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Gajawelli N, Paulli A, Deoni S, Paquette N, Darakjian D, Salazar C, Dean D, O'Muircheartaigh J, Nelson MD, Wang Y, Lepore N. Surface-based morphometry of the corpus callosum in young children of ages 1-5. Hum Brain Mapp 2024; 45:e26693. [PMID: 38924235 PMCID: PMC11199824 DOI: 10.1002/hbm.26693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 02/05/2024] [Accepted: 04/05/2024] [Indexed: 06/28/2024] Open
Abstract
The corpus callosum (CC) is a large white matter fiber bundle in the brain and is involved in various cognitive, sensory, and motor processes. While implicated in various developmental and psychiatric disorders, much is yet to be uncovered about the normal development of this structure, especially in young children. Additionally, while sexual dimorphism has been reported in prior literature, observations have not necessarily been consistent. In this study, we use morphometric measures including surface tensor-based morphometry (TBM) to investigate local changes in the shape of the CC in children between the ages of 12 and 60 months, in intervals of 12 months. We also analyze sex differences in each of these age groups. We observed larger significant clusters in the earlier ages between 12 v 24 m and between 48 v 60 m and localized differences in the anterior region of the body of the CC. Sex differences were most pronounced in the 12 m group. This study adds to the growing literature of work aiming to understand the developing brain and emphasizes the utility of surface TBM as a useful tool for analyzing regional differences in neuroanatomical morphometry.
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Affiliation(s)
- Niharika Gajawelli
- CIBORG Lab, Department of RadiologyChildren's Hospital Los AngelesLos AngelesCaliforniaUSA
| | - Athelia Paulli
- CIBORG Lab, Department of RadiologyChildren's Hospital Los AngelesLos AngelesCaliforniaUSA
| | - Sean Deoni
- Department of PediatricsWarren Alpert Medical School at Brown UniversityProvidenceRhode IslandUSA
- Bill & Melinda Gates FoundationSeattleWashingtonUSA
| | - Natacha Paquette
- CIBORG Lab, Department of RadiologyChildren's Hospital Los AngelesLos AngelesCaliforniaUSA
- Department of PsychologyCHU Sainte‐JustineMontrealQuebecCanada
| | - Danielle Darakjian
- CIBORG Lab, Department of RadiologyChildren's Hospital Los AngelesLos AngelesCaliforniaUSA
- College of MedicineCalifornia Northstate UniversityElk GroveCaliforniaUSA
| | - Carlos Salazar
- CIBORG Lab, Department of RadiologyChildren's Hospital Los AngelesLos AngelesCaliforniaUSA
- Department of Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Douglas Dean
- Waisman Laboratory for Brain Imaging and BehaviorUniversity of Wisconsin MadisonMadisonWisconsinUSA
| | | | - Marvin D. Nelson
- Department of PediatricsUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of RadiologyChildren's Hospital Los AngelesLos AngelesCaliforniaUSA
| | - Yalin Wang
- Department of Computer ScienceArizona State UniversityTempeArizonaUSA
| | - Natasha Lepore
- CIBORG Lab, Department of RadiologyChildren's Hospital Los AngelesLos AngelesCaliforniaUSA
- Department of Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of PediatricsUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of RadiologyChildren's Hospital Los AngelesLos AngelesCaliforniaUSA
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2
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Chen Y, Su Y, Wu J, Chen K, Atri A, Caselli RJ, Reiman EM, Wang Y. Combining Blood-Based Biomarkers and Structural MRI Measurements to Distinguish Persons with and without Significant Amyloid Plaques. J Alzheimers Dis 2024; 98:1415-1426. [PMID: 38578889 DOI: 10.3233/jad-231162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
Background Amyloid-β (Aβ) plaques play a pivotal role in Alzheimer's disease. The current positron emission tomography (PET) is expensive and limited in availability. In contrast, blood-based biomarkers (BBBMs) show potential for characterizing Aβ plaques more affordably. We have previously proposed an MRI-based hippocampal morphometry measure to be an indicator of Aβ plaques. Objective To develop and validate an integrated model to predict brain amyloid PET positivity combining MRI feature and plasma Aβ42/40 ratio. Methods We extracted hippocampal multivariate morphometry statistics from MR images and together with plasma Aβ42/40 trained a random forest classifier to perform a binary classification of participant brain amyloid PET positivity. We evaluated the model performance using two distinct cohorts, one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Banner Alzheimer's Institute (BAI), including prediction accuracy, precision, recall rate, F1 score, and AUC score. Results Results from ADNI (mean age 72.6, Aβ+ rate 49.5%) and BAI (mean age 66.2, Aβ+ rate 36.9%) datasets revealed the integrated multimodal (IMM) model's superior performance over unimodal models. The IMM model achieved prediction accuracies of 0.86 in ADNI and 0.92 in BAI, surpassing unimodal models based solely on structural MRI (0.81 and 0.87) or plasma Aβ42/40 (0.73 and 0.81) predictors. CONCLUSIONS Our IMM model, combining MRI and BBBM data, offers a highly accurate approach to predict brain amyloid PET positivity. This innovative multiplex biomarker strategy presents an accessible and cost-effective avenue for advancing Alzheimer's disease diagnostics, leveraging diverse pathologic features related to Aβ plaques and structural MRI.
