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Summary
This summary is machine-generated.

ADprep is a new, automated neuroimaging pipeline that harmonizes Alzheimer's disease (AD) data processing. It simplifies multimodal MRI and PET analysis for researchers, improving data standardization and sharing across studies.

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Area of Science:

  • Neuroimaging
  • Alzheimer's Disease Research
  • Computational Neuroscience

Background:

  • Processing large-scale multimodal MRI and PET data is vital for Alzheimer's disease (AD) research.
  • Current neuroimaging data processing is complex, requiring specialized expertise and leading to inter-site variability.
  • Standardized, user-friendly workflows are essential for harmonizing neuroimaging data and reducing preprocessing bias.

Purpose of the Study:

  • To develop ADprep, a containerized, automated neuroimaging toolbox for processing multimodal MRI and PET data.
  • To establish a user-friendly pipeline that requires no programming expertise, facilitating data harmonization and sharing.
  • To generate standardized outputs across various brain atlases for consistent neuroimaging analyses in AD research.

Main Methods:

  • ADprep utilizes the nipype library and processes data formatted according to the Brain Imaging Data Structure (BIDS).
  • It includes preprocessing for structural MRI (volumetrics, cortical thickness), functional MRI (connectivity), and multi-tracer PET (SUVR, partial-volume correction).
  • The pipeline generates standardized outputs in FreeSurfer space and is available via cloud-based (GRIP) and local cluster implementations.

Main Results:

  • ADprep was successfully tested on large-scale datasets (ADNI, ADNI-DOD, A4) with a processing failure rate below 4%.
  • The pipeline demonstrated high reproducibility, closely matching established ADNI PET core results for amyloid-PET (r=0.99) and tau-PET (r=0.98).
  • Processing times are efficient: approximately 1 hour for MRI scans and 30 minutes for PET scans.

Conclusions:

  • ADprep provides a user-friendly, harmonized pipeline for multimodal neuroimaging data.
  • It enables non-expert users to process diverse neuroimaging datasets, yielding outputs suitable for direct statistical analysis.
  • The toolbox significantly advances standardized neuroimaging analysis and data sharing within the Alzheimer's disease research community.