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Record W4414084143 · doi:10.1161/strokeaha.124.050269

fNIRS Biomarkers for Stratifying Poststroke Cognitive Impairment: Evidence From Frontal and Temporal Cortex Activation

2025· article· en· W4414084143 on OpenAlexaboutno aff
Han Zhang, Kang He, Yi Peng, Danling Feng, Qiang Gao

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionStroke (engine)Temporal cortexRehabilitationCognitive impairmentCortex (anatomy)Frontal cortex

Abstract

fetched live from OpenAlex

BACKGROUND: Poststroke cognitive impairment (PSCI) affects 30% to 50% of stroke survivors, severely impacting functional outcomes and quality of life. This study uses functional near-infrared spectroscopy (fNIRS) to assess task-evoked brain activation and its potential for stratifying the severity in patients with PSCI. METHODS: A cross-sectional study was conducted at Nanchong Central Hospital between June 2023 and April 2024. The Montreal Cognitive Assessment was used to evaluate cognitive function. Hemodynamic responses, including oxygenated hemoglobin concentration signals from the frontal and temporal cortices, were measured using fNIRS. Healthy controls were recruited from the local community, matched to the patient group by age and sex. Univariate and multivariate linear regression analyses were performed to assess the correlations between fNIRS features, clinical variables, and group classifications. Significant fNIRS features and clinical variables were then identified and included in a logistic regression model. The final model was developed using a stratified approach based on the severity of cognitive impairment, and the predictive performance was evaluated using receiver operating characteristic analysis. RESULTS: A total of 159 participants were included: 138 patients with PSCI and 21 healthy controls. Patients with PSCI (mean age=53.7, SD=10.2 years, 78.3% men) exhibited significantly reduced oxygenated hemoglobin responses in the left dorsal prefrontal cortex (L-DPFC) compared with healthy controls (mean age=52.8, SD=4.7 years, 76.2% men). Patients were in the subacute to chronic phase poststroke. A multivariate model combining L-DPFC features distinguished PSCI from healthy controls (area under the curve, 0.76). For severity stratification, a model distinguishing mild from moderate PSCI (area under the curve, 0.75 [95% CI, 0.65-0.85]) included age, education level, National Institutes of Health Stroke Scale score, recanalization therapy, and L-DPFC centroid. A model distinguishing moderate from severe PSCI (area under the curve, 0.84 [95% CI, 0.75-0.92]) included disease duration, lesion location, and L-DPFC centroid. CONCLUSIONS: This study revealed that individuals with PSCI exhibited significantly reduced cortical activation in the L-DPFC compared with healthy controls. fNIRS features, particularly L-DPFC centroid values combined with clinical variables, effectively stratify PSCI severity. These findings may inform individualized rehabilitation strategies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.343
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations39
Published2025
Admission routes1
Has abstractyes

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