A model of variability in brain stimulation evoked responses.

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The input-output (IO) curve of cortical neuron populations is a key measure of neural excitability and is related to other response measures including the motor threshold which is widely used for individualization of neurostimulation techniques, such as transcranial magnetic stimulation (TMS). The IO curve parameters provide biomarkers for changes in the state of the target neural population that could result from neurostimulation, pharmacological interventions, or neurological and psychiatric conditions. Conventional analyses of IO data assume a sigmoidal shape with additive Gaussian scattering that allows simple regression modeling. However, careful study of the IO curve characteristics reveals that simple additive noise does not account for the observed IO variability. We propose a consistent model that adds a second source of intrinsic variability on the input side of the IO response. We develop an appropriate mathematical method for calibrating this new nonlinear model. Finally, the modeling framework is applied to a representative IO data set. With this modeling approach, previously inexplicable stochastic behavior becomes obvious. This work could lead to improved algorithms for estimation of various excitability parameters including established measures such as the motor threshold and the IO slope, as well as novel measures relating to the variability characteristics of the IO response that could provide additional insight into the state of the targeted neural population.






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Goetz, SM, and AV Peterchev (2012). A model of variability in brain stimulation evoked responses. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2012. pp. 6434–6437. 10.1109/EMBC.2012.6347467 Retrieved from https://hdl.handle.net/10161/24068.

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Stefan M Goetz

Assistant Professor in Psychiatry and Behavioral Sciences

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 novel technology such as devices for transcranial magnetic stimulation (TMS) 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 within Duke and at other institutions to translate developments from the lab to research and clinical applications. For over 15 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.

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