Patterns of Structural Disconnection Driving Proprioceptive Deficits in Chronic Stroke
Bibliographic record
Abstract
BACKGROUND: Stroke is a leading cause of death and disability, with proprioceptive impairments affecting up to 64% of survivors. These impairments hinder sensorimotor recovery, significantly impacting poststroke quality of life. Proprioception depends on an integrated brain network but remains underexplored due to limitations in clinical assessments, hindering links between stroke-related damage and functional deficits. We combined quantitative proprioceptive measurements (arm position matching task) with connectome-based lesion-symptom mapping to identify white matter (WM) disconnection patterns underlying proprioceptive deficits in chronic sensorimotor stroke while controlling for motor impairment. METHODS: In this single-center observational study (Leipzig, Germany, 2015–2018), we investigated relationships between WM disconnection and proprioceptive deficits in chronic stroke survivors with paretic arm function using connectome-based lesion-symptom mapping and kinematic assessments. Lesions were manually delineated, and proprioception was quantified using the arm position matching task on the KINARM Exoskeleton. Patient-specific voxelwise WM disconnection maps were generated using the tractography-based lesion assessment standard, quantifying disconnection relative to a healthy WM connectome (n=1001; women=556; age=22–37 years). Proprioceptive scores were regressed against disconnection maps using voxelwise linear regressions (familywise error–corrected, controlled for age and sex). A secondary analysis included motor performance (visually guided reaching task) as a covariate to isolate proprioceptive-specific effects. RESULTS: Of 42 patients, 39 had valid arm position matching data, and 38 had valid visually guided reaching data included in the analyses (women=13; age=35–81 years). Arm position matching task scores were significantly associated with WM disconnection (d=0.58–1; P <0.005 familywise error; t=3.64–6.86) in a wide range of tracts previously implicated in proprioceptive function and beyond. Crucially, these associations persisted when controlling for motor performance using visually guided reaching task scores (d=0.44–0.93; P <0.05 familywise error; t=2.69–5.72). CONCLUSIONS: We provide evidence that proprioceptive impairments in chronic stroke may arise from network-wide WM disconnection in key tracts mediating proprioceptive function. Our findings highlight the benefits of connectome-based lesion-symptom mapping for assessing stroke-related proprioceptive deficits and offer a framework for network-informed assessments of functional impairments that could guide targeted therapies poststroke.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".