Detection of cognitive deficits years prior to clinical diagnosis across neurological conditions
Bibliographic record
Abstract
Abstract Understanding the cognitive trajectory of a neurological disease can provide important insight on underlying mechanisms and disease progression. Cognitive impairment is now well established as beginning many years before the diagnosis of Alzheimer's disease, but pre-diagnostic profiles are unclear for other neurological conditions that may be associated with cognitive impairment. We analysed data from the prospective UK Biobank cohort with study baseline assessment performed between 2006 and 2010 and participants followed until 2021. We examined data from 497 252 participants, aged between 38 and 72 years at baseline, with an imaging sub-sample of 42 468 participants. Using time-to-diagnosis and time-from-diagnosis data in relation to time of assessment, we compared a continuous measure of executive function and magnetic resonance imaging brain measures of total grey matter (GM) and hippocampal volume in individuals with ischaemic stroke, focal epilepsy, Parkinson's disease, multiple sclerosis, motor neurone disease (amyotrophic lateral sclerosis) and migraine. Of the 497 252 participants [226 206 (45.5%) men, mean (SD) age, 57.5(8.1) years], 12 755 had ischaemic stroke, 6758 had a diagnosis of focal epilepsy, 3315 had Parkinson's disease, 2315 had multiple sclerosis, 559 had motor neurone disease and 18 254 had migraine either at study baseline or diagnosed during the follow-up period. Apart from motor neurone disease, all conditions had lower pre-diagnosis executive function compared to controls (assessment performed median 7.4 years before diagnosis). At a group level, focal epilepsy and multiple sclerosis showed a gradual worsening in executive function up to 15 years prior to diagnosis, while ischaemic stroke was characterised by a modest decline for a few years followed by a substantial reduction at the time of diagnosis. By contrast, participants with migraine showed a mild reduction in pre-diagnosis cognition compared to controls which improved following clinical diagnosis. Pre-diagnosis MRI GM volume was lower than controls for stroke, Parkinson's disease and multiple sclerosis (scans performed median 1.7 years before diagnosis), while other conditions had lower volumes post-diagnosis. These cognitive trajectory models reveal disease-specific temporal patterns at a group level, including a long cognitive prodrome associated with focal epilepsy and multiple sclerosis. The findings may help to prioritise risk management of individual diseases and inform clinical decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".