QuNex – An Integrative Platform for Reproducible Neuroimaging Analytics

Abstract

Neuroimaging technology has experienced explosive growth and has transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges around method integration (1-3). Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. To address these challenges, we developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex is engineered for reproducible deployment of custom workflows, from onboarding raw data to generating analytic features, in a single "turnkey" command. The platform enables inter-operable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia (4), including multiple clinical datasets. Moreover, QuNex enables integration of non-human primate, rodent, and human workflows via a cohesive translational platform. Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.

Safety, tolerability, and clinical and neural effects of single-dose psilocybin in obsessive–compulsive disorder: protocol for a randomized, double-blind, placebo-controlled, non-crossover trial

“…Alphacorrected t-tests will be used to probe interaction effects. MRI preprocessing and group difference statistics will be evaluated using standard regression and general linear modeling approaches in a general-purpose MRI analysis package (QuNex), a processing platform that incorporates several state-of-the art neuroimaging tools (FSL, FreeSurfer, AFNI, SPM, PALM, and HCP-MPP) and thus offers comprehensive multimodal MRI analytic capabilities (92). To test the secondary hypothesis that at 48 h post-dosing, psilocybin, compared to placebo, will normalize frontostriatal connectivity at rest, we will conduct similar LMMs to examine the effect of psilocybin on activation of the corresponding regions.…”

Unique functional neuroimaging signatures of genetic versus clinical high risk for psychosis

“…All data from 22qDel, CHR, and TD controls were processed with the same workflow, as described in detail in previous publications (39,40). Functional and structural images were processed with the Quantitative Neuroimaging Environment and Toolbox (41), applying a modified version of the methods developed for the Human Connectome Project (HCP) (42), as well as motion scrubbing, i.e. censoring frames with displacement or intensity change thresholds exceeding those recommended by Power et al (43–45).…”

Static and dynamic functional connectomes represent largely similar information

“…Initial preprocessing was performed by the HCP team and included minimal preprocessing [26], ICA-FIX denoising [27] and MSMAll registration [28]. The data was then further processed using QuNex [29] to prepare them for functional connectivity analyzes. First, we identified frames with excessive movement and/or frame-to-frame signal changes.…”