Using computational modeling to assess decision‐making processes in a prodromal rat model of Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Toward establishing behavioral biomarkers of dementia, clinical studies have shown promising results for differentiating patient groups by mathematically modeling their decision-making processes during task performance (O'Callaghan et al., 2021; Ratcliff et al., 2022). The aim of our preclinical study was to include decision-based modeling into the behavioral analysis of a prodromal rat model of Alzheimer's disease (AD); an approach that we predicted would reveal genotype- and sex-specific behavioral effects that were not evident in the standard assessments of task performance. METHOD: Male/female wildtype and transgenic Fischer 344 rats (TgAPP, which over-express pathogenic human amyloid precursor protein, but do not spontaneously develop β-amyloid plaques) performed a two-alternative forced-choice task to visually discriminate a steady versus flashing light cue. In addition to measuring performance accuracy, the rats' reaction times were fit using an ExGauss function to assess the variability of the distribution; an important metric because increased reaction time variability is linked to age-related cognitive decline in humans. Consistent with past studies on dementia patients, the rats' decision-making processes were analyzed using a drift diffusion model (DDM), which computed their rate of evidence accumulation (drift), their threshold to make decisions (bounds), as well as their initial sensory processing and final motor execution (non-decision time). RESULT: Both male and female TgAPP rats performed the visual discrimination task at a modestly lower percent accuracy compared to wildtypes; however, only the female TgAPP rats showed slower reaction times, including increased variability in their timing to make decisions. Extending these genotype- and sex-specific results, the DDM analysis revealed (1) slower evidence accumulation in both TgAPP sexes; (2) increased decision-making thresholds in females compared to males, suggestive of increased response caution, and; (3) increased non-decision times only in the TgAPP female rats. CONCLUSION: We show that DDM is a useful tool for revealing changes in cognitive function in a prodromal animal model of AD that were otherwise not evident using standard task metrics. Ultimately, by combining this computational approach with other biomarkers (e.g. molecular; neurophysiological), we can enhance our understanding of the earliest stages of disease progression and improve the translational potential of preclinical research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".