青少年肌阵挛癫痫发作间期EEG-fMRI的研究

采用同步脑电与功能磁共振(Simultaneous electroencephalography-correlated functional magnetic resonance imaging,EEG-fMRI)技术,研究青少年肌阵挛癫痫患者发作间期痫样放电时脑部血氧水平依赖(Blood oxygen level-dependent,BOLD)信号变化。结果发现:双侧大脑半球的激活及失活信号变化普遍对称且各自独立存在,信号由枕顶至额区逐渐减少。阳性激活区有:楔叶、岛叶、额中部内侧、小脑中线两侧及丘脑。阴性激活区有:双侧额前部、顶部及扣带后回。由此推断:以棘慢复合波为表现形式的同步的神经元活动可能反映了丘脑皮层BOLD信号的激活,而失活区域反映了异常放电时的脑功能的静息状态;这类激活在神经元的活动(EEG)与fMRI结果之间有很好的对应关系;EEG-fMRI是研究脑功能状态有效的方式。     

正常中老年人词语联想任务功能磁共振成像研究

目的 采用功能磁共振成像(fMRI)技术对正常中老年人进行研究,检测与词语联想功能相关脑区的激活特点.方法 采用GE 1.5 T磁共振扫描仪对23例(男12例,女11例)正常中老年人行组块设计的词语联想任务fMRI研究.采用SPM 2软件进行数据处理和统计分析,通过组分析获得平均脑激活图,观察脑激活区的部位和激活强度.结果 12例符合入组条件,激活脑区为运动前区(PMC)、双侧额下回后部(Broca区及Broca镜像区)、双侧辅助运动区(SMA)、左侧顶后皮层、双侧岛叶、双侧扣带回前部、双侧基底节、左侧丘脑以及右侧小脑半球.全脑以左侧PMC激活强度最大.激活强度左侧大于右侧的脑区为额下回后部、背侧PMC及SMA;右侧大于左侧的脑区为腹侧PMC、岛叶、扣带回前部、基底节.结论 中老年人参与运动性语言表达的脑区,由包括Broca区在内的多个脑区组成复杂的神经网络,且相关脑区存在偏侧化现象,以左侧大脑半球和右侧小脑半球激活为主.     

基于fMRI的原发全面性癫痫脑活动的局域一致性研究

癫痫是一种以神经活动同步性异常增高为特征的中枢神经系统疾病.我们利用基于局域一致性(regional homogeneity,ReHo)分析方法的功能磁共振成像(functional Magnetic Resonance Imaging, fMRI)技术对原发全面性癫痫(idiopathic generalized epilepsy, IGE)进行研究.观察IGE患者相比正常对照组局域一致性的改变情况.结果表明:在静息状态下,患者大脑的局域一致性在某些脑区较正常人低,主要集中在楔前叶、内侧前额叶、双侧颞叶、双侧额中回;另外,在某些脑区较正常人高,主要集中在底丘脑等中缝核团.提示该方法可检出癫痫活动造成的局部脑组织血氧水平依赖(blood oxygen level dependent, BOLD)信号同步性的改变,从而有助于原发全面性癫痫的病理生理机制的探索.     

基于Granger因果性的功能磁共振成像对内侧颞叶癫痫活动的研究

在内侧颞叶癫痫(mTLE)中,内侧颞叶与皮层及皮层下结构参与了癫痫活动的起源与传播.基于Granger因果性(GC)检验方法,对脑电联合功能磁共振(EEG-fMRI)数据进行分析,研究内侧颞叶在mTLE中的作用.以内侧颞叶激活区域为参考区域,计算参考区域与大脑其余每个体素点之间的Granger因果关系,并映射到全脑,形成Granger因果图(GCM).结果表明,内侧颞叶将癫痫活动传播到外侧颞叶、额叶、顶叶及丘脑等区域,同时受到脑岛、壳核以及丘脑等区域脑活动的影响,提示内侧颞叶在mTLE痫样发放的传播环路中具有关键作用.     

