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常规MRI阴性的儿童及青少年特发性全面性癫痫脑动态功能连接研究

李清会, 张体江

李清会, 张体江. 常规MRI阴性的儿童及青少年特发性全面性癫痫脑动态功能连接研究[J]. 四川大学学报(医学版), 2024, 55(6): 1386-1395. DOI: 10.12182/20241160108
引用本文: 李清会, 张体江. 常规MRI阴性的儿童及青少年特发性全面性癫痫脑动态功能连接研究[J]. 四川大学学报(医学版), 2024, 55(6): 1386-1395. DOI: 10.12182/20241160108
LI Qinghui, ZHANG Tijiang. Brain Dynamic Functional Connectivity in Children and Adolescents With Conventional MRI-Negative Idiopathic Generalized Epilepsy[J]. Journal of Sichuan University (Medical Sciences), 2024, 55(6): 1386-1395. DOI: 10.12182/20241160108
Citation: LI Qinghui, ZHANG Tijiang. Brain Dynamic Functional Connectivity in Children and Adolescents With Conventional MRI-Negative Idiopathic Generalized Epilepsy[J]. Journal of Sichuan University (Medical Sciences), 2024, 55(6): 1386-1395. DOI: 10.12182/20241160108

常规MRI阴性的儿童及青少年特发性全面性癫痫脑动态功能连接研究

基金项目: 国家自然科学基金(No. 81960312)和贵州省高等学校智能医学影像工程研究中心〔No. 黔教技(2023)038〕资助
详细信息
    通讯作者:

    张体江: E-mail:tijzhang@163.com

Brain Dynamic Functional Connectivity in Children and Adolescents With Conventional MRI-Negative Idiopathic Generalized Epilepsy

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  • 摘要:
    目的 

    探讨常规MRI阴性的儿童及青少年特发性全面性癫痫(idiopathic generalized epilepsy, IGE)脑动态功能连接(dynamic functional connectivity, dFC)变化及其与临床变量的相关性。

    方法 

    收集颅脑常规MRI阴性的IGE患儿40例,对照组37例。所有受试者均采集T2-液体衰减反转恢复序列(T2 -fluid attenuated inversion recovery, T2-FLAIR)、三维T1加权成像(three-dimensional T1 weighted imaging, 3D-T1WI)及静息态功能磁共振成像(resting-state fMRI, rs-fMRI)图像,通过独立成分分析(independent component analysis, ICA)、滑动时间窗及k-means聚类法得到6个功能连接状态及dFC指标(时间分数、平均停留时间及转换次数)。基于SPSS18.0及GIFT软件Stats模块分析dFC组间差异及其与临床变量的相关性。通过改变滑动窗口大小验证dFC结果的可靠性及稳定性。

    结果 

    IGE组与对照组临床一般资料差异无统计学意义(P>0.05)。与对照组相比,在state 5中,IGE组默认模式网络(default mode network, DMN)内部、DMN与额顶网络(frontoparietal network, FPN)间dFC增强;DMN与视觉网络(visual network, VN)间dFC降低(P<0.001)。在state 6中,DMN与VN、基底节网络(basal ganglia network, BGN)与感觉运动网络(sensorimotor network, SMN)间dFC增强;DMN与注意网络(attention network, ATTN)间、VN内部dFC降低(P<0.001)。state 1时间分数(Z=-2.192,P=0.028)及平均停留时间(Z=-2.144,P=0.032)、state 4时间分数(Z=-2.444,P=0.015)及平均停留时间(Z=-2.368,P=0.018)和state 6时间分数(Z=-2.047,P=0.041)在两组间差异有统计学意义。IGE组state 1时间分数与病程呈负相关(r=-0.421,P=0.007,Bonferroni校正)。在验证分析中,当滑动窗口的大小及聚类数目改变时,IGE组与对照组之间的dFC指标仍然存在差异。

    结论 

    常规MRI阴性的儿童及青少年IGE患者存在全脑功能连接的动态属性异常,且IGE患者state 1时间分数与临床变量具有相关性,为儿童及青少年IGE患者神经机制研究提供新的影像学依据。

     

    Abstract:
    Objective 

    To investigate the changes in brain dynamic functional connectivity (dFC) in children and adolescents with idiopathic generalized epilepsy (IGE) who have negative findings for conventional magnetic resonance imaging (MRI) and to explore the correlation between dFC indicators and clinical variables.

    Methods 

    A total of 40 children and adolescents with IGE who have negative findings for routine brain MRI and 37 healthy controls were enrolled. T2-fluid attenuated inversion recovery (T2-FLAIR) was performed for all subjects. They also uinderwent 3-dimensional T1 weighted imaging (3D-T1WI) and resting-state functional MRI (rs-fMRI). Using independent component analysis (ICA), sliding time windows, and k-means clustering, we identified 6 functional connectivity states and derived dFC indicators, including fraction of time, mean dwell time, and the number of transitions. Then, SPSS18.0 and GIFT software Stats module were used to analyze the intergroup differences in dFC and its correlation with clinical variables. The reliability and stability of the dFC results were validated by changing the size of the sliding window.

