Biology subjects

Bosc, M.

Publications and source records attributed to Bosc, M..

3 recordsLinked to original sources

bioRxiv2026

The antipsychotic drug clozapine suppresses autoimmunity driving psychosis-like behavior in mice

Antipsychotic drugs are the first-line treatment for psychosis yet their mechanism of action remains poorly understood, largely due to the challenge to faithfully model psychosis preclinically. Here, we focus on the emerging concept that psychosis can be caused by brain autoimmunity and present a novel mouse model of anti-N-methyl-D-aspartate-receptor (anti-NMDAR) encephalitis, a condition that manifests with psychosis and autoanti-bodies against the NMDAR. We devised a new mRNA-based approach to immunize mice against the NMDAR. Immunized mice developed psychosis-like behaviors that were caused by anti-NMDAR autoantibodies leading to phagocytosis of NMDARs by brain microglia. The antipsychotic drug clozapine rescued psychosis-like behaviors and, remarkably, reduced anti-NMDAR autoantibody levels and antibody-mediated phagocytosis of NMDARs. The immunomodulatory effects of clozapine were confirmed in a mouse model of systemic lupus erythematosus. Our results demonstrate that clozapine suppresses autoimmunity driving psychosis-like behaviors, raising the possibility that immunomodulation contributes to antipsychotic drug action. HIGHLIGHTSO_LImRNA immunization against the NMDAR induces psychosis-like behavior in mice C_LIO_LIAnti-NMDAR autoantibodies are sufficient for psychosis-like behavior C_LIO_LIMicroglial phagocytosis of NMDARs mediates psychosis-like behavior induced by anti-NMDAR autoanti-bodies. C_LIO_LIClozapine reduces anti-NMDAR autoantibodies, microglial phagocytosis and psychosis-like behavior, consistent with immunomodulation as a potential mechanism of antipsychotic drug action. C_LI

neuroscience↗

bioRxiv2024

Bounded optimality of time investments in rats, mice, and humans

Time is our scarcest resource. Allocating time optimally presents a universal challenge for all organisms because the future benefits of time investments are uncertain. We developed a normative framework for assessing bounded optimality in time allocation, emphasizing the accuracy of future predictions, independent of subjective costs and benefits. In a common decision task across humans, rats, and mice, we varied uncertainty by titrating ambiguous sensory evidence and measured the time each subject was willing to invest post-decision. We observed that all species and subjects invested more time when they were more likely to be correct, which reflected a statistical confidence of uncertain evidence. Time allocation strategy approached the lower bound of optimality, indicating an accurate decision-by-decision assessment of confidence in the likelihood that waiting will pay off - independent of the subjective payoff values and time costs. We demonstrate that an elementary algorithm based on a drift-diffusion process algorithm can implement this optimal time investment strategy. These results illuminate the computational mechanisms governing rational time investment, showing that humans, rats, and mice can maximize payoffs via confidence-guided time allocation. HighlightsO_LIComputational and behavioral framework to assess bounded optimality of investments. C_LIO_LIHumans, rats, and mice invest more time to obtain more likely payoffs, in proportion to statistical confidence. C_LIO_LITime investment was close to optimal model predictions, reflecting bounded optimality of investments under uncertainty. C_LIO_LIBounded-optimal time investment may be an evolutionary ancient adaptive behavioral strategy. C_LI

animal behavior and cognition↗

bioRxiv2024

The neural mechanisms of fast versus slow decision-making

Is more haste less speed? Decision time variability has been attributed to speed/accuracy trade-offs(1), internal mental architecture(2), and noisy evidence accumulation(3-5). However, exploring these possibilities is difficult in rodents that consistently behave impulsively. Here, we demonstrate a novel floating-platform system that allows head-fixed mice to voluntarily vary decision times, akin to observed human behavior, in combination with sensitive neuroimaging approaches. We track the activity flow from medial to lateral frontal cortex (MFC to LFC) and record sequences of single-neuron activity. Choice-selective neurons displayed divergent temporal codes between MFC and LFC, with remarkable MFC susceptibility to optogenetic inhibition. These results suggest that LFC acts as an integrative motor threshold, while MFC plays a broader cognitive role in strategy and choice-selection.

neuroscience↗