Neural decoding and feature selection methods for closed-loop control of avoidance behavior.

Jinhan Liu, Rebecca Younk, Lauren M Drahos, Sumedh S Nagrale, Shreya Yadav, Alik S Widge, Mahsa Shoaran
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Abstract

Objective.Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach.We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results.Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance.Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

用于避让行为闭环控制的神经解码和特征选择方法。
目的:许多精神疾病都涉及过度回避或防御行为,例如焦虑症和创伤症中的回避或强迫症中的防御仪式。从局部场电位(LFP)中开发出预测这些行为的算法,可作为对此类疾病进行闭环控制的基础技术。一个重大挑战是确定编码这些防御行为的 LFP 特征。我们分析了接受音调冲击条件反射和消退的大鼠下边缘皮层和杏仁基底外侧的 LFP 信号,这是研究防御行为的标准。我们使用了一整套跨频谱、时间和连接域的神经标记物,并在光梯度提升机模型中使用 SHapley Additive exPlanations 进行特征重要性评估。我们的目标是解码三种常见的回避/防御行为:冻结、压杠抑制和运动(加速度测量),研究不同特征对解码性能的影响。频带功率和通道之间的频带功率比成为各次会议的最佳特征。高伽马(80-150 Hz)功率、功率比和区域间相关性比其他频段更有信息量,而其他频段与防御行为更有经典联系。专注于信息量大的特征可提高成绩。在对 16 名受试者进行的 4 次记录过程中,我们发现加速度测量挺举和杠铃按压率的平均决定系数分别为 0.5357 和 0.3476,皮尔逊相关系数分别为 0.7579 和 0.6092。仅利用信息量最大的特征就能发现加速度和压杆率之间的编码差异,前者主要通过局部频谱功率,后者则通过区域间连接。我们的方法显示了极低的训练/推理时间和内存使用率,只需要
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