Literature DB >> 22815197

Evolution of hippocampal shapes across the human lifespan.

Xianfeng Yang1, Alvina Goh, Shen-Hsing Annabel Chen, Anqi Qiu.   

Abstract

Aberrant hippocampal morphology plays an important role in the pathophysiology of aging. Volumetric analysis of the hippocampus has been performed in aging studies; however, the shape morphometry--which is potentially more informative in terms of related cognition--has yet to be examined. In this paper, we employed an advanced brain mapping technique, large deformation diffeomorphic metric mapping (LDDMM), and a dimensionality reduction approach, locally linear diffeomorphic metric embedding (LLDME), to explore age-related changes in hippocampal shape as delineated from magnetic resonance (MR) images of 302 healthy adults aged from 18 to 94 years. Compared with the hippocampal volumes, the hippocampal shapes clearly showed the nonlinear trajectory of biological aging across the human lifespan, where the variation of hippocampal shapes by age was characterized by a cubic polynomial. By integrating of LDDMM and LLDME, we were also able to illustrate the average hippocampal shapes in each individual decade. In addition, LDDMM and LLDME facilitated the identification of 63 years as a threshold beyond which hippocampal morphological changes were accelerated. Adults over 63 years of age showed the inward-deformation bilaterally in the head of the hippocampi and the left subiculum regardless of hippocampal volume reduction when compared to adults younger than 63. Hence, we demonstrated that the shape of anatomical structures added another dimension of structural morphological quantification beyond the volume in understanding aging.
Copyright © 2012 Wiley Periodicals, Inc.

Entities:  

Keywords:  diffeomorphic metric mapping; hippocampal shape; hippocampal volume; locally linear embedding; normal aging

Mesh:

Year:  2012        PMID: 22815197      PMCID: PMC6870440          DOI: 10.1002/hbm.22125

Source DB:  PubMed          Journal:  Hum Brain Mapp        ISSN: 1065-9471            Impact factor:   5.038


  55 in total

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