基于fMRI的原发全面性癫痫脑活动的局域一致性研究

癫痫是一种以神经活动同步性异常增高为特征的中枢神经系统疾病。我们利用基于局域一致性（regional homogeneity,ReHo）分析方法的功能磁共振成像（functional Magnetic Resonance Imaging,fMRI）技术对原发全面性癫痫（idiopathic generalized epilepsy,IGE）进行研究。观察IGE患者相比正常对照组局域一致性的改变情况。结果表明：在静息状态下,患者大脑的局域一致性在某些脑区较正常人低,主要集中在楔前叶、内侧前额叶、双侧颞叶、双侧额中回;另外,在某些脑区较正常人高,主要集中在底丘脑等中缝核团。提示该方法可检出癫痫活动造成的局部脑组织血氧水平依赖（blood oxygen level dependent,BOLD）信号同步性的改变,从而有助于原发全面性癫痫的病理生理机制的探索。     

一种基于静息态功能磁共振成像的快速丘脑分割算法

目的 探讨基于静息态功能磁共振成像(fMRI)的快速有效的丘脑分割方法.方法 静息态fMRI技术是通过测量血氧水平依赖(BOLD)信号的变化间接反映神经元的活动情况.利用丘脑内部的BOLD信号相关并结合聚类分析算法将丘脑进行功能性分割.结果 丘脑被划分为7个区域,同一区域内信号相似度高.此分割结果与利用丘脑-大脑皮层的功能连接强度所得的分割结果相似.结论 静息态fMRI不仅可以分析丘脑-大脑皮层之间的功能连接,还可分析丘脑内部的功能特征.仅利用丘脑内部信息分割丘脑具有运算量小、计算速度快的优点.     

功能磁共振在大脑皮质发育障碍患者的初步应用研究

目的：应用功能磁共振（fMRI）探讨6名正常受试者及2例皮质发育障碍（DCDs）患者在进行汉语词汇加工过程中脑激活模式，探讨DCDs患者与正常受试者之间脑激活区域的差异与临床意义。方法：本实验采用听觉刺激，以汉语单词理解为语言作业任务。结果：①正常受试者均出现显著的脑区激活，传统语言脑区Wemicke和Broca区激活在双侧大脑半球基本是对称的。除传统语言脑区激活外，还出现其它脑区激活现象。②两例DCDs患者表现为基本语言功能区的激活，诸如Wemicke区和Broca区激活。但脑激活表现为显著的不对称性，甚至完全偏侧性。结论：fMRI能够对大脑DCDs患者功能重塑皮质进行定位，为DCDs伴难治性癫痫患者手术治疗，避免损伤重要功能区提供了有益的保障。     

健康成人吞咽活动脑功能区的功能磁共振成像研究

目的 利用脑功能磁共振成像(fMRI)技术研究健康成人不同吞咽任务时激活脑区的特点.方法 8名健康志愿者参与实验性自主和反射性吞咽任务的fMRI研究.采用SPM2软件对功能成像数据进行处理及图像显示.配对t检验用于比较2种吞咽任务激活脑区的容积及信号值大小.结果 自主吞咽任务激活脑区有双侧初级感觉运动皮质、运动前区、前扣带回等.反射性吞咽任务相关脑区有双侧初级感觉皮层、双侧额盖、双侧顶后区.自主性吞咽激活双侧半球容积(体素)分别为1213±110(左)、1969±133(右).反射吞咽激活双侧半球容积(体素)分别为488±45(左)、398±35(右).自主性咽水双侧感觉运动区信号增加值分别为4.4±0.4(左)、4.1±0.2(右);双侧岛叶容积为1.2±0.5(左)、1.5±0.6(右).反射性咽水双侧感觉运动区信号增加值分别为2.6±0.3(左)、2.5±1.2(右);双侧岛叶信号增加值为0.6±0.4(左)、0.2±0.1(右).自主吞咽激活双侧半球容积、感觉运动区及岛叶信号值均大于反射性吞咽(均为P<0.05).两种吞咽任务的偏侧性指数(LI)值分别为(-16±9)%和(11±5)%.结论 自主吞咽任务激活脑区数量及容积大于反射性吞咽任务.两种吞咽任务激活脑区的不同与自主吞咽过程中计划、发动、情感等多种高级脑活动有关.     

