Identification of Focal Epileptogenic Networks in Generalized Epilepsy Using Brain Functional Connectivity Analysis of Bilateral Intracranial EEG Signals

Simultaneous bilateral onset and bi-synchrony epileptiform discharges in electroencephalogram (EEG) remain hallmarks for generalized seizures. However, the possibility of an epileptogenic focus triggering rapidly generalized epileptiform discharges has been documented in several studies. Previously, a new multi-stage surgical procedure using bilateral intracranial EEG (iEEG) prior to and post complete corpus callosotomy (CC) was developed to uncover seizure focus in non-lateralizing focal epilepsy. Five patients with drug-resistant generalized epilepsy who underwent this procedure were included in the study. Their bilateral iEEG findings prior to complete CC showed generalized epileptiform discharges with no clear lateralization. Nonetheless, the bilateral ictal iEEG findings post complete CC indicated lateralized or localized seizure onset. This study hypothesized that brain functional connectivity analysis, applied to the pre CC bilateral iEEG recordings, could help identify focal epileptogenic networks in generalized epilepsy. The results indicated that despite diffuse epileptiform discharges, focal features can still be observed in apparent generalized seizures through brain connectivity analysis. The seizure onset localization/lateralization from connectivity analysis demonstrated a good agreement with the bilateral iEEG findings post complete CC and final surgical outcomes. Our study supports the role of focal epileptic networks in generalized seizures.     

全面性棘慢复合波相关神经网络的EEG-fMRI研究

全面性癫痫的脑电图(EEG)呈全面性棘慢复合波放电(GSWDs),近来研究表明GSWDs并非"全面地"累及整个大脑,而是与特异的神经网络相关。同步脑电图联合功能磁共振(EEG-fMRI)技术可无创、高时空分辨率地了解痫性电活动时各脑区的代谢功能变化。EEG-fMRI研究已发现丘脑-基底节-皮质网络在GSWDs的产生与维持中起重要作用。与GSWDs相关的功能磁共振(fMRI)信号变化规律:前额叶、额顶叶、后扣带回、楔前叶出现相似的负激活(deactivation)信号,丘脑出现较为一致的激活(activation)信号,基底节出现负激活信号。进一步研究发现,fMRI信号随GSWDs出现的时程动态变化,皮质神经元活动可能先于丘脑出现,但不同EEG-fMRI研究的皮质激活部位存在个体化表现,散在地分布于额叶、顶叶等皮质区域。进一步发展更加复杂的分析方法揭示fMRI信号的时程变化规律,研究GSWDs产生的起始、传导和维持涉及的神经网络,揭示全面性癫痫的行为表现、药物治疗反应与神经网络的关系是未来EEG-fMRI研究的方向。     

Identifying and Characterizing Resting State Networks in Temporally Dynamic Functional Connectomes

An important application of resting state fMRI data has been to identify resting state networks (RSN). The conventional RSN studies attempted to discover consistent networks through functional connectivity analysis over the whole scan time, which implicitly assumes that RSNs are static. However, the brain undergoes dynamic functional state changes and the functional connectome patterns vary along with time, even in resting state. Hence, this study aims to characterize temporal brain dynamics in resting state. It utilizes the temporally dynamic functional connectome patterns to extract a set of resting state clusters and their corresponding RSNs based on the large-scale consistent, reproducible and predictable whole-brain reference system of dense individualized and common connectivity-based cortical landmarks (DICCCOL). Especially, an effective multi-view spectral clustering method was performed by treating each dynamic functional connectome pattern as one view, and this procedure was also applied on static multi-subject functional connectomes to obtain the static clusters for comparison. It turns out that some dynamic clusters exhibit high similarity with static clusters, suggesting the stability of those RSNs including the visual network and the default mode network. Moreover, two motor-related dynamic clusters show correspondence with one static cluster, which implies substantially more temporal variability of the motor resting network. Particularly, four dynamic clusters exhibited large differences in comparison with their corresponding static networks. Thus it is suggested that these four networks might play critically important roles in functional brain dynamics and interactions during resting state, offering novel insights into the brain function and its dynamics.     

