Dataset Information

Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

ABSTRACT: BACKGROUND:Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for other conditions including posttraumatic stress disorder (PTSD). Understanding the mechanisms of TMS action and developing biomarkers predicting response remain important goals. We applied a combination of machine learning and electroencephalography (EEG), testing whether machine learning analysis of EEG coherence would (1) predict clinical outcomes in individuals with comorbid MDD and PTSD, and (2) determine whether an individual had received a TMS course. METHODS:We collected resting-state 8-channel EEG before and after TMS (5?Hz to the left dorsolateral prefrontal cortex). We used Lasso regression and Support Vector Machine (SVM) to test the hypothesis that baseline EEG coherence predicted the outcome and to assess if EEG coherence changed after TMS. RESULTS:In our sample, clinical response to TMS were predictable based on pretreatment EEG coherence (n?=?29). After treatment, 13/29 had more than 50% reduction in MDD self-report score 12/29 had more than 50% reduction in PTSD self-report score. For MDD, area under roc curve was for MDD was 0.83 (95% confidence interval 0.69-0.94) and for PTSD was 0.71 (95% confidence interval 0.54-0.87). SVM classifier was able to accurately assign EEG recordings to pre- and post-TMS treatment. The accuracy for Alpha, Beta, Theta and Delta bands was 75.4?±?1.5%, 77.4?±?1.4%, 73.8?±?1.5%, and 78.6?±?1.4%, respectively, all significantly better than chance (50%, p?

SUBMITTER: Zandvakili A

PROVIDER: S-EPMC6520189 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

Zandvakili Amin A Philip Noah S NS Jones Stephanie R SR Tyrka Audrey R AR Greenberg Benjamin D BD Carpenter Linda L LL

Journal of affective disorders 20190330

<h4>Background</h4>Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for other conditions including posttraumatic stress disorder (PTSD). Understanding the mechanisms of TMS action and developing biomarkers predicting response remain important goals. We applied a combination of machine learning and electroencephalography (EEG), testing whether machine learning analysis of EEG coherence would (1) predict clinical out ...[more]

PMID: 30978624

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Project description:OBJECTIVE:Posttraumatic stress disorder (PTSD) is a highly prevalent psychiatric disorder associated with disruption in social and occupational function. Transcranial magnetic stimulation (TMS) represents a novel approach to PTSD, and intermittent theta-burst stimulation (iTBS) is a new, more rapid administration protocol with data supporting efficacy in depression. The authors conducted a sham-controlled study of iTBS for PTSD. METHODS:Fifty veterans with PTSD received 10 days of sham-controlled iTBS (1,800 pulses/day), followed by 10 unblinded sessions. Primary outcome measures included acceptability (retention rates), changes in PTSD symptoms (clinician- and self-rated), quality of life, social and occupational function, and depression, obtained at the end of 2 weeks; analysis of variance was used to compare active with sham stimulation. Secondary outcomes were evaluated 1 month after treatment, using mixed-model analyses. Resting-state functional MRI was acquired at pretreatment baseline on an eligible subset of participants (N=26) to identify response predictors. RESULTS:Retention was high, side effects were consistent with standard TMS, and blinding was successful. At 2 weeks, active iTBS was significantly associated with improved social and occupational function (Cohen's d=0.39); depression was improved with iTBS compared with the sham treatment (d=-0.45), but the difference fell short of significance, and moderate nonsignificant effect sizes were observed on self-reported PTSD symptoms (d=-0.34). One-month outcomes, which incorporated data from the unblinded phase of the study, indicated superiority of active iTBS on clinician- and self-rated PTSD symptoms (d=-0.74 and -0.63, respectively), depression (d=-0.47), and social and occupational function (d=0.93) (all significant). Neuroimaging indicated that clinical improvement was significantly predicted by stronger (greater positive) connectivity within the default mode network and by anticorrelated (greater negative) cross-network connectivity. CONCLUSIONS:iTBS appears to be a promising new treatment for PTSD. Most clinical improvements from stimulation occurred early, which suggests a need for further investigation of optimal iTBS time course and duration. Consistent with previous neuroimaging studies of TMS, default mode network connectivity played an important role in response prediction.

Project description:ObjectiveA minimally conscious state (MCS) is characterized by discernible behavioral evidence of consciousness that cannot be reproduced consistently. This condition is highly challenging to treat. Recent studies have demonstrated the potential therapeutic effect of non-invasive brain stimulation in patients with MCS. In one patient in an MCS, we delivered simultaneous transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS) based on an individual brain network analysis and evaluated the therapeutic effect.MethodsThe directional transfer function (DTF) was calculated based on electroencephalograph (EEG) analysis. Global brain connectivity was calculated based on functional magnetic resonance imaging (fMRI) analysis. By referring to the EEG and fMRI results, we identified inferior parietal lobes (IPLs) as targets. In the 2-week treatment period, 14 sessions were applied to the identified bilateral parietal regions. Simultaneous 1.5-mA anodal tDCS and 5-Hz rTMS were delivered for 20 min per hemisphere in each session. Clinical evaluation scores were recorded weekly throughout the treatment. A second patient given the routine treatment was evaluated as a control.ResultsThe clinical scores of patient 1 with MCS improved after 2 weeks of stimulation treatment, and the effect lasted for up to 1 month. EEG analysis showed a significant increase (p < 0.001) in the DTF value in the gamma band in a bilateral set of posterior regions, and fMRI showed a trend toward normalized activity in the IPLs. The clinical scores of patient 2 with coma did not improve much after 2 weeks of routine treatment. The EEG analysis showed a significant increase (p = 0.021) in the DTF value in the gamma band in a bilateral set of posterior regions.ConclusionThe application of EEG and fMRI to characterize the functional connectivity features of the network in an MCS patient provided a reasonable and accurate stimulation target and verified the changes in functional connectivity resulting from stimulation.

Dataset Information

Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

Publications

Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

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