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Affiliation(s)
- Yanxi Chen
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Jianfeng Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Kewei Chen
- College of Health Solutions, Arizona State University, Tempe, AZ, USA
| | - Alireza Atri
- Banner Alzheimer's Institute, Phoenix, AZ, USA
- Banner Sun Health Research Institute, Sun City, AZ, USA
- Department of Neurology, Center for Brain/Mind Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
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Zheng W, Liu H, Li Z, Li K, Wang Y, Hu B, Dong Q, Wang Z. Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics. CNS Neurosci Ther 2023; 29:2457-2468. [PMID: 37002795 PMCID: PMC10401169 DOI: 10.1111/cns.14189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/07/2023] [Accepted: 03/13/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus. AIMS We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC). METHODS We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification. RESULTS By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances. CONCLUSIONS The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.
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Affiliation(s)
- Weimin Zheng
- Department of Radiology, Aerospace Center Hospital, Beijing, China
| | - Honghong Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhigang Li
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, USA
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Qunxi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing, China
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4
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Li Z, Dong Q, Hu B, Wu H. Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability. Hum Brain Mapp 2023; 44:3343-3358. [PMID: 37051692 PMCID: PMC10171537 DOI: 10.1002/hbm.26285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 02/01/2023] [Accepted: 03/08/2023] [Indexed: 04/14/2023] Open
Abstract
Mentalising ability, indexed as the ability to understand others' beliefs, feelings, intentions, thoughts and traits, is a pivotal and fundamental component of human social cognition. However, considering the multifaceted nature of mentalising ability, little research has focused on characterising individual differences in different mentalising components. And even less research has been devoted to investigating how the variance in the structural and functional patterns of the amygdala and hippocampus, two vital subcortical regions of the "social brain", are related to inter-individual variability in mentalising ability. Here, as a first step toward filling these gaps, we exploited inter-subject representational similarity analysis (IS-RSA) to assess relationships between amygdala and hippocampal morphometry (surface-based multivariate morphometry statistics, MMS), connectivity (resting-state functional connectivity, rs-FC) and mentalising ability (interactive mentalisation questionnaire [IMQ] scores) across the participants ( N = 24 $$ N=24 $$ ). In IS-RSA, we proposed a novel pipeline, that is, computing patching and pooling operations-based surface distance (CPP-SD), to obtain a decent representation for high-dimensional MMS data. On this basis, we found significant correlations (i.e., second-order isomorphisms) between these three distinct modalities, indicating that a trinity existed in idiosyncratic patterns of brain morphometry, connectivity and mentalising ability. Notably, a region-related mentalising specificity emerged from these associations: self-self and self-other mentalisation are more related to the hippocampus, while other-self mentalisation shows a closer link with the amygdala. Furthermore, by utilising the dyadic regression analysis, we observed significant interactions such that subject pairs with similar morphometry had even greater mentalising similarity if they were also similar in rs-FC. Altogether, we demonstrated the feasibility and illustrated the promise of using IS-RSA to study individual differences, deepening our understanding of how individual brains give rise to their mentalising abilities.
