■许多精神疾病涉及过度的回避或防御行为,如焦虑和外伤障碍中的回避或强迫症中的防御性仪式。开发从局部场电位(LFP)预测这些行为的算法可以作为闭环控制此类疾病的基础技术。一个重要的挑战是识别编码这些防御行为的LFP特征。
■我们分析了经历音调休克调节和灭绝的大鼠的大脑下皮质和基底外侧杏仁核的LFP信号,研究防御行为的标准。我们利用了一组全面的神经标记,temporal,和连接域,采用SHapley添加剂扩张在光梯度提升机模型中进行特征重要性评估。我们的目标是解码三种通常研究的回避/防御行为:冻结,压杆抑制,和运动(加速度测量),研究不同特征对解码性能的影响。
■频带功率和信道之间的频带功率比成为会话中的最佳特征。高伽马(80-150Hz)功率,功率比,与其他与防御行为更经典相关的波段相比,区域间的相关性提供了更多的信息。专注于信息丰富的功能可增强性能。在16名受试者的4次录音中,我们实现了0.5357和0.3476的平均决定系数,以及0.7579和0.6092的皮尔逊相关系数,分别。仅利用信息最丰富的功能,揭示了加速度计和条形按压速率之间的差分编码,前者主要通过本地频谱功率,后者通过区域间连通性。我们的方法证明了非常低的时间复杂度,训练需要<110毫秒,推理需要<1毫秒。
■我们的结果证明了以最小延迟准确解码防御行为的可行性,使用来自神经回路的LFP特征与这些行为密切相关。这种方法有望实时解码,以识别闭环精神神经调节中的生理目标。
UNASSIGNED: 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 foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.
UNASSIGNED: 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.
UNASSIGNED: 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 time complexity, requiring <110 ms for training and <1 ms for inference.
UNASSIGNED: 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.