    Results 

    There were no significant differences in the general clinical data between the IGE group and the control group (P>0.05). Compared with the control group, the IGE group showed in state 5 increased dFC within the default mode network (DMN), increased dFC between DMN and the frontoparietal network (FPN), and decreased dFC between DMN and the visual network (VN) (P<0.001). In state 6, the IGE group showed increased dFC between DMN and VN, increased dFC between the basal ganglia network (BGN) and the sensorimotor network (SMN), decreased dFC between the DMN and the attention network (ATTN), and decreased dFC within the VN (P<0.001). There were statistically significant differences between the two groups in the fraction of time (Z=-2.192, P=0.028) and the mean dwell time (Z=-2.144, P=0.032) in state 1, in the fraction of time (Z=-2.444, P=0.015) and the mean dwell time (Z=-2.368, P=0.018) in state 4, and in the fraction of time (Z=-2.047, P=0.041) in state 6. There was a negative correlation between the duration of the disease and the fraction of time of state 1 in the IGE group (r=-0.421, P=0.007, Bonferroni correction). In the validation analysis, significant differences in dFC indicators between the IGE group and the control group persisted when the size of the sliding window and the number of clusters were changed.

    Conclusion 

    Children and adolescents with IGE and negative findings for conventional MRI exhibit abnormal dynamic properties of whole-brain functional connectivity, and the fraction of time of state 1 in IGE patients is correlated with clinical variables, providing new imaging evidence for research in the neural mechanisms of children and adolescents with IGE.

     

  • 癫痫是由多种因素引起的慢性脑疾病。全球患病人数超过7000万,近80%的患者生活在中、低等收入国家[1],其患病率每年约50.4~81.7/10万人[2],每年约有12.5万人死于癫痫相关疾病[3]。特发性全面性癫痫(idiopathic generalized epilepsy, IGE)是指发病机制尚不完全明确,表现为全面性发作的一组临床综合征。IGE好发于儿童及青少年阶段,易复发形成难治性癫痫[4-5],严重影响患儿生长发育,甚至导致认知障碍及精神行为异常,同时,与其相关的死亡率、共患病、残疾及经济负担等是亟须解决的重大问题,因此,迫切需要提高对癫痫病理生理机制的理解。基于静息态功能磁共振成像(resting-state fMRI, rs-fMRI)的静态功能连接(static functional connectivity, sFC)已被广泛运用于癫痫患者脑功能连接的研究[6-7]。但sFC分析是假设功能连接扫描期间是稳定的,忽略了功能相互作用随时间的潜在波动,大脑是动态复杂系统,能通过不断变化来适应微环境的改变[8]。动态功能连接(dynamic functional connectivity, dFC)反映静息态下不同时段脑网络间功能连接的动态变化,且已被用于研究功能变化和认知能力之间的相关性。已有研究[9-11]表明多种神经精神疾病都存在dFC的改变。然而既往关于IGE患者dFC的研究大多关注的是成人。因此,本研究拟运用dFC探讨常规MRI阴性的儿童及青少年IGE患者dFC变化及其与临床变量的相关性,加深对IGE发病机制的理解,以期为IGE患者脑功能改变及认知功能障碍提供影像学依据。

    收集2014 年 4 月–2021 年 8 月遵义医科大学附属医院确诊的IGE患者40例,同期招募性别、年龄及受教育程度与IGE组相匹配的37例作为对照组。IGE组所有患儿均在MRI检查前进行智力测试(专科医生采用中国韦氏儿童智力量表第二版,适用年龄:6~16岁),可得到言语智商(verbal intelligence quotient, VIQ)、操作智商(performance intelligence quotient, PIQ)及全量表智商(full scale intelligence quotient, FIQ)。

    IGE组纳入标准:①根据2010年国际抗癫痫联盟诊断标准确诊为IGE患者;②年龄6~16岁。IGE组排除标准:①常规MRI检查发现颅内器质性病变;②有其他神经精神疾病;③滥用药物、重大外伤、手术及颅内感染史;④患者依从性差,不能配合完成MRI检查或智力测试;⑤图像质量不合格。

    对照组纳入标准:①年龄6~16岁;②无重大外伤及手术史;无其他神经精神疾病、颅内感染及其他可能影响脑结构与功能的疾病。对照组排除标准:①依从性差,未能完成MRI检查;②颅脑MRI检查常规序列发现器质性病变;③MRI图像质量不合格。

    本研究经遵义医科大学附属医院生物医学研究伦理委员会批准通过(伦理审查批件号为:KLLY-2020-047),所有受试者均由监护人签署知情同意书。

    采用GE 3.0THDxt超导型MR扫描仪头部8通道线圈扫描。被试者闭目仰卧于检查床,用海绵垫固定头部以减少运动伪影,并配以耳塞降噪,保持清醒,避免入睡。扫描序列及参数:①T2-液体衰减反转恢复序列(T2 -fluid attenuated inversion recovery, T2-FLAIR):重复时间(repetition time, TR)7826 ms;回波时间(echo time, TE)165 ms;视野240 mm×240 mm;反转时间(inversion time, TI)2100 ms;层厚5 mm;层间距1.5 mm;翻转角90°;②三维T1加权成像(three-dimensional T1 weighted imaging, 3D-T1WI):TR 7.8 s;TE 3.0 ms;视野256 mm×256 mm;TI 450 ms;层厚1 mm;层间距0 mm;翻转角15°;③rs-fMRI:TR 2000 ms;TE 30 ms;视野240 mm×240 mm;层厚4 mm;层间距0 mm;翻转角15°,采集时相:210个。

