Struck Aaron F, Garcia-Ramos Camille, Prabhakaran Vivek, Nair Veena, Adluru Nagesh, Adluru Anusha, Almane Dace, Jones Jana E, Hermann Bruce P
Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Department of Neurology, William S Middleton Veterans Administration Hospital, Madison, Wisconsin, USA.
Epilepsia. 2025 Jan;66(1):253-264. doi: 10.1111/epi.18167. Epub 2024 Nov 2.
Application of cluster analytic procedures has advanced understanding of the cognitive heterogeneity inherent in diverse epilepsy syndromes and the associated clinical and neuroimaging features. Application of this unsupervised machine learning approach to the neuropsychological performance of persons with juvenile myoclonic epilepsy (JME) has yet to be attempted, which is the intent of this investigation.
A total of 77 JME participants, 19 unaffected siblings, and 44 unrelated controls, 12 to 25 years of age, were administered a comprehensive neuropsychological battery (intelligence, language, memory, executive function, and processing speed), which was subjected to factor analysis followed by K-means clustering of the resultant factor scores. Identified cognitive phenotypes were characterized and related to clinical, family, sociodemographic, and cortical and subcortical imaging features.
Factor analysis revealed three underlying cognitive dimensions (general ability, speed/response inhibition, and learning/memory), with JME participants performing worse than unrelated controls across all factor scores, and unaffected siblings performing worse than unrelated controls on the general mental ability and learning/memory factors, with no JME vs sibling differences. K-means clustering of the factor scores revealed three latent groups including above average (31.4% of participants), average (52.1%), and abnormal performance (16.4%). Participant groups differed in their distributions across the latent groups (p < 0.001), with 23% JME, 22% siblings, and 2% unrelated controls in the abnormal performance group; and 18% JME, 21% siblings, and 59% unrelated controls in the above average group. Clinical epilepsy variables were unassociated with cluster membership, whereas family factors (lower parental education) and abnormally increased thickness and/or volume in the frontal, parietal, and temporal-occipital regions were associated with the abnormal cognition group.
Distinct cognitive phenotypes characterize the spectrum of neuropsychological performance of patients with JME for which there is familial (sibling) aggregation. Phenotypic membership was associated with parental (education) and imaging characteristics (increased cortical thickness and volume) but not basic clinical seizure features.
聚类分析程序的应用加深了人们对多种癫痫综合征所固有的认知异质性以及相关临床和神经影像学特征的理解。将这种无监督机器学习方法应用于青少年肌阵挛癫痫（JME）患者的神经心理学表现尚未有人尝试，这正是本研究的目的。
共有77名JME参与者、19名未受影响的同胞以及44名12至25岁的无关对照者接受了一套全面的神经心理测试（智力、语言、记忆、执行功能和处理速度），对测试结果进行因子分析，然后对所得因子得分进行K均值聚类。对确定的认知表型进行特征描述，并将其与临床、家庭、社会人口统计学以及皮质和皮质下影像学特征相关联。
因子分析揭示了三个潜在的认知维度（一般能力、速度/反应抑制和学习/记忆），JME参与者在所有因子得分上的表现均比无关对照者差，未受影响的同胞在一般智力能力和学习/记忆因子上的表现比无关对照者差，JME与同胞之间无差异。因子得分的K均值聚类揭示了三个潜在组，包括高于平均水平（31.4%的参与者）、平均水平（52.1%）和异常表现（16.4%）。参与者组在潜在组中的分布存在差异（p < 0.001），异常表现组中JME占23%、同胞占22%、无关对照者占2%；高于平均水平组中JME占18%、同胞占21%、无关对照者占59%。临床癫痫变量与聚类成员身份无关，而家庭因素（父母教育程度较低）以及额叶、顶叶和颞枕叶区域厚度和/或体积异常增加与异常认知组相关。
不同的认知表型表征了JME患者神经心理表现的范围，且存在家族（同胞）聚集现象。表型成员身份与父母（教育程度）和影像学特征（皮质厚度和体积增加）相关，但与基本临床发作特征无关。
