Nedic Sanja, Stufflebeam Steven M, Rondinoni Carlo, Velasco Tonicarlo R, dos Santos Antonio C, Leite Joao P, Gargaro Ana C, Mujica-Parodi Lilianne R, Ide Jaime S
Department of Biomedical Engineering, Stony Brook University School of Medicine, Stony Brook, NY, 11794, USA.
Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
BMC Neurol. 2015 Dec 21;15:262. doi: 10.1186/s12883-015-0514-y.
Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series.
In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic measure of regulation and feedback, and performed follow-up seed-to-voxel functional connectivity analysis. Disruptions in connectivity of the region exhibiting abnormal dynamics were examined in relation to duration of epilepsy and patients' cognitive performance using a delayed verbal memory recall task.
ACF analysis revealed constrained (less chaotic) functional dynamics in left temporal lobe epilepsy patients, primarily localized to ipsilateral temporal pole, proximal to presumed focal points. Autocorrelation decay rates differentiated, with 100 % accuracy, between patients and healthy controls on a subject-by-subject basis within a leave-one-subject out classification framework. Regions identified via ACF analysis formed a less efficient network in patients, as compared to controls. Constrained dynamics were linked with locally increased and long-range decreased connectivity that, in turn, correlated significantly with impaired memory (local left temporal connectivity) and epilepsy duration (left temporal - posterior cingulate cortex connectivity).
Our current results suggest that data driven functional MRI methods that target network dynamics hold promise in providing clinically valuable tools for identification of epileptic regions.
癫痫是最常见的神经系统疾病之一。约三分之一的局灶性癫痫患者药物治疗无效，对于这些患者，准确确定引发癫痫发作的致痫区对于手术成功可能至关重要。现有的功能磁共振成像（fMRI）文献指出功能连接存在广泛的网络紊乱。根据先前的头皮和颅内脑电图研究，并与发作间期放电期间过度的局部同步一致，我们假设，相对于健康对照的相同区域，致痫灶将表现出较少的混沌动力学，可通过静息态fMRI时间序列的熵分析来识别。
为了首先在一组已知真实情况的患者中验证这一假设，我们在此测试具有明确致痫灶（左侧内侧颞叶癫痫）的个体。我们使用自相关函数（ACF）（一种调节和反馈的熵度量）分析体素水平的静息态fMRI时间序列，并进行后续的种子点到体素的功能连接分析。使用延迟言语记忆回忆任务，研究表现出异常动力学的区域的连接中断与癫痫持续时间和患者认知表现的关系。
ACF分析显示左侧颞叶癫痫患者的功能动力学受限（混沌较少），主要定位于同侧颞极，靠近推测的焦点。在留一法分类框架内，自相关衰减率在逐个受试者的基础上以100%的准确率区分患者和健康对照。与对照组相比，通过ACF分析确定的区域在患者中形成了一个效率较低的网络。受限动力学与局部连接增加和远程连接减少有关，而这又与记忆受损（左侧颞叶局部连接）和癫痫持续时间（左侧颞叶 - 后扣带回皮质连接）显著相关。
我们目前的结果表明，针对网络动力学的数据驱动功能磁共振成像方法有望为识别癫痫区域提供具有临床价值的工具。