    利用MRIcron软件包的dcm2nii工具将rs-fMRI原始DICOM图像转换为4DNIFTI格式。后续基于MATLAB2014a平台采用SPM12、RESTplus工具包进行预处理:①去除时间点:去除前10个时间点的数据;②时间层较正:以中间层作为参考层;③头动较正:剔除扫描过程中头动平移超过2 mm、旋转超过2°的数据;④空间标准化:将校正后的rs-fMRI图像转换采用欧洲标准脑模板进行配准,体素大小3 mm×3 mm×3 mm;⑤空间平滑:以6 mm×6 mm×6 mm半高全宽高斯核进行平滑,提高图像信噪比。

    采用GIFT软件包对预处理后的数据进行独立成分分析(independent component analysis, ICA),具体步骤如下:①数据降维:采用主成分分析对数据进行降维,得到100个主要成分;②成分数估计:基于Infomax算法对所有受试者进行独立成分(independent component, IC)估计,分离出75个ICs,为了提高分离的稳定性和一致性,该分析使用ICASSO算法重复20次;③反重建:得到受试者的空间分布图及与之相对应的时间序列;④成分挑选:使用GIFT软件的Sorting component GUI模块,基于多重空间回归分析方法,根据既往研究[12-14]按照空间网络模板的最大空间重叠、与模板相关系数最高的原则挑选成分。入选标准:①空间地图的峰值坐标主要位于灰质;②与白质、血管、脑室或磁敏感性伪影空间重叠较少;③以低频信号为主及以高动态范围为特征的时间进程。最终挑选出49个ICs作为内在连接网络用于后续分析,并根据解剖、功能特性及先验知识等将其归为7个静息态脑网络(resting state network, RSN),包括听觉网络(auditory network, AUD)、基底节网络(basal ganglia network, BGN)、默认模式网络(default mode network, DMN)、感觉运动网络(sensorimotor network, SMN)、视觉网络(visual network, VN)、注意网络(attention network, ATTN)及额顶网络(frontoparietal network, FPN)。各网络空间分布图见图1

    图  1  内在网络空间图
    Figure  1.  Spatial maps of the intrinsic networks
    IC: independent component; FPN: frontoparietal network; SMN: sensorimotor network; BGN: basal ganglia network; VN: visual network; DMN: default mode network; ATTN: attention network; AUD: auditory network.

    使用GIFT软件包的时间动态功能连接(Temporal dFC)分析模块,获得dFC相关指标。主要包括以下步骤:①滑动时间窗口设置:窗宽(W)为30 TRs(60 s),高斯值σ=3 TRs,步长1 TR,产生170个窗口。对于每个窗口,本研究以“L1法”正则化的逆协方差矩阵估计IC之间的dFC,重复10次以提高估计的稀疏性;②聚类分析:使用k均值算法(k-means algorithm)及曼哈坦距离(Manhattan distance)函数(迭代次数为500次,重复次数为150次)衡量不同时间窗之间的相似性;根据先验知识及既往研究确定聚类数目(k),将k设置为6,将全部受试者的dFC矩阵聚成6类,即6种功能连接状态(state),其中每个聚类的中心的功能连接矩阵称为质心(centroid);③提取dFC指标:a.时间分数(fraction of time),反映每个受试者在某个state的时间窗口数量占所有时间窗口数量的百分比;b.平均停留时间(mean dwell time),为每个受试者处于特定state的平均时间;c.转换次数(number of transitions),为每个受试者在扫描时间内于不同state之间转换的次数。

    使用SPSS18.0软件对受试者的一般资料及dFC指标进行两独立样本t检验或Mann-Whitney U检验,性别采用卡方检验,P<0.05为差异有统计学意义。基于GIFT软件包Stats模块,用两独立样本t检验统计IGE组与对照组不同state内dFC的差异,P<0.001为差异有统计学意义。使用Spearman秩相关分析dFC指标与年龄、病程及智商的相关性,采用Bonferroni方法对P值进行多重比较校正,P<0.01为差异有统计学意义。

    为了检验研究的可靠性,本研究验证了不同窗宽及聚类数目是否会影响研究的主要结果。根据以往研究[15-16],本研究另将窗宽设置为22 TRs,聚类数目设置为4。首先计算k=6、W=30 TRs/22 TRs及k=4、W=30 TRs/22 TRs时的功能连接矩阵的质心相关系数,相关系数最高的两个state被认为是相对应的state。然后,对不同窗宽及聚类数目下的dFC指标分别进行组间比较。观察组间差异在不同窗宽下是否稳定,组间差异是否为不同窗宽的对应状态。

