degrees to sophisticated graph theoretical measures have all been invoked in the study of the brain. At the time of this writing, dozens of studies examining the effects of genetics and disease on structural and functional brain connectivity have been published (Horovitz and Horwitz, 2012;Jahanshad et al, 2013;Jiang et al, 2019;Lynall et al, 2010;Shah et al, 2017;Sun et al, 2014;Xu et al, 2016). In nearly all of these, brain parcellation plays a crucial role.…”