CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: BRAIN SIGNATURES FROM MULTIVARIATE ANALYSIS

Brain signatures identified by bottom-up unsupervised machine learning: three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI). We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.本数据集通过自下而上无监督机器学习（bottom-up unsupervised machine learning）识别脑特征：研究基于三类诊断相关刺激所诱发的神经激活数据，提取其三组主成分，以此生成交叉验证标记，该标记可有效预测临床人群层面的变异，并最终划定诊断与分类组别。 上述刺激取自偏执抑郁自评量表的条目，采集过程与功能磁共振成像（fMRI）同步进行。 本研究借助多元线性模型与主成分分析，成功区分了本次研究的两种临床病症——精神分裂症与复发性抑郁症。 本研究证实了利用与临床评估测试相关的脑特征，实现精神障碍自下而上分类的可行性。