Neurology Department, Mayo Clinic, Rochester, Minnesota, United States of America.
PLoS Comput Biol. 2023 Feb 21;19(2):e1010527. doi: 10.1371/journal.pcbi.1010527. eCollection 2023 Feb.
The relationship between macroscale electrophysiological recordings and the dynamics of underlying neural activity remains unclear. We have previously shown that low frequency EEG activity (<1 Hz) is decreased at the seizure onset zone (SOZ), while higher frequency activity (1-50 Hz) is increased. These changes result in power spectral densities (PSDs) with flattened slopes near the SOZ, which are assumed to be areas of increased excitability. We wanted to understand possible mechanisms underlying PSD changes in brain regions of increased excitability. We hypothesized that these observations are consistent with changes in adaptation within the neural circuit. We developed a theoretical framework and tested the effect of adaptation mechanisms, such as spike frequency adaptation and synaptic depression, on excitability and PSDs using filter-based neural mass models and conductance-based models. We compared the contribution of single timescale adaptation and multiple timescale adaptation. We found that adaptation with multiple timescales alters the PSDs. Multiple timescales of adaptation can approximate fractional dynamics, a form of calculus related to power laws, history dependence, and non-integer order derivatives. Coupled with input changes, these dynamics changed circuit responses in unexpected ways. Increased input without synaptic depression increases broadband power. However, increased input with synaptic depression may decrease power. The effects of adaptation were most pronounced for low frequency activity (< 1Hz). Increased input combined with a loss of adaptation yielded reduced low frequency activity and increased higher frequency activity, consistent with clinical EEG observations from SOZs. Spike frequency adaptation and synaptic depression, two forms of multiple timescale adaptation, affect low frequency EEG and the slope of PSDs. These neural mechanisms may underlie changes in EEG activity near the SOZ and relate to neural hyperexcitability. Neural adaptation may be evident in macroscale electrophysiological recordings and provide a window to understanding neural circuit excitability.
宏观电生理记录与潜在神经活动之间的关系尚不清楚。我们之前曾表明，低频 EEG 活动（<1Hz）在癫痫发作起始区（SOZ）减少，而高频活动（1-50Hz）增加。这些变化导致在 SOZ 附近斜率平坦的功率谱密度（PSD），这被认为是兴奋性增加的区域。我们想了解在兴奋性增加的脑区 PSD 变化的可能机制。我们假设这些观察结果与神经回路中适应变化一致。我们开发了一个理论框架，并使用基于滤波器的神经质量模型和基于电导率的模型测试了适应机制（如尖峰频率适应和突触抑制）对兴奋性和 PSD 的影响。我们比较了单时间尺度适应和多时间尺度适应的贡献。我们发现，多时间尺度适应会改变 PSD。多时间尺度的适应可以近似分数阶动力学，这是一种与幂律、历史依赖性和非整数阶导数相关的微积分形式。与输入变化相结合，这些动力学以意想不到的方式改变了电路响应。没有突触抑制的增加输入会增加宽带功率。但是，具有突触抑制的增加输入可能会降低功率。适应的影响在低频活动（<1Hz）中最为明显。增加输入加上适应丧失会导致低频活动减少和高频活动增加，这与 SOZ 处的临床 EEG 观察结果一致。尖峰频率适应和突触抑制是两种多时间尺度适应形式，它们会影响低频 EEG 和 PSD 的斜率。这些神经机制可能是 SOZ 附近 EEG 活动变化的基础，并与神经超兴奋性有关。神经适应可能在宏观电生理记录中明显，并为理解神经回路兴奋性提供一个窗口。