Resting state fMRI in Alzheimer's disease: beyond the default mode network

Using resting state (RS) functional magnetic resonance imaging (fMRI), the connectivity patterns of the default mode (DMN), frontoparietal, executive, and salience networks were explored in 13 Alzheimer's disease (AD) patients, 12 amnestic mild cognitive impairment (aMCI) patients, and 13 healthy controls. Compared with controls and aMCI, AD was associated with opposing connectivity effects in the DMN (decreased) and frontal networks (enhanced). The only RS abnormality found in aMCI patients compared with controls was a precuneus connectivity reduction in the DMN. RS fMRI group differences were only partly related to gray matter atrophy. In AD patients, the mean executive network connectivity was positively associated with frontal-executive and language neuropsychological scores. These results suggest that AD is associated with an alteration of large-scale functional brain networks, which extends well beyond the DMN. In AD, the limited resources of the DMN may be paralleled, in an attempt to maintain cognitive efficiency, by an increased prefrontal connectivity. A medial parietal RS fMRI signal change seems to be present since the early phase of AD.     

动态因果模型在脑网络研究中的应用进展

大脑各功能区之间的有效连接是脑科学研究领域的一个重要内容.研究在不同情形下相关脑区之间有效连接所构成的大脑网络,对于全面理解大脑的功能机制,治疗各种与大脑相关疾病,开发脑功能具有重要意义.动态因果模型是一种分析大脑有效连接的优势方法.结合功能性磁共振成像、脑电、近红外脑功能成像等3种检测技术,综述动态因果模型的相关研究...     

Seizure Onset Zone Localization from Ictal High-Density EEG in Refractory Focal Epilepsy

Epilepsy surgery is the most efficient treatment option for patients with refractory epilepsy. Before surgery, it is of utmost importance to accurately delineate the seizure onset zone (SOZ). Non-invasive EEG is the most used neuroimaging technique to diagnose epilepsy, but it is hard to localize the SOZ from EEG due to its low spatial resolution and because epilepsy is a network disease, with several brain regions becoming active during a seizure. In this work, we propose and validate an approach based on EEG source imaging (ESI) combined with functional connectivity analysis to overcome these problems. We considered both simulations and real data of patients. Ictal epochs of 204-channel EEG and subsets down to 32 channels were analyzed. ESI was done using realistic head models and LORETA was used as inverse technique. The connectivity pattern between the reconstructed sources was calculated, and the source with the highest number of outgoing connections was selected as SOZ. We compared this algorithm with a more straightforward approach, i.e. selecting the source with the highest power after ESI as the SOZ. We found that functional connectivity analysis estimated the SOZ consistently closer to the simulated EZ/RZ than localization based on maximal power. Performance, however, decreased when 128 electrodes or less were used, especially in the realistic data. The results show the added value of functional connectivity analysis for SOZ localization, when the EEG is obtained with a high-density setup. Next to this, the method can potentially be used as objective tool in clinical settings.     

大尺度脑网络交互支持内外部指向认知的研究进展

大量神经成像研究表明,人脑的高级认知功能不是由单个脑区负责的,而是通过多个与认知活动相关的脑区构成的特异性脑网络的协同活动实现。其中,额顶控制网络动态调控默认网络和背侧注意网络之间的信息交互,在高级认知加工中发挥重要作用。从几个方面综述这一领域的研究进展,包括默认网络、背侧注意网络和额顶控制网络的神经解剖,以及各个脑网络和网络间交互在内外部注意指向任务中的功能角色。未来研究需要对3个脑网络进行更精确的功能定义,进一步探索网络内部各个亚网络的功能角色,同时借助效应连接的手段,考察网络内部和网络间信息传递的方向性和动态性,从而更深入理解默认网络、背侧注意网络和额顶控制网络在内外部注意指向的认知活动中信息交互的神经机制。     

