颞叶癫痫患者脑部静息态下多模态磁共振成像研究
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摘要
第一部分颞叶癫痫患者脑白质损伤DTI-TBSS与结构网络研究
目的
利用磁共振扩散张量成像技术(DTI)和基于纤维束体素的空间统计(TBSS)方法观测颞叶癫痫患者脑白质微结构改变情况,同时探讨DTI两个参数(FA、MD)值在评价颞叶癫痫患者脑白质微结构损伤中的应用价值,从而为颞叶癫痫患者的临床诊断、分析、疗效与预后评估提供客观的影像学依据。另外,基于DTI图像数据,构建大脑拓扑结构网络,探讨颞叶癫痫患者脑结构网络“小世界”属性的变化情况及其临床意义。
材料与方法
1.研究对象
研究被试包括50人,其中颞叶癫痫组26人,全部来自广东三九脑科医院住院病人,健康对照组24人。所有颞叶癫痫患者均为住院病人,均行常规MR检查,以及视频脑电检查,脑电图包括头皮电极或/与蝶骨电极。颞叶癫痫纳入标准：①癫痫发作类型与临床症状符合1981年以及1989年国际抗癫联盟的诊断标准与分类标准；②视频脑电图显示异常痫性放电(Interical epileptiformdischarges, IEDs)起源于颞部。MR检查显示单侧或双侧海马硬化、颞叶皮层局灶性发育不良。临床发作具有以下特征：内侧颞叶癫痫(mTLE)发作先兆包括胃气上升感、胸闷、心慌等。外侧颞叶癫痫(1TLE)发作先兆包括幻听、精神症状、躯体感觉异常等。综合临床表现、脑电图(Electrocephalographic, EEG)与MR结果最终诊断为TLE。排除标准,MRI检查示颅内占位性病变(如肿瘤、寄生虫、血管畸形等)、白质变性、软化灶等其它病变,EEG示可疑不正常,EEG定侧结果与临床表现不相符者。8例被试者从本研究中排除(包括6例颞叶癫痫患者与2例健康对照组受试者),剩余42例被纳入本次研究,即：20例颞叶癫痫患者(男性：16例,女性：4例,平均年龄：25.55±8.67岁),22例正常健康对照组(男性：13例,女性：9例,平均年龄：25.23±6.67岁)。
2.磁共振DTI数据采集
磁共振DTI数据采集使用飞利浦1.5T磁共振扫描仪(Philips Gyroscan Intera1.5T),并采用6通道相位陈列(神经血管线圈6,NV6)线圈接收核磁共振信号。DTI数据采集之前均进行全脑轴位T1WI、T2WI及FLAIR扫描,以排除脑部疾患。DTI数据采集采用单次激发自旋回波平面回波序列,平行于大脑前后联合得到全脑轴位弥散加成像。b=800s/mm2共32组图像(32个扩散梯度方向),和一组扩散敏感系数b=0的非扩散加权图像。磁共振扫描具体参数如下：重复时间(TR)=11000ms；回波时间(TE)=71.614ms；翻转角(flip angle)=90°;矩阵(matrix size)=144×144;视野(FOV)=230mm×230mm;激励次数(NEX)=1；层厚(thickness)=2.Omm;层数(slice)=67；层间距(slice gap)=0。
3.DTI数据预处理与TBSS分析
首先利用MRIcro(Chris Rorden, http://www.psychology.nottingham.c.uk/staff/crl/mricro.html)将每个被试者的1组Bo图像与相应的32组扩散权重二维图像DICOM数据转化为包含三维影像与代表不同扩散权重梯度场强方向的四维NIFTI格式数据。
DTI数据分析采用FSL (FMRIB Software Library, www.fmrib.ox.ac.uk/fsl, version4.19)软件。具体处理步骤包括：①首先使用FSL软件FDT套件中的eddy correct函式来矫正图像的失真。②利用FSL软件中BET套件以每个被试者的B0图像作为依据产生各自的脑mask。③将梯度磁场方向、磁场强度的数值以及四维NIFTI格式图像,连同各个被试的mask输入FDT套件中的dtifit函式中来进行扩散张量的计算。输出的结果将包含FA(fractional anisotropy)、MD(mean diffusivity)图像。以FA值、MD值进行基于纤维束体素的空间统计的群组分析。
4.大脑结构网络构建与可视化
应用基于Matlab R2009b分析软件平台的SPM5(http://www.fil.ion.ucl.ac. uk/spm)脑成像处理软件进行图像数据处理分析。结构网络流程：①ICOM数据格式转换为NIFTI格式；②刚性配准将T1像转换到DTI空间,将配准后的T1像通过非线性转换转至MNI空间；③用第二步转换得到的翻转矩阵将MNI空间中的AAL-90模板反向转换到DTI空间；④利用DTIstudio计算扩散张量矩阵并得到特征值和特征向量；⑤利用DTIstudio里的FACT算法重建全脑白质纤维束；⑥用90个脑区作为节点与纤维束作为边构建大脑结构网络。大脑结构网络图的可视化是基于Brainnet viewer Version1.1软件实现。
5.统计学分析
应用SPSS13.0软件采用两独立样本t检验的统计方法评价颞叶癫痫患者组与正常被试组之间年龄、受教育程度,采用卡方检验评价两组被试之间性别差异。全脑平均FA值、MD值进行组间分析,P<0.05认为有统计学意义。采用Pearson相关分析方法对颞叶癫痫组全脑平均FA值与癫痫患者病程作相关分析。
应用SPSS13.0软件采用两独立样本t检验的统计方法评价颞叶癫痫患者组大脑结构网络各“小世界”属性参数(λ、σ、Cp、Lp、Eglob、Eloc、Cost)与正常对照组之间的差异。采用Pearson相关分析方法对颚叶癫痫患者病程分别与大脑效率(Eglob)、代价(Cost)作相关性分析,P<0.05认为有统计学意义。
结果
1.TBSS结果：
颞叶癫痫患者多个脑区FA值显著降低：双侧颞叶、额叶、顶叶及部分枕叶,双侧半卵圆中心、双侧内囊前、后肢(P<0.05)。颞叶癫痫患者多个脑区MD值显著升高：左侧额叶、颞叶、顶叶、枕叶(P<0.05)。颞叶癫痫患者脑白质纤维骨架平均FA值与癫痫病程呈负相关(r=-0.5535,P=0.0114)。
2.结构小世界网络属性结果：
颞叶癫痫患者大脑全局效率显著降低,而且左侧额中回、双侧枕下回、左侧角回、左侧颞横回,右侧扣带回与扣带旁回前部的局部效率显著降低(P<0.05)；颞叶癫痫患者大脑最短路径(t＝-4.18,P=1.55e-04)与成本(t=-4.04,P=2.35e-04)显著增大。采用Pearson相关分析方法,结果显示左侧缘上回局部节点效率与病程呈显著负相关(r=-0.63,P=0.0053),左侧颞中回局部节点效率与病程呈显著负相关(r=-0.53,P=0.0233),颞叶癫痫患者大脑最短路径(Lp)、全局效率(Eglob)、局部效率(Eloc)、代价(Cost)均有显著性差异,而集群系数(Cp)两组间无显著性差异。
结论
颞叶癫痫患者脑白质微结构发生广泛损伤,且损伤程度随着病程的延长而加重,而且左侧大脑半球白质受损较右侧显著。通过基于弥散张量图像构建大脑结构网络显示,颞叶癫痫患者大脑结构网络仍然具有“小世界”属性,但是该网络拓扑结构发生改变,主要表现为全局效率与局部效率下降,而最短路径增加,代价增高；另外,大脑结构网络的核心节点分布亦发生了变化。以上结果表明癫痫电活动导致大脑白质广泛损伤,信息传递效率降低,这可能与癫痫患者情感低落、记忆力减退,认知与学习能力下降等临床症状有关。