    两组间性别、年龄、受教育程度差异无统计学意义(P>0.05),见表1

    表  1  IGE组与对照组一般资料比较
    Table  1.  Comparison of the general data between the IGE group and the control group
    Subject data IGE group (n=40) HC group (n=37) t/χ2 P
    (Male/female)/case 18/22 22/15 1.610 0.205a
    Age/yr.* 11.0 (7.3-13.0) 12.0 (10.0-14.0) −1.290 0.197b
    Education/year* 6.0 (2.3-7.8) 7.0 (5.0-8.5) −1.658 0.097b
    Hand dominance (right/left)/case 40/0 37/0
    Duration of illness/month* 36.5 (4.8-60.0)
    VIQ 88.4±24.2
    PIQ 81.6±19.5
    FIQ 83.7±23.0
     IGE: idiopathic generalized epilepsy; HC: healthy control; VIQ: verbal intelligence quotient; PIQ: performance intelligence quotient; FIQ: full scale intelligence quotient. Statistically significant differences between groups were defined as P<0.05. a Chi-square test was performed for statistical analysis. b Nonparametric tests were performed for statistical analysis. * Median (Q1-Q3).
    下载: 导出CSV 
    | 显示表格

    当W为30 TRs、步长为1 TR、k为6时,得到6种功能连接模式,即6个状态(state),不同状态间功能连接模式及强度不同(图2图3)。state 1表示较广泛的局部网络内、网络间的功能连接增强;state 2特征是强连接主要存在于高级认知网络中;state 3表示所有RSN内部及之间的功能连接明显弱于state 1;state 4特征是强连接主要存在于某些高级认知网络内、知觉网络内及其网络间;state 5表示所有RSN内或之间大规模的功能连接增强;state 6表示所有RSN内或之间呈“稀疏连接状态”,即全脑大规模的功能连接减弱。

    图  2  聚类数为6,窗宽为30 TRs,聚类分析结果(state 1~state 3)
    Figure  2.  The cluster analysis results derived with the cluster number of 6 and the window width of 30 TRs (state 1-state 3)
    The left column indicates the dFC matrices, with the number of windowed dFC in every state, the corresponding percentage, as well as the number of participants from every group who entered into the state. The right column represents the visualization of functional connectivity in each state. The functional connectivity matrix was screened using a threshold of 0.1 to display all independent components of functional networks. All abbreviations are explained in the notes to Fig 1 and Table 1.

    所有受试者的所有时间窗的功能连接矩阵相关指标结果如图4所示。图A显示每种状态中出现频率的折线图,在state 6中出现频率最高,在state 1中出现的频率最低;图B显示的是在每种状态的平均停留时间的折线图,在state 6的平均停留时间最长,在state 1的平均停留时间最短;图C显示转换次数,表示在特定时间从一个状态切换到另一个状态的可能性,结果表明每个state都倾向于继续保持这个状态,而不向其他state转换。

    图  3  聚类数为6,窗宽为30 TRs,聚类分析结果(state 4~state 6)
    Figure  3.  The cluster analysis results derived with the cluster number of 6 and the window width of 30 TRs (state 4-state 6)
    The notes are the same as those for Fig 2.
    图  4  所有受试者所有时间窗的功能连接矩阵相关指标
    Figure  4.  The functional connection matrix correlation indicators for all time windows for all subjects
    A, Frequency; B, mean dwell time; C, number of transitions.

    与对照组相比,IGE组在state 5中DMN内部、DMN与FPN之间dFC增强,以DMN内部dFC强度最高;DMN与VN之间dFC降低。在state 6中DMN与VN、BGN与SMN之间dFC增强;DMN与ATTN之间、VN内部dFC降低,以DMN与ATTN之间dFC降低较明显(上述P均<0.001)(图5)。

    图  5  IGE组与对照组dFC组间差异
    Figure  5.  Difference of dFC between the IGE group and the HC group
    The horizontal axis represents the 49 selected components, and the vertical axis represents the 49 components and the functional network to which they belong. All the abbreviations are explained in the note to Fig 1.

    state 1、state 4的时间分数(Z=-2.192,-2.444,P<0.05)及平均停留时间(Z=-2.144,-2.368,P<0.05)和state 6的时间分数(Z=-2.047,P<0.05)在IGE组和对照组间的差异有统计学意义(P<0.05)。其余dFC指标在2组间的差异均无统计学意义(P>0.05)(图6)。

    图  6  IGE组与对照组dFC指标组间差异
    Figure  6.  Difference in the dFC indicators between the IGE group and the HC group
    IGE: idiopathic generalized epilepsy; HC: healthy control. The graphs were made using violin plots. The horizontal lines indicate group medians and the interquartile range (in solid and dashed line, respectively). The width of the violin plot represents the distribution density of each set of objects at the corresponding ordinate level. * P<0.05.

    IGE组state 1时间分数与病程呈负相关(r=-0.421,P=0.007,Bonferroni校正)(图7)。其余dFC指标与临床变量之间无明显相关性。

    图  7  IGE组state 1时间分数与病程相关分析
    Figure  7.  Correlation between state 1 time fraction and disease duration in the IGE group
    There was a negative correlation between disease duration and the fraction of time of state 1 in the IGE group (Spearman correlation, P<0.01, adjusted by Bonferroni correction).