Association Between the Basal Ganglia and Large-Scale Brain Networks in Epilepsy

Epilepsy may affect connectivity between the putamen and cortex even during the resting state. Putamen is part of the basal ganglia resting state network (BG-RSN) which is anti-correlated with the default mode network (DMN) in healthy subjects. Therefore, we aimed at studying the functional brain connectivity (FC) of the putamen with the cortical areas engaged in the DMN as well as with the primary somatomotor cortex which is a cortical region engaged in the BG-RSN. We compared the data obtained in patients with epilepsy with that in healthy controls (HC). Functional magnetic resonance imaging (fMRI) was performed in 10 HC and 24 patients with epilepsy: 14 patients with extratemporal epilepsy (PE) and 10 patients with temporal epilepsy (PT). Resting state fMRI data was obtained using the 1.5 T Siemens Symphony scanner. The Group ICA of fMRI Toolbox (GIFT) program was used for independent component analysis. The component representing the DMN was chosen according to a spatial correlation with a mask typical for DMN. The FC between the putamen and the primary somatomotor cortex was studied to assess the connectivity of the putamen within the BG-RSN. A second-level analysis was calculated to evaluate differences among the groups using SPM software. In patients with epilepsy as compared to HC, the magnitude of anti-correlation between the putamen and brain regions engaged in the DMN was significantly lower. In fact, the correlation changed the connectivity direction from negative in HC to positive in PE and PT. The disturbed FC of the BG in patients with epilepsy as compared with HC was further illustrated by a significant decrease in connectivity between the left/right putamen and the left/right somatomotor cortex, i.e. between regions that are engaged in the BG-RSN. The FC between the putamen and the cortex is disturbed in patients with epilepsy. This may reflect an altered function of the BG in epilepsy.     

Resting state connectivity of the medial prefrontal cortex covaries with individual differences in high‐frequency heart rate variability

Resting high‐frequency heart rate variability (HF‐HRV) relates to cardiac vagal control and predicts individual differences in health and longevity, but its functional neural correlates are not well defined. The medial prefrontal cortex (mPFC) encompasses visceral control regions that are components of intrinsic networks of the brain, particularly the default mode network (DMN) and the salience network (SN). Might individual differences in resting HF‐HRV covary with resting state neural activity in the DMN and SN, particularly within the mPFC? This question was addressed using fMRI data from an eyes‐open, 5‐min rest period during which echoplanar brain imaging yielded BOLD time series. Independent component analysis yielded functional connectivity estimates defining the DMN and SN. HF‐HRV was measured in a rest period outside of the scanner. Midlife (52% female) adults were assessed in two studies (Study 1, N = 107; Study 2, N = 112). Neither overall DMN nor SN connectivity strength was related to HF‐HRV. However, HF‐HRV related to connectivity of one region within mPFC shared by the DMN and SN, namely, the perigenual anterior cingulate cortex, an area with connectivity to other regions involved in autonomic control. In sum, HF‐HRV does not seem directly related to global resting state activity of intrinsic brain networks, but rather to more localized connectivity. A mPFC region was of particular interest as connectivity related to HF‐HRV was shared by the DMN and SN. These findings may indicate a functional basis for the coordination of autonomic cardiac control with engagement and disengagement from the environment.     

Epileptic seizures as condensed sleep: an analysis of network dynamics from electroencephalogram signals

Both deepening sleep and evolving epileptic seizures are associated with increasing slow‐wave activity. Larger‐scale functional networks derived from electroencephalogram indicate that in both transitions dramatic changes of communication between brain areas occur. During seizures these changes seem to be ‘condensed’, because they evolve more rapidly than during deepening sleep. Here we set out to assess quantitatively functional network dynamics derived from electroencephalogram signals during seizures and normal sleep. Functional networks were derived from electroencephalogram signals from wakefulness, light and deep sleep of 12 volunteers, and from pre‐seizure, seizure and post‐seizure time periods of 10 patients suffering from focal onset pharmaco‐resistant epilepsy. Nodes of the functional network represented electrical signals recorded by single electrodes and were linked if there was non‐random cross‐correlation between the two corresponding electroencephalogram signals. Network dynamics were then characterized by the evolution of global efficiency, which measures ease of information transmission. Global efficiency was compared with relative delta power. Global efficiency significantly decreased both between light and deep sleep, and between pre‐seizure, seizure and post‐seizure time periods. The decrease of global efficiency was due to a loss of functional links. While global efficiency decreased significantly, relative delta power increased except between the time periods wakefulness and light sleep, and pre‐seizure and seizure. Our results demonstrate that both epileptic seizures and deepening sleep are characterized by dramatic fragmentation of larger‐scale functional networks, and further support the similarities between sleep and seizures.     

