Drug Development

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.

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).

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.

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.