Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia.
Department of Neurosurgery, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, United States of America.
PLoS One. 2021 Mar 9;16(3):e0240147. doi: 10.1371/journal.pone.0240147. eCollection 2021.
When presented with an oscillatory sensory input at a particular frequency, F [Hz], neural systems respond with the corresponding frequency, f [Hz], and its multiples. When the input includes two frequencies (F1 and F2) and they are nonlinearly integrated in the system, responses at intermodulation frequencies (i.e., n1f1+n2f2 [Hz], where n1 and n2 are non-zero integers) emerge. Utilizing these properties, the steady state evoked potential (SSEP) paradigm allows us to characterize linear and nonlinear neural computation performed in cortical neurocircuitry. Here, we analyzed the steady state evoked local field potentials (LFPs) recorded from the primary (S1) and secondary (S2) somatosensory cortex of anesthetized cats (maintained with alfaxalone) while we presented slow (F1 = 23Hz) and fast (F2 = 200Hz) somatosensory vibration to the contralateral paw pads and digits. Over 9 experimental sessions, we recorded LFPs from N = 1620 and N = 1008 bipolar-referenced sites in S1 and S2 using electrode arrays. Power spectral analyses revealed strong responses at 1) the fundamental (f1, f2), 2) its harmonic, 3) the intermodulation frequencies, and 4) broadband frequencies (50-150Hz). To compare the computational architecture in S1 and S2, we employed simple computational modeling. Our modeling results necessitate nonlinear computation to explain SSEP in S2 more than S1. Combined with our current analysis of LFPs, our paradigm offers a rare opportunity to constrain the computational architecture of hierarchical organization of S1 and S2 and to reveal how a large-scale SSEP can emerge from local neural population activities.
当呈现特定频率（F [Hz]）的振荡感觉输入时，神经系统以相应的频率（f [Hz]）及其倍数做出响应。当输入包括两个频率（F1 和 F2）并且它们在系统中非线性地整合时，会出现互调频率（即 n1f1+n2f2 [Hz]，其中 n1 和 n2 是非零整数）的响应。利用这些特性，稳态诱发电位（SSEP）范式使我们能够描述皮质神经回路中进行的线性和非线性神经计算。在这里，我们分析了麻醉猫（用阿法索烷维持）的初级（S1）和次级（S2）体感皮层记录的稳态诱发局部场电位（LFPs），同时向对侧爪垫和手指呈现缓慢（F1 = 23Hz）和快速（F2 = 200Hz）体感振动。在 9 个实验过程中，我们使用电极阵列从 S1 和 S2 中的 N = 1620 和 N = 1008 个双极参考位点记录 LFPs。功率谱分析显示出强烈的响应 1）在基本频率（f1、f2），2）其谐波，3）互调频率，和 4）宽带频率（50-150Hz）。为了比较 S1 和 S2 中的计算架构，我们采用了简单的计算模型。我们的建模结果需要非线性计算才能更好地解释 S2 中的 SSEP 而不是 S1。结合我们当前对 LFPs 的分析，我们的范式提供了一个难得的机会来约束 S1 和 S2 的分层组织的计算架构，并揭示如何从局部神经群体活动中出现大规模的 SSEP。
