The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing
Bibliographic record
Abstract
BACKGROUND: As greater numbers of us are living longer, it is increasingly important to understand how we can age healthily. Although old age is often stereotyped as a time of declining mental abilities and inflexibility, cognitive neuroscience reveals that older adults use neural and cognitive resources flexibly, recruiting novel neural regions and cognitive processes when necessary. Our aim in this project is to understand how age-related changes to neural structure and function interact to support cognitive abilities across the lifespan. METHODS/DESIGN: We are recruiting a population-based cohort of 3000 adults aged 18 and over into Stage 1 of the project, where they complete an interview including health and lifestyle questions, a core cognitive assessment, and a self-completed questionnaire of lifetime experiences and physical activity. Of those interviewed, 700 participants aged 18-87 (100 per age decile) continue to Stage 2 where they undergo cognitive testing and provide measures of brain structure and function. Cognition is assessed across multiple domains including attention and executive control, language, memory, emotion, action control and learning. A subset of 280 adults return for in-depth neurocognitive assessment in Stage 3, using functional neuroimaging experiments across our key cognitive domains.Formal statistical models will be used to examine the changes that occur with healthy ageing, and to evaluate age-related reorganisation in terms of cognitive and neural functions invoked to compensate for overall age-related brain structural decline. Taken together the three stages provide deep phenotyping that will allow us to measure neural activity and flexibility during performance across a number of core cognitive functions. This approach offers hypothesis-driven insights into the relationship between brain and behaviour in healthy ageing that are relevant to the general population. DISCUSSION: Our study is a unique resource of neuroimaging and cognitive measures relevant to change across the adult lifespan. Because we focus on normal age-related changes, our results may contribute to changing views about the ageing process, lead to targeted interventions, and reveal how normal ageing relates to frail ageing in clinicopathological conditions such as Alzheimer's disease.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".