散发型克雅病的首发症状、脑电图及影像学分析

目的:探讨散发型克雅病(Creutzfeldt-Jakob diseaes,CJD)的首发症状、影像学及脑电图特点。方法:回顾性分析8例疑似CJD患者的临床资料。结果:首发症状有认知功能下降、精神行为异常,吐字不清、行走困难、单侧肢体无力、头晕。8例患者脑电图均出现典型的周期性尖慢波或三相波,其中一例在晚期记录到爆发-抑制样波形。8例患者在大脑皮层或/及基底节、岛叶、尾状核出现异常高信号。结论:CJD首发症状特异性不强,早期鉴别很重要。脑电图、磁共振检查阳性率高,对于疑似CJD患者应多次行脑电图及磁共振检查。     

健康人大脑和小脑空间记忆认知功能的fMRI研究

本研究应用功能磁共振成像(functional magnetic resonance imaging,fMRI)技术,检测了健康人大脑和小脑参与空间记忆的认知过程。通过对10名右利手健康志愿者进行一项短时空间记忆任务作业的同时进行脑功能磁共振扫描,实验采用组块设计,任务与对照任务交替进行,数据采用SPM99软件进行数据分析和脑功能区定位。结果显示:当统计阈值设定为P<0.0001时,大脑皮层和右侧小脑一起被显著激活;大脑皮层所激活的脑区有双侧顶叶的楔前叶、顶上小叶、缘上回(BA7/40,BA:Brodma-nn Area),双侧前额上、中、下回(BA6/9/47),双侧枕叶和枕颞交界处(BA18/19/37),右侧海马回;左侧中脑黑质及被盖部也被激活。上述结果提示:小脑和大脑皮层一起参与了空间记忆的认知过程。     

Generalized Spike and Waves: Effect of Discharge Duration on Brain Networks as Revealed by BOLD fMRI

In the past decade, the possibility of combining recordings of EEG and functional MRI (EEG–fMRI), has brought a new insight into the brain network underlying generalized spike wave discharges (GSWD). Nevertheless, how GSWD duration influences this network is not fully understood. In this study we aim to investigate whether GSWD duration had a threshold (non-linear) and/or a linear effect on the amplitude of the associated BOLD changes in any brain regions. This could help in elucidating if there is an hemodynamic background supporting the differentiation between interictal and ictal events. We studied a population of 42 patients with idiopathic generalized epilepsies (IGE) who underwent resting-state EEG–fMRI recordings in three centres (London, UK; Modena, Italy; Rome, Italy), applying a parametric analysis of the GSWD duration. Patients were classified as having Childhood Absence epilepsy, Juvenile Absence Epilepsy, or Juvenile Myoclonic Epilepsy. At the population level linear GSWD duration-related BOLD signal changes were found in a network of brain regions: mainly BOLD increase in thalami and cerebral ventricles, and BOLD decrease in posterior cingulate, precuneus and bilateral parietal regions. No region of significant BOLD change was found in the group analysis for the non-linear effect of GSWD duration. To explore the possible effect of both the different IGE sub-syndromes and the different protocols and scanning equipment used in the study, a full-factorial ANOVA design was performed revealing no significant differences. These findings support the idea that the amplitude of the BOLD changes is linearly related to the GSWD duration with no universal threshold effect of spike and wave duration on the brain network supporting this activity.     

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.     

