Deformetrica 4: An Open-Source Software for Statistical Shape Analysis

Deformation-based Statistical Shape Analysis of the Corpus Callosum in Mild Cognitive Impairment and Alzheimer’s Disease

Current Alzheimer Research ◽

10.2174/1567205015666180813145935 ◽

2018 ◽

Vol 15 (12) ◽

pp. 1151-1160 ◽

Cited By ~ 1

Author(s):

Zihan Jiang ◽

Huilin Yang ◽

Xiaoying Tang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Corpus Callosum ◽

Shape Analysis ◽

Disease Assessment ◽

Statistical Shape Analysis ◽

Superior Part ◽

Statistical Shape

Objective: In this study, we investigated the influence that the pathology of Alzheimer’s disease (AD) exerts upon the corpus callosum (CC) using a total of 325 mild cognitive impairment (MCI) subjects, 155 AD subjects, and 185 healthy control (HC) subjects. Method: Regionally-specific morphological CC abnormalities, as induced by AD, were quantified using a large deformation diffeomorphic metric curve mapping based statistical shape analysis pipeline. We also quantified the association between the CC shape phenotype and two cognitive measures; the Mini Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale-Cognitive Behavior Section (ADAS-cog). To identify AD-relevant areas, CC was sub-divided into three subregions; the genu, body, and splenium (gCC, bCC, and sCC). Results: We observed significant shape compressions in AD relative to that in HC, mainly concentrated on the superior part of CC, across all three sub-regions. The HC-vs-MCI shape abnormalities were also concentrated on the superior part, but mainly occurred on bCC and sCC. The significant MCI-vs-AD shape differences, however, were only detected in part of sCC. In the shape-cognition association, significant negative correlations to ADAS-cog were detected for shape deformations at regions belonging to gCC and sCC and significant positive correlations to MMSE at regions mainly belonging to sCC. Conclusion: Our results suggest that the callosal shape deformation patterns, especially those of sCC, linked tightly to the cognitive decline in AD, and are potentially a powerful biomarker for monitoring the progression of AD.

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Statistical Shape Analysis of Headlights

2011 International Conference on Biometrics and Kansei Engineering ◽

10.1109/icbake.2011.52 ◽

2011 ◽

Cited By ~ 1

Author(s):

Shigekazu Ishihara ◽

Keiko Ishihara ◽

Mitsuo Nagamachi

Keyword(s):

Shape Analysis ◽

Statistical Shape Analysis ◽

Statistical Shape

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Automatic statistical shape analysis of human cerebral asymmetry in 3D MRI

NeuroImage ◽

10.1016/s1053-8119(09)70838-9 ◽

2009 ◽

Vol 47 ◽

pp. S99

Author(s):

A Pepe ◽

L Zhao ◽

J Tohka ◽

J Koikkalainen ◽

J Hietala ◽

...

Keyword(s):

Shape Analysis ◽

Statistical Shape Analysis ◽

3D Mri ◽

Cerebral Asymmetry ◽

Statistical Shape

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Statistical shape analysis of putamen in early-onset Parkinson’s disease

Clinical Neurology and Neurosurgery ◽

10.1016/j.clineuro.2021.106936 ◽

2021 ◽

Vol 209 ◽

pp. 106936

Author(s):

Deniz Sigirli ◽

Senem Turan Ozdemir ◽

Sevda Erer ◽

Ibrahim Sahin ◽

Ilker Ercan ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Shape Analysis ◽

Early Onset ◽

Statistical Shape Analysis ◽

Statistical Shape ◽

Early Onset Parkinson’S Disease

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Hippocampal and amygdalar morphological abnormalities in Alzheimer’s disease based on three Chinese MRI datasets

Current Alzheimer Research ◽

10.2174/1567205018666210218150223 ◽

2021 ◽

Vol 18 ◽

Author(s):

Yuanyuan Wei ◽

Nianwei Huang ◽

Yong Liu ◽

Xi Zhang ◽

Silun Wang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Shape Analysis ◽

Early Stage ◽

Disease Status ◽

Brain Structures ◽

Statistical Shape Analysis ◽

Social Significance ◽

Statistical Shape ◽

Shape Characteristics

Background: Early detection of Alzheimer’s disease (AD) and its early stage, the mild cognitive impairment (MCI), has important scientific, clinical and social significance. Magnetic resonance imaging (MRI) based statistical shape analysis provides an opportunity to detect regional structural abnormalities of brain structures caused by AD and MCI. Objective: In this work, we aimed to employ a well-established statistical shape analysis pipeline, in the framework of large deformation diffeomorphic metric mapping, to identify and quantify the regional shape abnormalities of the bilateral hippocampus and amygdala at different prodromal stages of AD, using three Chinese MRI datasets collected from different domestic hospitals. Methods: We analyzed the region-specific shape abnormalities at different stages of the neuropathology of AD by comparing the localized shape characteristics of the bilateral hippocampi and amygdalas between healthy controls and two disease groups (MCI and AD). In addition to group comparison analyses, we also investigated the association between the shape characteristics and the Mini Mental State Examination (MMSE) of each structure of interest in the disease group (MCI and AD combined) as well as the discriminative power of different morphometric biomarkers. Results: We found the strongest disease pathology (regional atrophy) at the subiculum and CA1 subregions of the hippocampus and the basolateral, basomedial as well as centromedial subregions of the amygdala. Furthermore, the shape characteristics of the hippocampal and amygdalar subregions exhibiting the strongest AD related atrophy were found to have the most significant positive associations with the MMSE. Employing the shape deformation marker of the hippocampus or the amygdala for automated MCI or AD detection yielded a significant accuracy boost over the corresponding volume measurement. Conclusion: Our results suggested that the amygdalar and hippocampal morphometrics, especially those of shape morphometrics, can be used as auxiliary indicators for monitoring the disease status of an AD patient.

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