    当k=6、W=30 TRs/22 TRs及k=4、W=30 TRs/22 TRs时,计算功能连接矩阵Pearson相关系数。对不同窗宽及聚类数目下的dFC指标分别进行组间比较,发现主要结果是可重复的。即当窗宽及聚类数目改变后,IGE患者在dFC指标上与对照组仍有明显差异。结果还显示当k=6、W=22 TRs时,state 3的时间分数及平均停留时间与病程呈负相关,state 5的时间分数及平均停留时间与VIQ、FIQ呈负相关,相关分析部分结果与主要研究结果相似。具体数据详见网络资源附件。

    本研究基于rs-fMRI,通过ICA、滑动时间窗口技术及聚类分析方法得到6种功能连接状态(state)。发现RSN之间的状态特异性功能连接异常,主要包括DMN、FPN、ATTN、BGN、SMN、VN等脑网络,并且异常的功能连接主要与DMN有关。IGE组在state 1、state 4的时间分数及平均停留时间和state 6的时间分数与对照组存在差异,同时state 1的时间分数与病程呈负相关。在验证分析中发现结果具有可重复性。这些结果揭示了IGE患者认知功能改变的潜在神经生物学基础,为其认知功能改变提供了新的影像学依据。

    本研究发现state 6出现频率最高,其特征为全脑大规模的功能连接减弱,WANG等[15]将其称为“稀疏连接状态”,这种弱功能连接可能代表了各种不够明显或不够频繁而无法分离的其他状态的平均值[17]。MARUSAK等[17]在对儿童神经认知网络dFC进行研究时发现较弱的功能连接状态与自我集中思维有关。state 6平均停留时间最长,提示IGE患者更容易处于功能连接减弱的状态,功能连接是大脑不同区域之间信息传递的基础,反复出现这种连接模式可能意味着大脑更倾向保持在一个信息传输效率较低的状态[18]。与对照组相比,IGE组在“稀疏连接状态”(state 6)的时间分数增加,提示IGE患者dFC改变更倾向于这种状态,其中以ATTN与DMN之间、VN内部功能连接降低为主,进一步提示患者在这些脑网络区域的信息交流和传输减少,推测这可能是IGE患者脑功能连接损害的特征,可能为其脑功能损害提供新的线索,或许可以作为IGE患者发病机制的潜在神经影像标志物。

    本研究发现IGE组在state 5、state 6中存在dFC异常的脑网络,且大多数与DMN有关,主要表现为DMN内部及其与FPN、VN和ATTN之间的功能连接改变。以往研究[19-21]表明各种类型的癫痫都存在DMN功能的异常,揭示了其常见及特定的功能连接改变,DMN在意识、记忆、环境监测等各种认知过程中发挥着重要作用。LI等[22]发现全面性强直阵挛性癫痫患者DMN、FPN、VN及ATTN之间功能连接降低,表明患者这些子网络之间的功能关系异常,可能主要影响各网络之间的信息交流及传输。JIANG等[23]运用ICA分析发现难治性癫痫患者双侧内侧前额叶皮层功能连接降低,而双侧楔前叶功能连接升高,认为癫痫的发作过程可能会导致网络行为障碍,这可能影响DMN一致性,导致整体功能下降。本研究结果与上述研究结果相似。DMN的功能异常可能会影响其与其他网络的信息流通,导致DMN与其他RSN之间的功能整合受损。结合本研究结果,推测认知相关网络(尤其是DMN)之间过度频繁的信息交流可能在患者癫痫活动、认知功能障碍中起一定作用。

    本研究发现两组的dFC模式及强度在每个state中均相似,进一步分析两组间dFC指标,结果显示IGE组state 1的时间分数、平均停留时间及state 6的时间分数均高于对照组,结合dFC聚类分析结果,state 1特征为较广泛的局部网络内、网络间功能连接增强,主要表现在知觉网络内部及其与部分高级认知网络(ATTN与DMN)之间,在此状态的平均停留时间长,说明脑网络间功能连接相互作用更加稳定,提示IGE患者存在一定的认知功能障碍。此外,IGE组state 4的时间分数及平均停留时间均明显低于对照组,结合dFC聚类分析结果,state 4中强连接主要存在于部分高级认知网络内、知觉网络内及其网络间,即局部网络内、网络间功能连接增强。说明IGE患者在此状态中停留时间较正常对照组更短,更容易向其他state转换,提示IGE患者在state 4中局部网络内、网络间功能连接更活跃。NOMI等[24]研究认知灵活性与执行功能时发现在正常对照组中state间转换的概率与执行功能指标呈正相关,表明功能连接动态特性与认知活动的灵活性有关。本研究此状态的局部高级认知网络内、知觉网络内及网络间功能连接增强,推测可能是反映IGE患者思维灵活性及执行功能的细微变化的潜在指标。 “稀疏连接状态”下的平均停留时间更长,在多种脑部疾病的研究中被发现,例如精神分裂症[25]、脑卒中[15]、帕金森病[26]、双相障碍及抑郁症[27]和阿尔茨海默病[28]等。提示dFC的这种异常可能是不同大脑疾病类似功能障碍的潜在机制。