第二部分基于BOLD-fMRI的颞叶癫痫患者脑功能网络研究
目的
采用基于血氧水平依赖成像的图像,通过功能连接技术与构建功能结构网络的方法,探讨颞叶癫痫患者静息态脑部脑区间功能连接变化情况,同时探讨脑功能网络“小世界”属性的变化情况,并评价这些指标变化的临床意义。
材料与方法
1.研究对象
研究被试包括63人,其中颞叶癫痫组35人,全部来自广东三九脑科医院住院病人,健康对照组28人,9例被试者从本研究中排除,剩余54例被纳入本次研究：25例正常健康对照组(男性：17例,女性：8例,年龄：24.24±5.31岁),16例左侧颞叶癫痫患者(男性：11例,女性：5例,年龄：23.13±7.14岁)13例右侧颞叶癫痫患者(男性：8例,女性：5例,年龄：26.31±10.10岁)。颞叶癫痫纳入标准：①癫痫发作类型与临床症状符合1981年以及1989年国际抗癫联盟的诊断标准与分类标准；②视频脑电图显示异常痫性放电(Interical epileptiform discharges, IEDs)起源于单侧颞部。排除标准：MRI检查示颅内占位性病变、白质变性、软化灶等其它病变,EEG示可疑不正常,或双侧IEDs(或无单侧定侧意义),EEG定侧结果与临床表现不相符者。正常对照组入选标准同第一部分。
2.数据采集
磁共振仪器设备同前；静息态BOLD-fMRI数据采集前,并告之被试者在进行MR检查时务必避免系统性思考问题,保持清醒状态、闭眼,不能睡觉。BOLD数据采集采用梯度回波平面回波序列(GRE-EPI),平行于大脑前、后联合得到全脑轴位成像,具体参数如下：TR=3000ms; TE=50ms;翻转角=900；究康复治疗Matrix=128×128; FOV=230mm×230mm;层厚=4.5mm；层间距=0；共160个动态。静息态扫描时间约7分钟。
3.图像数据处理分析
3.1数据预处理
对采集到的原始DICOM数据首先应用基于Matlab R2009b分析软件平台的SPM5(http://www.fil.ion.ucl.ac.uk/spm)软件进行图像数据处理。具体预处理步骤包括：①将原始图像的DICOM格式转化为用于数据分析的NIFTI格式；②手动去除前十个时间点的扫描图像,保持磁场稳定性；③头动校正(realignment),被试者头动范围超过±1mm或者±1。的数据从研究中剔除；④时间序列校正(slice timing)以扫描位置中心点为参考层,把被试者所有个体静息态BOLD-fMRI图像进行时间差异校正；⑤标准化(normalization)处理,将头动校正后的数据标准化到SPM5内MNI模板并转化到Talairach and Tournoux空间。⑥进行去线性飘移(linearly detrended)与低频滤过(1ow-frequency filtering)处理。预处理之后的时间序列信号做进一步的功能连接分析与图论分析。
3.2图像分割
应用AAL-90模板将图像分割为90个感兴趣区(ROIs),每一侧半球为45个ROIs。。再利用MarsBaR工具箱(http://marsbar.sourceforge.net)对每一个感兴趣区提取预处理之后的时间序列信号。
3.3计算相关矩阵
将90个脑区之间的静息态BOLD-fMRI时间序列信号进行相关计算,结果得到每一位受试者的90×90的相关矩阵图,由于相关矩阵的对称性,我们对4005(C920=90×89/2=4005)个相关系数进行下一步统计分析。
3.4大脑功能网络图的构建与可视化
基于上述构造的相关矩阵,我们以每个脑区为节点,脑区间的相关系数为边,来构建二值矩阵。最后我们基于二值矩阵来计算全局网络参数和节点参数。大脑功能连接网络图的可视化是基于Brainnet viewer Version11软件实现的。
4.统计学分析
4.1功能连接比较
基于matlab2009b,采用两独立样本的t检验的方法分别比较左侧颞叶癫痫病人和正常人之间有差异的功能连接,右侧颞叶癫痫病人和正常人之间有差异的功能连结。由于多重比较的问题,p<0.001认为有统计学意义。
4.2网络参数的比较
基于matlab2009b,针对每一个网络参数,采用两独立样本的t检验的方法分别检测左侧颞叶癫痫病人和正常人之间、右侧颞叶癫痫病人和正常人之间是否有差异。由于多重比较的问题,p<0.001认为有统计学意义。
结果
1.功能连接分析结果：
左侧TLE较正常对照组出现多个节点间功能连接增强,如右侧中央前回与右侧海马,右侧额下回眶部与右侧中央后回等；功能连接减弱的节点如左侧扣带回后部与左侧丘脑。右侧TLE较正常对照组出现多个脑区间功能连接增强,如左侧中央前回与左侧海马旁回,左侧额上回背外侧部与左侧嗅皮质等,功能连接减弱的脑区,包括右侧额上回内侧部与右侧颞中回颞极部,左侧扣带回后部与右侧颞中回颞极部,右侧海马与右侧尾状核。
2.功能网络分析结果：
2.1左侧颞叶癫痫的集群系数(CP)与局部效率(Eloc)增大,右侧颞叶癫痫患者大脑功能网络拓扑结构参数无显著差异性。
2.2左侧颞叶癫痫患者右侧颞中回与右侧海马旁回节点参数增加,而右侧丘脑与左侧额中回眶部节点参数下降,尤其以左侧额中回眶部下降显著(P=0.0057)；与正常对照组比较,右侧颞叶癫痫患者右侧顶下小叶、双侧角回、右侧中央后回节点节参数增加,其中以右侧顶下小叶的Nbc。增加显著(P=6.8817e-4),而左侧颞下回节点参数减少。
结论
左、右侧TLE多个脑区之间功能连接发生异常改变,通过构建脑功能网络方法,显示左侧TLE患者大脑集群系数与局部效率较正常人对照组增加,而右侧TLE患者与正常对照组比较无显著差异；此外,左侧TLE患者的右侧丘脑与左侧额叶眶部节点参数显著下降。以上结果表明左侧TLE对大脑的损害较右侧TLE显著,同时不同脑区间功能连接变化以及多个脑区节点参数变化与癫痫患者记忆受损、情感改变、认知能力下降等有关。
Part one:White matter microstructural damage in temporal lobe epilepsy individuals DTI-TBSS and structure network study
Objective
The purpose of present study is to explore white matter microstructure abnormalities of temporal lobe epilepsy (TLE) by using diffusion tensor imaging (DTI) and tract based spatial statistic (TBSS) methods, and the differences between two index of DTI (FA value and MD value) on evaluating white matter microstructure of TLE, in order to setup image basis for clinical diagnosis, analysis, assessment of treatment on TLE. In addition, we explored the change of "small world" properties and their clinical significance through the topology network of brain that created through DTI data.
