Neural circuits for decision making

项目摘要 不同的人面对类似的情况会做出不同的选择，不同的选择会导致 不同的人生轨迹尽管对个人的生活有着广泛的影响， 特殊的决策仍然在很大程度上未知。开始讨论这个重要的话题， 最近，我们定量地描述了特质选择行为，并确定了一个后验 网络由扣带皮层，后顶叶皮层和纹状体组成，其中 特质选择偏差很可能被加工、传递，并与其他决策整合 变量灌输特质的后验网络可能与众所周知的 用于基于规则和价值的决策的前端网络和用于 刺激-反应关联，所有这些都动态地有助于最终选择。然而，在这方面， 后验网络中的详细电路机制尚未被发现。我们提出 一系列在老鼠身上进行的实验， 网络，利用完善的实验室工具，包括行为建模，顺行和 逆行跨突触标记、光遗传学和双光子成像。了解 控制着每时每刻特殊历史偏见的神经回路将揭示 特异性决定的神经起源。此外，生活干扰，适应不良的选择， 行为可能部分是极端偏离的特质偏见的表现，从而阐述了 不太为人所知的后验网络可能会为病理决策带来新的见解， 神经系统疾病，如衰老、痴呆和成瘾。

PROJECT SUMMARY Different people facing similar situations make different choices, and different choices result in divergent life trajectories. Despite the pervasive impact on an individual’s life, the neural basis of idiosyncratic decision-making remains largely unknown. Beginning to address this important topic, we recently characterized idiosyncratic choice behavior quantitatively and identified a posterior network comprised of the cingulate cortex, posterior parietal cortex, and striatum in which the idiosyncratic choice biases are likely processed, transmitted, and integrated with other decision variables. The posterior network imbuing idiosyncrasy might operate in parallel to the well-known frontal network for rule- and value-based decision-making and the sensorimotor network for stimulus-response association, all of which dynamically contribute to the final choice. However, the detailed circuit mechanisms in the posterior network have yet to be discovered. We propose a series of experiments in mice to elaborate information flows and processing in the posterior network, utilizing well-established in-lab tools including behavior modeling, anterograde- and retrograde-transsynaptic labeling, optogenetics, and two-photon imaging. Understanding the neural circuits governing the moment-by-moment idiosyncratic history bias will shed light on the neural origins of idiosyncratic decisions. Furthermore, life-interfering, maladaptive choice behavior might be in part a manifestation of extremely deviated idiosyncratic bias, thus elaborating the less known posterior network may bring new insights into pathological decision-making in neurological conditions such as aging, dementia, and addiction.