ObjectiveMisdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD.MethodThe module allegiance from resting‐fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD.ResultsBased on our classification model, difference mainly reflected in default‐mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well‐overlapped, except for cognitive control network (CCN).ConclusionThe functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.

Background Major depressive disorder (MDD) is characterized by core functional deficits in cognitive inhibition, which is crucial for emotion regulation. To assess the response to ruminative and negative mood states, it was hypothesized that MDD patients have prolonged disparities in the oscillatory dynamics of the frontal cortical regions across the life course of the disease. Method A “go/no-go” response inhibition paradigm was tested in 31 MDD patients and 19 age-matched healthy controls after magnetoencephalography (MEG) scanning. The use of minimum norm estimates (MNE) examined the changes of inhibitory control network which included the right inferior frontal gyrus ( r IFG), pre-supplementary motor area (preSMA), and left primary motor cortex ( l M1). The power spectrum (PS) within each node and the functional connectivity (FC) between nodes were compared between two groups. Furthermore, Pearson correlation was calculated to estimate the relationship between altered FC and clinical features. Result PS was significantly reduced in left motor and preSMA of MDD patients in both beta (13–30 Hz) and low gamma (30–50 Hz) bands. Compared to the HC group, the MDD group demonstrated higher connectivity between l M1 and preSMA in the beta band ( t = 3.214, p = 0.002, FDR corrected) and showed reduced connectivity between preSMA and r IFG in the low gamma band ( t = −2.612, p = 0.012, FDR corrected). The FC between l M1 and preSMA in the beta band was positively correlated with illness duration ( r = 0.475, p = 0.005, FDR corrected), while the FC between preSMA and r IFG in the low gamma band was negatively correlated with illness duration ( r = −0.509, p = 0.002, FDR corrected) and retardation factor scores ( r = −0.288, p = 0.022, uncorrected). Conclusion In this study, a clinical neurophysiological signature of cognitive inhibition leading to sustained negative affect as well as functional non-recovery in MDD patients is highlighted. Duration of illness (DI) plays a key role in negative emotional processing, heighten rumination, impulsivity, and disinhibition.

In major depressive disorder (MDD), processing of facial affect is thought to reflect a perceptual bias (toward negative emotion, away from positive emotion, and interpretation of neutral as emotional). However, it is unclear to what extent and which specific perceptual bias is represented in MDD at the behavior and neuronal level. The present report examined 48 medication naive MDD patients and 41 healthy controls (HCs) performing a facial affect judgment task while magnetoencephalography was recorded. MDD patients were characterized by overall slower response times and lower perceptual judgment accuracies. In comparison with HC, MDD patients exhibited less somatosensory beta activity (20–30 Hz) suppression, more visual gamma activity (40–80 Hz) modulation and somatosensory beta and visual gamma interaction deficit. Moreover, frontal gamma activity during positive facial expression judgment was found to be negatively correlated with depression severity. Present findings suggest that perceptual bias in MDD is associated with distinct spatio-spectral manifestations on the neural level, which potentially establishes aberrant pathways during facial emotion processing and contributes to MDD pathology.

Objectives In clinical practice, bipolar depression (BD) and unipolar depression (UD) appear to have similar symptoms, causing BD being frequently misdiagnosed as UD, leading to improper treatment decision and outcome. Therefore, it is in urgent need of distinguishing BD from UD based on clinical objective biomarkers as early as possible. Here, we aimed to integrate brain neuroimaging data and an advanced machine learning technique to predict different types of mood disorder patients at the individual level. Methods Eyes closed resting‐state magnetoencephalography (MEG) data were collected from 23 BD, 30 UD, and 31 healthy controls (HC). Individual power spectra were estimated by Fourier transform, and statistic spectral differences were assessed via a cluster permutation test. A support vector machine classifier was further applied to predict different mood disorder types based on discriminative oscillatory power. Results Both BD and UD showed decreased frontal‐central gamma/beta ratios comparing to HC, in which gamma power (30‐75 Hz) was decreased in BD while beta power (14‐30 Hz) was increased in UD vs HC. The support vector machine model obtained significant high classification accuracies distinguishing three groups based on mean gamma and beta power (BD: 79.9%, UD: 81.1%, HC: 76.3%, P < .01). Conclusions In combination with resting‐state MEG data and machine learning technique, it is possible to make an individual and objective prediction for mode disorder types, which in turn has implications for diagnosis precision and treatment decision of mood disorder patients.