Materials and Methods
1. Subjects
Fifty subjects have participated in present study, including26TLE patients and24normal adults. All patients were recruited from Guangdong999hospital, and underwent MR scan, Video-EEG monitoring, scalp EEG and/or sphenoidal electrodes. The inclusion criteria included the following:①seizure types and epileptic syndromes as diagnosed according to the classification of the International League Against Epilepsy (Anon.,1981,1989);②TLE diagnosis when continuous interictal-ictal scalp video electroencephalography showed interical epileptiform discharges (IEDs) of temporal origin. Unilateral or bilateral hippocampal sclerosis, focal cortical dysplasia (FCD) of temporal lobe were showed on MR imaging. Main clinical symptoms as follows:hoven, dyspnea and dither were indicators of a possible medial TLE, acousma, psychotic symptoms, somatosensory abnormality were indicators of a possible lateral TLE. The final diagnosis of TLE from patients'clinical performance and electro-cephalographic results. The exclusive criteria:with ace-occupying lesion (such as tumor, parasite and vascular malformation), white matter lesion, encephalomalacia on MRI, EEG showed suspicious abnormality, the results of EEG and clinical performance were not consistent. Eight subjects were excluded from this study (including6TLE patients and2health controls), remaining42subjects were included in this study, which included20TLE patients (mean age=25.55±8.67years,16males,4females),22health controls (mean age=25.23±6.67years,13males,9females).
2. DTI-MRI data acquisition
DTI-MRI data were collected using a1.5-Tesla scanner (Philips Gyroscan Intera) with a6channel neurovascular (NV) coil to receive the signal. Firstly, axial T1-weighted, T2-weighted images and FLAIR images were acquired for detecting incranial lesions. A Single-shot turbo spin echo sequence was used to obtain the DTI data, TR (repetition time)=11000ms; TE (echo time)=71.614ms; flip angle=90°; matrix size=144×144; FOV (field of view)=230X230; NEX=1; thickness=2mm; slice=67; slice gap=0. Thirty-two diffusion gradient directions with b-value=800s/mm2. In addition, images without diffusion weighting were acquired corresponding to b-value=0.
3. DTI data preprocessing and TBSS analysis
The DICOM data format of B0image and32diffusion weighted images of each subject were converted to NIFTI data format with four dimension
DTI data preprocessing was partly carried out using FSL (FMRIB Software Library, www.fmrib.ox.ac.uk/fsl, version4.19). The procedure of DTI data process included:①Diffusion tensor images were corrected for head movement by using FDT tool of the FSL software.②Mask for each brain was created by using each subject'Bo image with BET tool of the FSL software.③Fractional anisotropy (FA) and mean diffusivity (MD) were obtained with diffusion tensor calculation by using dtifit function, the FA and MD output images were used as input for tract based spatial statistic (TBSS).
4. Brain structure network graph visualization
DTI data preprocessing was partly carried out using SPM5software (http://www.fil. ion.ucl.ac.uk/spm) based on Matlab R2009b. The reprocessing as follows:①Dsta with DICOM format were converted to NIFTI format;②Each individual structural image (Ti-weighted image) was coregistered to the Bo image in the DTI space using a linear transformation, the transformed structural image was then mapped to the T1template in the Montreal Neurological Institute (MNI) space using a nonlinear transformation;③The resulting inverse transformation was then used to warp the AAL-90mask from the MNI space to the DTI native space;④The diffusion tensor matrix was then calculated voxel-by-voxel and diagonalization was performed to yield eigenvalues;⑤Fiber bundles of white matter were reconstructed by using FACT algorithm with DTIstudio software;⑥The topological properties of brain structural networks were defined on the basis of a90X90binary graph, which was consisted of nodes and edges, the edges between nodes could be constructed by applying a correlation matrice, the regional centroid of each node was positioned by using Brainnet viewer Version1.1software.
5. Statistic analysis
Two-sample t-test was performed with age, years of education between TLE patients and controls, chi-square test was performed with sex between TLE patients and controls. Two-sample t-test was performed with whole brain mean FA value and MD value between TLE patients and controls, we used a statistical significance level of P<0.05. Then, we used Pearson correlation analysis to investigate the underlying relationship between mean FA value of TLE patients and epilepsy duration.
Two-sample t-test was performed with network topological measures(λ、σ、Cp、 Lp、Eglob、Eloc、Cost) between TLE patients and controls. Then, we used Pearson correlation analysis to investigate the underlying relationship between properties measures (Eglob, Cost) of the brain structure networks and epilepsy duration (P<0.05).
Results
1. The results of TBSS:
Reduced FA values were found in many brain regions, which included bilateral temporal lobes, frontal lobes, parietal lobes and part of the occipital lobes, bilateral internal capsule, centrum semiovale (P<0.05). Increased MD values were found almost in left hemisphere, including left frontal lobe, temporal lobe, parietal lobe and occipital lobe (P<0.05). Moreover, it has a significant negative correlation (r=-0.5535, P=0.0114) for the mean FA value in FA skeleton of each patient with the epilepsy duration.
2. The results of structural network topological measures:
Compared to normal controls, the global efficiency (Eglob) showed significantly decreased in TLE patients, and the local efficiency (Eglob) showed significantly decreased also in TLE patients, which included left middle frontal gyrus, bilateral inferior occipital gyrus, left angular gyrus, left Heschl gyrus, right anterior cingulate and paracingulate gyri. Moreover, the shortest path length (t=-4.18, P=1.55e-04) and cost (t=-4.04, P=2.35e-04) showed significantly increased in TLE. It also showed a significant negative correlation (r=-0.63, P=0.0053) for Eglob of left Heschl gyrus in patients with the epilepsy duration. The TLE patients showed statistically significant decrease inEglob, Eloc, and increase in Lp, Cost, compared to health controls. However, it has no statistically significant difference for Cp between TLE patients and controls.