基于动态功能连接分析的青少年吸烟者大脑静息态默认模式网络

采集30名青少年吸烟者和相匹配的30名非吸烟者的静息态功能性磁共振成像数据。通过独立成分分析、滑动窗相关法和k-均值聚类分析探讨青少年吸烟者的默认模式网络的动态功能连接变化。同时,使用斯皮尔曼相关分析评估动态功能连接指标（时间分数、平均停留时间和转换次数）与吸烟统计学数据（烟龄、包年和FTND）的关系。结果显示,相比于非吸烟组,青少年吸烟者大脑的默认模式网络的功能连接性降低,进一步的相关性分析表明动态功能连接指标中的平均停留时间与烟龄呈显著性正相关。此项研究从动态功能网络连接的角度分析吸烟对青少年吸烟者默认模式网络的影响,可以为进一步探索青少年吸烟成瘾机制提供新的视角。     

Ventromedial prefrontal cortex connectivity during and after psychological stress in women

The ventromedial prefrontal cortex (vmPFC) integrates sensory, affective, memory‐related, and social information from diverse brain systems to coordinate behavioral and peripheral physiological responses according to contextual demands that are appraised as stressful. However, the functionality of the vmPFC during stressful experiences is not fully understood. Among 40 female participants, the present study evaluated (a) functional connectivity of the vmPFC during exposure to and recovery following an acute psychological stressor, (b) associations among vmPFC functional connectivity, heart rate, and subjective reports of stress across individuals, and (c) whether patterns of vmPFC functional connectivity were associated with distributed brain networks. Results showed that psychological stress increased vmPFC functional connectivity with individual brain areas implicated in stressor processing (e.g., insula, amygdala, anterior cingulate cortex) and decreased connectivity with the posterior cingulate cortex and thalamus. There were no statistical differences in vmPFC connectivity to individual brain areas during recovery, as compared with baseline. Spatial similarity analyses revealed stressor‐evoked increased connectivity of the vmPFC with the so‐called dorsal attention, ventral attention, and frontoparietal networks, as well as decreased connectivity with the default mode network. During recovery, vmPFC connectivity increased with the frontoparietal network. Finally, individual differences in heart rate and perceived stress were associated with vmPFC connectivity to the ventral attention, frontoparietal, and default mode networks. Psychological stress appears to alter network‐level functional connectivity of the vmPFC in a manner that further relates to individual differences in stressor‐evoked cardiovascular and affective reactivity.     

Synchrony in normal and focal epileptic brain: the seizure onset zone is functionally disconnected

Synchronization of local and distributed neuronal assemblies is thought to underlie fundamental brain processes such as perception, learning, and cognition. In neurological disease, neuronal synchrony can be altered and in epilepsy may play an important role in the generation of seizures. Linear cross-correlation and mean phase coherence of local field potentials (LFPs) are commonly used measures of neuronal synchrony and have been studied extensively in epileptic brain. Multiple studies have reported that epileptic brain is characterized by increased neuronal synchrony except possibly prior to seizure onset when synchrony may decrease. Previous studies using intracranial electroencephalography (EEG), however, have been limited to patients with epilepsy. Here we investigate neuronal synchrony in epileptic and control brain using intracranial EEG recordings from patients with medically resistant partial epilepsy and control subjects with intractable facial pain. For both epilepsy and control patients, average LFP synchrony decreases with increasing interelectrode distance. Results in epilepsy patients show lower LFP synchrony between seizure-generating brain and other brain regions. This relative isolation of seizure-generating brain underlies the paradoxical finding that control patients without epilepsy have greater average LFP synchrony than patients with epilepsy. In conclusion, we show that in patients with focal epilepsy, the region of epileptic brain generating seizures is functionally isolated from surrounding brain regions. We further speculate that this functional isolation may contribute to spontaneous seizure generation and may represent a clinically useful electrophysiological signature for mapping epileptic brain.     

