Evidence map›Paper›PMID 39229238›Full record

ArticlebioRxiv : the preprint server for biology2024

Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time.

Uttara U Khatri, Kristen Pulliam, Muskan Manesiya, Melanie Vieyra Cortez, José Del R Millán, Sara J Hussain

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Uttara U KhatriMovement and Cognitive Rehabilitation Science Program, Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, USA.
Kristen PulliamMovement and Cognitive Rehabilitation Science Program, Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, USA.
Muskan ManesiyaMovement and Cognitive Rehabilitation Science Program, Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, USA.
Melanie Vieyra CortezMovement and Cognitive Rehabilitation Science Program, Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, USA.
José Del R MillánChandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.ORCID 0000-0001-5819-1522
Sara J HussainMovement and Cognitive Rehabilitation Science Program, Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, USA.

Funding

Developing real-time personalized TMS to target residual corticospinal connections after strokeR21NS133605 · NINDS · UNIVERSITY OF TEXAS AT AUSTIN · PI HUSSAIN, SARA J · 2024 to 2025
$451k
NINDS NIH HHS R21 NS133605
6 · The paper itself

Abstract

backgroundTranscranial magnetic stimulation (TMS) interventions could feasibly treat stroke-related motor impairments, but their effects are highly variable. Brain state-dependent TMS approaches are a promising solution to this problem, but inter-individual variation in lesion location and oscillatory dynamics can make translating them to the poststroke brain challenging. Personalized brain state-dependent approaches specifically designed to address these challenges are therefore needed.

methodsAs a first step towards this goal, we tested a novel machine learning-based EEG-TMS system that identifies personalized brain activity patterns reflecting strong and weak corticospinal tract (CST) output (strong and weak CST states) in healthy adults in real-time. Participants completed a single-session study that included the acquisition of a TMS-EEG-EMG training dataset, personalized classifier training, and real-time EEG-informed single pulse TMS during classifier-predicted personalized CST states.

resultsMEP amplitudes elicited in real-time during personalized strong CST states were significantly larger than those elicited during personalized weak and random CST states. MEP amplitudes elicited in real-time during personalized strong CST states were also significantly less variable than those elicited during personalized weak CST states. Personalized CST states lasted for ~1-2 seconds at a time and ~1 second elapsed between consecutive similar states. Individual participants exhibited unique differences in spectro-spatial EEG patterns between personalized strong and weak CST states.

conclusionOur results show for the first time that personalized whole-brain EEG activity patterns predict CST activation in real-time in healthy humans. These findings represent a pivotal step towards using personalized brain state-dependent TMS interventions to promote poststroke CST function.

Indexed as

brain stimulationelectroencephalographymachine learningmotor cortextranscranial magnetic stimulation

Identifiers

PMID39229238
PMCPMC11370398

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LicenceCC BY-NC-ND
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Registered trials

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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.