Conclusion
It offered the evidence that there have extensive damages of brain white matter on TLE patients, and the severity of this damage increased with the epilepsy duration. Moreover, it seen to be more sensitivity to damage for left hemisphere than right hemisphere. Though graph-theory analysis, we found that the structural network of TLE patients also have "small-word" attributes. However, the topology of structural network parameters have altered remarkably in TLE patients compared to health controls, which included increase of Cp and Cost, decrease of Eglob and Eloc, the location of brain Hubs have also altered. This demonstrated that there have extensive damages of brain white matter on TLE patients, which mean decrease in efficiency of information transfer, that would be related to hypothymergasia, memory losses, learning ability and cognitive decline in TLE patients.
Part two:Research on brain functional network of temporal lobe epilepsy via BOLD-fMRI and graph theory
Objective
To investigate alterations of functional connectivity and small-world topological properties related to temporal lobe epilepsy through functional connection technology and graph-theory based on blood oxygen level-dependent functional MRI and its clinical significance.
Materials and Methods
1. Subjects
Sixty-three subjects participated in the study, including35TLE patients and28 health adults, all TLE patients were recruited from Guangdong999hospital. The inclusion criteria included the following:①seizure types and epileptic syndromes as diagnosed according to the classification of the International League Against Epilepsy (Anon.,1981,1989);②TLE diagnosis when continuous interictal-ictal scalp video electroencephalography showed interical epileptiform discharges (IEDs) of unilateral temporal origin. The exclusive criteria:with ace-occupying lesion (such as tumor, parasite and vascular malformation), white matter lesion, encephalomalacia on MR image, suspicious abnormality of the results for EEG, the results of EEG suggested bilateral TLE, or not consistent to clinical performance of TLE patients. The inclusion criteria for health controls was as same as part one. Nine participants were excluded from this study, remaining54subjects were included in this study, which included25health controls (mean age=24.24±5.31years,17males,8females),16left TLE patients (mean age=23.13±7.14years,11males,5females),13right TLE patients (mean age=26.31±10.10,8males,5females).
2. Data acquisition
MRI data were collected using a1.5-Tesla scanner, participants were instructed to rest with their eyes closed and to be still, without considering the specific problems, and not to fall asleep. BOLD functional images covering the whole brain were acquired axially using an gradient echo-echo planar imaging sequence (GRE-EPI), TR=3000ms, TE=50ms, flip angle=90, matrix=128×128, FOV=230mm×230mm, slice=4.5mm, slice gap=0. For each subjects, the resting state fMRI scanning lasted seven minutes, thus collecting160volumes.
3. Data processing
3.1Data preprocessing
Data preprocessing was partly carried out using SPM5software (http://www.fil. ion.ucl.ac.uk/spm) based on Matlab R2009b. The reprocessing as follows:①Data with DICOM format were converted to NIFTI format;②The first10images were discarded to ensure the magnetization equilibrium;③Then the remaining images were realigned, the subjects would be excluded for head translation or rotation exceeded±1mm or±1°;④lice timing, correct differences in image acquisition time between slices;⑤Normalization, normalize images into a standard Talairach and Tournoux space by MNI template images which supplied with SPM5;⑥The images were proceed with linear detrend and low-frequency filtering, then the preprocessed time series were used for further functional connectivity and graph-theory analysis.
3.2Image segment
The images were segmented into90anatomical regions of interests (ROIs)(45ROIs for each hemisphere) using anatomically labeled-90(AAL-90) template. These anatomical ROIs were extracted by the MarsBaR toolbox (http://marsbar.sourceforge. net)
3.3Computation of correlation matrix
The resting state BOLD time series were correlated region by region for each subject across the full length of the resting time series. Then a square90X90correlation matrix was obtained for each subject,4005(C920=90X89/2=4005) inter-regional correlations were subjected to statistic analysis.
3.4functional network graph visualization
The topological properties of the brain functional networks were defined on the basis of a90X90binary graph, which was consisted of nodes and edges, the edges between nodes could be constructed by applying a correlation matrice, the regional centroid of each node was positioned by using Brainnet viewer Version1.1software.
4. Statistic analysis
4.1Function connectivity comparison
Two-sample t-test was performed with functional connectivity between left TLE patients and controls, and right TLE patients and controls base on matlab R2009b. To account for multiple comparisons, the false discovery rate method was applied, P﹤0.001was supposed to be a significantly functional connectivity.
4.2Network topological measures comparison
Two-sample t-test was performed with network topological measures between left TLE patients and controls, and right TLE patients and controls base on matlab2009b. To account for multiple comparisons, the false discovery rate method was applied, P﹤0.001was supposed to be a significantly functional connectivity.
Results
1. Results of functional connectivity:
Left TLE patients produced significantly stronger connectivity than healthy controls between specific ROIs, e.g. right precentral gyrus vs right hippocampus; right inferior frontal gyrus orbital part vs right postcentral gyrus, and produced significantly lower connectivity than healthy controls between left posterior cingulated gyrus vs left thalamus. Right TLE patients produced significantly stronger connectivity than healthy controls between specific ROIs, e.g. left precentral gyrus vs left parahippocampal gyrus; left dorsolateral superior frontal gyrus vs left olfactory cortex, and produced significantly lower connectivity than healthy controls between right medial superior frontal gyrus vs temporal pole of right middle temporal gyrus; left posterior cingulated gyrus vs temporal pole of right middle temporal gyrus; right hippocampus vs right caudate nucleus.
2. Results of brain functional network:
2.1Clustering coefficients (Cp) and local efficiency (Eloc) showed significantly larger value in left TLE patients when compared to normal controls. However, there have no alteration with graph theory measures between right TLE and normal controls.
2.2In the left TLE patients, some nodal parameters showed significant increase, e.g. right middle temporal gyrus; right parahippocampal gyrus, and some nodal parameters showed significant decrease, e.g. right thalamus, left middle frontal gyrus orbital part, especially for left middle frontal gyrus orbital part (P=0.0057). In the right TLE patients, some nodal parameters showed significant increase, e.g. right inferior parietal gyrus, bilateral angular gyrus, right postcentral gyrus, especially for right inferior parietal gyrus (P=6.8817e-4). However, the left inferior temporal gyrus showed significant decrease with nodal parameters.
Conclusion
It showed that there have alterations of functional connectivity between many brain regions both in left TLEs or right TLEs, though graph-theory analysis, we found that the clustering coefficients (Cp) and local efficiency (Eloc) showed significantly larger value in left TLE patients when compared to normal controls. However, there has no alteration with graph theory measures between right TLE and normal controls. Furthermore, the nodal parameters of right thalamus and left middle frontal gyrus orbital part showed significant decrease in left TLE patients. It demonstrated that the damages of brain with left TLE were more serious than right TLE. Furthermore, it suggested that the alternation of functional connectivity and nodal parameters would be related to memory losses, emotional changes and cognitive decline in TLE patients.
