Distinct Patterns of Adaptive Decision-Making in Mouse Models of Neurodevelopmental Disorders
- 1RIKEN Center for Brain Science
Abstract
Neurodevelopmental disorders (NDDs) arise from diverse genetic alterations that can affect adaptive decision-making, but quantifying specific components of cognitive processes remains challenging. We developed a sequence of deterministic and probabilistic reversal learning tasks using an automated operant platform and applied reinforcement-learning models to quantify latent cognitive processes in mice. We applied this framework to three genetic NDD models: Mecp2+/- (Rett syndrome), MECP2-Tg1 (MECP2 duplication syndrome), and Kmt2c+/fs (KMT2C haploinsufficiency) mice. Mecp2+/- mice showed heterogeneous impairment in acquiring deterministic reversal learning, accompanied by reduced task engagement and increased premature responses. MECP2-Tg1 mice exhibited mild impairment of deterministic reversal learning but preserved performance in probabilistic reversal learning. Although Kmt2c+/fs mice showed largely preserved behavioral performance, reinforcement-learning modeling revealed increased learning from unrewarded outcomes and faster forgetting of unchosen options, which were not apparent from overall performance. These findings demonstrate that combining quantitative reversal learning tasks with computational modeling can distinguish specific alterations in cognitive processes, providing an effective framework for characterizing cognitive dysfunction across mouse models of NDDs.
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BibTeX
@misc{hilber2026distinct,
title = {{Distinct Patterns of Adaptive Decision-Making in Mouse Models of Neurodevelopmental Disorders}},
author = {J. Hilber and K. Pulk and T. Nakamura and K. Honda and T. Kato and A. Takata and T. Matsuda and A. Ito-Ishida},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.14.759086},
url = {https://www.biorxiv.org/content/10.64898/2026.10.14.759086v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Distinct Patterns of Adaptive Decision-Making in Mouse Models of Neurodevelopmental Disorders
AU - J. Hilber
AU - K. Pulk
AU - T. Nakamura
AU - K. Honda
AU - T. Kato
AU - A. Takata
AU - T. Matsuda
AU - A. Ito-Ishida
PY - 2026
DA - 2026/10/14
PB - bioRxiv
DO - 10.64898/2026.10.14.759086
UR - https://www.biorxiv.org/content/10.64898/2026.10.14.759086v1
AB - Neurodevelopmental disorders (NDDs) arise from diverse genetic alterations that can affect adaptive decision-making, but quantifying specific components of cognitive processes remains challenging. We developed a sequence of deterministic and probabilistic reversal learning tasks using an automated operant platform and applied reinforcement-learning models to quantify latent cognitive processes in mice. We applied this framework to three genetic NDD models: Mecp2+/- (Rett syndrome), MECP2-Tg1 (MECP2 duplication syndrome), and Kmt2c+/fs (KMT2C haploinsufficiency) mice. Mecp2+/- mice showed heterogeneous impairment in acquiring deterministic reversal learning, accompanied by reduced task engagement and increased premature responses. MECP2-Tg1 mice exhibited mild impairment of deterministic reversal learning but preserved performance in probabilistic reversal learning. Although Kmt2c+/fs mice showed largely preserved behavioral performance, reinforcement-learning modeling revealed increased learning from unrewarded outcomes and faster forgetting of unchosen options, which were not apparent from overall performance. These findings demonstrate that combining quantitative reversal learning tasks with computational modeling can distinguish specific alterations in cognitive processes, providing an effective framework for characterizing cognitive dysfunction across mouse models of NDDs.
ER -