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Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: Multivariate analysis of task-free fMRI

机译:严重癫痫Lennox-Gastaut表型网络特征异常的脑区:无任务fmRI的多变量分析

摘要

OBJECTIVE: Lennox-Gastaut syndrome, and the similar but less tightly defined Lennox-Gastaut phenotype, describe patients with severe epilepsy, generalized epileptic discharges, and variable intellectual disability. Our previous functional neuroimaging studies suggest that abnormal diffuse association network activity underlies the epileptic discharges of this clinical phenotype. Herein we use a data-driven multivariate approach to determine the spatial changes in local and global networks of patients with severe epilepsy of the Lennox-Gastaut phenotype. METHODS: We studied 9 adult patients and 14 controls. In 20 min of task-free blood oxygen level-dependent functional magnetic resonance imaging data, two metrics of functional connectivity were studied: Regional homogeneity or local connectivity, a measure of concordance between each voxel to a focal cluster of adjacent voxels; and eigenvector centrality, a global connectivity estimate designed to detect important neural hubs. Multivariate pattern analysis of these data in a machine-learning framework was used to identify spatial features that classified disease subjects. RESULTS: Multivariate pattern analysis was 95.7% accurate in classifying subjects for both local and global connectivity measures (22/23 subjects correctly classified). Maximal discriminating features were the following: increased local connectivity in frontoinsular and intraparietal areas; increased global connectivity in posterior association areas; decreased local connectivity in sensory (visual and auditory) and medial frontal cortices; and decreased global connectivity in the cingulate cortex, striatum, hippocampus, and pons. SIGNIFICANCE: Using a data-driven analysis method in task-free functional magnetic resonance imaging, we show increased connectivity in critical areas of association cortex and decreased connectivity in primary cortex. This supports previous findings of a critical role for these association cortical regions as a final common pathway in generating the Lennox-Gastaut phenotype. Abnormal function of these areas is likely to be important in explaining the intellectual problems characteristic of this disorder.
机译:目的:Lennox-Gastaut综合征和相似但定义不严的Lennox-Gastaut表型描述了患有严重癫痫,全身性癫痫发作和智力障碍的患者。我们以前的功能性神经影像学研究表明,异常弥漫性缔合网络活动是该临床表型的癫痫放电的基础。在这里,我们使用数据驱动的多元方法来确定伦诺克斯-盖斯特表型严重癫痫患者的局部和全局网络中的空间变化。方法:我们研究了9名成年患者和14名对照。在20分钟无任务血氧水平依赖性功能磁共振成像数据中,研究了两个功能连通性指标:区域同质性或局部连通性,一种度量每个体素与相邻体素的焦点簇之间的一致性的方法;和特征向量中心性,一种用于检测重要神经中枢的全局连通性估计。在机器学习框架中对这些数据进行多元模式分析,以识别对疾病受试者进行分类的空间特征。结果:多变量模式分析在对主题进行局部和全局连通性度量的分类中准确率为95.7%(正确分类的主题为22/23)。最大的区别特征如下:额叶和顶叶内区域的局部连通性增加;后部关联区域的全球连接性增强;感觉(视觉和听觉)和额叶内侧皮质的局部连通性降低;以及扣带回皮层,纹状体,海马和脑桥的整体连通性降低。重要性:在无任务功能磁共振成像中使用数据驱动的分析方法,我们显示了关联皮层关键区域的连接性增加,而初级皮层的连接性下降。这支持了先前的发现,即这些关联的皮质区域在产生Lennox-Gastaut表型中作为最终的通用途径至关重要。这些区域的异常功能对于解释这种疾病的智力问题可能很重要。

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