Drug Development

Area of Science:

• Neuroscience
• Computational Psychiatry
• Animal Models of Disease

Background:

• Clinical studies demonstrate the utility of mathematical decision-making models for identifying behavioral biomarkers in dementia.
• Preclinical research is essential for understanding early-stage neurodegenerative diseases like Alzheimer's disease (AD).

Purpose of the Study:

• To integrate decision-making modeling into behavioral analysis of a prodromal rat model of AD.
• To uncover genotype- and sex-specific behavioral effects not apparent through standard performance assessments.

Main Methods:

• Male and female wildtype and transgenic Fischer 344 rats (TgAPP) performed a visual discrimination task.
• Reaction times were analyzed using an ExGauss function to assess variability.
• Decision-making processes were modeled using a drift diffusion model (DDM) to quantify evidence accumulation, decision thresholds, and non-decision time.

Main Results:

• Transgenic APP (TgAPP) rats exhibited lower accuracy and slower reaction times compared to wildtypes, with females showing increased timing variability.
• DDM analysis revealed slower evidence accumulation in both male and female TgAPP rats.
• Female TgAPP rats displayed higher decision thresholds and increased non-decision times, indicating enhanced response caution and altered processing.

Conclusions:

• Drift diffusion modeling effectively identifies cognitive function changes in a prodromal AD rat model, surpassing standard metrics.
• Combining computational modeling with other biomarkers can improve understanding of early AD and preclinical research translation.