Cross‐Species Insights into Cognition: Translating Findings from Mice to Humans at‐risk for Alzheimer's Disease
Bibliographic record
Abstract
BACKGROUND: The cross-species translation of cognitive testing is a critical approach for advancing our understanding of Alzheimer's disease (AD). Mouse models of AD enable causal mechanistic insights into how genetic and biological factors influence cognitive decline. Recent developments in touchscreen-based cognitive tasks allow for consistent testing paradigms in both humans and mice, bridging the gap between preclinical and clinical research. METHOD: Through an ongoing collaboration between Western and McGill Universities, we are leveraging homologous touchscreen tests of cognition in parallel studies of next-generation mouse models of AD and humans at risk for AD. Mouse models with humanized knock-in genes (amyloid, tau, ApoE ε3 or ε4) are being used at Western University to explore how genetic factors interact with age and sex to influence executive function. These experiments use the Continuous Performance Task (CPT), a sensitive touchscreen-based probe of selective attention. RESULT: Since 2021, we have been integrating the CPT into the longitudinal PREVENT-AD study led by McGill University and the Douglas Research Centre. Over 425 individuals (aged 60-80 years) with intact cognition at baseline and family history of AD are enrolled in PREVENT-AD. Since 2011, this cohort has contributed a comprehensive dataset that includes multimodal neuroimaging, cerebrospinal fluid biomarkers, neurosensory measures, and standard cognitive assessments. We have acquired CPT in >220 individuals, with longitudinal data collected from approximately 80 of these participants and counting. CONCLUSION: This talk will explore the integration of touchscreen-based cognitive testing into both human and mouse studies and its role in elucidating the mechanisms of AD risk. I will discuss how PREVENT-AD human and mouse touchscreen CPT data can shed light on the interaction of ApoE genotype, age, and sex in early cognitive dysfunction. Additionally, I will explore whether touchscreen tests of cognition differ from standard neuropsychological tests in their sensitivity to early AD-related decline. Finally, I will outline plans to expand the use of touchscreen tests within PREVENT-AD and other large-scale consortia. By linking genetic risk, cognitive performance, and neurobiological markers, this cross-species approach will advance our understanding of AD's earliest manifestations and support the development of targeted interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".