    本研究发现IGE患者在state 1时间分数与病程呈负相关,即病程越长,state 1时间分数越小、平均停留时间越短,功能连接更容易向其他state转换,那么此状态网络内及网络间的功能连接就越活跃。推测此状态IGE患者所有RSN的异常功能连接更频繁,可能导致IGE患者的认知、知觉功能受损越严重,进而证明病程越长,IGE患者认知、知觉功能受损越严重。先前研究[29]表明IGE患者存在广泛的认知功能、知觉功能的障碍,本研究与之相似,这对探究IGE患者认知功能障碍及其神经机制具有重要意义。

    既往研究[30-32]表明IGE患者脑结构(皮层厚度、脑沟深度)、脑功能连接及疾病发作频率与智商具有相关性。本研究未发现dFC指标与智商(包括VIQ、PIQ及FIQ)之间的相关性。分析其原因可能有以下几点:①本研究未对IGE不同亚型进行分组研究,不同亚型的癫痫发病机制不同,对智商的影响也可能存在差异性;②本研究未排除抗癫痫药物的影响,可能也是导致本研究结果的原因。结合既往研究,笔者认为通过分析dFC指标与病程、年龄及智商等临床变量之间的相关性,可能为IGE患者认知功能改变提供新的影像学依据。

    对于使用滑动窗口法进行dFC分析时,滑动窗口的最适长度设置尚无明确定论。既往研究[33]认为滑动窗口设置为30~60 s时能有效获取大脑的动态改变,能很好地反映认知状态的变化。有研究[15-16]验证分析并证实了动态功能连接方法具有可重复性和稳定性。本研究的验证分析中,设置了不同的滑动窗口长度及聚类数目验证结果的稳定性和可靠性。结果发现其主要结果具有可重复性,在与临床变量进行相关性分析时发现大部分结果具有一致性。因此,可以认为IGE组与对照组之间dFC的差异及与临床变量之间的相关性是稳定存在的。

    本研究尚存局限性:首先,小样本横断面研究难以体现结果的普适性,未针对IGE不同亚型进行分组研究、未排除抗癫痫药物的影响及对照组未做智力测试是本研究中存在的不足,后续将扩大样本量,将对照组智力测试、发作频率、最后一次发作时间等更多有意义的临床指标纳入研究范围,进行更精确的分组研究,制定更严格的纳排标准以减小抗癫痫药物对研究结果的影响;其次本研究属于观察性研究的范畴,虽然采用了匹配的方法控制混杂,但仍然无法完全消除混杂的影响,这也是一个重要的局限性;最后本研究为横断面研究,缺乏纵向对比研究,结果可能具有一定的局限性,未来可以从纵向角度对IGE患者认知功能改变进行更全面的研究。

    综上,常规MRI阴性的儿童及青少年IGE患者存在全脑功能连接的动态属性异常,并且IGE患者state 1时间分数与临床变量具有相关性,为儿童及青少年IGE患者神经机制研究提供新的影像学依据。

    *    *    *

    作者贡献声明 李清会负责论文构思、数据审编、正式分析、调查研究、研究方法、验证、可视化和初稿写作,张体江负责经费获取、研究项目管理、提供资源、软件、监督指导和审读与编辑写作。所有作者已经同意将文章提交给本刊,且对将要发表的版本进行最终定稿,并同意对工作的所有方面负责。

    Author Contribution  LI Qinghui is responsible for conceptualization, data curation, formal analysis, investigation, methodology, validation, visualization, and writing--original draft. ZHANG Tijiang is responsible for funding acquisition, project administration, resources, software, supervision, and writing--review and editing. All authors consented to the submission of the article to the Journal. All authors approved the final version to be published and agreed to take responsibility for all aspects of the work.

    利益冲突 所有作者均声明不存在利益冲突

    Declaration of Conflicting Interests All authors declare no competing interests.

  • 图  1   内在网络空间图

    Figure  1.   Spatial maps of the intrinsic networks

    IC: independent component; FPN: frontoparietal network; SMN: sensorimotor network; BGN: basal ganglia network; VN: visual network; DMN: default mode network; ATTN: attention network; AUD: auditory network.

    图  2   聚类数为6,窗宽为30 TRs,聚类分析结果(state 1~state 3)

    Figure  2.   The cluster analysis results derived with the cluster number of 6 and the window width of 30 TRs (state 1-state 3)

    The left column indicates the dFC matrices, with the number of windowed dFC in every state, the corresponding percentage, as well as the number of participants from every group who entered into the state. The right column represents the visualization of functional connectivity in each state. The functional connectivity matrix was screened using a threshold of 0.1 to display all independent components of functional networks. All abbreviations are explained in the notes to Fig 1 and Table 1.

    图  3   聚类数为6,窗宽为30 TRs,聚类分析结果(state 4~state 6)

    Figure  3.   The cluster analysis results derived with the cluster number of 6 and the window width of 30 TRs (state 4-state 6)

    The notes are the same as those for Fig 2.

    图  4   所有受试者所有时间窗的功能连接矩阵相关指标

    Figure  4.   The functional connection matrix correlation indicators for all time windows for all subjects

    A, Frequency; B, mean dwell time; C, number of transitions.

