Engaged decision-makers align spontaneous movements to stereotyped task demands.
Engaged decision-makers align spontaneous movements to stereotyped task demands.
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积极参与的决策者根据刻板的任务要求调整自发的行动。
DOI：
10.1101/2023.06.26.546404
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Churchland,AnneK
中科院分区：
文献类型：
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作者：
Yin,Chaoqun;Melin,MaxwellD;Rojas-Bowe,Gabriel;Sun,XiaonanRichard;Gluf,Steven;Couto,João;Kostiuk,Alex;Musall,Simon;Churchland,AnneK
感官引导决策过程中的神经活动受到动物运动的强烈调节。尽管运动对神经活动的影响现已得到充分记录，但这些运动与行为表现之间的关系仍不清楚。为了理解这种关系，我们首先测试了动物运动的幅度（通过对 28 个身体部位的姿势分析进行评估）是否与感知决策任务的表现相关。不存在很强的关系，这表明任务绩效不受运动幅度的影响。然后我们测试了性能是否取决于运动时间和轨迹。我们将运动分为两组：由任务事件（例如感觉刺激或选择的开始）很好地预测的任务相关运动和独立于任务事件发生的任务独立运动（TIM）。 TIM 与头部受约束的小鼠和自由活动的大鼠的表现具有可靠的负相关性。这认为，某些运动（由其与任务事件相关的时间和轨迹定义）可能表明参与或脱离任务的时期。为了证实这一点，我们将 TIM 与隐马尔可夫模型与伯努利广义线性模型观察 (GLM-HMM) 恢复的潜在行为状态进行了比较，并再次发现它们呈负相关。最后，我们研究了这些行为状态对通过宽场钙成像测量的神经活动的影响。参与状态与广泛增加的活动相关，特别是在延迟期间。然而，线性编码模型可以解释脱离状态下神经活动的更多总体差异。我们的分析表明，这可能是因为在脱离过程中未经指导的运动对神经活动产生了更大的影响。总而言之，这些发现表明 TIM 可以提供有关参与的内部状态的信息，并且运动和状态共同对神经活动产生重大影响。
Neural activity during sensory-guided decision-making is strongly modulated by animal movements. Although the impact of movements on neural activity is now well-documented, the relationship between these movements and behavioral performance remains unclear. To understand this relationship, we first tested whether the magnitude of animal movements (assessed with posture analysis of 28 individual body parts) was correlated with performance on a perceptual decision-making task. No strong relationship was present, suggesting that task performance is not affected by the magnitude of movements. We then tested if performance instead depends on movement timing and trajectory. We partitioned the movements into two groups: task-aligned movements that were well predicted by task events (such as the onset of the sensory stimulus or choice) and task independent movement (TIM) that occurred independently of task events. TIM had a reliable, inverse correlation with performance in head-restrained mice and freely moving rats. This argues that certain movements, defined by their timing and trajectories relative to task events, might indicate periods of engagement or disengagement in the task. To confirm this, we compared TIM to the latent behavioral states recovered by a hidden Markov model with Bernoulli generalized linear model observations (GLM-HMM) and found these, again, to be inversely correlated. Finally, we examined the impact of these behavioral states on neural activity measured with widefield calcium imaging. The engaged state was associated with widespread increased activity, particularly during the delay period. However, a linear encoding model could account for more overall variance in neural activity in the disengaged state. Our analyses demonstrate that this is likely because uninstructed movements had a greater impact on neural activity during disengagement. Taken together, these findings suggest that TIM is informative about the internal state of engagement, and that movements and state together have a major impact on neural activity.