Magnetoencephalography resting‐state spectral fingerprints distinguish bipolar depression and unipolar depression

Abstract

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.

Efficacy of acupuncture at ghost points combined with fluoxetine in treating depression: A randomized study

“…Previous studies have reported significantly lower TCM syndrome scores in an acupuncture group compared to a fluoxetine group after 8 wk of treatment; a greater clinical efficacy was achieved with acupuncture treatment[ 19 , 20 ]. In addition, the incidence of adverse reactions in the acupuncture group (5.71%) was significantly lower than that in the fluoxetine group (25.71%).…”

Alterations in Patients With First-Episode Depression in the Eyes-Open and Eyes-Closed Conditions: A Resting-State EEG Study

“…Despite many studies on Rs-EEG oscillations, which have improved the diagnostics of MDD, the findings differ markedly and are partly contradictory. For example, compared to HC subjects, an EEG study reported increased γ in the frontal and temporal regions in MDD patients under the EC condition [31], whereas MDD patients showed no significant difference in the γ band under the EC condition in another work [32]. Previous studies have identified a frontal α asymmetry in MDD.…”

Connectivity of the Frontal Cortical Oscillatory Dynamics Underlying Inhibitory Control During a Go/No-Go Task as a Predictive Biomarker in Major Depression

“…Result: PS was significantly reduced in left motor and preSMA of MDD patients in both beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and low gamma bands. Compared to the HC group, the MDD group demonstrated higher connectivity between lM1 and preSMA in the beta band (t = 3.214, p = 0.002, FDR corrected) and showed reduced connectivity between preSMA and rIFG in the low gamma band (t = −2.612, p = 0.012, FDR corrected).…”

“…Connections between lM1, preSMA, and rIFG have been widely demonstrated to be associated with inhibition mechanisms (6,(14)(15)(16). MDD show abnormal PS in the beta and gamma bands compared to healthy controls (HC) (27,28). Authors of previous studies have demonstrated that the FC and PS in the beta and gamma bands among these regions may be a strong indicator of cognitive deficit in depression (25,26,(29)(30)(31).…”