Evidence map›Paper›PMID 31379476›Full record

ArticleFrontiers in neuroscience2019

Non-motor Brain Regions in Non-dominant Hemisphere Are Influential in Decoding Movement Speed.

Macauley Smith Breault, Zachary B Fitzgerald, Pierre Sacré, John T Gale, Sridevi V Sarma, Jorge A González-Martínez

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Deep Learning-Based Decoding and Feature Visualization of Motor Imagery Speeds From EEG Signals.IEEE open journal of engineering in medicine and biology · 2026
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  8. Neuromodulation in Beta-Band Power Between Movement Execution and Inhibition in the Human Hippocampus.Neuromodulation : journal of the International Neuromodulation Society · 2022
    Article
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

6 authors.

Macauley Smith BreaultNeuromedical Control Systems Laboratory, Department of Biomedical Engineering, Institute of Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.
Zachary B FitzgeraldDepartment of Neurosurgery, Cleveland Clinic, Epilepsy Center, Neurological Institute, Cleveland, OH, United States.
Pierre SacréNeuromedical Control Systems Laboratory, Department of Biomedical Engineering, Institute of Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.
John T GaleGale Neurotechnologies Inc., Smoke Rise, GA, United States.
Sridevi V SarmaNeuromedical Control Systems Laboratory, Department of Biomedical Engineering, Institute of Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.
Jorge A González-MartínezDepartment of Neurosurgery, Cleveland Clinic, Epilepsy Center, Neurological Institute, Cleveland, OH, United States.

Funding

NEURAL BASIS OF COMMUNICATION SOUND PERCEPTIONR01DC003180 · NIDCD · JOHNS HOPKINS UNIVERSITY · PI WANG, XIAOQIN · 1997 to 2024
$12.0M
Training Program in NeuroengineeringT32EB003383 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI NITISH VYOMESH THAKOR · 2004 to 2026
$6.5M
NIBIB NIH HHS T32 EB003383NIDCD NIH HHS R01 DC003180
6 · The paper itself

Abstract

Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.

Indexed as

generalized linear model (GLM)Least Absolute Shrinkage and Selection Operator (LASSO)Local Field Potential (LFP)movement speednon-dominant hemispherenon-motor brain regionsregressionStereoelEctroencEphalography (SEEG)

Identifiers

PMID31379476
PMCPMC6660252

What OpenQuestion holds

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LicenceCC BY
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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.