Pergola Giulio, Trizio Silvestro, Di Carlo Pasquale, Taurisano Paolo, Mancini Marina, Amoroso Nicola, Nettis Maria Antonietta, Andriola Ileana, Caforio Grazia, Popolizio Teresa, Rampino Antonio, Di Giorgio Annabella, Bertolino Alessandro, Blasi Giuseppe
Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari 'Aldo Moro', Piazza Giulio Cesare 11, 70124 Bari, Italy.
National Institute of Nuclear of Physics-Branch of Bari, Via E. Orabona 4, 70125 Bari, Italy; Interuniversity Department of Physics 'M. Merlin', University of Bari 'Aldo Moro', Via E. Orabona 4, 70125 Bari, Italy.
Schizophr Res. 2017 Feb;180:13-20. doi: 10.1016/j.schres.2016.07.005. Epub 2016 Jul 21.
Previous evidence suggests reduced thalamic grey matter volume (GMV) in patients with schizophrenia (SCZ). However, it is not considered an intermediate phenotype for schizophrenia, possibly because previous studies did not assess the contribution of individual thalamic nuclei and employed univariate statistics. Here, we hypothesized that multivariate statistics would reveal an association of GMV in different thalamic nuclei with familial risk for schizophrenia. We also hypothesized that accounting for the heterogeneity of thalamic GMV in healthy controls would improve the detection of subjects at familial risk for the disorder. We acquired MRI scans for 96 clinically stable SCZ, 55 non-affected siblings of patients with schizophrenia (SIB), and 249 HC. The thalamus was parceled into seven regions of interest (ROIs). After a canonical univariate analysis, we used GMV estimates of thalamic ROIs, together with total thalamic GMV and premorbid intelligence, as features in Random Forests to classify HC, SIB, and SCZ. Then, we computed a Misclassification Index for each individual and tested the improvement in SIB detection after excluding a subsample of HC misclassified as patients. Random Forests discriminated SCZ from HC (accuracy=81%) and SIB from HC (accuracy=75%). Left anteromedial thalamic volumes were significantly associated with both multivariate classifications (p<0.05). Excluding HC misclassified as SCZ improved greatly HC vs. SIB classification (Cohen's d=1.39). These findings suggest that multivariate statistics identify a familial background associated with thalamic GMV reduction in SCZ. They also suggest the relevance of inter-individual variability of GMV patterns for the discrimination of individuals at familial risk for the disorder.
先前的证据表明，精神分裂症（SCZ）患者的丘脑灰质体积（GMV）减少。然而，它不被认为是精神分裂症的中间表型，可能是因为先前的研究没有评估各个丘脑核的贡献，并且采用的是单变量统计方法。在此，我们假设多变量统计将揭示不同丘脑核中的GMV与精神分裂症家族风险之间的关联。我们还假设，考虑健康对照中丘脑GMV的异质性将提高对该疾病家族风险个体的检测能力。我们对96名临床稳定的SCZ患者、55名精神分裂症患者的未患病同胞（SIB）和249名健康对照（HC）进行了MRI扫描。丘脑被划分为七个感兴趣区域（ROI）。在进行典型的单变量分析后，我们将丘脑ROI的GMV估计值，连同丘脑总GMV和病前智力，作为随机森林中的特征，对HC、SIB和SCZ进行分类。然后，我们计算每个个体的错误分类指数，并在排除被误分类为患者的HC子样本后，测试SIB检测的改善情况。随机森林能够区分SCZ和HC（准确率=81%）以及SIB和HC（准确率=75%）。左前内侧丘脑体积与两种多变量分类均显著相关（p<0.05）。排除被误分类为SCZ的HC后，HC与SIB的分类有了很大改善（科恩d值=1.39）。这些发现表明，多变量统计识别出了与SCZ中丘脑GMV减少相关的家族背景。它们还表明GMV模式的个体间变异性对于区分该疾病家族风险个体具有相关性。
