Use of Neuroanatomical Pattern Classification to Identify Subjects in At-Risk Mental States of Psychosis and Predict Disease Transition

作者：Nikolaos Koutsouleris, Eva Meisenzahl, Christos Davatzikos, Ronald Bottlender, Thomas Frodl, Johanna Scheuerecker, Gisela Schmitt, Thomas Zetzsche, P. Decker, Maximilian F. Reiser, Hans‐Jürgen Möller, Christian Gaser · 发表于：Archives of General Psychiatry · 年份：2009 · DOI：10.1001/archgenpsychiatry.2009.62 · 被引用次数：408 · 研究领域：Schizophrenia research and treatment、Functional Brain Connectivity Studies、Bipolar Disorder and Treatment

CONTEXT: Identification of individuals at high risk of developing psychosis has relied on prodromal symptomatology. Recently, machine learning algorithms have been successfully used for magnetic resonance imaging-based diagnostic classification of neuropsychiatric patient populations. OBJECTIVE: To determine whether multivariate neuroanatomical pattern classification facilitates identification of individuals in different at-risk mental states (ARMS) of psychosis and enables the prediction of disease transition at the individual level. DESIGN: Multivariate neuroanatomical pattern classification was performed on the structural magnetic resonance imaging data of individuals in early or late ARMS vs healthy controls (HCs). The predictive power of the method was then evaluated by categorizing the baseline imaging data of individuals with transition to psychosis vs those without transition vs HCs after 4 years of clinical follow-up. Classification generalizability was estimated by cross-validation and by categorizing an independent cohort of 45 new HCs. SETTING: Departments of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany. PARTICIPANTS: The first classification analysis included 20 early and 25 late at-risk individuals and 25 matched HCs. The second analysis consisted of 15 individuals with transition, 18 without transition, and 17 matched HCs. MAIN OUTCOME MEASURES: Specificity, sensitivity, and accuracy of classification. RESULTS: The 3-group, cross...