Fırat Zeynep, Er Füsun, Noyan Handan, Ekinci Gazanfer, Üçok Alp, Uluğ Aziz M, Aktekin Berrin
Department of Radiology, Yeditepe University Hospitals, Kosuyolu, 34718, Istanbul, Turkey.
Department of Information Systems Engineering, Piri Reis University, Istanbul, Turkey.
Neuroradiology. 2024 Jul;66(7):1083-1092. doi: 10.1007/s00234-024-03317-y. Epub 2024 Feb 28.
This study aims to assess the diagnostic power of brain asymmetry indices and neuropsychological tests for differentiating mesial temporal lobe epilepsy (MTLE) and schizophrenia (SCZ).
We studied a total of 39 women including 13 MTLE, 13 SCZ, and 13 healthy individuals (HC). A neuropsychological test battery (NPT) was administered and scored by an experienced neuropsychologist, and NeuroQuant (CorTechs Labs Inc., San Diego, California) software was used to calculate brain asymmetry indices (ASI) for 71 different anatomical regions of all participants based on their 3D T1 MR imaging scans.
Asymmetry indices measured from 10 regions showed statistically significant differences between the three groups. In this study, a multi-class linear discriminant analysis (LDA) model was built based on a total of fifteen variables composed of the most five significantly informative NPT scores and ten significant asymmetry indices, and the model achieved an accuracy of 87.2%. In pairwise classification, the accuracy for distinguishing MTLE from either SCZ or HC was 94.8%, while the accuracy for distinguishing SCZ from either MTLE or HC was 92.3%.
The ability to differentiate MTLE from SCZ using neuroradiological and neuropsychological biomarkers, even within a limited patient cohort, could make a substantial contribution to research in larger patient groups using different machine learning techniques.
本研究旨在评估脑不对称指数和神经心理学测试对鉴别内侧颞叶癫痫（MTLE）和精神分裂症（SCZ）的诊断能力。
我们共研究了39名女性，其中包括13名内侧颞叶癫痫患者、13名精神分裂症患者和13名健康个体（HC）。由一位经验丰富的神经心理学家实施并评分一套神经心理学测试组合（NPT），并使用NeuroQuant（加利福尼亚州圣地亚哥的CorTechs Labs公司）软件根据所有参与者的3D T1磁共振成像扫描计算71个不同解剖区域的脑不对称指数（ASI）。
从10个区域测量的不对称指数在三组之间显示出统计学上的显著差异。在本研究中，基于总共15个变量构建了一个多类线性判别分析（LDA）模型，这些变量由五个最具显著信息的NPT分数和十个显著的不对称指数组成，该模型的准确率达到了87.2%。在两两分类中，区分内侧颞叶癫痫与精神分裂症或健康个体的准确率为94.8%，而区分精神分裂症与内侧颞叶癫痫或健康个体的准确率为92.3%。
即使在有限的患者队列中，使用神经放射学和神经心理学生物标志物区分内侧颞叶癫痫和精神分裂症的能力，也可能对使用不同机器学习技术的更大患者群体的研究做出重大贡献。