Mutual information in natural position order of electroencephalogram is significantly increased at seizure onset

Epilepsy affects an estimated 60 million people worldwide. As many as 50% of people with epilepsy will continue to have seizures despite therapeutic dosages of appropriately selected antiepileptic drugs. Among proposed treatment modalities for persons with medication refractory epilepsy are implantable devices that rapidly detect and abort seizures. Computational resources in these devices are limited and much effort is directed to improving the efficiency of seizure detection. The goal of this study is to determine if electroencephalogram (EEG) may be reduced by the method of natural position order in a way that increases computation speed and reduces system memory requirements while preserving features relevant to detecting seizure onset. In this study we show increased mutual information (MI) at seizure onset in simultaneous channels of EEG reduced by natural position order with a 40-fold reduction in computation time and a fivefold reduction in system memory requirements. The trade-offs to EEG reduction by natural position order include decreased bandwidth and increased noise sensitivity.     

Space–time network connectivity and cortical activations preceding spike wave discharges in human absence epilepsy: a MEG study

To describe the spatial and temporal profiles of connectivity networks and sources preceding generalized spike-and-wave discharges (SWDs) in human absence epilepsy. Nonlinear associations of MEG signals and cluster indices obtained within the framework of graph theory were determined, while source localization in the frequency domain was performed in the low frequency bands with dynamic imaging of coherent sources. The results were projected on a three-dimensional surface rendering of the brain using a semi-realistic head model and MRI images obtained for each of the five patients studied. An increase in clustering and a decrease in path length preceding SWD onset and a rhythmic pattern of increasing and decreasing connectivity were seen during SWDs. Beamforming showed a consistent appearance of a low frequency frontal cortical source prior to the first generalized spikes. This source was preceded by a low frequency occipital source. The changes in the connectivity networks with the onset of SWDs suggest a pathologically predisposed state towards synchronous seizure networks with increasing connectivity from interictal to preictal and ictal state, while the occipital and frontal low frequency early preictal sources demonstrate that SWDs are not suddenly arising but gradually build up in a dynamic network.     

默认模式网络在精神疾病诊断中的研究进展

脑影像技术的发展为探究精神类疾病的发病成因及治疗方案提供了更多途径。默认模式网络（DMN）是大脑静息状态下的主要工作模式。本研究综述近期精神疾病的DMN研究进展,明确了DMN的结构,总结主流的磁共振数据预处理工具箱以及与DMN相关的功能连接计算方法,进而分析利用DMN功能连接研究各类精神疾病的结果。最后提出现有研究中的结果与不足,并展望未来可能的研究方向。     

基于自适应多尺度脑功能连接的局灶性癫痫发作检测方法研究

利用长时程脑电图检测癫痫发作是临床中较为广泛的应用,然而这项工作乏味、耗时,且很大程度上依赖于临床医生的自身经验和主观判断,准确性和可重复性也较低。针对长时程脑电图检测癫痫中存在的问题,提出一种基于自适应多尺度脑功能连接的癫痫发作检测方法(AMBFC),并选取10例局灶性癫痫患者的发作期和非发作期的样本作为研究对象。首先在一个滑动时间窗内,通过多元经验模态分解(MEMD)提取19通道脑电信号的7个本征模函数(IMF)分量及残差;然后建立多变量自回归(MVAR)模型,利用有向传递函数(DTF)提取流出信息强度,进行特征组合,并通过主成分分析(PCA)降维保留原始特征数目的85%;最后经代价敏感支持向量机(CSVM)分类区分发作期和非发作期脑电,并通过五重交叉验证进行癫痫发作检测算法的效果评价。结果表明,AMBFC算法检测脑电癫痫发作得到的平均准确率为98.6%,精确率为81.9%,召回率为81.4%,F2值为0.80。与各IMF分量、DTF-CSVM算法等检测结果相比,AMBFC算法更具有优越性。有望应用于长时程脑电的实时监测。     