    图  5   IGE组与对照组dFC组间差异

    Figure  5.   Difference of dFC between the IGE group and the HC group

    The horizontal axis represents the 49 selected components, and the vertical axis represents the 49 components and the functional network to which they belong. All the abbreviations are explained in the note to Fig 1.

    图  6   IGE组与对照组dFC指标组间差异

    Figure  6.   Difference in the dFC indicators between the IGE group and the HC group

    IGE: idiopathic generalized epilepsy; HC: healthy control. The graphs were made using violin plots. The horizontal lines indicate group medians and the interquartile range (in solid and dashed line, respectively). The width of the violin plot represents the distribution density of each set of objects at the corresponding ordinate level. * P<0.05.

    图  7   IGE组state 1时间分数与病程相关分析

    Figure  7.   Correlation between state 1 time fraction and disease duration in the IGE group

    There was a negative correlation between disease duration and the fraction of time of state 1 in the IGE group (Spearman correlation, P<0.01, adjusted by Bonferroni correction).

    表  1   IGE组与对照组一般资料比较

    Table  1   Comparison of the general data between the IGE group and the control group

    Subject data IGE group (n=40) HC group (n=37) t/χ2 P
    (Male/female)/case 18/22 22/15 1.610 0.205a
    Age/yr.* 11.0 (7.3-13.0) 12.0 (10.0-14.0) −1.290 0.197b
    Education/year* 6.0 (2.3-7.8) 7.0 (5.0-8.5) −1.658 0.097b
    Hand dominance (right/left)/case 40/0 37/0
    Duration of illness/month* 36.5 (4.8-60.0)
    VIQ 88.4±24.2
    PIQ 81.6±19.5
    FIQ 83.7±23.0
     IGE: idiopathic generalized epilepsy; HC: healthy control; VIQ: verbal intelligence quotient; PIQ: performance intelligence quotient; FIQ: full scale intelligence quotient. Statistically significant differences between groups were defined as P<0.05. a Chi-square test was performed for statistical analysis. b Nonparametric tests were performed for statistical analysis. * Median (Q1-Q3).
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  • [1]

    THIJS R D, SURGES R, O'BRIEN T J, et al. Epilepsy in adults. Lancet, 2019, 393(10172): 689-701. doi: 10.1016/S0140-6736(18)32596-0.

    [2]

    FALCO-WALTER J. Epilepsy-definition, classification, pathophysiology, and epidemiology. Semin Neurol, 2020, 40(6): 617-623. doi: 10.1055/s-0040-1718719.

    [3]

    SINGH G, SANDER J W. The global burden of epilepsy report: Implications for low- and middle-income countries. Epilepsy Behav, 2020, 105: 106949. doi: 10.1016/j.yebeh.2020.106949.

    [4]

    NILO A, GELISSE P, CRESPEL A. Genetic/idiopathic generalized epilepsies: not so good as that. Rev Neurol (Paris), 2020, 176(6): 427-438. doi: 10.1016/j.neurol.2020.03.018.

    [5]

    GESCHE J, CHRISTENSEN J, HJALGRIM H, et al. Epidemiology and outcome of idiopathic generalized epilepsy in adults. Eur J Neurol, 2020, 27(4): 676-684. doi: 10.1111/ene.14142.

    [6]

    YANG Y, WANG F, ANDRADE-MACHADO R, et al. Disrupted functional connectivity patterns of the left inferior frontal gyrus subregions in benign childhood epilepsy with centrotemporal spikes. Transl Pediatr, 2022, 11(9): 1552-1561. doi: 10.21037/tp-22-270.

    [7]

    LI X, LIU H, ZHANG T. Resting-state functional MRI study of conventional MRI-negative intractable epilepsy in children. Front Hum Neurosci, 2024, 18: 1337294. doi: 10.3389/fnhum.2024.1337294.

    [8]

    PRETI M G, BOLTON T A, Van De VILLE D. The dynamic functional connectome: state-of-the-art and perspectives. Neuroimage, 2017, 160: 41-54. doi: 10.1016/j.neuroimage.2016.12.061.

    [9]

    LIU F, WANG Y, LI M, et al. Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic-clonic seizure. Hum Brain Mapp, 2017, 38(2): 957-973. doi: 10.1002/hbm.23430.

    [10]

    JIA X, XIE Y, DONG D, et al. Reconfiguration of dynamic large-scale brain network functional connectivity in generalized tonic-clonic seizures. Hum Brain Mapp, 2020, 41(1): 67-79. doi: 10.1002/hbm.24787.

    [11]

    ZHENG R, CHEN Y, JIANG Y, et al. Abnormal dynamic functional connectivity in first-episode, drug-naïve adolescents with major depressive disorder. J Neurosci Res, 2022, 100(7): 1463-1475. doi: 10.1002/jnr.25047.

    [12]

    ALLEN E A, ERHARDT E B, DAMARAJU E, et al. A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci, 2011, 5: 2. doi: 10.3389/fnsys.2011.00002.

    [13]

    SMITH S M, FOX P T, MILLER K L, et al. Correspondence of the brain's functional architecture during activation and rest. Proc Natl Acad Sci U S A, 2009, 106(31): 13040-13045. doi: 10.1073/pnas.0905267106.

