Disrupted Regional Homogeneity in Melancholic and Non-melancholic Major Depressive Disorder at Rest

Abstract

Background: Melancholic depression has been viewed as one severe subtype of major depressive disorder (MDD). However, it is unclear whether melancholic depression has distinct changes in brain imaging. We aimed to explore specific or distinctive alterations in melancholic MDD and whether the alterations could be used to separate melancholic MDD from non-melancholic MDD or healthy controls.Materials and Methods: Thirty-one outpatients with melancholic MDD and thirty-three outpatients with non-melancholic MDD and thirty-two age- and gender-matched healthy controls were recruited. All participants were scanned by resting-state functional magnetic resonance imaging (fMRI). Imaging data were analyzed with the regional homogeneity (ReHo) and support vector machine (SVM) methods.Results: Melancholic MDD patients exhibited lower ReHo in the right superior occipital gyrus/middle occipital gyrus than non-melancholic MDD patients and healthy controls. Merely for non-melancholic MDD patients, decreased ReHo in the right middle frontal gyrus was negatively correlated with the total HRSD-17 scores. SVM analysis results showed that a combination of abnormal ReHo in the right fusiform gyrus/cerebellum Crus I and the right superior occipital gyrus/middle occipital gyrus exhibited the highest accuracy of 83.05% (49/59), with a sensitivity of 90.32% (28/31), and a specificity of 75.00% (21/28) for discriminating patients with melancholic MDD from patients with non-melancholic MDD. And a combination of abnormal ReHo in the right fusiform gyrus/cerebellum VI and left postcentral gyrus/precentral gyrus exhibited the highest accuracy of 98.41% (62/63), with a sensitivity of 96.77% (30/31), and a specificity of 100.00%(32/32) for separating patients with melancholic MDD from healthy controls.Conclusion: Our findings showed the distinctive ReHo pattern in patients with melancholic MDD and found brain area that may be associated with the pathophysiology of non-melancholic MDD. Potential imaging markers for discriminating melancholic MDD from non-melancholic MDD or healthy controls were reported.

Changes in Regional Homogeneity of Medication-Free Major Depressive Disorder Patients With Different Onset Ages

“…Orbitofrontal cortex plays an important role in decisionmaking, and decision-making impairment is associated with a susceptibility to suicidal symptoms (70), which is included in the cognitive disturbance. A previous study also reported the decreased ReHo in the right middle frontal gyrus was negatively correlated with depression symptoms in MDD patients (60). On the contrary with LOD patients, we found that the correlation between ReHo in the inferior frontal triangular gyrus and cognitive disturbance of EOD was positive (not significant).…”

“…The DLPFC dysfunction may be an early manifestation of MDD and that the right inferior frontal triangular gyrus plays an important role in the pathogenesis of the early stage of MDD; patients with mild DLPFC damage often appear to be uninterested in things, with slow thinking, poor memory, and lack of motivation (56)(57)(58)(59). Recently, one study using machine learning methods reported the abnormal ReHo in the left postcentral gyrus and right fusiform gyrus with a high accuracy for discriminating MDD from HC (60). These results and our study provide further evidence for the important role of the prefrontal lobe in MDD, which may help distinguish the depression subtypes for accurate diagnosis and treatment in the future.…”

Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis

“…In the recent years, computational and statistical models have been used to differentiate melancholic from nonmelancholic depression (9). The complexity and design of these models range from fundamental statistical analysis (10)(11)(12)(13) to machine learning models (14)(15)(16). In most of these studies, they analyzed neuroimaging data to identify the brain activities involved in melancholic depression (11)(12)(13)(14)(15)(16).…”

“…The complexity and design of these models range from fundamental statistical analysis (10)(11)(12)(13) to machine learning models (14)(15)(16). In most of these studies, they analyzed neuroimaging data to identify the brain activities involved in melancholic depression (11)(12)(13)(14)(15)(16). In particular, they extracted functional connections from resting state functional MRI (rs-fMRI) data (13)(14)(15)(16).…”

“…In most of these studies, they analyzed neuroimaging data to identify the brain activities involved in melancholic depression (11)(12)(13)(14)(15)(16). In particular, they extracted functional connections from resting state functional MRI (rs-fMRI) data (13)(14)(15)(16). Functional connectivity analysis (FCA) provides insights on which brain regions are temporally correlated, and is useful for studying brain networks and neural circuit dynamics (14).…”