There has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such open-source framework is the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) that works on Windows, macOS, and Linux operating systems and leverages containerized analysis pipelines to analyze neuroimaging data stored locally across multiple physical locations without the need for pooling the data at any point during the analysis. In this paper, the COINSTAC team partnered with a data collection consortium to implement the first-ever decentralized voxelwise analysis of brain imaging data performed outside the COINSTAC development group. Decentralized voxel-based morphometry analysis of over 2000 structural magnetic resonance imaging data sets collected at 14 different sites across two cohorts and co-located in different countries was performed to study the structural changes in brain gray matter which linked to age, body mass index (BMI), and smoking. Results produced by the decentralized analysis were consistent with and extended previous findings in the literature. In particular, a widespread cortical gray matter reduction (resembling a ‘default mode network’ pattern) and hippocampal increase with age, bilateral increases in the hypothalamus and basal ganglia with BMI, and cingulate and thalamic decreases with smoking. This work provides a critical real-world test of the COINSTAC framework in a “Large-N” study. It showcases the potential benefits of performing multivoxel and multivariate analyses of large-scale neuroimaging data located at multiple sites.

开发框架呈上升趋势，使神经影像学研究人员能够通过利用世界各地多个站点的数据来解决具有挑战性的问题。一个这样的开源框架是用于匿名计算的协作信息学和神经成像套件工具包 (COINSTAC)，它适用于 Windows、macOS 和 Linux 操作系统，并利用容器化分析管道来分析跨多个物理位置本地存储的神经成像数据，而无需在分析过程中的任何时候汇集数据。在本文中，COINSTAC 团队与数据收集联盟合作，在 COINSTAC 开发组之外对脑成像数据进行了首次分散体素分析。对在两个队列的 14 个不同地点收集并位于不同国家的 2000 多个结构磁共振成像数据集进行分散的基于体素的形态测量分析，以研究与年龄、体重指数相关的脑灰质结构变化。 BMI）和吸烟。分散分析产生的结果与文献中先前的发现一致并扩展了。特别是，广泛的皮质灰质减少（类似于“默认模式网络”模式）和海马随着年龄的增长而增加，下丘脑和基底神经节的双边增加与 BMI，以及扣带回和丘脑随着吸烟而减少。这项工作在“Large-N”研究中提供了对 COINSTAC 框架的关键现实世界测试。