title: Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification.
abstract: Transcranial direct current stimulation (tDCS) is a promising treatment for major depressive disorder (MDD). Standard tDCS treatment involves numerous sessions running over a few weeks. However, not all participants respond to this type of treatment. This study aims to investigate the feasibility of identifying MDD patients that respond to tDCS treatment based on resting-state electroencephalography (EEG) recorded prior to treatment commencing.
  
  
  
  We used machine learning to predict improvement in mood and cognition during tDCS treatment from baseline EEG power spectra. Ten participants with a current diagnosis of MDD were included. Power spectral density was assessed in five frequency bands: delta (0.5-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (13-30Hz) and gamma (30-100Hz). Improvements in mood and cognition were assessed using the Montgomery-Åsberg Depression Rating Scale and Symbol Digit Modalities Test, respectively. We trained the classifiers using three algorithms (support vector machine, extreme learning machine and linear discriminant analysis) and a leave-one-out cross-validation approach.
  
  
  
  Mood labels were accurately predicted in 8 out of 10 participants using EEG channels FC4-AF8 (accuracy=76%, p=0.034). Cognition labels were accurately predicted in 10 out of 10 participants using channels pair CPz-CP2 (accuracy=92%, p=0.004).
  
  
  
  Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept.
  
  
  
  These finding demonstrate the feasibility of using machine learning to identify patients that will respond to tDCS treatment. These promising results warrant a larger study to determine the clinical utility of this approach.
authors.affiliation: Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia. Electronic address: alaa.m.dawood@student.uts.edu.au.
authors.forename: Alaa M
authors.initials: AM
authors.lastname: Al-Kaysi
authors.affiliation: Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Al-Ani
authors.affiliation: School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, University of New South Wales, Sydney, Australia; St George Hospital, Kogarah, Australia.
authors.forename: Colleen K
authors.initials: CK
authors.lastname: Loo
authors.affiliation: Black Dog Institute, University of New South Wales, Sydney, Australia; QIMR Berghofer Medial Research Institute, Brisbane, Australia.
authors.forename: Tamara Y
authors.initials: TY
authors.lastname: Powell
authors.affiliation: School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, University of New South Wales, Sydney, Australia.
authors.forename: Donel M
authors.initials: DM
authors.lastname: Martin
authors.affiliation: QIMR Berghofer Medial Research Institute, Brisbane, Australia; St George Hospital, Kogarah, Australia.
authors.forename: Michael
authors.initials: M
authors.lastname: Breakspear
authors.affiliation: Black Dog Institute, University of New South Wales, Sydney, Australia; QIMR Berghofer Medial Research Institute, Brisbane, Australia.
authors.forename: Tjeerd W
authors.initials: TW
authors.lastname: Boonstra
mesh_terms: D000328:Adult; D003071:Cognition; D003865:Major Depressive Disorder / Q000503:physiopathology / Q000628:therapy*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011597:Psychomotor Performance; D060388:Support Vector Machine; D065908:Transcranial Direct Current Stimulation; D016896:Treatment Outcome
keywords: Brain rhythms; Machine learning; Major depressive disorder; Neuromodulation; Resting-state EEG