利用皮层几何本征模态约束欠定神经系统中的源成像估计
Temporally constraining source imaging estimates in an underdetermined neural system with eigenmodes of cortical geometry
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中文总结 AI 辅助
该研究探究能否用神经场理论推导的传递函数为EEG源成像引入时间约束，发现纳入经验估计的跨本征模态耦合项可显著提升定位性能，推动了结合空间本征模态与跨模态动态的源成像方法发展。
中文摘要 AI 辅助
几何本征模态为大规模神经活动提供了紧凑且符合生物学基础的表征。先前研究表明，这些模态可缓解脑电图（EEG）和脑磁图（MEG）源定位的欠定性，该问题是从无创记录中重建神经活动的不适定逆问题。除空间结构外，神经场理论通过解析推导的传递函数预测本征模态的时间演化。受该框架启发，本研究探究这些传递函数是否可用于为EEG源成像引入时间约束。该方法通过耦合Epileptor神经质量模型生成的模拟癫痫发作动态进行评估。直接从神经场理论推导的传递函数作为源定位的时间约束总体无效，主要因为它们忽略了跨模态耦合。纳入经验估计的耦合项可显著提升定位性能，尤其在噪声环境中。尽管从实验数据估计这些本征模态耦合相互作用仍具挑战性，但研究结果推动了结合空间本征模态结构与经验驱动跨模态动态的动态源成像方法的发展。
英文摘要
Geometric eigenmodes provide a compact and biologically grounded representation of large-scale neural activity. Previous work demonstrated that they can mitigate the underdetermined nature of electroencephalographic (EEG) and magnetoencephalographic (MEG) source localisation, an ill-posed inverse problem in which neural activity is reconstructed from non-invasive recordings. Beyond their spatial structure, neural field theory predicts the temporal evolution of eigenmodes through analytically derived transfer functions. Motivated by this framework, the present work investigates whether these transfer functions can be used to introduce temporal constraints into EEG source imaging. The approach is evaluated using simulated seizure dynamics generated by coupled Epileptor neural mass models. Transfer functions derived directly from neural field theory were found to be generally ineffective as temporal constraints for source localisation, primarily because they neglect cross-eigenmode coupling. Incorporating empirically estimated coupling terms substantially improves localisation performance, particularly in noisy conditions. Although estimating these eigenmode coupling interactions from experimental data remains challenging, the findings motivate dynamical source imaging approaches that combine spatial eigenmode structure with empirically informed cross-modal dynamics.
发表机构
- The University of Melbourne（墨尔本大学）
- Graeme Clark Institute, The University of Melbourne（格雷姆·克拉克研究所，墨尔本大学）
- Grenoble Insitut des Neurosciences, INSERM（格勒诺布尔神经科学研究所，法国国家健康与医学研究院）
- St Vincent’s Hospital, Melbourne（圣文森特医院，墨尔本）
机构由 AI 辅助整理，请以论文原文为准。
