Valli Isabel, Marquand Andre F, Mechelli Andrea, Raffin Marie, Allen Paul, Seal Marc L, McGuire Philip
Department of Psychosis Studies, Institute of Psychiatry, King's College London , London , UK.
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands; Centre for Neuroimaging Sciences, King's College London, London, UK.
Front Psychiatry. 2016 Apr 8;7:52. doi: 10.3389/fpsyt.2016.00052. eCollection 2016.
The identification of individuals at high risk of developing psychosis is entirely based on clinical assessment, associated with limited predictive potential. There is, therefore, increasing interest in the development of biological markers that could be used in clinical practice for this purpose. We studied 25 individuals with an at-risk mental state for psychosis and 25 healthy controls using structural MRI, and functional MRI in conjunction with a verbal memory task. Data were analyzed using a standard univariate analysis, and with support vector machine (SVM), a multivariate pattern recognition technique that enables statistical inferences to be made at the level of the individual, yielding results with high translational potential. The application of SVM to structural MRI data permitted the identification of individuals at high risk of psychosis with a sensitivity of 68% and a specificity of 76%, resulting in an accuracy of 72% (p < 0.001). Univariate volumetric between-group differences did not reach statistical significance. By contrast, the univariate fMRI analysis identified between-group differences (p < 0.05 corrected), while the application of SVM to the same data did not. Since SVM is well suited at identifying the pattern of abnormality that distinguishes two groups, whereas univariate methods are more likely to identify regions that individually are most different between two groups, our results suggest the presence of focal functional abnormalities in the context of a diffuse pattern of structural abnormalities in individuals at high clinical risk of psychosis.
对有患精神病高风险个体的识别完全基于临床评估，其预测潜力有限。因此，人们对开发可用于此目的临床实践的生物标志物越来越感兴趣。我们使用结构磁共振成像（MRI）以及结合言语记忆任务的功能MRI，对25名有精神病风险精神状态的个体和25名健康对照进行了研究。数据采用标准单变量分析以及支持向量机（SVM）进行分析，支持向量机是一种多变量模式识别技术，能够在个体水平上进行统计推断，产生具有高转化潜力的结果。将支持向量机应用于结构MRI数据能够识别出有精神病高风险的个体，其灵敏度为68%，特异性为76%，准确率为72%（p < 0.001）。组间单变量体积差异未达到统计学显著性。相比之下，单变量功能MRI分析发现了组间差异（校正后p < 0.05），而将支持向量机应用于相同数据时则未发现。由于支持向量机非常适合识别区分两组的异常模式，而单变量方法更有可能识别两组之间个体差异最大的区域，我们的结果表明，在临床精神病高风险个体的弥漫性结构异常背景下存在局灶性功能异常。
