Department of Neurobiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8553, Japan
Laboratory for Neural Coding and Brain Computing, RIKEN Center for Brain Science, Saitama 351-0198, Japan.
eNeuro. 2024 Aug 5;11(8). doi: 10.1523/ENEURO.0172-24.2024. Print 2024 Aug.
The frontal cortex-striatum circuit plays a pivotal role in adaptive goal-directed behaviors. However, it remains unclear how decision-related signals are mediated through cross-regional transmission between the medial frontal cortex and the striatum by neuronal ensembles in making decision based on outcomes of past action. Here, we analyzed neuronal ensemble activity obtained through simultaneous multiunit recordings in the secondary motor cortex (M2) and dorsal striatum (DS) in rats performing an outcome-based left-or-right choice task. By adopting tensor component analysis (TCA), a single-trial-based unsupervised dimensionality reduction approach, for concatenated ensembles of M2 and DS neurons, we identified distinct three spatiotemporal neural dynamics (TCA components) at the single-trial level specific to task-relevant variables. Choice-position-selective neural dynamics reflected the positions chosen and was correlated with the trial-to-trial fluctuation of behavioral variables. Intriguingly, choice-pattern-selective neural dynamics distinguished whether the incoming choice was a repetition or a switch from the previous choice before a response choice. Other neural dynamics was selective to outcome and increased within-trial activity following response. Our results demonstrate how the concatenated ensembles of M2 and DS process distinct features of decision-related signals at various points in time. Thereby, the M2 and DS collaboratively monitor action outcomes and determine the subsequent choice, whether to repeat or switch, for action selection.
前额叶皮层-纹状体回路在适应性目标导向行为中起着关键作用。然而,目前尚不清楚在基于过去行为结果做出决策时,神经元集合如何通过内侧前额叶皮层和纹状体之间的跨区域传递来介导与决策相关的信号。在这里,我们分析了大鼠在执行基于结果的左右选择任务时,通过对次级运动皮层(M2)和背侧纹状体(DS)进行同步多单元记录获得的神经元集合活动。通过采用张量成分分析(TCA),一种基于单试的无监督降维方法,对 M2 和 DS 神经元的串联集合进行分析,我们在单个试次水平上识别出与任务相关变量特异性相关的三个独特的时空神经动力学(TCA 成分)。选择位置选择神经动力学反映了所选位置,并与行为变量的试次间波动相关。有趣的是,选择模式选择神经动力学区分了传入的选择是来自前一次选择的重复还是切换。其他神经动力学对结果具有选择性,并在响应选择后增加了试内活动。我们的研究结果表明,M2 和 DS 的串联集合如何在不同时间点处理与决策相关信号的不同特征。因此,M2 和 DS 共同监测动作结果,并确定后续选择,是重复还是切换,用于动作选择。