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Affiliation(s)
- Zhaoning Li
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, China
| | - Qunxi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Taipa, China
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Wu J, Su Y, Zhu W, Mallak NJ, Lepore N, Reiman EM, Caselli RJ, Thompson PM, Chen K, Wang Y. Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding. J Alzheimers Dis 2023; 91:637-651. [PMID: 36463452 PMCID: PMC9940990 DOI: 10.3233/jad-220812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development). OBJECTIVE To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. METHODS With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. RESULTS We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics. CONCLUSION The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.
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Affiliation(s)
- Jianfeng Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, AZ, USA
| | - Wenhui Zhu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Negar Jalili Mallak
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | | | | | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, USA
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
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Wu J, Su Y, Chen Y, Zhu W, Reiman EM, Caselli RJ, Chen K, Thompson PM, Wang J, Wang Y. A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry. J Alzheimers Dis 2023; 93:1153-1168. [PMID: 37182882 PMCID: PMC10329869 DOI: 10.3233/jad-230034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits, potentially preventing or delaying up to 40% of dementia cases. Tau neurofibrillary tangles are the primary driver of downstream neurodegeneration and subsequent cognitive impairment in AD, resulting in structural deformations such as hippocampal atrophy that can be observed in magnetic resonance imaging (MRI) scans. OBJECTIVE To build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline. METHODS Using data obtained from different institutions, we develop a surface-based federated Chow test model to study the synergistic effects of APOE, a previously reported significant risk factor of AD, and tau on hippocampal surface morphometry. RESULTS We illustrate that the APOE-specific morphometry features correlate with AD progression and better predict future AD conversion than other MRI biomarkers. For example, a strong association between atrophy and abnormal tau was identified in hippocampal subregion cornu ammonis 1 (CA1 subfield) and subiculum in e4 homozygote cohort. CONCLUSION Our model allows for identifying MRI biomarkers for AD and cognitive decline prediction and may uncover a corner of the neural mechanism of the influence of APOE and tau deposition on hippocampal morphology.
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Affiliation(s)
- Jianfeng Wu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, USA
| | - Yanxi Chen
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA
| | - Wenhui Zhu
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA
| | | | | | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, USA
| | - Junwen Wang
- Division of Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, USA
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7
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Cong Z, Fu Y, Chen N, Zhang L, Yao C, Wang Y, Yao Z, Hu B. Individuals with cannabis use are associated with widespread morphological alterations in the subregions of the amygdala, hippocampus, and pallidum. Drug Alcohol Depend 2022; 239:109595. [PMID: 35961268 DOI: 10.1016/j.drugalcdep.2022.109595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/02/2022] [Accepted: 07/30/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Cannabis is the most frequently used illicit drug worldwide. Although multiple structural MRI studies of individuals with cannabis use (CB) have been undertaken, the reports of the volume alterations in the amygdala, hippocampus, and pallidum are not consistent. This study aims to detect subregion-level morphological alterations, analyze the correlation areas with cannabis usage characteristics, and gain new insights into the neuro mechanisms of CB. METHODS By leveraging the novel surface-based subcortical morphometry method, 20 CB and 22 age- and sex-matched healthy controls (HC) were included to explore their volumetric and morphological differences in the three subcortical structures. Afterward, the correlation analysis between surface morphological eigenvalues and cannabis usage characteristics was performed. RESULTS Compared with volumetric measures, the surface-based subcortical morphometry method detected more significant global morphological deformations in the left amygdala, right hippocampus, and right pallidum (overall-p < 0.05, corrected). More obvious morphological alterations (atrophy or expansion) were observed in specific subregions (vertex-based p-value<0.05, uncorrected) of the three subcortical structures. Both positive and negative subregional correlation areas were reported by the correlation analysis. CONCLUSIONS The current study illuminated new pathophysiologic mechanisms in the amygdala, hippocampus, and pallidum at the subregion level, which may inform the subsequent smaller-scale CB research.
