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.

Accumulating evidence suggests that early improvement after two-week antidepressant treatment is predictive of later outcomes of patients with major depressive disorder (MDD); however, whether this early improvement is associated with baseline neural architecture remains largely unknown. Utilizing resting-state functional MRI data and graph-based network approaches, this study calculated voxel-wise degree centrality maps for 24 MDD patients at baseline and linked them with changes in the Hamilton Rating Scale for Depression (HAMD) scores after two weeks of medication. Six clusters exhibited significant correlations of their baseline degree centrality with treatment-induced HAMD changes for the patients, which were mainly categorized into the posterior default-mode network (i.e., the left precuneus, supramarginal gyrus, middle temporal gyrus, and right angular gyrus) and frontal regions. Receiver operating characteristic curve and logistic regression analyses convergently revealed excellent performance of these regions in discriminating the early improvement status for the patients, especially the angular gyrus (sensitivity and specificity of 100%). Moreover, the angular gyrus was identified as the optimal regressor as determined by stepwise regression. Interestingly, these regions possessed higher centrality than others in the brain (P < 10(-3)) although they were not the most highly connected hubs. Finally, we demonstrate a high reproducibility of our findings across several factors (e.g., threshold choice, anatomical distance, and temporal cutting) in our analyses. Together, these preliminary exploratory analyses demonstrate the potential of neuroimaging-based network analysis in predicting the early therapeutic improvement of MDD patients and have important implications in guiding earlier personalized therapeutic regimens for possible treatment-refractory depression.

ObjectiveOverlap of obstructive sleep apnea (OSA) complicates diagnosis of depressive disorder and renders antidepressant treatment challenging. Previous studies have reported that the incidence of OSA is higher in patients with depression than in the general population. The purpose of this article was to investigate clinical risk factors to predict OSA in depression disorders.MethodsA total of 115 patients diagnosed with major depressive disorder (MDD) and bipolar disorder (in a major depressive episode), who underwent overnight polysomnography, were studied retrospectively. They were divided into two groups: non-OSA and OSA. The patients who had apnea–hypopnea index (AHI) <5 were defined as the non-OSA group, whereas the OSA group was defined as those with an AHI ≥5. Logistic regression was used to analyze the association among AHI and clinical factors, including sex, age, body mass index (BMI), Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale, Pittsburgh Sleep Quality Index (PSQI), and diagnosis (MDD or bipolar disorder [in a major depressive episode]).ResultsIn 115 patients, 51.3% had OSA. Logistic regression analysis showed significant associations between AHI and diagnosis (MDD or bipolar disorder [in a major depressive episode]), BMI, HAMD, and PSQI (P<0.05).ConclusionThe findings of our study suggested that the rate of depression being comorbid with OSA is remarkably high and revealed that there is a high rate of undetected OSA among depressive disorder patients and untreated OSA among mood disorder patients. The clinical risk factors (diagnosis [MDD or bipolar disorder {in a major depressive episode}], BMI, HAMD, and PSQI) could predict AHI or OSA diagnosis and contribute to OSA screening in depressive disorder patients.