Structural brain networks correlating with poststroke cognition
Bibliographic record
Abstract
Cognitive deficits are a common and debilitating consequence of stroke, yet our understanding of the structural neurobiological biomarkers predicting recovery of cognition after stroke remains limited. In this longitudinal observational study, we set out to investigate the effect of both focal lesions and structural connectivity on poststroke cognition. Sixty-two patients with stroke underwent advanced brain imaging and cognitive assessment, utilizing the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE), at 3-month and 12-month poststroke. We first evaluated the relationship between lesions and cognition at 3 months using voxel-based lesion-symptom mapping. Next, a novel correlational tractography approach, using multi-shell diffusion-weighted magnetic resonance imaging (MRI) data collected at both time points, was used to evaluate the relationship between the white matter connectome and cognition cross-sectionally at 3 months, and longitudinally (12 minus 3 months). Lesion-symptom mapping did not yield significant findings. In turn, correlational tractography analyses revealed positive associations between both MoCA and MMSE scores and bilateral cingulum and the corpus callosum, both cross-sectionally at the 3-month stage, and longitudinally. These results demonstrate that rather than focal neural structures, a consistent structural connectome underpins the performance of two frequently used cognitive screening tools, the MoCA and the MMSE, in people after stroke. This finding should encourage clinicians and researchers to not only suspect cognitive decline when lesions affect these tracts, but also to refine their investigation of novel approaches to differentially diagnosing pathology associated with cognitive decline, regardless of the aetiology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".