Finding the balance between model complexity and performance: Using ventral striatal oscillations to classify feeding behavior in rats

The ventral striatum (VS) is a central node within a distributed network that controls appetitive behavior, and neuromodulation of the VS has demonstrated therapeutic potential for appetitive disorders. Local field potential (LFP) oscillations recorded from deep brain stimulation (DBS) electrodes within the VS are a pragmatic source of neural systems-level information about appetitive behavior that could be used in responsive neuromodulation systems. Here, we recorded LFPs from the bilateral nucleus accumbens core and shell (subregions of the VS) during limited access to palatable food across varying conditions of hunger and food palatability in male rats. We used standard statistical methods (logistic regression) as well as the machine learning algorithm lasso to predict aspects of feeding behavior using VS LFPs. We were able to predict the amount of food eaten, the increase in consumption following food deprivation, and the type of food eaten. Further, we were able to predict whether the initiation of feeding was imminent up to 42.5 seconds before feeding began and classify current behavior as either feeding or not-feeding. In classifying feeding behavior, we found an optimal balance between model complexity and performance with models using 3 LFP features primarily from the alpha and high gamma frequencies. As shown here, unbiased methods can identify systems-level neural activity linked to domains of mental illness with potential application to the development and personalization of novel treatments.

伏隔核（ventral striatum, VS）是调控摄食寻求行为的分布式神经网络中的核心节点，针对VS的神经调控已在摄食相关障碍的治疗中展现出应用潜力。通过伏隔核内深部脑刺激（deep brain stimulation, DBS）电极记录得到的局部场电位（local field potential, LFP）振荡信号，是获取摄食行为相关神经系统级信息的实用来源，可用于响应式神经调控系统。本研究在雄性大鼠处于不同饥饿状态与食物适口性条件下、实施限时接触适口性食物的过程中，记录了双侧伏隔核核心区与壳区（VS的两个亚区）的局部场电位信号。我们采用标准统计方法（逻辑回归）与机器学习算法套索回归（Lasso），基于VS的LFP信号预测摄食行为的多项特征，成功实现了进食量、食物剥夺后进食量增幅以及食物种类的预测。此外，我们可在摄食开始前最长42.5秒预判摄食启动时机，并能将当前行为分类为摄食或非摄食状态。在摄食行为分类任务中，我们发现当模型仅使用3个主要源自α频段与高γ频段的LFP特征时，可在模型复杂度与分类性能间达到最优平衡。正如本研究所示，无偏分析方法可识别与精神疾病领域相关的神经系统级神经活动，其成果有望应用于新型治疗手段的开发与个性化定制。