Transcranial Magnetic Stimulation in Tourette Syndrome: A Historical Perspective, Its Current Use and the Influence of Comorbidities in Treatment Response

Background Repetitive Transcranial Magnetic Stimulation [rTMS] is increasingly being used to treat Major Depressive Disorder [MDD]. Given that not all patients respond to rTMS, it would be clinically useful to have reliable biomarkers that predict treatment response. Oxidized phosphatidylcholine [OxPC] and some oxylipins are important plasma biomarkers of oxidative stress and inflammation. Not only is depression associated with oxidative stress, but rTMS has been shown to have anti-oxidative effects. Objectives To investigate whether plasma oxolipidomics profiles could predict treatment response in patients with treatment resistant MDD. Methods Fourty-eight patients undergoing rTMS treatment for MDD were recruited along with nine healthy control subjects. Plasma OxPCs and oxylipins were extracted and analyzed through high performance liquid chromatography coupled with mass spectrometry. Patients with a Hamilton Depression Rating Scale score [Ham-D] ≤7 post-treatment were defined as having entered remission. Results Fifty-seven OxPC and 32 oxylipin species were identified in our subjects. MDD patients who entered remission following rTMS had significantly higher pre-rTMS levels of total and fragmented OxPCs compared to non-remitters and controls [one-way ANOVA, p<0.05]. However, no significant changes in OxPC levels were found as a result of rTMS, regardless of treatment response [p>0.05]. No differences in plasma oxylipins were found between remitters and non-remitters at baseline. Conclusion Certain categories of OxPCs may be useful predictive biomarkers for response to rTMS treatment in MDD. Given that elevated oxidized lipids may indicate higher levels of oxidative stress and inflammation in the brain, patients with this phenotype of depression may be more receptive to rTMS treatment.

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Repetitive Transcranial Magnetic Stimulation of the Supplementary Motor Area in the treatment of Tourette Syndrome: Report of two cases

Clinical Neurophysiology ◽

10.1016/j.clinph.2007.07.011 ◽

2007 ◽

Vol 118 (10) ◽

pp. 2314-2315 ◽

Cited By ~ 64

Author(s):

Antonio Mantovani ◽

James F. Leckman ◽

Heidi Grantz ◽

Robert A. King ◽

Alexandra L. Sporn ◽

...

Keyword(s):

Transcranial Magnetic Stimulation ◽

Tourette Syndrome ◽

Magnetic Stimulation ◽

Supplementary Motor Area ◽

Repetitive Transcranial Magnetic Stimulation ◽

Motor Area ◽

Syndrome Report ◽

Stimulation Of

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Use of EEG for Predicting Treatment Response to Transcranial Magnetic Stimulation in Obsessive Compulsive Disorder

Clinical EEG and Neuroscience ◽

10.1177/1550059419879569 ◽

2019 ◽

Vol 51 (3) ◽

pp. 139-145 ◽

Cited By ~ 1

Author(s):

Sinem Zeynep Metin ◽

Tugçe Balli Altuglu ◽

Baris Metin ◽

Turker Tekin Erguzel ◽

Selin Yigit ◽

...

Keyword(s):

Machine Learning ◽

Transcranial Magnetic Stimulation ◽

Treatment Response ◽

Obsessive Compulsive Disorder ◽

Magnetic Stimulation ◽

Low Frequency ◽

Quantitative Eeg ◽

Measurement Scale ◽

Obsessive Compulsive ◽

Compulsive Disorder

Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment response to right frontal transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD) using a machine learning approach. Method. The study included 50 OCD patients (35 responsive to TMS, 15 nonresponsive) who were treated with right frontal low frequency stimulation and identified retrospectively from Uskudar Unversity, NPIstanbul Brain Hospital outpatient clinic. All patients were diagnosed with OCD according to the DSM-IV-TR and DSM-5 criteria. We first extracted pretreatment band powers for patients. To explore the prediction accuracy of pretreatment EEG, we employed machine learning methods using an artificial neural network model. Results. Among 4 EEG bands, theta power successfully discriminated responsive from nonresponsive patients. Responsive patients had more theta powers for all electrodes as compared to nonresponsive patients. Discussion. qEEG could be helpful before deciding about treatment strategy in OCD. The limitations of our study are moderate sample size and limited number of nonresponsive patients and that treatment response was defined by clinicians and not by using a formal symptom measurement scale. Future studies with larger samples and prospective design would show the role of qEEG in predicting TMS response better.

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