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A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

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Multi-contrast anatomical subcortical structures parcellation.

Pierre-Louis Bazin^1,2, Anneke Alkemade¹, Martijn J Mulder^1,3

¹Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, Netherlands.

|December 16, 2020

Summary

This summary is machine-generated.

Researchers developed an open-source algorithm to automatically map the human subcortex, a brain region previously uncharted due to its small structures. This tool aids neuroimaging research into these deep brain nuclei.

Keywords:

anatomical parcellation human neuroscience quantitative MRI subcortex

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Area of Science:

Neuroimaging
Human Anatomy
Brain Mapping

Background:

The human subcortex contains over 450 nuclei, but standard MRI atlases depict only 7% due to their small size and proximity.
This limits detailed study of subcortical structures, leaving much of it as 'terra incognita'.

Purpose of the Study:

To present a novel, open-source algorithm for automated subcortical parcellation.
To improve the mapping and study of small subcortical nuclei using quantitative MRI.

Main Methods:

Developed and tested an open-source parcellation algorithm on 17 prominent subcortical structures.
Utilized a large quantitative MRI dataset acquired at 7 Tesla.
Validated the algorithm against expert human raters and existing methods.

Main Results:

The algorithm successfully mapped 17 key subcortical structures.
Demonstrated high accuracy and reliability when compared to expert assessments.
The method is adaptable to other subcortical nuclei and various quantitative MRI datasets.

Conclusions:

The novel parcellation algorithm significantly advances the ability to study small subcortical nuclei.
This tool is expected to facilitate functional and structural neuroimaging research, aiding exploration of the uncharted subcortex.