Enhanced network synchronization connectivity following transcranial direct current stimulation (tDCS) in bipolar depression: Effects on EEG oscillations and deep learning-based predictors of clinical remission

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Wenyi Xiao - , University of East London (Author)
  • Jijomon C. Moncy - , University of East London (Author)
  • Ali Reza Ghazi-Noori - , University of East London (Author)
  • Rachel D. Woodham - , University of East London (Author)
  • Hakimeh Rezaei - , Department of Psychiatry and Psychotherapy, University of East London, TUD Dresden University of Technology, Centre for Affective Disorders, King's College London (KCL) (Author)
  • Elvira Bramon - , University College London (Author)
  • Philipp Ritter - , Department of Psychiatry and Psychotherapy, TUD Dresden University of Technology (Author)
  • Michael Bauer - , Department of Psychiatry and Psychotherapy, TUD Dresden University of Technology (Author)
  • Allan H. Young - , Department of Psychiatry and Psychotherapy, Centre for Affective Disorders, King's College London (KCL), University of Oxford, South London and Maudsley NHS Foundation Trust (Author)
  • Cynthia H.Y. Fu - , University of East London, King's College London (KCL), South London and Maudsley NHS Foundation Trust (Author)

Abstract

Aim: To investigate oscillatory networks in bipolar depression, effects of a home-based tDCS treatment protocol, and potential predictors of clinical response. Methods: 20 participants (14 women) with bipolar disorder, mean age 50.75 ± 10.46 years, in a depressive episode of severe severity (mean Montgomery-Åsberg Rating Scale (MADRS) score 24.60 ± 2.87) received home-based transcranial direct current stimulation (tDCS) treatment for 6 weeks. Clinical remission defined as MADRS score < 10. Resting-state EEG data were acquired at baseline, prior to the start of treatment, and at the end of treatment, using a portable 4-channel EEG device (electrode positions: AF7, AF8, TP9, TP10). EEG band power was extracted for each electrode and phase locking value (PLV) was computed as a functional connectivity measure of phase synchronization. Deep learning was applied to pre-treatment PLV features to examine potential predictors of clinical remission. Results: Following treatment, 11 participants (9 women) attained clinical remission. A significant positive correlation was observed with improvements in depressive symptoms and delta band PLV in frontal and temporoparietal regional channel pairs. An interaction effect in network synchronization was observed in beta band PLV in temporoparietal regions, in which participants who attained clinical remission showed increased synchronization following tDCS treatment, which was decreased in participants who did not achieve clinical remission. Main effects of clinical remission status were observed in several PLV bands: clinical remission following tDCS treatment was associated with increased PLV in frontal and temporal regions and in several frequency bands, including delta, theta, alpha and beta, as compared to participants who did not achieve clinical remission. The highest deep learning prediction accuracy 69.45 % (sensitivity 71.68 %, specificity 66.72 %) was obtained from PLV features combined from theta, beta, and gamma bands. Conclusions: tDCS treatment enhances network synchronization, potentially increasing inhibitory control, which underscores improvement in depressive symptoms. Baseline EEG-based measures might aid predicting clinical response.

Details

Original languageEnglish
Pages (from-to)576-587
Number of pages12
JournalJournal of Affective Disorders
Volume369
Publication statusPublished - 15 Jan 2025
Peer-reviewedYes

External IDs

PubMed 39293596

Keywords

Keywords

  • Bipolar depression, Bipolar disorder, Brain connectivity, EEG, Phase locking value, Prediction treatment response, Transcranial direct current stimulation