Alaa M Al-Kaysi1, Ahmed Al-Ani2, Colleen K Loo3, Tamara Y Powell4, Donel M Martin5, Michael Breakspear6, Tjeerd W Boonstra4. 1. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia. Electronic address: alaa.m.dawood@student.uts.edu.au. 2. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia. 3. 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. 4. Black Dog Institute, University of New South Wales, Sydney, Australia; QIMR Berghofer Medial Research Institute, Brisbane, Australia. 5. School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, University of New South Wales, Sydney, Australia. 6. QIMR Berghofer Medial Research Institute, Brisbane, Australia; St George Hospital, Kogarah, Australia.
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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). LIMITATIONS: Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept. CONCLUSIONS: 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.
BACKGROUND: 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 MDDpatients that respond to tDCS treatment based on resting-state electroencephalography (EEG) recorded prior to treatment commencing. METHODS: 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. RESULTS: 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). LIMITATIONS: Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept. CONCLUSIONS: 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: Alik S Widge; M Taha Bilge; Rebecca Montana; Weilynn Chang; Carolyn I Rodriguez; Thilo Deckersbach; Linda L Carpenter; Ned H Kalin; Charles B Nemeroff Journal: Am J Psychiatry Date: 2018-10-03 Impact factor: 18.112
Authors: Nicholas L Balderston; Emily M Beydler; Madeline Goodwin; Zhi-De Deng; Thomas Radman; Bruce Luber; Sarah H Lisanby; Monique Ernst; Christian Grillon Journal: Transl Psychiatry Date: 2020-02-17 Impact factor: 6.222