    [14]

    ABROL A, DAMARAJU E, MILLER R L, et al. Replicability of time-varying connectivity patterns in large resting state fMRI samples. Neuroimage, 2017, 163: 160-176. doi: 10.1016/j.neuroimage.2017.09.020.

    [15]

    WANG Y, WANG C, MIAO P, et al. An imbalance between functional segregation and integration in patients with pontine stroke: a dynamic functional network connectivity study. Neuroimage Clin, 2020, 28: 102507. doi: 10.1016/j.nicl.2020.102507.

    [16]

    LIU J, LI X, XUE K, et al. Abnormal dynamics of functional connectivity in first-episode and treatment-naive patients with obsessive-compulsive disorder. Psychiatry Clin Neurosci, 2021, 75(1): 14-22. doi: 10.1111/pcn.13162.

    [17]

    MARUSAK H A, CALHOUN V D, BROWN S, et al. Dynamic functional connectivity of neurocognitive networks in children. Hum Brain Mapp, 2017, 38(1): 97-108. doi: 10.1002/hbm.23346.

    [18]

    GU Y, LIN Y, HUANG L, et al. Abnormal dynamic functional connectivity in Alzheimer's disease. CNS Neurosci Ther, 2020, 26(9): 962-971. doi: 10.1111/cns.13387.

    [19]

    ZANÃO T A, LOPES T M, De CAMPOS B M, et al. Patterns of default mode network in temporal lobe epilepsy with and without hippocampal sclerosis. Epilepsy Behav, 2021, 121(Pt B): 106523. doi: 10.1016/j.yebeh.2019.106523.

    [20]

    CACIAGLI L, PAQUOLA C, HE X, et al. Disorganization of language and working memory systems in frontal versus temporal lobe epilepsy. Brain, 2023, 146(3): 935-953. doi: 10.1093/brain/awac150.

    [21]

    LI Y, RAN Y, YAO M, et al. Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: a resting-state fMRI study. Neurobiol Dis, 2024, 192: 106425. doi: 10.1016/j.nbd.2024.106425.

    [22]

    LI Y, RAN Y, CHEN Q. Abnormal static and dynamic functional network connectivity of the whole-brain in children with generalized tonic-clonic seizures. Front Neurosci, 2023, 17: 1236696. doi: 10.3389/fnins.2023.1236696.

    [23]

    JIANG L W, QIAN R B, FU X M, et al. Altered attention networks and DMN in refractory epilepsy: a resting-state functional and causal connectivity study. Epilepsy Behav, 2018, 88: 81-86. doi: 10.1016/j.yebeh.2018.06.045.

    [24]

    NOMI J S, VIJ S G, DAJANI D R, et al. Chronnectomic patterns and neural flexibility underlie executive function. Neuroimage, 2017, 147: 861-871. doi: 10.1016/j.neuroimage.2016.10.026.

    [25]

    YANG W, XU X, WANG C, et al. Alterations of dynamic functional connectivity between visual and executive-control networks in schizophrenia. Brain Imaging Behav, 2022, 16(3): 1294-1302. doi: 10.1007/s11682-021-00592-8.

    [26]

    CAO Y, SI Q, TONG R, et al. Abnormal dynamic functional connectivity changes correlated with non-motor symptoms of Parkinson's disease. Front Neurosci, 2023, 17: 1116111. doi: 10.3389/fnins.2023.1116111.

    [27]

    WANG J, WANG Y, HUANG H, et al. Abnormal dynamic functional network connectivity in unmedicated bipolar and major depressive disorders based on the triple-network model. Psychol Med, 2020, 50(3): 465-474. doi: 10.1017/S003329171900028X.

    [28]

    ZANG F, LIU X, FAN D, et al. Dynamic functional network connectivity and its association with lipid metabolism in Alzheimer's disease. CNS Neurosci Ther, 2024, 30(9): e70029. doi: 10.1111/cns.70029.

    [29]

    RATCLIFFE C, WANDSCHNEIDER B, BAXENDALE S, et al. Cognitive function in genetic generalized epilepsies: insights from neuropsychology and neuroimaging. Front Neurol, 2020, 11: 144. doi: 10.3389/fneur.2020.00144.

    [30]

    HU J, RAN H, CHEN G, et al. Altered neurovascular coupling in children with idiopathic generalized epilepsy. CNS Neurosci Ther, 2023, 29(2): 609-618. doi: 10.1111/cns.14039.

    [31]

    TOSUN D, SIDDARTH P, TOGA A W, et al. Effects of childhood absence epilepsy on associations between regional cortical morphometry and aging and cognitive abilities. Hum Brain Mapp, 2011, 32(4): 580-591. doi: 10.1002/hbm.21045.

    [32]

    THOMPSON P J, DUNCAN J S. Cognitive decline in severe intractable epilepsy. Epilepsia, 2005, 46(11): 1780-1787. doi: 10.1111/j.1528-1167.2005.00279.

    [33]

    FAGHIRI A, STEPHEN J M, WANG Y P, et al. Changing brain connectivity dynamics: From early childhood to adulthood. Hum Brain Mapp, 2018, 39(3): 1108-1117. doi: 10.1002/hbm.23896.

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  • 收稿日期:  2024-08-01
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