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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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Related Experiment Video

Updated: Jun 18, 2026

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
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Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

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Population-based Brain Templates for Ultra-Low-Field MRI.

Kh Tohidul Islam1, Parisa Zakavi1, Shenjun Zhong1

  • 1Monash Biomedical Imaging, Monash University, Melbourne, Australia.

Scientific Data
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces open, standardized ultra-low-field (ULF) MRI brain templates for 100 adults, improving spatial analysis and comparability across ULF studies. The resource aids normalization and registration method development.

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

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Last Updated: Jun 18, 2026

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Area of Science:

  • Neuroimaging
  • Medical Physics

Background:

  • Ultra-low-field (ULF) MRI lacks population-representative spatial priors, hindering robust alignment, normalization, and cross-study comparability.
  • Standardized spatial priors are crucial for advancing ULF neuroimaging research and clinical applications.

Purpose of the Study:

  • To provide an open, standardized ULF brain template resource with data and code.
  • To enable reproducible spatial analysis and method development across diverse ULF studies.
  • To establish ULF-specific spatial priors for improved normalization and registration.

Main Methods:

  • Generated group-average brain templates from 64 mT MRI scans of 100 healthy adults (T1- and T2-weighted contrasts).
  • Stratified participants into three age groups for age-specific and population-level analyses.
  • Employed open-source neuroimaging tools and iterative averaging for preprocessing and registration, addressing intensity variability.

Main Results:

  • Developed population and age-specific ULF brain templates covering the full adult lifespan.
  • Provided accompanying example segmentations and complete scripts for full reproducibility.
  • Ensured data and code are openly available on Zenodo and GitHub, adhering to FAIR principles.

Conclusions:

  • The ULF brain template resource offers essential spatial priors for ULF neuroimaging.
  • Facilitates normalization, registration benchmarking, and method development in comparable acquisition settings.
  • Promotes cross-study comparability and reproducibility in ULF MRI research.