Virtual Neuronavigation for Parcel-guided TMS
Truong, D. Q.; Datta, A.; Moreno, J.; Valter, Y. Z.; Mejia, R.; Nazim, K.; Gao, Y.; Huang, Y.; Berman, J. A.; Javitt, D. C.
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BackgroundrTMS for depression is partially effective which is likely in part due to the treatment coil positioning being based on scalp-based approaches. Such an approach results in not accounting for individual anatomy and therefore delivering electrical currents to non-optimal brain targets. In a pilot study, we demonstrated that rTMS targeted to parcel 46 (p46) of the HCP atlas led to 100% response in patients who were resistant to the standard TMS. Implementing parcel-guided targeting requires neuronavigation. However, neuronavigation use in clinical practice is limited. Besides added cost, need for individual MRI, leveraging new targeting advances requires expertise in 3D brain reconstruction and processing. MethodsWe sought to develop a virtual neuronavigation system in which MRI images are processed on the backend to determine p46 and a corresponding individualized 3D printed scalp headgear is developed. The headgear includes markings for the location corresponding to p46 and other anatomical guides. Headgear development involved an iterative sequence of 3D CAD modeling, printing, and testing to meet the gold standard neuronavigation-guided location. Individual headgear accuracy is then verified by positioning on the intended subject and comparing alignment with the location on the scalp and brain intersecting the neuronavigation beam targeting p46. Reproducibility testing was also performed. ResultsAcross 16 test subjects on which the final version of the headgear could be tested, individualized headgear was found to be 8.31{+/-} 4.93 mm from the neuronavigation-determined scalp location. The corresponding distances on the brain was 7.94{+/-} 4.99 mm. Within-subject reproducibility (against centroid formed by 3 measurements) was 1.21 {+/-} 0.84 mm across n= 8 subjects. ConclusionIndividualized headgear targeting presents a viable option for practices to leverage state-of-the-art targeting advances without investing in neuronavigation hardware.
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