A Pipeline for ADNI Resting-State Functional MRI Processing and Quality Control

TL;DR

This paper presents a comprehensive, reproducible pipeline for processing and quality controlling ADNI resting-state fMRI data, enabling large-scale, reliable brain connectivity studies in Alzheimer's disease research.

Contribution

It introduces an integrated, scalable pipeline that handles data download, alignment, preprocessing, and quality control for ADNI rs-fMRI data, supporting reproducibility and large-scale analysis.

Findings

Supports ADNI-GO, ADNI-2, and ADNI-3 data releases

Generates quality metrics and reports for each session

Outputs high-quality, BIDS-compliant rs-fMRI data

Abstract

The Alzheimer's Disease Neuroimaging Initiative (ADNI) provides a comprehensive multimodal neuroimaging resource for studying aging and Alzheimer's disease (AD). Since its second wave, ADNI has increasingly collected resting-state functional MRI (rs-fMRI), a valuable resource for discovering brain connectivity changes predictive of cognitive decline and AD. A major barrier to its use is the considerable variability in acquisition protocols and data quality, compounded by missing imaging sessions and inconsistencies in how functional scans temporally align with clinical assessments. As a result, many studies only utilize a small subset of the total rs-fMRI data, limiting statistical power, reproducibility, and the ability to study longitudinal functional brain changes at scale. Here, we describe a pipeline for ADNI rs-fMRI data that encompasses the download of necessary imaging and clinical data, temporally aligning the clinical and imaging data, preprocessing, and quality control. We integrate data curation and preprocessing across all ADNI sites and scanner types using a combination of open-source software (Clinica, fMRIPrep, and MRIQC) and bespoke tools. Quality metrics and reports are generated for each subject and session to facilitate rigorous data screening. All scripts and configuration files are available to enable reproducibility. The pipeline, which currently supports ADNI-GO, ADNI-2, and ADNI-3 data releases, outputs high-quality rs-fMRI time series data adhering to the BIDS-derivatives specification. This protocol provides a transparent and scalable framework for curating and utilizing ADNI fMRI data, empowering large-scale functional biomarker discovery and integrative multimodal analyses in Alzheimer's disease research.