Rutgers University, 195 University Ave, Newark, NJ 07102, United States.
Neuroimage Clin. 2018 Feb 1;18:367-376. doi: 10.1016/j.nicl.2018.01.014. eCollection 2018.
Autism and schizophrenia share overlapping genetic etiology, common changes in brain structure and common cognitive deficits. A number of studies using resting state fMRI have shown that machine learning algorithms can distinguish between healthy controls and individuals diagnosed with either autism spectrum disorder or schizophrenia. However, it has not yet been determined whether machine learning algorithms can be used to distinguish between the two disorders. Using a linear support vector machine, we identify features that are most diagnostic for each disorder and successfully use them to classify an independent cohort of subjects. We find both common and divergent connectivity differences largely in the default mode network as well as in salience, and motor networks. Using divergent connectivity differences, we are able to distinguish autistic subjects from those with schizophrenia. Understanding the common and divergent connectivity changes associated with these disorders may provide a framework for understanding their shared cognitive deficits.
自闭症和精神分裂症具有重叠的遗传病因，大脑结构存在共同变化，认知缺陷也相同。多项使用静息态 fMRI 的研究表明，机器学习算法可以区分健康对照者和被诊断为自闭症谱系障碍或精神分裂症的个体。然而，目前还不清楚机器学习算法是否可以用于区分这两种疾病。我们使用线性支持向量机来识别对每种疾病最具诊断意义的特征，并成功地将它们用于分类一个独立的受试者队列。我们发现，无论是在默认模式网络还是在突显网络和运动网络中，都存在共同和不同的连通性差异。我们利用不同的连通性差异，能够将自闭症患者与精神分裂症患者区分开来。了解与这些疾病相关的共同和不同的连通性变化可能为理解它们共同的认知缺陷提供一个框架。