Linking visual gamma to task‐related brain networks—a simultaneous EEG‐fMRI study

There is a growing interest in human gamma‐band oscillatory activity due to its direct link to neuronal populations, its associations with many cognitive processes, and its positive relationship with fMRI BOLD signal. Visual gamma has been successfully detected using concurrent EEG‐fMRI recordings and linked to activity in the visual cortex using voxel‐wise regression analysis. As gamma‐band oscillations reflect predominantly feedforward projections between brain regions, its inclusion in functional connectivity analysis is highly recommended; however, very few studies have investigated this line of research. In the current study, we aimed to explore this gap by asking which fMRI brain network is related to gamma activity induced by the color discrimination task. Advanced denoising strategies and multitaper spectral decomposition were applied to EEG data to detect gamma oscillations, and group independent component analysis was performed on fMRI data to identify task‐related neural networks. Despite using only trials without motor response (50% of the trials), the two neural measures were successfully coupled. One of the six task‐related networks, the occipito‐parietal network, exhibited significant trial‐by‐trial covariations with gamma oscillations. In addition to the expected extrastriate visual cortex, the network encompasses extensive brain activations in the precuneus, bilateral intraparietal, and anterior insular cortices. We argue that the visual cortex is the source of gamma, whereas the remaining brain regions exhibit feedforward and feedback connections related to this oscillatory activity. Our findings provide evidence for the electrophysiological basis of the connectivity revealed by BOLD signal and impart novel insights into the neural mechanism of color discrimination.     

The Change of Functional Connectivity Specificity in Rats Under Various Anesthesia Levels and its Neural Origin

Spatiotemporal correlations of spontaneous blood oxygenation level dependent (BOLD) signals measured in the resting brain have been found to imply many resting-state coherent networks under both awake/conscious and anesthetized/unconscious conditions. To understand the resting-state brain networks in the unconscious state, spontaneous BOLD signals from the rat sensorimotor cortex were studied across a wide range of anesthesia levels induced by isoflurane. Distinct resting-state networks covering functionally specific sub-regions of the sensorimotor system were observed under light anesthesia with 1.0 % isoflurane; however, they gradually merged into a highly synchronized and spatially less-specific network under deep anesthesia with 1.8 % isoflurane. The EEG power correlations recorded using three electrodes from a separate group of rats showed similar dependency on anesthesia depth, suggesting the neural origin of the change in functional connectivity specificity. The specific-to-less-specific transition of resting-state networks may reflect a functional reorganization of the brain at different anesthesia levels or brain states.     

Dynamical intrinsic functional architecture of the brain during absence seizures

Epilepsy is characterized by recurrent and temporary brain dysfunction due to discharges of interconnected groups of neurons. The brain of epilepsy patients has a dynamic bifurcation that switches between epileptic and normal states. The dysfunctional state involves large-scale brain networks. It is very important to understand the network mechanisms of seizure initiation, maintenance, and termination in epilepsy. Absence epilepsy provides a unique model for neuroimaging investigation on dynamic evolutions of brain networks over seizure repertoire. By using a dynamic functional connectivity and graph theoretical analyses to study absence seizures (AS), we aimed to obtain transition of network properties that account for seizure onset and offset. We measured resting-state functional magnetic resonance imaging and simultaneous electroencephalography (EEG) from children with AS. We used simultaneous EEG to define the preictal, ictal and postictal intervals of seizures. We measured dynamic connectivity maps of the thalamus network and the default mode network (DMN), as well as functional connectome topologies, during the three different seizure intervals. The analysis of dynamic changes of anti-correlation between the thalamus and the DMN is consistent with an inhibitory effect of seizures on the default mode of brain function, which gradually fades out after seizure onset. Also, we observed complex transitions of functional network topology, implicating adaptive reconfiguration of functional brain networks. In conclusion, our work revealed novel insights into modifications in large-scale functional connectome during AS, which may contribute to a better understanding the network mechanisms of state bifurcations in epileptogenesis.     

