Detection of cognitive deficits years prior to clinical diagnosis across neurological conditions
Bibliographic record
Abstract
Abstract Importance 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. Objective Compare pre-diagnostic and post-diagnostic cognition and global brain volume in ischaemic stroke, focal epilepsy, Parkinson’s disease, multiple sclerosis, motor neurone disease (amyotrophic lateral sclerosis) and migraine using time-to-diagnosis and time-from-diagnosis data in relation to time of assessment. Design Analysis the prospective UK Biobank cohort with study baseline assessment performed between 2006-2010 and participants followed until 2021. Setting Multicenter, population-based study. Participants Sample of 497,252 participants, aged between 38 and 72 years, at baseline with an imaging sub-sample of 42,468 participants. Exposure Participants with each neurological condition were compared to a healthy control group. Main outcomes and measures A continuous measure of executive function and magnetic resonance imaging brain measures of total grey matter and hippocampal volume. Results Of the 497,252 participants (226,206 [45.5%] men, mean [SD] age, 57.5[8.1] years), 12,755 had ischaemic stroke, 6,758 had a diagnosis of focal epilepsy, 3,315 had Parkinson’s disease, 2,315 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). Participants with 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 improved post-diagnosis cognitive scores. Pre-diagnosis MRI grey matter 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. Conclusion These cognitive trajectory models reveal disease-specific temporal patterns, 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. Key Points Question What is the pre-diagnosis cognitive profile across neurological conditions of ischaemic stroke, focal epilepsy, Parkinson’s disease, multiple sclerosis, motor neurone disease and migraine? Findings This cohort study of 495 149 participants, aged between 38 and 72 years, identified gradual worsening of cognition up to 15 years prior to clinical diagnosis in participants with focal epilepsy and multiple sclerosis while stroke was associated with a modest decline for a few years followed by a substantial reduction at the time of diagnosis. Migraine was associated with post-diagnosis improvement in cognitive scores. Meaning Cognitive trajectory models identify disease-specific temporal patterns that 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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".