Associations between white and grey matter damage and gait impairment in cerebral amyloid angiopathy
Bibliographic record
Abstract
BACKGROUND: Cerebral amyloid angiopathy (CAA) is associated with white matter damage and neurodegeneration. Gait is impaired in CAA; however, the neural basis of this impairment is unclear. RESEARCH QUESTION: Are gait impairments in patients with CAA associated with altered cerebral white matter diffusivity and/or atrophy of cortical and subcortical grey matter. METHODS: Participants with CAA (n=29), Alzheimer's disease (AD; n=16), and normal controls (n=47) were included. Gait was assessed using a 6 m walkway with parameters categorized into rhythm, pace, postural control, and variability domains. The dual-task cost (DTC) of gait speed was calculated for counting backwards, animal fluency, and serial sevens tasks. Whole-brain white matter disruption was quantified using the peak width of skeletonized mean diffusivity (PSMD), and thickness and volume of select cortical, subcortical, and cerebellar regions were quantified using FreeSurfer. RESULTS: In CAA participants, associations were found between PSMD and pace (standardized parameter estimate (β), 95 % confidence interval (CI): 0.17, 0.03-0.32), and medial orbital frontal cortical thickness and counting backwards DTC (parameter estimate (PE), 95 % CI: -5.7 %/SD, -0.24 to -11.23). Across all groups, including CAA, associations were found between PSMD and pace, variability, counting backwards DTC, and animal fluency DTC; between frontal cortical thickness and pace, counting backwards DTC, and animal fluency DTC; between cortical regions affected by AD (inferior parietal cortex, inferior and middle temporal gyrus) and counting backwards DTC; and between thalamus volume and postural control. SIGNIFICANCE: Reduced white matter structural integrity and grey matter loss is associated with poor overall gait performance in CAA, AD, and normal controls.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".