Objective To evaluate the safety and assess the different symptom improvements found after a combined low-frequency primary motor cortex and high-frequency prefrontal cortex (PFC) stimulation using the deep TMS (dTMS) H-coil, as an add-on treatment for Parkinson’s disease (PD). Methods Forty-five PD patients underwent 14 dTMS sessions; each consisting of 1 Hz stimulation of the primary motor cortex for 15 min, followed by 10 Hz stimulation of the PFC for 15 min. Clinical assessments were performed, BEFORE, at the MIDDLE, and END of therapy as well as at FOLLOW-UP after 30 days, using Movement Disorder Society-Unified Parkinson’s Disease Rating Scale, TINETTI, UP&GO, SCOPA, HDRS21, Beck Depression Inventory, and self-applied daily motor assessment scales. Results Treatment was well-tolerated, without serious adverse effects. dTMS-induced significant PD symptom improvements at END and at FOLLOW-UP, in all subscales of the UPDRS, gait speed, depressive symptoms, balance, autonomic symptoms, and a 73% increase in daily ON time. Conclusion In the cohort of PD patients treated, dTMS was well-tolerated with only minor adverse effects. The dTMS-induced significant improvements in motor, postural, and motivational symptoms of PD patients and may potentiate concurrent levodopa treatment. Significance The present study demonstrates that dTMS may have a much wider spectrum of beneficial effects than previously reported for TMS, including enhancement of levodopa effects, suggesting that future clinical trials with dTMS should include a broader range of symptom measurements.
Retrospective Evaluation of Deep Transcranial Magnetic Stimulation as Add-On Treatment for Parkinson’s Disease
Francisco Torres,E. Villalón,P. Poblete,R. Moraga-Amaro,Sergio Linsambarth,Raúl Riquelme,A. Zangen,J. Stehberg
Published 2015 in Frontiers in Neurology
ABSTRACT
PUBLICATION RECORD
- Publication year
2015
- Venue
Frontiers in Neurology
- Publication date
2015-10-26
- Fields of study
Medicine, Psychology
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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