Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility

Abstract

Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC (The Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC's federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration, and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.

Keywords: COINSTAC; collaborative analysis; datasets; federated learning; neuroimaging; open science; privacy; reproducibility.

Figure 1

Adding vault data to an analysis pipeline.

Figure 2

Different types of participants interacting with COINSTAC.

Figure 3

Process of creating a vault in COINSTAC.

Figure 4

Architecture of vaults in COINSTAC.

Figure 5

(A–C) Rendered images show voxel-wise β values corresponding to the age, sex, and diagnosis covariates using COBRE VBM data Vault in COINSTAC. For age, negative values show that the gray matter volume decreases with age. For sex, positive values indicate male's gray matter volume is greater than female's gray matter volume and vice versa. For diagnosis, positive values indicate control's gray matter volume is greater than patient's gray matter volume and vice versa.

Figure 6

(A–C) Rendered images show voxel-wise β values corresponding to the age, sex, and diagnosis covariates using MCIC sMRI data in COINSTAC. For age, negative values show that the gray matter volume decreases with age. For sex, positive values indicate male's gray matter volume is greater than female's gray matter volume and vice versa. For diagnosis, positive values indicate control's gray matter volume is greater than patient's gray matter volume and vice versa.

Figure 7

(A–C) MCIC+COBRE vault: rendered images show voxel-wise β values corresponding to the age, sex, and diagnosis covariates using MCIC sMRI data along with the data in the COBRE Vault in COINSTAC. For age, negative values show that the gray matter volume decreases with age. For sex, positive values indicate male's gray matter volume is greater than female's gray matter volume and vice versa. For diagnosis, positive values indicate control's gray matter volume is greater than patient's gray matter volume and vice versa.

Figure 8

(A, B) Rendered images show voxel-wise β values corresponding to the age and sex covariates using CMI VBM Vault data in COINSTAC. For age, negative values show that the gray matter volume decreases with age. For sex, positive values indicate male's gray matter volume is greater than female's gray matter volume and vice versa.

Figure 9

(A–C) Rendered images show univariate regression results demonstrating the effects of age and sex on correlation between the 53 independent components and FNC correlation map using vault data in COINSTAC.

Figure 10

The Neuromark fMRI 1.0 template with 53 intrinsic networks (components) from 7 major networks.