The development of FEDUPP: feeding experimentation device users processing package to assess learning and cognitive...

Area of Science:

• Neuroscience
• Behavioral Science
• Computational Biology

Background:

• Cognitive flexibility is crucial for adaptive decision-making but is often impaired in neuropsychiatric disorders.
• Traditional rodent assays for cognitive flexibility lack ecological validity and temporal resolution due to restrictive environments and experimenter control.
• Developing automated, home-cage paradigms is essential for more naturalistic and continuous behavioral assessment.

Purpose of the Study:

• To develop and validate a fully automated, home-cage paradigm using the Feeding Experimentation Device 3 (FED3) and the Feeding Experimentation Device Users Processing Package (FEDUPP) for assessing learning and cognitive flexibility.
• To implement multi-scale learning metrics, including machine learning-based meal accuracy, for a sensitive evaluation of motivated, goal-directed feeding.
• To investigate the utility of this paradigm in detecting subtle behavioral changes in a mouse model with dorsal hippocampal CASK knockdown.

Main Methods:

• A novel automated home-cage paradigm combining a single-day fixed-ratio 1 (FR1) task with a multi-day reversal learning task.
• Utilizing the Feeding Experimentation Device 3 (FED3) for automated pellet dispensing and response recording.
• Employing the open-source Feeding Experimentation Device Users Processing Package (FEDUPP) for data analysis, including multi-scale learning metrics and machine learning-based meal accuracy classification.

Main Results:

• The automated paradigm successfully detected rapid FR1 task acquisition and progressive adaptation during reversal learning in wild-type mice.
• Mice with dorsal hippocampal CASK knockdown exhibited faster FR1 acquisition and higher overall accuracy compared to controls.
• The CASK knockdown group showed a faster onset of the first accurate meal post-reversal, suggesting improved updating of goal-directed feeding behavior.

Conclusions:

• The FED3/FEDUPP automated system provides high-resolution, continuous assessment of learning and cognitive flexibility in ethologically relevant settings.
• Meal-based accuracy, analyzed via machine learning, serves as a sensitive metric for detecting subtle alterations in cognitive flexibility.
• This paradigm advances the study of cognitive flexibility and its impairments in neuropsychiatric disorders by offering improved ecological validity and temporal resolution.