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.

Functional connectome through the human life span

“…For individuals aged three years and older, we utilized the publicly available, containerized HCP structural preprocessing pipelines (v4.4.0-rc-MOD-e7a6af9) 96 , which have been standardized through the QuNex platform (v0.93.2) 97 . In brief, this pipeline involves three stages: (1) The PreFreeSurfer stage focused on the normalization of anatomical MRI data, involving a sequence of preprocessing steps that include brain extraction, denoising, and bias field correction on anatomical T1- and T2w MRI data (if T2w data were available).…”

Human brain state dynamics reflect individual neuro-phenotypes

“…4,000 time-frames/subject), we used the Cole-Anticevic Brain Network Parcellation (CAP-NP) that involves 718 cortical surface and subcortical volumetric parcels (43). We averaged the preprocessed BOLD signals in the voxels belonging to each parcel (44). Therefore, within each split, a 4,000 × 718 array of individual rsfMRI data are temporally concatenated across subjects.…”

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.…”