目的
利用磁共振扩散张量成像技术(DTI)和基于纤维束体素的空间统计(TBSS)方法观测颞叶癫痫患者脑白质微结构改变情况,同时探讨DTI两个参数(FA、MD)值在评价颞叶癫痫患者脑白质微结构损伤中的应用价值,从而为颞叶癫痫患者的临床诊断、分析、疗效与预后评估提供客观的影像学依据。另外,基于DTI图像数据,构建大脑拓扑结构网络,探讨颞叶癫痫患者脑结构网络“小世界”属性的变化情况及其临床意义。
材料与方法
1.研究对象
研究被试包括50人,其中颞叶癫痫组26人,全部来自广东三九脑科医院住院病人,健康对照组24人。所有颞叶癫痫患者均为住院病人,均行常规MR检查,以及视频脑电检查,脑电图包括头皮电极或/与蝶骨电极。颞叶癫痫纳入标准：①癫痫发作类型与临床症状符合1981年以及1989年国际抗癫联盟的诊断标准与分类标准；②视频脑电图显示异常痫性放电(Interical epileptiformdischarges, IEDs)起源于颞部。MR检查显示单侧或双侧海马硬化、颞叶皮层局灶性发育不良。临床发作具有以下特征：内侧颞叶癫痫(mTLE)发作先兆包括胃气上升感、胸闷、心慌等。外侧颞叶癫痫(1TLE)发作先兆包括幻听、精神症状、躯体感觉异常等。综合临床表现、脑电图(Electrocephalographic, EEG)与MR结果最终诊断为TLE。排除标准,MRI检查示颅内占位性病变(如肿瘤、寄生虫、血管畸形等)、白质变性、软化灶等其它病变,EEG示可疑不正常,EEG定侧结果与临床表现不相符者。8例被试者从本研究中排除(包括6例颞叶癫痫患者与2例健康对照组受试者),剩余42例被纳入本次研究,即：20例颞叶癫痫患者(男性：16例,女性：4例,平均年龄：25.55±8.67岁),22例正常健康对照组(男性：13例,女性：9例,平均年龄：25.23±6.67岁)。
2.磁共振DTI数据采集
磁共振DTI数据采集使用飞利浦1.5T磁共振扫描仪(Philips Gyroscan Intera1.5T),并采用6通道相位陈列(神经血管线圈6,NV6)线圈接收核磁共振信号。DTI数据采集之前均进行全脑轴位T1WI、T2WI及FLAIR扫描,以排除脑部疾患。DTI数据采集采用单次激发自旋回波平面回波序列,平行于大脑前后联合得到全脑轴位弥散加成像。b=800s/mm2共32组图像(32个扩散梯度方向),和一组扩散敏感系数b=0的非扩散加权图像。磁共振扫描具体参数如下：重复时间(TR)=11000ms；回波时间(TE)=71.614ms；翻转角(flip angle)=90°;矩阵(matrix size)=144×144;视野(FOV)=230mm×230mm;激励次数(NEX)=1；层厚(thickness)=2.Omm;层数(slice)=67；层间距(slice gap)=0。
3.DTI数据预处理与TBSS分析
首先利用MRIcro(Chris Rorden, http://www.psychology.nottingham.c.uk/staff/crl/mricro.html)将每个被试者的1组Bo图像与相应的32组扩散权重二维图像DICOM数据转化为包含三维影像与代表不同扩散权重梯度场强方向的四维NIFTI格式数据。
DTI数据分析采用FSL (FMRIB Software Library, www.fmrib.ox.ac.uk/fsl, version4.19)软件。具体处理步骤包括：①首先使用FSL软件FDT套件中的eddy correct函式来矫正图像的失真。②利用FSL软件中BET套件以每个被试者的B0图像作为依据产生各自的脑mask。③将梯度磁场方向、磁场强度的数值以及四维NIFTI格式图像,连同各个被试的mask输入FDT套件中的dtifit函式中来进行扩散张量的计算。输出的结果将包含FA(fractional anisotropy)、MD(mean diffusivity)图像。以FA值、MD值进行基于纤维束体素的空间统计的群组分析。
4.大脑结构网络构建与可视化
应用基于Matlab R2009b分析软件平台的SPM5(http://www.fil.ion.ucl.ac. uk/spm)脑成像处理软件进行图像数据处理分析。结构网络流程：①ICOM数据格式转换为NIFTI格式；②刚性配准将T1像转换到DTI空间,将配准后的T1像通过非线性转换转至MNI空间；③用第二步转换得到的翻转矩阵将MNI空间中的AAL-90模板反向转换到DTI空间；④利用DTIstudio计算扩散张量矩阵并得到特征值和特征向量；⑤利用DTIstudio里的FACT算法重建全脑白质纤维束；⑥用90个脑区作为节点与纤维束作为边构建大脑结构网络。大脑结构网络图的可视化是基于Brainnet viewer Version1.1软件实现。
5.统计学分析
应用SPSS13.0软件采用两独立样本t检验的统计方法评价颞叶癫痫患者组与正常被试组之间年龄、受教育程度,采用卡方检验评价两组被试之间性别差异。全脑平均FA值、MD值进行组间分析,P<0.05认为有统计学意义。采用Pearson相关分析方法对颞叶癫痫组全脑平均FA值与癫痫患者病程作相关分析。
应用SPSS13.0软件采用两独立样本t检验的统计方法评价颞叶癫痫患者组大脑结构网络各“小世界”属性参数(λ、σ、Cp、Lp、Eglob、Eloc、Cost)与正常对照组之间的差异。采用Pearson相关分析方法对颚叶癫痫患者病程分别与大脑效率(Eglob)、代价(Cost)作相关性分析,P<0.05认为有统计学意义。
结果
1.TBSS结果：
颞叶癫痫患者多个脑区FA值显著降低：双侧颞叶、额叶、顶叶及部分枕叶,双侧半卵圆中心、双侧内囊前、后肢(P<0.05)。颞叶癫痫患者多个脑区MD值显著升高：左侧额叶、颞叶、顶叶、枕叶(P<0.05)。颞叶癫痫患者脑白质纤维骨架平均FA值与癫痫病程呈负相关(r=-0.5535,P=0.0114)。
2.结构小世界网络属性结果：
颞叶癫痫患者大脑全局效率显著降低,而且左侧额中回、双侧枕下回、左侧角回、左侧颞横回,右侧扣带回与扣带旁回前部的局部效率显著降低(P<0.05)；颞叶癫痫患者大脑最短路径(t＝-4.18,P=1.55e-04)与成本(t=-4.04,P=2.35e-04)显著增大。采用Pearson相关分析方法,结果显示左侧缘上回局部节点效率与病程呈显著负相关(r=-0.63,P=0.0053),左侧颞中回局部节点效率与病程呈显著负相关(r=-0.53,P=0.0233),颞叶癫痫患者大脑最短路径(Lp)、全局效率(Eglob)、局部效率(Eloc)、代价(Cost)均有显著性差异,而集群系数(Cp)两组间无显著性差异。
结论
颞叶癫痫患者脑白质微结构发生广泛损伤,且损伤程度随着病程的延长而加重,而且左侧大脑半球白质受损较右侧显著。通过基于弥散张量图像构建大脑结构网络显示,颞叶癫痫患者大脑结构网络仍然具有“小世界”属性,但是该网络拓扑结构发生改变,主要表现为全局效率与局部效率下降,而最短路径增加,代价增高；另外,大脑结构网络的核心节点分布亦发生了变化。以上结果表明癫痫电活动导致大脑白质广泛损伤,信息传递效率降低,这可能与癫痫患者情感低落、记忆力减退,认知与学习能力下降等临床症状有关。
第二部分基于BOLD-fMRI的颞叶癫痫患者脑功能网络研究
目的
