Yun Je-Yeon, Kim Sung Nyun, Lee Tae Young, Chon Myong-Wuk, Kwon Jun Soo
Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
SNU-MRC, Institute of Human Behavioral Medicine, Seoul, Republic of Korea.
Hum Brain Mapp. 2016 Mar;37(3):1051-65. doi: 10.1002/hbm.23083. Epub 2015 Dec 17.
Neocortical phenotype of cortical surface area (CSA) and thickness (CT) are influenced by distinctive genetic factors and undergo differential developmental trajectories, which could be captured using the individualized cortical structural covariance (ISC). Disturbed patterns of neocortical development and maturation underlie the perceptual disturbance of psychosis including auditory hallucination (AH). To demonstrate the utility of selected ISC features as primal biomarker of AH in first-episode psychosis (FEP) subjects experiencing AH (FEP-AH), we employed herein a support vector machine (SVM). A total of 147 subjects (FEP-AH, n = 27; FEP-NAH, n = 24; HC, n = 96) underwent T1 -weighted magnetic resonance imaging at 3T. The FreeSurfer software suite was used for cortical parcellation, with the CSA-ISC and CT-ISC then calculated. The most informative ISCs showing statistical significance (P < 0.001) across every run of leave-one-out group-comparison were aligned according to the absolute value of averaged t-statistics and were packaged into candidate feature sets for classification analysis using the SVM. An optimal feature set comprising three CSA-ISCs, including the intraparietal sulcus, Broca's complex, and the anterior insula, distinguished FEP-AH from FEP-NAH subjects with 83.6% accuracy (sensitivity = 82.8%; specificity = 85.7%). Furthermore, six CT-ISCs encompassing the executive control network and Wernicke's module classified FEP-AH from FEP-NAH subjects with 82.3% accuracy (sensitivity = 79.5%; specificity = 88.6%). Finally, extended sets of ISCs related to the default-mode network distinguished FEP-AH or FEP-NAH from HC subjects with 89.0-93.0% accuracy (sensitivity = 88.4-93.4%; specificity = 89.0-94.1%). This study established a distinctive intermediate phenotype of biological proneness for AH in FEP using CSA-ISCs as well as a state marker of disease progression using CT-ISCs.
新皮质表面积(CSA)和厚度(CT)的表型受独特的遗传因素影响,并经历不同的发育轨迹,这可以通过个体化皮质结构协方差(ISC)来捕捉。新皮质发育和成熟的紊乱模式是包括幻听(AH)在内的精神病感知障碍的基础。为了证明所选ISC特征作为首次发作精神病(FEP)患者中AH的原始生物标志物的效用,我们在此采用了支持向量机(SVM)。共有147名受试者(FEP-AH组,n = 27;FEP-无AH组,n = 24;健康对照组,n = 96)在3T条件下接受了T1加权磁共振成像。使用FreeSurfer软件套件进行皮质分区,然后计算CSA-ISC和CT-ISC。在每次留一法组间比较中显示出统计学显著性(P < 0.001)的最具信息性的ISC,根据平均t统计量的绝对值进行排列,并打包成候选特征集,用于使用SVM进行分类分析。一个由三个CSA-ISC组成的最佳特征集,包括顶内沟、布洛卡区和前岛叶,以83.6%的准确率区分FEP-AH组和FEP-无AH组受试者(敏感性 = 82.8%;特异性 = 85.7%)。此外,六个涵盖执行控制网络和韦尼克模块的CT-ISC以82.3%的准确率区分FEP-AH组和FEP-无AH组受试者(敏感性 = 79.5%;特异性 = 88.6%)。最后,与默认模式网络相关的扩展ISC集以89.0 - 93.0%的准确率区分FEP-AH组或FEP-无AH组与健康对照组受试者(敏感性 = 88.4 - 93.4%;特异性 = 89.0 - 94.1%)。本研究使用CSA-ISC建立了FEP中AH的独特生物学易感性中间表型,并使用CT-ISC建立了疾病进展的状态标志物。