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Record W4320898302 · doi:10.1016/j.brs.2023.01.796

The impact of individual stroke lesions on tDCS current flow compared to neurotypical age-matched controls

2023· article· en· W4320898302 on OpenAlexaff
Jenn Lee, Ainslie Johnstone, Carys Evans, Catharina Zich, Bethany Lo, Michael R. Borich, Lara A. Boyd, Jessica M. Cassidy, Steven C. Cramer, Miranda R. Donnelly, Colleen A. Hanlon, Brenton Hordacre, Steven A. Kautz, Jingchun Liu, Christian Schranz, Na Jin Seo, Surjo R. Soekadar, Srivastava Shraddha, Carolee J. Winstein, Chunshui Yu, Artemis Zavaliangos‐Petropulu, Sook‐Lei Liew, Nick Ward, Sven Bestmann

Bibliographic record

VenueBrain stimulation · 2023
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsVancouver Coastal Health
Fundersnot available
KeywordsTranscranial direct-current stimulationNeurotypicalStroke (engine)Stroke recoveryNeuroplasticityPhysical medicine and rehabilitationMedicineLesionBrain stimulationPopulationPsychologyRehabilitationNeurosciencePhysical therapyStimulationSurgeryPsychiatry

Abstract

fetched live from OpenAlex

Abstract Transcranial direct current stimulation (tDCS) has promise as a tool to aid rehabilitation after stroke by altering cortical excitability to promote neuroplasticity (Kubis, 2016; Ward, 2017). However, tDCS outcomes are subject to high variability, an effect which is amplified in stroke. Current flow models (CFMs) can be used to estimate tDCS electric field (E-field) in the brain, though they do not account for brain lesions (Lee et al., 2021). Previous work investigating the impact of stroke lesions on tDCS current flow is limited by small sample size or simulated lesions which may not reflect the broader stroke population (Piastra et al., 2021; Johnstone et al., 2021; Minjoli et al., 2017). Here we describe the impact of manually segmented individual stroke lesions on tDCS E-field when a commonly used symptom-targeted tDCS protocol is used. We compare our findings to a neurotypical age-matched control group. 123 stroke survivor and 147-age matched control MRI scans from the ENIGMA Stroke Recovery working group database were provided with manually segmented lesion masks. Inclusion criteria for stroke survivor scans were upper limb impairment (<66 FMA >28), unilateral cortical or subcortical stroke, and 1mm3 MRI collected >3 months post-stroke. A classic bipolar electrode montage (C3-Fp1) was simulated, targeting the primary motor cortex (M1). An adapted version of ROAST CFM software (Huang et al., 2019, 2018; Johnstone et al., 2021) was used to account for manually segmented stroke lesions. Lesion conductivity was specified (McCann et al., 2019). E-field intensity in the M1 target is more variable in stroke compared to control scans, even though the average E-field was comparable between groups. Within the stroke survivor group, lesion size, location, and proximity to the M1 ROI impacted current flow in M1. These findings highlight the necessity for individual CFM-informed protocol design, especially for tDCS application in stroke. Research Category and Technology and Methods Clinical Research: 9. Transcranial Direct Current Stimulation (tDCS) Keywords: Computational modelling, tDCS, Stroke, Non-invasive brain stimulation

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.117
GPT teacher head0.384
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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