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Related Concept Videos

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Amir Dehsarvi1, Lukas Frontzkowski1, Anna Dewenter1

  • 1Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Bavaria, Germany.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

ADprep is a new, automated neuroimaging pipeline that harmonizes Alzheimer's disease (AD) data processing. This user-friendly tool enables non-experts to analyze multimodal MRI and PET scans, facilitating research and data sharing.

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Area of Science:

  • Neuroimaging
  • Alzheimer's Disease Research
  • Computational Neuroscience

Background:

  • Large-scale multimodal MRI and PET data processing is vital for Alzheimer's disease (AD) research.
  • Inconsistent preprocessing across sites hinders data harmonization and introduces bias.
  • Standardized, user-friendly workflows are essential for reliable neuroimaging analysis.

Purpose of the Study:

  • To develop ADprep, a containerized, automated neuroimaging toolbox.
  • To integrate preprocessing for structural/functional MRI and multi-tracer PET scans.
  • To generate standardized outputs across various brain atlases for harmonized analyses.

Main Methods:

  • ADprep utilizes the nipype library and processes BIDS-formatted data.
  • It performs volumetric and cortical thickness assessments for multiple brain atlases.
  • Functional MRI processing includes motion correction and connectivity analysis; PET processing involves SUVR generation and partial-volume correction.

Main Results:

  • ADprep was successfully tested on thousands of scans from ADNI, ADNI-DOD, and A4 datasets with a <4% failure rate.
  • The pipeline accurately reproduces amyloid-PET (r=0.99) and tau-PET (r=0.98) SUVRs.
  • Processing times are approximately 1 hour for MRI and 30 minutes for PET scans.

Conclusions:

  • ADprep provides a user-friendly, harmonized pipeline for multimodal neuroimaging.
  • It enables non-expert users to analyze diverse neuroimaging datasets.
  • The generated outputs are directly applicable to statistical analyses, promoting data sharing and standardization.