Shape Analysis for Brain Structures

2014 ◽  
pp. 3-49 ◽  
Author(s):  
Bernard Ng ◽  
Matthew Toews ◽  
Stanley Durrleman ◽  
Yonggang Shi
Keyword(s):  
Shape Analysis ◽  
Brain Structures

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

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.

scholarly journals Elastic Shape Analysis of Brain Structures for Predictive Modeling of PTSD

2021 ◽  
Author(s):  
Yuexuan Wu ◽  
Suprateek Kundu ◽  
Jennifer S. Stevens ◽  
Negar Fani ◽  
Anuj Srivastava
Keyword(s):  
Predictive Modeling ◽  
Shape Analysis ◽  
Predictive Power ◽  
Predictive Performance ◽  
Brain Structures ◽  
Morphological Features ◽  
Modeling And Prediction ◽  
Clinical Covariates ◽  
Elastic Shape Analysis ◽  
Clinical Measures

Predictive modeling involving brain morphological features and other covariates is of paramount interest in such heterogeneous mental disorders as PTSD. We propose a comprehensive shape analysis framework representing brain substructures, such as the hippocampus, amygdala, and putamen, as parameterized surfaces and quantifying their shape differences using an elastic shape metric. Under this metric, we compute shape summaries (mean, covariance, PCA) of subcortical data and represent individual shapes by their principal scores under a shape PCA basis. These representations are rich enough to allow visualizations of full 3D structures and help understand localized changes. Subsequently, we use these PCs, the auxiliary exposure variables, and their interactions for regression modeling and prediction. We apply our method to data from the Grady Trauma Project (GTP), where the goal is to predict clinical measures of PTSD. The framework seamlessly integrates accurate morphological features and other clinical covariates to yield superior predictive performance when modeling PTSD outcomes. This approach reveals considerably greater predictive power under the elastic shape analysis than the current approaches and helps identify local deformations in brain shapes associated with PTSD severity.

scholarly journals A pilot study of basal ganglia and thalamus structure by high dimensional mapping in children with Tourette syndrome

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 207 ◽  
Author(s):  
Alton C. Williams ◽  
Marie E. McNeely ◽  
Deanna J. Greene ◽  
Jessica A. Church ◽  
Stacie L. Warren ◽  
...  
Keyword(s):  
Basal Ganglia ◽  
Pilot Study ◽  
Tourette Syndrome ◽  
Shape Analysis ◽  
Brain Structures ◽  
Small Sample ◽  
Effect Sizes ◽  
High Dimensional ◽  
Group Differences ◽  
Structural Abnormalities

Background: Prior brain imaging and autopsy studies have suggested that structural abnormalities of the basal ganglia (BG) nuclei may be present in Tourette Syndrome (TS). These studies have focused mainly on the volume differences of the BG structures and not their anatomical shapes.  Shape differences of various brain structures have been demonstrated in other neuropsychiatric disorders using large-deformation, high dimensional brain mapping (HDBM-LD).  A previous study of a small sample of adult TS patients demonstrated the validity of the method, but did not find significant differences compared to controls. Since TS usually begins in childhood and adult studies may show structure differences due to adaptations, we hypothesized that differences in BG and thalamus structure geometry and volume due to etiological changes in TS might be better characterized in children.Objective: Pilot the HDBM-LD method in children and estimate effect sizes.Methods: In this pilot study, T1-weighted MRIs were collected in 13 children with TS and 16 healthy, tic-free, control children. The groups were well matched for age.  The primary outcome measures were the first 10 eigenvectors which are derived using HDBM-LD methods and represent the majority of the geometric shape of each structure, and the volumes of each structure adjusted for whole brain volume. We also compared hemispheric right/left asymmetry and estimated effect sizes for both volume and shape differences between groups.Results: We found no statistically significant differences between the TS subjects and controls in volume, shape, or right/left asymmetry.  Effect sizes were greater for shape analysis than for volume.Conclusion: This study represents one of the first efforts to study the shape as opposed to the volume of the BG in TS, but power was limited by sample size. Shape analysis by the HDBM-LD method may prove more sensitive to group differences.

scholarly journals Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM

2006 ◽  
Author(s):  
Martin Styner ◽  
Ipek Oguz ◽  
Shun Xu ◽  
Christian Brechbuehler ◽  
Dimitrios Pantazis ◽  
...  
Keyword(s):  
Shape Analysis ◽  
Morphological Changes ◽  
Multiple Comparisons ◽  
Brain Structures ◽  
Statistical Shape Analysis ◽  
Brain Morphometry ◽  
Triangulated Surfaces ◽  
Statistical Shape ◽  
Spherical Topology ◽  
Binary Segmentation

Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology.The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T^2 T2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information.The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives. New NITRC page for binary distribution

Volume and shape analysis of subcortical brain structures and ventricles in euthymic bipolar I disorder

2015 ◽  
Vol 233 (3) ◽  
pp. 324-330 ◽  
Author(s):  
Stephen J. Quigley ◽  
Cathy Scanlon ◽  
Liam Kilmartin ◽  
Louise Emsell ◽  
Camilla Langan ◽  
...  
Keyword(s):  
Shape Analysis ◽  
Brain Structures ◽  
Bipolar I Disorder ◽  
Subcortical Brain ◽  
Subcortical Brain Structures

scholarly journals Elastic Shape Analysis of Brain Structures for Predictive Modeling of PTSD

2021 ◽  
Author(s):  
Yuexuan Wu ◽  
Suprateek Kundu ◽  
Jennifer S. Stevens ◽  
Negar Fani ◽  
Anuj Srivastava
Keyword(s):  
Predictive Modeling ◽  
Shape Analysis ◽  
Predictive Power ◽  
Predictive Performance ◽  
Brain Structures ◽  
Morphological Features ◽  
Modeling And Prediction ◽  
Clinical Covariates ◽  
Elastic Shape Analysis ◽  
Clinical Measures

Predictive modeling involving brain morphological features and other covariates is of paramount interest in such heterogeneous mental disorders as PTSD. We propose a comprehensive shape analysis framework representing brain substructures, such as the hippocampus, amygdala, and putamen, as parameterized surfaces and quantifying their shape differences using an elastic shape metric. Under this metric, we compute shape summaries (mean, covariance, PCA) of subcortical data and represent individual shapes by their principal scores under a shape PCA basis. These representations are rich enough to allow visualizations of full 3D structures and help understand localized changes. Subsequently, we use these PCs, the auxiliary exposure variables, and their interactions for regression modeling and prediction. We apply our method to data from the Grady Trauma Project (GTP), where the goal is to predict clinical measures of PTSD. The framework seamlessly integrates accurate morphological features and other clinical covariates to yield superior predictive performance when modeling PTSD outcomes. This approach reveals considerably greater predictive power under the elastic shape analysis than the current approaches and helps identify local deformations in brain shapes associated with PTSD severity.

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