Biology subjects

Berwian, I. M.

Publications and source records attributed to Berwian, I. M..

3 recordsLinked to original sources

bioRxiv2023

Emotion-induced frontal α asymmetry predictsrelapse after discontinuation of antidepressantmedication

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSOne in three patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of Major Depressive Disorder (MDD). However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by fMRI has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor. MethodsPatients (n=56) who had remitted from a depressive episode while taking antidepressants underwent EEG recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a six-months follow-up period. 35 healthy controls were also tested. Current source density of the EEG power in the band (8-13Hz) was extracted and alpha-asymmetry was computed by comparing the power across two hemispheres at frontal electrodes (F5 and F6). OutcomesSad mood induction was robust across all groups. Reactivity of -asymmetry to sad mood did not distinguish healthy controls from patients with remitted MDD on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in -asymmetry compared to patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy). InterpretationA simple EEG-based measure of emotional reactivity may have clinical utility in the management of antidepressant discontinuation. FundingSwiss National Science Foundation project grant 320030L_153449 / 1 to QJMH, Stiftung Deutsche Depressionshilfe to HW and QJMH, a Deutsche Forschungsgemeinschaft (DFG) grant (WA 1539/5-1) to HW, EMDO Stiftung to QJMH and the Rene and Susanne Braginsky Foundation and Clinical Research Priority Programme "Molecular Imaging" at the University of Zurich to KES.

neuroscience↗

bioRxiv2020

The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse

The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal differences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This finding requires replication in a larger dataset.

neuroscience↗

bioRxiv2020

Low predictive power of clinical features for relapse prediction after antidepressant discontinuation in a naturalistic setting

1BackgroundThe risk of relapse after antidepressant medication (ADM) discontinuation is high. Predictors of relapse could guide clinical decision-making, but are yet to be established. MethodWe assessed demographic and clinical variables in a longitudinal observational study before antidepressant discontinuation. State-dependent variables were re-assessed either after discontinuation or before discontinuation after a waiting period. Relapse was assessed during six months after discontinuation. We applied logistic general linear models in combination with least absolute shrinkage and selection operator and elastic nets to avoid overfitting in order to identify predictors of relapse and estimated their generalisability using cross-validation. ResultsThe final sample included 104 patients (age: 34.86 (11.1), 77% female) and 57 healthy controls (age: 34.12 (10.6), 70% female). 36% of the patients experienced a relapse. Treatment by a general practitioner increased the risk of relapse. Although within-sample statistical analyses suggested reasonable sensitivity and specificity, out-of-sample prediction of relapse was at chance level. Residual symptoms increased with discontinuation, but did not relate to relapse. Conclusion and RelevanceDemographic and standard clinical variables appear to carry little predictive power and therefore are of limited use for patients and clinicians in guiding clinical decision-making.

neuroscience↗