采用基于血氧水平依赖成像的图像,通过功能连接技术与构建功能结构网络的方法,探讨颞叶癫痫患者静息态脑部脑区间功能连接变化情况,同时探讨脑功能网络“小世界”属性的变化情况,并评价这些指标变化的临床意义。
材料与方法
1.研究对象
研究被试包括63人,其中颞叶癫痫组35人,全部来自广东三九脑科医院住院病人,健康对照组28人,9例被试者从本研究中排除,剩余54例被纳入本次研究：25例正常健康对照组(男性：17例,女性：8例,年龄：24.24±5.31岁),16例左侧颞叶癫痫患者(男性：11例,女性：5例,年龄：23.13±7.14岁)13例右侧颞叶癫痫患者(男性：8例,女性：5例,年龄：26.31±10.10岁)。颞叶癫痫纳入标准：①癫痫发作类型与临床症状符合1981年以及1989年国际抗癫联盟的诊断标准与分类标准；②视频脑电图显示异常痫性放电(Interical epileptiform discharges, IEDs)起源于单侧颞部。排除标准：MRI检查示颅内占位性病变、白质变性、软化灶等其它病变,EEG示可疑不正常,或双侧IEDs(或无单侧定侧意义),EEG定侧结果与临床表现不相符者。正常对照组入选标准同第一部分。
2.数据采集
磁共振仪器设备同前；静息态BOLD-fMRI数据采集前,并告之被试者在进行MR检查时务必避免系统性思考问题,保持清醒状态、闭眼,不能睡觉。BOLD数据采集采用梯度回波平面回波序列(GRE-EPI),平行于大脑前、后联合得到全脑轴位成像,具体参数如下：TR=3000ms; TE=50ms;翻转角=900；究康复治疗Matrix=128×128; FOV=230mm×230mm;层厚=4.5mm；层间距=0；共160个动态。静息态扫描时间约7分钟。
3.图像数据处理分析
3.1数据预处理
对采集到的原始DICOM数据首先应用基于Matlab R2009b分析软件平台的SPM5(http://www.fil.ion.ucl.ac.uk/spm)软件进行图像数据处理。具体预处理步骤包括：①将原始图像的DICOM格式转化为用于数据分析的NIFTI格式；②手动去除前十个时间点的扫描图像,保持磁场稳定性；③头动校正(realignment),被试者头动范围超过±1mm或者±1。的数据从研究中剔除；④时间序列校正(slice timing)以扫描位置中心点为参考层,把被试者所有个体静息态BOLD-fMRI图像进行时间差异校正；⑤标准化(normalization)处理,将头动校正后的数据标准化到SPM5内MNI模板并转化到Talairach and Tournoux空间。⑥进行去线性飘移(linearly detrended)与低频滤过(1ow-frequency filtering)处理。预处理之后的时间序列信号做进一步的功能连接分析与图论分析。
3.2图像分割
应用AAL-90模板将图像分割为90个感兴趣区(ROIs),每一侧半球为45个ROIs。。再利用MarsBaR工具箱(http://marsbar.sourceforge.net)对每一个感兴趣区提取预处理之后的时间序列信号。
3.3计算相关矩阵
将90个脑区之间的静息态BOLD-fMRI时间序列信号进行相关计算,结果得到每一位受试者的90×90的相关矩阵图,由于相关矩阵的对称性,我们对4005(C920=90×89/2=4005)个相关系数进行下一步统计分析。
3.4大脑功能网络图的构建与可视化
基于上述构造的相关矩阵,我们以每个脑区为节点,脑区间的相关系数为边,来构建二值矩阵。最后我们基于二值矩阵来计算全局网络参数和节点参数。大脑功能连接网络图的可视化是基于Brainnet viewer Version11软件实现的。
4.统计学分析
4.1功能连接比较
基于matlab2009b,采用两独立样本的t检验的方法分别比较左侧颞叶癫痫病人和正常人之间有差异的功能连接,右侧颞叶癫痫病人和正常人之间有差异的功能连结。由于多重比较的问题,p<0.001认为有统计学意义。
4.2网络参数的比较
基于matlab2009b,针对每一个网络参数,采用两独立样本的t检验的方法分别检测左侧颞叶癫痫病人和正常人之间、右侧颞叶癫痫病人和正常人之间是否有差异。由于多重比较的问题,p<0.001认为有统计学意义。
结果
1.功能连接分析结果：
左侧TLE较正常对照组出现多个节点间功能连接增强,如右侧中央前回与右侧海马,右侧额下回眶部与右侧中央后回等；功能连接减弱的节点如左侧扣带回后部与左侧丘脑。右侧TLE较正常对照组出现多个脑区间功能连接增强,如左侧中央前回与左侧海马旁回,左侧额上回背外侧部与左侧嗅皮质等,功能连接减弱的脑区,包括右侧额上回内侧部与右侧颞中回颞极部,左侧扣带回后部与右侧颞中回颞极部,右侧海马与右侧尾状核。
2.功能网络分析结果：
2.1左侧颞叶癫痫的集群系数(CP)与局部效率(Eloc)增大,右侧颞叶癫痫患者大脑功能网络拓扑结构参数无显著差异性。
2.2左侧颞叶癫痫患者右侧颞中回与右侧海马旁回节点参数增加,而右侧丘脑与左侧额中回眶部节点参数下降,尤其以左侧额中回眶部下降显著(P=0.0057)；与正常对照组比较,右侧颞叶癫痫患者右侧顶下小叶、双侧角回、右侧中央后回节点节参数增加,其中以右侧顶下小叶的Nbc。增加显著(P=6.8817e-4),而左侧颞下回节点参数减少。
结论
左、右侧TLE多个脑区之间功能连接发生异常改变,通过构建脑功能网络方法,显示左侧TLE患者大脑集群系数与局部效率较正常人对照组增加,而右侧TLE患者与正常对照组比较无显著差异；此外,左侧TLE患者的右侧丘脑与左侧额叶眶部节点参数显著下降。以上结果表明左侧TLE对大脑的损害较右侧TLE显著,同时不同脑区间功能连接变化以及多个脑区节点参数变化与癫痫患者记忆受损、情感改变、认知能力下降等有关。
Part one:White matter microstructural damage in temporal lobe epilepsy individuals DTI-TBSS and structure network study
Objective
The purpose of present study is to explore white matter microstructure abnormalities of temporal lobe epilepsy (TLE) by using diffusion tensor imaging (DTI) and tract based spatial statistic (TBSS) methods, and the differences between two index of DTI (FA value and MD value) on evaluating white matter microstructure of TLE, in order to setup image basis for clinical diagnosis, analysis, assessment of treatment on TLE. In addition, we explored the change of "small world" properties and their clinical significance through the topology network of brain that created through DTI data.
