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Multi‐atlas thalamic nuclei segmentation on standard T1‐weighed MRI with application to normal aging.

Authors :
Pfefferbaum, Adolf
Sullivan, Edith V.
Zahr, Natalie M.
Pohl, Kilian M.
Saranathan, Manojkumar
Source :
Human Brain Mapping; Feb2023, Vol. 44 Issue 2, p612-628, 17p
Publication Year :
2023

Abstract

Specific thalamic nuclei are implicated in healthy aging and age‐related neurodegenerative diseases. However, few methods are available for robust automated segmentation of thalamic nuclei. The threefold aims of this study were to validate the use of a modified thalamic nuclei segmentation method on standard T1 MRI data, to apply this method to quantify age‐related volume declines, and to test functional meaningfulness by predicting performance on motor testing. A modified version of THalamus Optimized Multi‐Atlas Segmentation (THOMAS) generated 22 unilateral thalamic nuclei. For validation, we compared nuclear volumes obtained from THOMAS parcellation of white‐matter‐nulled (WMn) MRI data to T1 MRI data in 45 participants. To examine the effects of age/sex on thalamic nuclear volumes, T1 MRI available from a second data set of 121 men and 117 women, ages 20–86 years, were segmented using THOMAS. To test for functional ramifications, composite regions and constituent nuclei were correlated with Grooved Pegboard test scores. THOMAS on standard T1 data showed significant quantitative agreement with THOMAS from WMn data, especially for larger nuclei. Sex differences revealing larger volumes in men than women were accounted for by adjustment with supratentorial intracranial volume (sICV). Significant sICV‐adjusted correlations between age and thalamic nuclear volumes were detected in 20 of the 22 unilateral nuclei and whole thalamus. Composite Posterior and Ventral regions and Ventral Anterior/Pulvinar nuclei correlated selectively with higher scores from the eye‐hand coordination task. These results support the use of THOMAS for standard T1‐weighted data as adequately robust for thalamic nuclear parcellation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10659471
Volume :
44
Issue :
2
Database :
Complementary Index
Journal :
Human Brain Mapping
Publication Type :
Academic Journal
Accession number :
161328767
Full Text :
https://doi.org/10.1002/hbm.26088