Kohl Oliver, Gohil Chetan, Zokaei Nahid, Hu Michele T M, Nobre Anna C, Woolrich Mark, Quinn Andrew
Oxford Centre for Human Brain Activity (OHBA), Welcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford OX3 7JK, UK.
Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, UK.
Brain Commun. 2025 Jul 23;7(4):fcaf282. doi: 10.1093/braincomms/fcaf282. eCollection 2025.
Non-invasive recordings of magnetoencephalography have been used for developing biomarkers for neural changes associated with Parkinson's disease that can be measured across the entire course of the disease. These studies, however, have yielded inconsistent findings. Here, we investigated whether analysing motor cortical activity within the context of large-scale brain network activity provides a more sensitive marker of changes in Parkinson's disease using magnetoencephalography. We extracted motor cortical beta power and beta bursts from resting-state magnetoencephalography scans of patients with Parkinson's disease ( = 28) and well-matched healthy controls ( = 36). To situate beta bursts in their brain network contexts, we used a time-delay-embedded hidden Markov model to extract brain network activity and investigated co-occurrence patterns between brain networks and beta bursts. Parkinson's disease was associated with decreased beta power in motor cortical power spectra, but no significant differences in motor cortical beta-burst dynamics occurred when using a conventional beta-burst analysis. Dynamics of a large-scale sensorimotor network extracted with the time-delay-embedded hidden Markov model approach revealed significant decreases in the occurrence of this network with Parkinson's disease. By comparing conventional burst and time-delay-embedded hidden Markov model state occurrences, we observed that motor beta bursts occurred during both sensorimotor and non-sensorimotor network activations. When using the large-scale network information provided by the time-delay-embedded hidden Markov model to focus on bursts that were active during sensorimotor network activations, significant decreases in burst dynamics could be observed in patients with Parkinson's disease. In conclusion, our findings suggest that decreased motor cortical beta power in Parkinson's disease is prominently associated with changes in sensorimotor network dynamics using magnetoencephalography. Thus, investigating large-scale networks or considering the large-scale network context of motor cortical activations may be crucial for identifying alterations in the sensorimotor network that are prevalent in Parkinson's disease and might help resolve contradicting findings in the literature.
脑磁图的无创记录已被用于开发与帕金森病相关的神经变化生物标志物，这些变化可在疾病的整个过程中进行测量。然而，这些研究结果并不一致。在这里，我们研究了在大规模脑网络活动的背景下分析运动皮层活动是否能通过脑磁图提供更敏感的帕金森病变化标志物。我们从帕金森病患者（n = 28）和匹配良好的健康对照（n = 36）的静息态脑磁图扫描中提取运动皮层β功率和β爆发。为了将β爆发置于其脑网络背景中，我们使用时延嵌入隐马尔可夫模型提取脑网络活动，并研究脑网络与β爆发之间的共现模式。帕金森病与运动皮层功率谱中β功率降低有关，但使用传统的β爆发分析时，运动皮层β爆发动力学没有显著差异。用时延嵌入隐马尔可夫模型方法提取的大规模感觉运动网络动力学显示，帕金森病患者该网络的出现频率显著降低。通过比较传统爆发和时延嵌入隐马尔可夫模型状态的出现情况，我们观察到运动β爆发在感觉运动和非感觉运动网络激活期间均有发生。当使用时延嵌入隐马尔可夫模型提供的大规模网络信息来关注感觉运动网络激活期间活跃的爆发时，帕金森病患者的爆发动力学可观察到显著下降。总之，我们的研究结果表明，帕金森病中运动皮层β功率降低与使用脑磁图的感觉运动网络动力学变化密切相关。因此，研究大规模网络或考虑运动皮层激活的大规模网络背景对于识别帕金森病中普遍存在的感觉运动网络改变可能至关重要，并且可能有助于解决文献中的矛盾发现。