Materials and Methods
1. Subjects
Fifty subjects have participated in present study, including26TLE patients and24normal adults. All patients were recruited from Guangdong999hospital, and underwent MR scan, Video-EEG monitoring, scalp EEG and/or sphenoidal electrodes. The inclusion criteria included the following:①seizure types and epileptic syndromes as diagnosed according to the classification of the International League Against Epilepsy (Anon.,1981,1989);②TLE diagnosis when continuous interictal-ictal scalp video electroencephalography showed interical epileptiform discharges (IEDs) of temporal origin. Unilateral or bilateral hippocampal sclerosis, focal cortical dysplasia (FCD) of temporal lobe were showed on MR imaging. Main clinical symptoms as follows:hoven, dyspnea and dither were indicators of a possible medial TLE, acousma, psychotic symptoms, somatosensory abnormality were indicators of a possible lateral TLE. The final diagnosis of TLE from patients'clinical performance and electro-cephalographic results. The exclusive criteria:with ace-occupying lesion (such as tumor, parasite and vascular malformation), white matter lesion, encephalomalacia on MRI, EEG showed suspicious abnormality, the results of EEG and clinical performance were not consistent. Eight subjects were excluded from this study (including6TLE patients and2health controls), remaining42subjects were included in this study, which included20TLE patients (mean age=25.55±8.67years,16males,4females),22health controls (mean age=25.23±6.67years,13males,9females).
2. DTI-MRI data acquisition
DTI-MRI data were collected using a1.5-Tesla scanner (Philips Gyroscan Intera) with a6channel neurovascular (NV) coil to receive the signal. Firstly, axial T1-weighted, T2-weighted images and FLAIR images were acquired for detecting incranial lesions. A Single-shot turbo spin echo sequence was used to obtain the DTI data, TR (repetition time)=11000ms; TE (echo time)=71.614ms; flip angle=90°; matrix size=144×144; FOV (field of view)=230X230; NEX=1; thickness=2mm; slice=67; slice gap=0. Thirty-two diffusion gradient directions with b-value=800s/mm2. In addition, images without diffusion weighting were acquired corresponding to b-value=0.
3. DTI data preprocessing and TBSS analysis
The DICOM data format of B0image and32diffusion weighted images of each subject were converted to NIFTI data format with four dimension
DTI data preprocessing was partly carried out using FSL (FMRIB Software Library, www.fmrib.ox.ac.uk/fsl, version4.19). The procedure of DTI data process included:①Diffusion tensor images were corrected for head movement by using FDT tool of the FSL software.②Mask for each brain was created by using each subject'Bo image with BET tool of the FSL software.③Fractional anisotropy (FA) and mean diffusivity (MD) were obtained with diffusion tensor calculation by using dtifit function, the FA and MD output images were used as input for tract based spatial statistic (TBSS).
4. Brain structure network graph visualization
DTI data preprocessing was partly carried out using SPM5software (http://www.fil. ion.ucl.ac.uk/spm) based on Matlab R2009b. The reprocessing as follows:①Dsta with DICOM format were converted to NIFTI format;②Each individual structural image (Ti-weighted image) was coregistered to the Bo image in the DTI space using a linear transformation, the transformed structural image was then mapped to the T1template in the Montreal Neurological Institute (MNI) space using a nonlinear transformation;③The resulting inverse transformation was then used to warp the AAL-90mask from the MNI space to the DTI native space;④The diffusion tensor matrix was then calculated voxel-by-voxel and diagonalization was performed to yield eigenvalues;⑤Fiber bundles of white matter were reconstructed by using FACT algorithm with DTIstudio software;⑥The topological properties of brain structural networks were defined on the basis of a90X90binary graph, which was consisted of nodes and edges, the edges between nodes could be constructed by applying a correlation matrice, the regional centroid of each node was positioned by using Brainnet viewer Version1.1software.
5. Statistic analysis
Two-sample t-test was performed with age, years of education between TLE patients and controls, chi-square test was performed with sex between TLE patients and controls. Two-sample t-test was performed with whole brain mean FA value and MD value between TLE patients and controls, we used a statistical significance level of P<0.05. Then, we used Pearson correlation analysis to investigate the underlying relationship between mean FA value of TLE patients and epilepsy duration.
Two-sample t-test was performed with network topological measures(λ、σ、Cp、 Lp、Eglob、Eloc、Cost) between TLE patients and controls. Then, we used Pearson correlation analysis to investigate the underlying relationship between properties measures (Eglob, Cost) of the brain structure networks and epilepsy duration (P<0.05).
Results
1. The results of TBSS:
Reduced FA values were found in many brain regions, which included bilateral temporal lobes, frontal lobes, parietal lobes and part of the occipital lobes, bilateral internal capsule, centrum semiovale (P<0.05). Increased MD values were found almost in left hemisphere, including left frontal lobe, temporal lobe, parietal lobe and occipital lobe (P<0.05). Moreover, it has a significant negative correlation (r=-0.5535, P=0.0114) for the mean FA value in FA skeleton of each patient with the epilepsy duration.
2. The results of structural network topological measures:
Compared to normal controls, the global efficiency (Eglob) showed significantly decreased in TLE patients, and the local efficiency (Eglob) showed significantly decreased also in TLE patients, which included left middle frontal gyrus, bilateral inferior occipital gyrus, left angular gyrus, left Heschl gyrus, right anterior cingulate and paracingulate gyri. Moreover, the shortest path length (t=-4.18, P=1.55e-04) and cost (t=-4.04, P=2.35e-04) showed significantly increased in TLE. It also showed a significant negative correlation (r=-0.63, P=0.0053) for Eglob of left Heschl gyrus in patients with the epilepsy duration. The TLE patients showed statistically significant decrease inEglob, Eloc, and increase in Lp, Cost, compared to health controls. However, it has no statistically significant difference for Cp between TLE patients and controls.
