Evidence map›Paper›PMID 35397278›Full record

ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2022

Isolating two sources of variability of subcortical stimulation to quantify fluctuations of corticospinal tract excitability.

Stefan M Goetz, Bryan Howell, Boshuo Wang, Zhongxi Li, Marc A Sommer, Angel V Peterchev, Warren M Grill

Open access · greenAbstract read
In one paragraph

Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.8field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 23 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 2 countries.

Stefan M GoetzDepartment of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK; Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC 27710, USA; Department of Neurosurgery, Duke University, Durham, NC 27710, USA; Department of Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC 27710, USA. Electronic address: stefan.goetz@duke.edu.
Bryan HowellDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.
Boshuo WangDepartment of Psychiatry & Behavioral Sciences, Duke University, Durham, NC 27710, USA.
Zhongxi LiDepartment of Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA.
Marc A SommerDuke Institute of Brain Sciences, Duke University, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; Department of Neurobiology, Duke University, Durham, NC 27710, USA.
Angel V PeterchevDepartment of Psychiatry & Behavioral Sciences, Duke University, Durham, NC 27710, USA; Department of Neurosurgery, Duke University, Durham, NC 27710, USA; Department of Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Warren M GrillDepartment of Neurosurgery, Duke University, Durham, NC 27710, USA; Department of Electrical & Computer Engineering, Duke University, Durham, NC 27708, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; Department of Neurobiology, Duke University, Durham, NC 27710, USA.
Duke University · USCase Western Reserve University · USUniversity of Cambridge · GB

Funding

Robotically-actuated, low-noise, concurrent TMS-EEG-fMRI systemR01MH127104 · NIMH · UNIVERSITY OF CALIFORNIA BERKELEY · PI LIU, CHUNLEI, LUSTIG, MICHAEL · 2021 to 2024
$6.5M
Temporal Patterns of Deep Brain StimulationR37NS040894 · NINDS · DUKE UNIVERSITY · PI GRILL, WARREN M. · 2015 to 2021
$4.2M
Temporal Patterns of Deep Brain StimulationR01NS040894 · NINDS · DUKE UNIVERSITY · PI GRILL, WARREN M. · 2000 to 2013
$3.4M
Biology and Biophysics of the Cortical Response to Transcranial Magnetic StimulationR01NS117405 · NINDS · DUKE UNIVERSITY · PI PETERCHEV, ANGEL V, SOMMER, MARC A · 2020 to 2024
$3.3M
MPS-TMS: Modular Pulse Synthesizer for Transcranial Magnetic Stimulation with Fully Adjustable Pulse Shape and SequenceTBDRF1MH124943 · NIMH · DUKE UNIVERSITY · PI PETERCHEV, ANGEL V · 2020 to 2020
$1.6M
NIMH NIH HHS R01 MH127104NIMH NIH HHS RF1 MH124943NINDS NIH HHS R01 NS040894NINDS NIH HHS R01 NS117405NINDS NIH HHS R37 NS040894
6 · The paper itself

Abstract

objectiveInvestigate the variability previously found with cortical stimulation and handheld transcranial magnetic stimulation (TMS) coils, criticized for its high potential of coil position fluctuations, bypassing the cortex using deep brain electrical stimulation (DBS) of the corticospinal tract with fixed electrodes where both latent variations of the coil position of TMS are eliminated and cortical excitation fluctuations should be absent.

methodsTen input-output curves were recorded from five anesthetized cats with implanted DBS electrodes targeting the corticospinal tract. Goodness of fit of regressions with a conventional single variability source as well as a dual variability source model was quantified using a Schwarz Bayesian Information approach to avoid overfitting.

resultsMotor evoked potentials (MEPs) through DBS of the corticospinal tract revealed short-term fluctuations in excitability of the targeted neuron pathway reflecting endogenous input-side variability at similar magnitude as TMS despite bypassing cortical networks.

conclusionInput-side variability, i.e., variability resulting in changing MEP amplitudes as if the stimulation strength was modulated, also emerges in electrical stimulation at a similar degree and is not primarily a result of varying stimulation, such as minor coil movements in TMS. More importantly, this variability component is present, although the cortex is bypassed. Thus, it may be of spinal origin, which can include cortical input from spinal projections. Further, the nonlinearity of the compound variability entails complex heteroscedastic non-Gaussian distributions and typically does not allow simple linear averages in statistical analysis of MEPs. As the average is dominated by outliers, it risks bias. With appropriate regression, the net effects of excitatory and inhibitory inputs to the targeted neuron pathways become noninvasively observable and quantifiable. SIGNIFICANCE: The neural responses evoked by artificial stimulation in the cerebral cortex are variable. For example, MEPs in response to repeated presentations of the same stimulus can vary from no response to saturation across trials. Several sources of such variability have been suggested, and most of them may be technical in nature, but localization is missing.

Indexed as

Motor CortexPyramidal TractsBayes TheoremElectrodes, ImplantedEvoked Potentials, MotorHumansTranscranial Magnetic StimulationDose–response curveInput–output (IO) curveIsolation of excitabilityRecruitment curveSpinal contribution to short-term fluctuation of excitabilitySubcortical electrical stimulationTrial-to-trial variability

Identifiers

PMID35397278
PMCPMC9271909
OpenAlexW4213443442

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.