Dataset Information

Treating major depression and comorbid disorders with transcranial magnetic stimulation.

ABSTRACT:

Background

Major Depressive Disorder (MDD) is a global disorder that negatively affects mood and quality of life. Antidepressant medication and psychotherapy are the most commonly prescribed treatments, but prior research has called their clinical efficacy into question. These treatments may be less effective when the patient has a diagnosed comorbid disorder.

Method

A systematic review of the literature was conducted to investigate whether an alternative method of treatment, Transcranial Magnetic Stimulation (TMS), is effective for MDD with a diagnosed comorbidity. 110 articles were identified, of which 8 were included in the current review.

Results

Response and remission rates vary. A range of 39.5-70% of participants experienced an antidepressant response to treatment, and 16.6-76.9% of patients achieved remission from MDD. A range of 48.6-84.6% of participants responded to treatment of their comorbid disorder, and 50-84.6% achieved remission of comorbid symptoms.

Limitations

Limitations of the current review include small sample sizes, limited statistical power, homogenous samples, and a lack of sham or placebo-controlled studies.

Conclusion

Preliminary results support that TMS is effective at treating symptoms of MDD and a comorbid disorder. Additional studies are needed to confirm these results.

SUBMITTER: Thompson L

PROVIDER: S-EPMC7505211 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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?<?0.001). LIMITATION:Limitations of this work include lack of sham condition, modest sample size, and a sparse electrode array. Despite these methodological limitations, we found validated and clinically meaningful results. CONCLUSIONS:Machine learning successfully predicted non-response to TMS with high specificity, and identified pre- and post-TMS status using EEG coherence. This approach may provide mechanistic insights and may also become a clinically useful screening tool for TMS candidates.