Sederberg Audrey, Pala Aurélie, Stanley Garrett B
Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.
Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States.
Front Comput Neurosci. 2024 Oct 23;18:1445621. doi: 10.3389/fncom.2024.1445621. eCollection 2024.
As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics.
Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity.
A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity.
Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.
随着新兴技术能够在不断扩大的规模上测量微电路内活动的精确细节，越来越需要识别神经群体中代表网络生理和行为相关方面的显著特征和模式。来自大量神经群体记录的证据不断积累，表明神经群体活动经常表现出相对低维的结构，少数变量就能解释大部分活动结构。虽然这种结构在整个大脑中都有观察到，但尚不清楚神经群体活动的降维表示与通常根据局部场电位（LFP）波动、单细胞活动和行为指标描述的“脑状态”经典指标之间的关系。
将隐状态模型拟合到清醒小鼠初级体感皮层胡须区域记录的神经元群体的自发尖峰活动。将S1中皮质状态的经典测量指标，包括LFP和胡须运动活动，与从尖峰活动推断出的状态动态进行比较。
一个隐马尔可夫模型以相对较少的状态很好地拟合了群体尖峰数据，并且假定的抑制性神经元在确定潜在状态动态方面发挥了超大作用。从模型推断出的尖峰状态比单个神经元或群体的尖峰活动直接读数更能反映皮质状态。此外，尖峰状态预测了感觉反应的逐次试验变异性和行为的一个方面，即胡须运动活动。
我们的结果展示了脑状态的经典测量指标如何与微电路尺度上的神经群体尖峰动态相关，并提供了一种跨脑区定量绘制脑状态动态的方法。