Conclusion
It offered the evidence that there have extensive damages of brain white matter on TLE patients, and the severity of this damage increased with the epilepsy duration. Moreover, it seen to be more sensitivity to damage for left hemisphere than right hemisphere. Though graph-theory analysis, we found that the structural network of TLE patients also have "small-word" attributes. However, the topology of structural network parameters have altered remarkably in TLE patients compared to health controls, which included increase of Cp and Cost, decrease of Eglob and Eloc, the location of brain Hubs have also altered. This demonstrated that there have extensive damages of brain white matter on TLE patients, which mean decrease in efficiency of information transfer, that would be related to hypothymergasia, memory losses, learning ability and cognitive decline in TLE patients.
Part two:Research on brain functional network of temporal lobe epilepsy via BOLD-fMRI and graph theory
Objective
To investigate alterations of functional connectivity and small-world topological properties related to temporal lobe epilepsy through functional connection technology and graph-theory based on blood oxygen level-dependent functional MRI and its clinical significance.
Materials and Methods
1. Subjects
Sixty-three subjects participated in the study, including35TLE patients and28 health adults, all TLE patients were recruited from Guangdong999hospital. The inclusion criteria included the following:①seizure types and epileptic syndromes as diagnosed according to the classification of the International League Against Epilepsy (Anon.,1981,1989);②TLE diagnosis when continuous interictal-ictal scalp video electroencephalography showed interical epileptiform discharges (IEDs) of unilateral temporal origin. The exclusive criteria:with ace-occupying lesion (such as tumor, parasite and vascular malformation), white matter lesion, encephalomalacia on MR image, suspicious abnormality of the results for EEG, the results of EEG suggested bilateral TLE, or not consistent to clinical performance of TLE patients. The inclusion criteria for health controls was as same as part one. Nine participants were excluded from this study, remaining54subjects were included in this study, which included25health controls (mean age=24.24±5.31years,17males,8females),16left TLE patients (mean age=23.13±7.14years,11males,5females),13right TLE patients (mean age=26.31±10.10,8males,5females).
2. Data acquisition
MRI data were collected using a1.5-Tesla scanner, participants were instructed to rest with their eyes closed and to be still, without considering the specific problems, and not to fall asleep. BOLD functional images covering the whole brain were acquired axially using an gradient echo-echo planar imaging sequence (GRE-EPI), TR=3000ms, TE=50ms, flip angle=90, matrix=128×128, FOV=230mm×230mm, slice=4.5mm, slice gap=0. For each subjects, the resting state fMRI scanning lasted seven minutes, thus collecting160volumes.
3. Data processing
3.1Data preprocessing
Data preprocessing was partly carried out using SPM5software (http://www.fil. ion.ucl.ac.uk/spm) based on Matlab R2009b. The reprocessing as follows:①Data with DICOM format were converted to NIFTI format;②The first10images were discarded to ensure the magnetization equilibrium;③Then the remaining images were realigned, the subjects would be excluded for head translation or rotation exceeded±1mm or±1°;④lice timing, correct differences in image acquisition time between slices;⑤Normalization, normalize images into a standard Talairach and Tournoux space by MNI template images which supplied with SPM5;⑥The images were proceed with linear detrend and low-frequency filtering, then the preprocessed time series were used for further functional connectivity and graph-theory analysis.
3.2Image segment
The images were segmented into90anatomical regions of interests (ROIs)(45ROIs for each hemisphere) using anatomically labeled-90(AAL-90) template. These anatomical ROIs were extracted by the MarsBaR toolbox (http://marsbar.sourceforge. net)
3.3Computation of correlation matrix
The resting state BOLD time series were correlated region by region for each subject across the full length of the resting time series. Then a square90X90correlation matrix was obtained for each subject,4005(C920=90X89/2=4005) inter-regional correlations were subjected to statistic analysis.
3.4functional network graph visualization
The topological properties of the brain functional networks were defined on the basis of a90X90binary graph, which was consisted of nodes and edges, the edges between nodes could be constructed by applying a correlation matrice, the regional centroid of each node was positioned by using Brainnet viewer Version1.1software.
4. Statistic analysis
4.1Function connectivity comparison
Two-sample t-test was performed with functional connectivity between left TLE patients and controls, and right TLE patients and controls base on matlab R2009b. To account for multiple comparisons, the false discovery rate method was applied, P﹤0.001was supposed to be a significantly functional connectivity.
4.2Network topological measures comparison
Two-sample t-test was performed with network topological measures between left TLE patients and controls, and right TLE patients and controls base on matlab2009b. To account for multiple comparisons, the false discovery rate method was applied, P﹤0.001was supposed to be a significantly functional connectivity.
Results
1. Results of functional connectivity:
Left TLE patients produced significantly stronger connectivity than healthy controls between specific ROIs, e.g. right precentral gyrus vs right hippocampus; right inferior frontal gyrus orbital part vs right postcentral gyrus, and produced significantly lower connectivity than healthy controls between left posterior cingulated gyrus vs left thalamus. Right TLE patients produced significantly stronger connectivity than healthy controls between specific ROIs, e.g. left precentral gyrus vs left parahippocampal gyrus; left dorsolateral superior frontal gyrus vs left olfactory cortex, and produced significantly lower connectivity than healthy controls between right medial superior frontal gyrus vs temporal pole of right middle temporal gyrus; left posterior cingulated gyrus vs temporal pole of right middle temporal gyrus; right hippocampus vs right caudate nucleus.
2. Results of brain functional network:
2.1Clustering coefficients (Cp) and local efficiency (Eloc) showed significantly larger value in left TLE patients when compared to normal controls. However, there have no alteration with graph theory measures between right TLE and normal controls.
2.2In the left TLE patients, some nodal parameters showed significant increase, e.g. right middle temporal gyrus; right parahippocampal gyrus, and some nodal parameters showed significant decrease, e.g. right thalamus, left middle frontal gyrus orbital part, especially for left middle frontal gyrus orbital part (P=0.0057). In the right TLE patients, some nodal parameters showed significant increase, e.g. right inferior parietal gyrus, bilateral angular gyrus, right postcentral gyrus, especially for right inferior parietal gyrus (P=6.8817e-4). However, the left inferior temporal gyrus showed significant decrease with nodal parameters.
Conclusion
It showed that there have alterations of functional connectivity between many brain regions both in left TLEs or right TLEs, though graph-theory analysis, we found that the clustering coefficients (Cp) and local efficiency (Eloc) showed significantly larger value in left TLE patients when compared to normal controls. However, there has no alteration with graph theory measures between right TLE and normal controls. Furthermore, the nodal parameters of right thalamus and left middle frontal gyrus orbital part showed significant decrease in left TLE patients. It demonstrated that the damages of brain with left TLE were more serious than right TLE. Furthermore, it suggested that the alternation of functional connectivity and nodal parameters would be related to memory losses, emotional changes and cognitive decline in TLE patients.
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