Update on rTMS and the first year roll-out experience in the Edmonton Zone of a provincial program of rTMS for treatment-resistant depression. Quality improvement with training simulations, multi-modal neuroimaging and biomarker detection.

Learning Objectives:

1. Learn about pharmacotherapy that may hasten relapse in patients receiving rTMS;
2. Learn about applications of Virtual Reality and Augmented Reality in rTMS training simulations; and
3. Learn about AI, machine learning and EEG biomarkers predicting response to rTMS.

Abstract:

In January 2019, a new provincial program of repetitive Transcranial Magnetic Stimulation (rTMS) was initiated at Alberta Hospital Edmonton (AHE). The program is supported by the Addictions and Mental Health Strategic Clinical Network with 2 MagVenture rTMS stimulators being provided by the Mental Health Foundation. The implementation was part of a unique day hospital program at AHE. With the successful implementation of the service at AHE, a further 3 rTMS stimulators were installed at the Edmonton Mental Health Clinic in downtown Edmonton. All referrals are received and processed at AHE for rTMS treatments provided there and at the downtown clinic. The rTMS service at AHE has assumed a key educational role in ensuring that all staff are trained and instructed in best practices in providing safe and effective rTMS treatments. We have identified some key areas that could be explored to improve outcomes. Specifically, we have identified that development of an educational program with simulations for training may be useful. We have also identified pharmacotherapy that may be preventing response to rTMS treatment or hastening relapse. In addition, the recent identification of EEG biomarkers that may be predictive of response or that may inform clinicians of network neuropathology and allow rational coil placement and protocol selection which could significantly improve delivery of rTMS service and clinical outcomes. Our literature review revealed advances in VR/AR in rTMS applications related to neurorehabilitation but not as much in training applications, unlike training programs in neurosurgery which is very active in adopting this technology. Our exhaustive review of pharmacological factors related to lack of response to treatment or hastened relapse of illness, revealed that being on benzodiazepines during or after rTMS treatment appears to be the most significant factor that is associated with relapse. Clinicians need to be aware of this and minimize benzodiazepines as much as possible, ideally before patients are referred for rTMS. EEG biomarkers that are measures of network connectivity and network entropy are the most promising predictive markers of treatment response especially when combined with machine-learning approaches. An rTMS clinical study is planned examining EEG biomarkers with machine learning/AI.

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