Owkin Lab, Paris, France.
Parietal, Inria, Saclay, France.
Sci Rep. 2022 Apr 29;12(1):7050. doi: 10.1038/s41598-022-10710-1.
Associating brain systems with mental processes requires statistical analysis of brain activity across many cognitive processes. These analyses typically face a difficult compromise between scope-from domain-specific to system-level analysis-and accuracy. Using all the functional Magnetic Resonance Imaging (fMRI) statistical maps of the largest data repository available, we trained machine-learning models that decode the cognitive concepts probed in unseen studies. For this, we leveraged two comprehensive resources: NeuroVault-an open repository of fMRI statistical maps with unconstrained annotations-and Cognitive Atlas-an ontology of cognition. We labeled NeuroVault images with Cognitive Atlas concepts occurring in their associated metadata. We trained neural networks to predict these cognitive labels on tens of thousands of brain images. Overcoming the heterogeneity, imbalance and noise in the training data, we successfully decoded more than 50 classes of mental processes on a large test set. This success demonstrates that image-based meta-analyses can be undertaken at scale and with minimal manual data curation. It enables broad reverse inferences, that is, concluding on mental processes given the observed brain activity.
将大脑系统与心理过程联系起来需要对许多认知过程的大脑活动进行统计分析。这些分析通常在范围（从特定于领域的分析到系统级分析）和准确性之间面临着艰难的妥协。我们使用了最大的可用数据存储库中的所有功能磁共振成像 (fMRI) 统计地图，训练了机器学习模型，这些模型可以解码在未见研究中探测到的认知概念。为此，我们利用了两个全面的资源：NeuroVault——一个具有无约束注释的 fMRI 统计地图的开放存储库——和认知图谱（Cognitive Atlas）。我们用在其相关元数据中出现的认知图谱概念来标记 NeuroVault 图像。我们训练神经网络在数万个大脑图像上预测这些认知标签。通过克服训练数据中的异质性、不平衡和噪声，我们在一个大型测试集上成功解码了 50 多种心理过程。这一成功表明，可以在不进行大量手动数据整理的情况下大规模进行基于图像的元分析。它可以进行广泛的反向推理，也就是说，可以根据观察到的大脑活动来推断心理过程。
