TAP: targeting and analysis pipeline for optimization and verification of coil placement in transcranial magnetic stimulation.

Abstract

Objective.Transcranial magnetic stimulation (TMS) can modulate brain function via an electric field (E-field) induced in a brain region of interest (ROI). The ROI E-field can be computationally maximized and set to match a specific reference using individualized head models to find the optimal coil placement and stimulus intensity. However, the available software lacks many practical features for prospective planning of TMS interventions and retrospective evaluation of the experimental targeting accuracy.Approach.The TMS targeting and analysis pipeline (TAP) software uses an MRI/fMRI-derived brain target to optimize coil placement considering experimental parameters such as the subject's hair thickness and coil placement restrictions. The coil placement optimization is implemented in SimNIBS 3.2, for which an additional graphical user interface (TargetingNavigator) is provided to visualize/adjust procedural parameters. The coil optimization process also computes the E-field at the target, allowing the selection of the TMS device intensity setting to achieve specific E-field strengths. The optimized coil placement information is prepared for neuronavigation software, which supports targeting during the TMS procedure. The neuronavigation system can record the coil placement during the experiment, and these data can be processed in TAP to quantify the accuracy of the experimental TMS coil placement and induced E-field.Main results.TAP was demonstrated in a study consisting of three repetitive TMS sessions in five subjects. TMS was delivered by an experienced operator under neuronavigation with the computationally optimized coil placement. Analysis of the experimental accuracy from the recorded neuronavigation data indicated coil location and orientation deviations up to about 2 mm and 2°, respectively, resulting in an 8% median decrease in the target E-field magnitude compared to the optimal placement.Significance.TAP supports navigated TMS with a variety of features for rigorous and reproducible stimulation delivery, including planning and evaluation of coil placement and intensity selection for E-field-based dosing.

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Published Version (Please cite this version)

10.1088/1741-2552/ac63a4

Publication Info

Dannhauer, Moritz, Ziping Huang, Lysianne Beynel, Eleanor Wood, Noreen Bukhari-Parlakturk and Angel V Peterchev (2022). TAP: targeting and analysis pipeline for optimization and verification of coil placement in transcranial magnetic stimulation. Journal of neural engineering, 19(2). pp. 026050–026050. 10.1088/1741-2552/ac63a4 Retrieved from https://hdl.handle.net/10161/32056.

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Scholars@Duke

Bukhari-Parlakturk

Noreen Bukhari-Parlakturk

Assistant Professor of Neurology

I have a long standing interest in developing disease-modifying therapies for movement disorders, a major unmet clinical need. I work at the interface of neuroscience and neurology to apply mechanistic understanding of neurological disease to develop targeted neuromodulatory therapies and in the process further disease mechanisms and medical therapy.

Peterchev

Angel V Peterchev

Professor in Psychiatry and Behavioral Sciences

I direct the Brain Stimulation Engineering Lab (BSEL) which focuses on the development, modeling, and application of devices and paradigms for transcranial brain stimulation. Transcranial brain stimulation involves non-invasive delivery of fields (e.g., electric and magnetic) to the brain that modulate neural activity. It is widely used as a tool for research and a therapeutic intervention in neurology and psychiatry, including several FDA-cleared indications. BSEL develops devices for transcranial magnetic stimulation (TMS) and other forms of magnetic stimulation such as magnetogenetics that leverage design techniques from power electronics and computational electromagnetics to enable more flexible stimulus control, focal stimulation, and quiet operation. We also deploy these devices in experimental studies to characterize and optimize the brain response to TMS. Another line of work is multi-scale computational models that couple simulations of the electromagnetic fields, single neuron responses, and neural population modulation induced by electric and magnetic brain stimulation. These models are calibrated and validated with experimental neural recordings through various collaborations. Apart from understanding of mechanisms, we develop modeling, algorithmic, and targeting tools for response estimation, dose individualization, and precise localization of transcranial brain stimulation using advanced techniques such as artificial neural networks and machine learning. Moreover, BSEL is involved in the integration of transcranial brain stimulation with robotics, neuronavigation, intracranial electrophysiology recordings, and imaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), as well as the evaluation of the safety of device–device interactions, for example between transcranial stimulators and implants. Importantly, we collaborate widely with neuroscientists and clinicians at Duke and other institutions to translate developments from the lab to research and clinical applications. For over 17 years, BSEL has been continuously supported with multiple NIH grants as well as funding by DARPA, NSF, Brain & Behavior Research Foundation, Coulter Foundation, Duke Institute for Brain Sciences, MEDx, Duke University Energy Initiative, and industry. Further, some of our technology has been commercialized, for example as ElevateTMS cTMS, or incorporated in free software packages, such as SimNIBS and SAMT. In recognition of “excellence in non-invasive brain stimulation research that stimulates further work at a higher scientific level” I received the Brainbox Initiative John Rothwell Award in 2024.


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