Noninvasive Detection of Motor-Evoked Potentials in Response to Brain Stimulation Below the Noise Floor-How Weak Can a Stimulus Be and Still Stimulate.
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Motor-evoked potentials (MEP) are one of the most important responses to brain stimulation, such as supra-threshold transcranial magnetic stimulation (TMS) and electrical stimulation. The understanding of the neurophysiology and the determination of the lowest stimulation strength that evokes responses requires the detection of even smallest responses, e.g., from single motor units, but available detection and quantization methods are rather simple and suffer from a large noise floor. The paper introduces a more sophisticated matched-filter detection method that increases the detection sensitivity and shows that activation occurs well below the conventional detection level. In consequence, also conventional threshold definitions, e.g., as 50 μV median response amplitude, turn out to be substantially higher than the point at which first detectable responses occur. The presented method uses a matched-filter approach for improved sensitivity and generates the filter through iterative learning from the presented data. In contrast to conventional peak-to-peak measures, the presented method has a higher signal-to-noise ratio (≥14 dB). For responses that are reliably detected by conventional detection, the new approach is fully compatible and provides the same results but extends the dynamic range below the conventional noise floor. The underlying method is applicable to a wide range of well-timed biosignals and evoked potentials, such as in electroencephalography.
Published Version (Please cite this version)
Goetz, SM, Z Li and AV Peterchev (2018). Noninvasive Detection of Motor-Evoked Potentials in Response to Brain Stimulation Below the Noise Floor-How Weak Can a Stimulus Be and Still Stimulate. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2018. pp. 2687–2690. 10.1109/embc.2018.8512765 Retrieved from https://hdl.handle.net/10161/28783.
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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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