Divergent brain network connectivity in amyotrophic lateral sclerosis

Using resting state (RS) functional magnetic resonance imaging and independent component analysis, the integrity of brain networks related to cognition and behavior was investigated in 20 nondemented patients with amyotrophic lateral sclerosis (ALS). The association between RS functional connectivity and executive functions was assessed in 16 patients with neuropsychological assessment. ALS patients compared with control subjects showed a decreased connectivity of the right orbitofrontal cortex, and an enhanced connectivity of the left precuneus in the default mode network; a decreased connectivity of the left inferior frontal cortex, and an increased connectivity of the right angular gyrus in the right frontoparietal network; and an increased connectivity of the parietal cortex in the left frontoparietal network. The enhanced parietal connectivity was associated with the clinical and cognitive deficits of the patients. In ALS, an alteration of large-scale functional brain networks associated with cognition does occur, even in the absence of overt dementia. The increased parietal connectivity may have a role in an attempt to maintain cognitive efficiency in the presence of structural frontotemporal injury.     

青少年肌阵挛癫痫患者的大尺度脑网络研究

采用静息态功能性磁共振成像(rs-fMRI)数据探究青少年肌阵挛癫痫(JME)患者大尺度脑网络的变化。采集17例JME患者和15名正常志愿者的脑部静息态功能磁共振成像数据,两组均使用偏相关系数构建静息态脑网络。分别计算JME患者组与正常对照组的阈值,构建二值化脑网络。计算两组被试各个脑区的介数值,采用双样本t检验对比两组脑网络介数值的差异(Bonferroni 校正,P<0.01),找出介数值发生显著变化脑区。结果表明,偏相关系数构造的脑网络具有小世界属性。JME患者组脑网络中脑区的介数值相比正常对照组有显著性差异。与正常对照组相比,JME患者组介数值显著降低的脑区有2个,介数值显著升高的脑区有17个。其中属于默认模式网络(DMN)的脑区有8个,属于突显网络(SN)的脑区有5个。JME患者组介数值显著降低的脑区有右侧中央旁小叶和右侧后扣带回,介数值显著增高脑区主要是右侧背外侧额上回、左侧枕中回、右侧楔前叶和右侧舌回等。JME患者介数值发生显著改变的脑区主要属于默认模式网络和突显网络。可以推断出默认模式网络和突显网络内部脑区间连接发生改变,信息传递产生变化。由此可能会导致JME患者大脑功能发生改变,造成患者的认知能力与执行能力等功能受损。     

Basal ganglia circuits changes in Parkinson's disease patients

Functional changes in basal ganglia circuitry are responsible for the major clinical features of Parkinson's disease (PD). Current models of basal ganglia circuitry can only partially explain the cardinal symptoms in PD. We used functional MRI to investigate the causal connectivity of basal ganglia networks from the substantia nigra pars compacta (SNc) in PD in the movement and resting state. In controls, SNc activity predicted increased activity in the supplementary motor area, the default mode network, and dorsolateral prefrontal cortex, but, in patients, activity predicted decreases in the same structures. The SNc had decreased connectivity with the striatum, globus pallidus, subthalamic nucleus, thalamus, supplementary motor area, dorsolateral prefrontal cortex, insula, default mode network, temporal lobe, cerebellum, and pons in patients compared to controls. Levodopa administration partially normalized the pattern of connectivity. Our findings show how the dopaminergic system exerts influences on widespread brain networks, including motor and cognitive networks. The pattern of basal ganglia network connectivity is abnormal in PD secondary to dopamine depletion, and is more deviant in more severe disease. Use of functional MRI with network analysis appears to be a useful method to demonstrate basal ganglia pathways in vivo in human subjects.