The presence, nature and network characteristics of behavioural phenotypes in temporal lobe epilepsy.
The presence, nature and network characteristics of behavioural phenotypes in temporal lobe epilepsy.
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DOI：
10.1093/braincomms/fcad095
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发表时间：
2023
影响因子：
4.8
通讯作者：
Hermann, Bruce P.
中科院分区：
文献类型：
--
作者：
Struck, Aaron F.;Garcia-Ramos, Camille;Nair, Veena A.;Prabhakaran, Vivek;Dabbs, Kevin;Boly, Melanie;Conant, Lisa L.;Binder, Jeffrey R.;Meyerand, Mary E.;Hermann, Bruce P.
颞叶癫痫和精神病理学之间的关系有一个长期的和有争议的历史，不同的意见，关于存在，性质和严重程度的情绪行为问题，在这个病人群体。为了解决这些争议，我们采取了一种新的以人为本的方法，通过应用无监督机器学习技术来识别潜在的潜在群体或行为表型。解决的是不同的精神病理学的配置文件，其相关的频率，模式和严重程度和破坏的形态和网络特性，所确定的潜在群体。来自癫痫连接组项目的114名患者和83名对照者接受了Achenbach基于经验的评估量表，通过无监督的机器学习分析法分析了6个面向精神障碍诊断和统计手册的量表，以识别潜在的患者群体。将识别的聚类与对照组以及彼此进行对比，以表征其与社会人口统计学、临床癫痫以及形态和功能成像网络特征的关联。通过行为和生活质量的其他措施的行为表型的同时有效性进行了检查。与对照组相比，患者总体表现出显著更高（异常）的评分。然而，聚类分析确定了三个潜在的群体：（i）未受影响，与对照组相比没有规模升高（第1组，37%）;（ii）轻度精神障碍，其特征为与对照组相比，几个精神障碍诊断和统计手册导向量表显著升高（第2组，42%）;和（iii）与对照组和其他颞叶癫痫行为表型组相比，所有量表均显著升高的重度癫痫（第3组，21%）。行为表型分组的并行有效性通过与独立测量的异常的相同逐步联系来证明，包括国立卫生研究院情绪测试和生活质量指标。聚类成员与社会人口统计学（惯用手和教育），认知（处理速度），临床癫痫（强直阵挛发作的存在和终身数量）和神经影像学特征（皮质体积和厚度以及形态学和静息态功能MRI的全局图论指标）之间存在显著相关性。日益分散的体积异常和底层网络特性的广泛破坏与最异常的行为表型相关。这些患者的精神病理学特征是一系列离散的潜伏组，这些潜伏组具有伴随的社会人口统计学、临床和神经影像学相关性。潜在的神经生物学模式表明，精神病理学的程度与日益分散的异常大脑网络有关。与认知类似，机器学习方法支持癫痫合并症的新发展分类。Struck等人证明，虽然与对照组（n = 83）相比，颞叶癫痫患者（n = 114）在精神病理学指标上表现出显著异常的评分，但聚类分析确定了三个潜在的患者组，其特征在于从无到严重的不同程度的情绪-行为困扰，这些聚类与社会人口统计学、临床、成像和网络特征有关。
The relationship between temporal lobe epilepsy and psychopathology has had a long and contentious history with diverse views regarding the presence, nature and severity of emotional–behavioural problems in this patient population. To address these controversies, we take a new person-centred approach through the application of unsupervised machine learning techniques to identify underlying latent groups or behavioural phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity and the disruptions in morphological and network properties that underlie the identified latent groups. A total of 114 patients and 83 controls from the Epilepsy Connectome Project were administered the Achenbach System of Empirically Based Assessment inventory from which six Diagnostic and Statistical Manual of Mental Disorders-oriented scales were analysed by unsupervised machine learning analytics to identify latent patient groups. Identified clusters were contrasted to controls as well as to each other in order to characterize their association with sociodemographic, clinical epilepsy and morphological and functional imaging network features. The concurrent validity of the behavioural phenotypes was examined through other measures of behaviour and quality of life. Patients overall exhibited significantly higher (abnormal) scores compared with controls. However, cluster analysis identified three latent groups: (i) unaffected, with no scale elevations compared with controls (Cluster 1, 37%); (ii) mild symptomatology characterized by significant elevations across several Diagnostic and Statistical Manual of Mental Disorders-oriented scales compared with controls (Cluster 2, 42%); and (iii) severe symptomatology with significant elevations across all scales compared with controls and the other temporal lobe epilepsy behaviour phenotype groups (Cluster 3, 21%). Concurrent validity of the behavioural phenotype grouping was demonstrated through identical stepwise links to abnormalities on independent measures including the National Institutes of Health Toolbox Emotion Battery and quality of life metrics. There were significant associations between cluster membership and sociodemographic (handedness and education), cognition (processing speed), clinical epilepsy (presence and lifetime number of tonic–clonic seizures) and neuroimaging characteristics (cortical volume and thickness and global graph theory metrics of morphology and resting-state functional MRI). Increasingly dispersed volumetric abnormalities and widespread disruptions in underlying network properties were associated with the most abnormal behavioural phenotype. Psychopathology in these patients is characterized by a series of discrete latent groups that harbour accompanying sociodemographic, clinical and neuroimaging correlates. The underlying neurobiological patterns suggest that the degree of psychopathology is linked to increasingly dispersed abnormal brain networks. Similar to cognition, machine learning approaches support a novel developing taxonomy of the comorbidities of epilepsy. Struck et al. demonstrate that while temporal lobe epilepsy patients (n = 114) exhibit significantly abnormal scores across measures of psychopathology compared with controls (n = 83), cluster analysis identifies three latent patient groups characterized by varying levels of emotional–behavioural distress ranging from none to severe, the clusters linked to sociodemographic, clinical, imaging and network characteristics.
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