Interhemispheric integration at different spatial scales: the evidence from EEG coherence and FMRI

The early visual system processes different spatial frequencies (SFs) separately. To examine where in the brain the scale-specific information is integrated, we mapped the neural assemblies engaged in interhemispheric coupling with electroencephalographic (EEG) coherence and blood-oxygen-level dependent (BOLD) signal. During similar EEG and functional magnetic resonance imaging (fMRI) experiments, our subjects viewed centrally presented bilateral gratings of different SF (0.25-8.0 cpd), which either obeyed Gestalt grouping rules (iso-oriented, IG) or violated them (orthogonally oriented, OG). The IG stimuli (0.5-4.0 cpd) synchronized EEG at discrete beta frequencies (beta1, beta2) and increased BOLD (0.5 and 2.0 cpd tested) in ventral (around collateral sulcus) and dorsal (parieto-occipital fissure) regions compared with OG. At both SF, the beta1 coherence correlated with the ventral activations, whereas the beta2 coherence correlated with the dorsal ones. Thus distributed neural substrates mediated interhemispheric integration at single SF. The relative impact of the ventral versus dorsal networks was modulated by the SF of the stimulus.     

Connecting Mean Field Models of Neural Activity to EEG and fMRI Data

Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.     

Correspondence of Visual Evoked Potentials with FMRI Signals in Human Visual Cortex

Functional magnetic resonance imaging (FMRI) and event related potentials (ERPs) are tools that can be used to image brain activity with relatively good spatial and temporal resolution, respectively. Utilizing both of these methods is therefore desirable in neuroimaging studies to explore the spatio-temporal characteristics of brain function. While several studies have investigated the relationship between EEG and positive (+) BOLD (activation), little is known about the relationship between EEG signals and negative (−) BOLD (deactivation) responses. In this study, we used a visual stimuli designed to shift cortical activity from anterior to posterior regions of the visual cortex. Using EEG and FMRI, we investigated how shifts in +BOLD and −BOLD location were correlated to shifts in the N75 and P100 visual evoked potential (VEP) dipolar sources. The results show that the N75 dipole along with +BOLD, were indeed shifted from posterior to anterior regions of the visual cortex. The P100 VEP component, along with the −BOLD were not shifted to the same extent, indicating that N75 is better correlated to +BOLD than to −BOLD. These findings indicate how different components of the EEG signal are related to the positive and negative BOLD responses, which may aid in interpreting the relationship between visually evoked EEG and FMRI signals. An erratum to this article can be found at     

Brain areas activated in fMRI during self-regulation of slow cortical potentials (SCPs)

In humans, surface-negative slow cortical potentials (SCPs) originating in the apical dendritic layers of the neocortex reflect synchronized depolarization of large groups of neuronal assemblies. They are recorded during states of behavioural or cognitive preparation and during motivational states of apprehension and fear. Surface positive SCPs are thought to indicate reduction of cortical excitation of the underlying neural networks and appear during behavioural inhibition and motivational inertia (e.g. satiety). SCPs at the cortical surface constitute summated population activity of local field potentials (LFPs). SCPs and LFPs may share identical neural substrates. In this study the relationship between negative and positive SCPs and changes in the BOLD signal of the fMRI were examined in ten subjects who were trained to successfully self-regulate their SCPs. FMRI revealed that the generation of negativity (increased cortical excitation) was accompanied by widespread activation in central, pre-frontal, and parietal brain regions as well as the basal ganglia. Positivity (decreased cortical excitation) was associated with widespread deactivations in several cortical sites as well as some activation, primarily in frontal and parietal structures as well as insula and putamen. Regression analyses revealed that cortical positivity was predicted with high accuracy by pallidum and putamen activation and supplementary motor area (SMA) and motor cortex deactivation, while differentiation between cortical negativity and positivity was revealed primarily in parahippocampal regions. These data suggest that negative and positive electrocortical potential shifts in the EEG are related to distinct differences in cerebral activation detected by fMRI and support animal studies showing parallel activations in fMRI and neuroelectric recordings.Supported by the Deutsche Forschungsgemeinschaft (DFG)