Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada; Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Québec, Canada; Département de Psychiatrie, Université de Montréal, Montréal, Québec, Canada.
Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Québec, Canada; Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Montréal, Québec, Canada.
Schizophr Res. 2018 Feb;192:167-171. doi: 10.1016/j.schres.2017.05.027. Epub 2017 Aug 24.
Our objective was to assess the generalizability, across sites and cognitive contexts, of schizophrenia classification based on functional brain connectivity. We tested different training-test scenarios combining fMRI data from 191 schizophrenia patients and 191 matched healthy controls obtained at 6 scanning sites and under different task conditions. Diagnosis classification accuracy generalized well to a novel site and cognitive context provided data from multiple sites were used for classifier training. By contrast, lower classification accuracy was achieved when data from a single distinct site was used for training. These findings indicate that it is beneficial to use multisite data to train fMRI-based classifiers intended for large-scale use in the clinical realm.
我们的目标是评估基于功能脑连接的精神分裂症分类在不同地点和认知环境下的泛化能力。我们测试了不同的训练-测试场景，这些场景结合了 191 名精神分裂症患者和 191 名匹配的健康对照组的 fMRI 数据，这些数据来自 6 个扫描地点和不同的任务条件。诊断分类的准确性很好地推广到了一个新的地点和认知环境，如果使用来自多个地点的数据进行分类器训练，则可以实现更好的分类效果。相比之下，当使用单个独特的地点的数据进行训练时，分类的准确性较低。这些发现表明，使用多站点数据来训练基于 fMRI 的分类器是有益的，这有利于在临床领域进行大规模使用。
