Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, People's Republic of China.
Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, People's Republic of China.
J Neural Eng. 2022 Aug 19;19(4). doi: 10.1088/1741-2552/ac86a3.
Brain-machine interfaces (BMIs) translate neural activity into motor commands to restore motor functions for people with paralysis. Local field potentials (LFPs) are promising for long-term BMIs, since the quality of the recording lasts longer than single neuronal spikes. Inferring neuronal spike activity from population activities such as LFPs is challenging, because LFPs stem from synaptic currents flowing in the neural tissue produced by various neuronal ensembles and reflect neural synchronization. Existing studies that combine LFPs with spikes leverage the spectrogram of the former, which can neither detect the transient characteristics of LFP features (here, neuromodulation in a specific frequency band) with high accuracy, nor correlate them with relevant neuronal activity with a sufficient time resolution.We propose a feature extraction and validation framework to directly extract LFP neuromodulations related to synchronized spike activity using recordings from the primary motor cortex of six Sprague Dawley rats during a lever-press task. We first select important LFP frequency bands relevant to behavior, and then implement a marked point process (MPP) methodology to extract transient LFP neuromodulations. We validate the LFP feature extraction by examining the correlation with the pairwise synchronized firing probability of important neurons, which are selected according to their contribution to behavioral decoding. The highly correlated synchronized firings identified by the LFP neuromodulations are fed into a decoder to check whether they can serve as a reliable neural data source for movement decoding.We find that the gamma band (30-80 Hz) LFP neuromodulations demonstrate significant correlation with synchronized firings. Compared with traditional spectrogram-based method, the higher-temporal resolution MPP method captures the synchronized firing patterns with fewer false alarms, and demonstrates significantly higher correlation than single neuron spikes. The decoding performance using the synchronized neuronal firings identified by the LFP neuromodulations can reach 90% compared to the full recorded neuronal ensembles.Our proposed framework successfully extracts the sparse LFP neuromodulations that can identify temporal synchronized neuronal spikes with high correlation. The identified neuronal spike pattern demonstrates high decoding performance, which suggest LFP can be used as an effective modality for long-term BMI decoding.
脑机接口 (BMI) 将神经活动转化为运动指令，以恢复瘫痪患者的运动功能。局部场电位 (LFP) 是长期 BMI 的有前途的选择，因为记录的质量比单个神经元尖峰持续时间更长。从群体活动（如 LFP）推断神经元尖峰活动具有挑战性，因为 LFP 源自流经由各种神经元集合产生的神经组织的突触电流，并反映神经同步。将 LFP 与尖峰结合使用的现有研究利用前者的声谱图，该声谱图既不能高精度地检测 LFP 特征的瞬态特征（此处为特定频带中的神经调制），也不能以足够的时间分辨率将其与相关神经元活动相关联。我们提出了一种特征提取和验证框架，该框架使用六只 Sprague Dawley 大鼠在按压杠杆任务期间的初级运动皮层记录，直接提取与同步尖峰活动相关的 LFP 神经调制。我们首先选择与行为相关的重要 LFP 频带，然后实现标记点过程 (MPP) 方法来提取瞬态 LFP 神经调制。我们通过检查与根据其对行为解码的贡献而选择的重要神经元的成对同步发射概率的相关性来验证 LFP 特征提取。通过 LFP 神经调制识别出高度相关的同步发射被送入解码器，以检查它们是否可以作为运动解码的可靠神经数据源。我们发现，30-80 Hz 的 LFP 神经调制与同步发射具有显著相关性。与传统基于声谱图的方法相比，具有更高时间分辨率的 MPP 方法以更少的误报捕获同步发射模式，并且与单个神经元尖峰相比具有显著更高的相关性。使用 LFP 神经调制识别的同步神经元发射进行解码的性能可以达到 90%，而不是使用完整记录的神经元集合。我们提出的框架成功提取了稀疏的 LFP 神经调制，这些调制可以以高相关性识别时间同步的神经元尖峰。所识别的神经元尖峰模式表现出高解码性能，这表明 LFP 可以用作长期 BMI 解码的有效模态。
