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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Basics of Multivariate Analysis in Neuroimaging Data
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Building Multivariate Molecular Imaging Brain Atlases Using the NeuroMark PET Independent Component Analysis

Cyrus Eierud1, Martin Norgaard2,3, Murat Bilgel4

  • 1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.

Biorxiv : the Preprint Server for Biology
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

NeuroMark PET uses spatially constrained independent component analysis to create reproducible amyloid-beta networks (AβNs) for molecular brain imaging. This automated approach offers higher sensitivity for detecting age-related changes compared to traditional methods.

Keywords:
18F-florbetaben18F-florbetapirNeuroMarkPET templateamyloid-betaindependent component analysis (ICA)positron emission tomography (PET)

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Area of Science:

  • Neuroimaging
  • Molecular Imaging
  • Computational Neuroscience

Background:

  • Traditional positron emission tomography (PET) analyses use macro-anatomical regions of interest (ROIs) that may not align with chemo-architectural boundaries.
  • Independent component analysis (ICA) has been used to address this, but its data-driven nature complicates cross-study comparisons.

Purpose of the Study:

  • Introduce NeuroMark PET, a novel approach using spatially constrained ICA to define overlapping brain regions reflecting molecular architecture.
  • Develop a fully automated pipeline for generating replicable amyloid-beta networks (AβNs) from PET data.

Main Methods:

  • Generated an ICA template for florbetapir (FBP) PET targeting amyloid-beta (Aβ) using blind ICA on large datasets.
  • Defined Aβ networks (AβNs) by selecting components targeting Aβ and omitting others.
  • Validated the NeuroMark PET pipeline against a standard neuroanatomical atlas using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Main Results:

  • NeuroMark PET captures biologically meaningful, participant-specific features and shows higher sensitivity for detecting age-related changes than traditional ROIs.
  • The most age-associated AβN (cognitive control network, CC1) showed a stronger association with age compared to macro-anatomical ROIs.
  • The approach successfully differentiated white matter components from AβNs, improving artifact separation.

Conclusions:

  • NeuroMark PET provides a fully automated, accurate, and reproducible framework for defining brain AβNs.
  • This method enhances the investigation of molecular underpinnings of brain function and pathology.
  • NeuroMark PET offers a valuable alternative to traditional ROI-based analyses in molecular imaging.