Task-specific functional MRI (fMRI) images provide excellent modalities for studying the neuronal basis of cognitive processes. We use fMRI data to formulate and solve the problem of deconvolving task-specific aggregate neuronal networks into a set of basic building blocks called canonical networks, to use these networks for functional characterization, and to characterize the physiological basis of these responses by mapping them to regions of the brain. Our results show excellent task-specificity of canonical networks, i.e., the expression of a small number of canonical networks can be used to accurately predict tasks; generalizability across cohorts, i.e., canonical networks are conserved across diverse populations, studies, and acquisition protocols; and that canonical networks have strong anatomical and physiological basis. From a methods perspective, the problem of identifying these canonical networks poses challenges rooted in the high dimensionality, small sample size, acquisition variability, and noise. Our deconvolution technique is based on non-negative matrix factorization (NMF) that identifies canonical networks as factors of a suitably constructed matrix. We demonstrate that our method scales to large datasets, yields stable and accurate factors, and is robust to noise.

翻译：任务特异性功能磁共振成像（fMRI）图像为研究认知过程的神经元基础提供了极佳的模态。我们利用fMRI数据，将任务特异性聚合神经元网络解卷积为一组称为规范网络的基本构建块，利用这些网络进行功能表征，并通过将其映射到大脑区域来表征这些反应的生理基础。我们的结果表明，规范网络具有出色的任务特异性，即少量规范网络的表达可用于准确预测任务；在队列间具有普适性，即规范网络在不同人群、研究和采集协议中具有保守性；并且规范网络具有坚实的解剖学和生理学基础。从方法学角度看，识别这些规范网络的问题源于高维度、小样本量、采集变异性和噪声带来的挑战。我们的解卷积技术基于非负矩阵分解（NMF），该方法将规范网络识别为适当构建矩阵的因子。我们证明，该方法可扩展至大型数据集，产生稳定且准确的因子，并对噪声具有鲁棒性。