Rikandi E, Pamilo S, Mäntylä T, Suvisaari J, Kieseppä T, Hari R, Seppä M, Raij T T
Mental Health Unit,National Institute for Health and Welfare,Helsinki,Finland.
Department of Neuroscience and Biomedical Engineering, andAdvanced Magnetic Imaging Centre,Aalto NeuroImaging,Aalto University School of Science,Espoo,Finland.
Psychol Med. 2017 Feb;47(3):495-506. doi: 10.1017/S0033291716002609. Epub 2016 Oct 25.
While group-level functional alterations have been identified in many brain regions of psychotic patients, multivariate machine-learning methods provide a tool to test whether some of such alterations could be used to differentiate an individual patient. Earlier machine-learning studies have focused on data collected from chronic patients during rest or simple tasks. We set out to unravel brain activation patterns during naturalistic stimulation in first-episode psychosis (FEP).
We recorded brain activity from 46 FEP patients and 32 control subjects viewing scenes from the fantasy film Alice in Wonderland. Scenes with varying degrees of fantasy were selected based on the distortion of the 'sense of reality' in psychosis. After cleaning the data with a novel maxCorr method, we used machine learning to classify patients and healthy control subjects on the basis of voxel- and time-point patterns.
Most (136/194) of the voxels that best classified the groups were clustered in a bilateral region of the precuneus. Classification accuracies were up to 79.5% (p = 5.69 × 10-8), and correct classification was more likely the higher the patient's positive-symptom score. Precuneus functioning was related to the fantasy content of the movie, and the relationship was stronger in control subjects than patients.
These findings are the first to show abnormalities in precuneus functioning during naturalistic information processing in FEP patients. Correlational findings suggest that these alterations are associated with positive psychotic symptoms and processing of fantasy. The results may provide new insights into the neuronal basis of reality distortion in psychosis.
虽然已在精神病患者的许多脑区中识别出组水平的功能改变，但多变量机器学习方法提供了一种工具，可用于测试其中一些改变是否可用于区分个体患者。早期的机器学习研究主要集中于从慢性患者在休息或简单任务期间收集的数据。我们着手揭示首发精神病（FEP）患者在自然主义刺激期间的脑激活模式。
我们记录了46名FEP患者和32名对照受试者观看奇幻电影《爱丽丝梦游仙境》场景时的脑活动。根据精神病中“现实感”的扭曲程度选择了不同程度奇幻的场景。在用一种新颖的最大相关方法清理数据后，我们使用机器学习根据体素和时间点模式对患者和健康对照受试者进行分类。
最能区分组别的大多数（136/194）体素聚集在双侧楔前叶区域。分类准确率高达79.5%（p = 5.69×10-8），并且患者的阳性症状评分越高，正确分类的可能性越大。楔前叶功能与电影的奇幻内容相关，并且在对照受试者中这种关系比患者中更强。
这些发现首次表明FEP患者在自然主义信息处理期间楔前叶功能存在异常。相关性发现表明这些改变与阳性精神病症状和奇幻处理有关。这些结果可能为精神病中现实扭曲的神经元基础提供新的见解。