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Affiliation(s)
- Zhaoyang Cong
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province 730000, China
| | - Yu Fu
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang Province 310027, China
| | - Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province 730000, China
| | - Lingyu Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province 730000, China
| | - Chaofan Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province 730000, China
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province 730000, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province 730000, China; Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, Gansu Province 730000, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200233, China; Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University), Ministry of Education, Lanzhou, Gansu Province 730000, China.
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8
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Taheri M, Schulz J. Statistical analysis of locally parameterized shapes. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2116445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Mohsen Taheri
- Department of Mathematics & Physics, University of Stavanger (UiS)
| | - Jörn Schulz
- Department of Mathematics & Physics, University of Stavanger (UiS)
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Li S, An N, Chen N, Wang Y, Yang L, Wang Y, Yao Z, Hu B. The impact of Alzheimer's disease susceptibility loci on lateral ventricular surface morphology in older adults. Brain Struct Funct 2022; 227:913-924. [PMID: 35028746 DOI: 10.1007/s00429-021-02429-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 11/13/2021] [Indexed: 11/25/2022]
Abstract
The enlargement of ventricular volume is a general trend in the elderly, especially in patients with Alzheimer's disease (AD). Multiple susceptibility loci have been reported to have an increased risk for AD and the morphology of brain structures are affected by the variations in the risk loci. Therefore, we hypothesized that genes contributed significantly to the ventricular surface, and the changes of ventricular surface were associated with the impairment of cognitive functions. After the quality controls (QC) and genotyping, a lateral ventricular segmentation method was employed to obtain the surface features of lateral ventricle. We evaluated the influence of 18 selected AD susceptibility loci on both volume and surface morphology across 410 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI). Correlations were conducted between radial distance (RD) and Montreal Cognitive Assessment (MoCA) subscales. Only the C allele at the rs744373 loci in BIN1 gene significantly accelerated the atrophy of lateral ventricle, including the anterior horn, body, and temporal horn of left lateral ventricle. No significant effect on lateral ventricle was found at other loci. Our results revealed that most regions of the bilateral ventricular surface were significantly negatively correlated with cognitive scores, particularly in delayed recall. Besides, small areas of surface were negatively correlated with language, orientation, and visuospatial scores. Together, our results indicated that the genetic variation affected the localized areas of lateral ventricular surface, and supported that lateral ventricle was an important brain structure associated with cognition in the elderly.
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Affiliation(s)
- Shan Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, 730000, Gansu Province, People's Republic of China
| | - Na An
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, 730000, Gansu Province, People's Republic of China
| | - Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, 730000, Gansu Province, People's Republic of China
| | - Yin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, 730000, Gansu Province, People's Republic of China
| | - Lin Yang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, 730000, Gansu Province, People's Republic of China
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, 730000, Gansu Province, People's Republic of China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, 730000, Gansu Province, People's Republic of China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, ShangHai, China.
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, LanZhou, China.
- Engineering Research Center of Open Source Software and Real-Time System, Ministry of Education, Lanzhou University, Lanzhou, China.
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10
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Wang G, Zhou W, Kong D, Qu Z, Ba M, Hao J, Yao T, Dong Q, Su Y, Reiman EM, Caselli RJ, Chen K, Wang Y. Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker. J Alzheimers Dis 2022; 85:1233-1250. [PMID: 34924383 PMCID: PMC10498787 DOI: 10.3233/jad-215149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes. OBJECTIVE We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61). METHODS Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs. RESULTS The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC. CONCLUSION The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.
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Affiliation(s)
- Gang Wang
- School of Ulsan Ship and Ocean College, Ludong University, Yantai, China
| | - Wenju Zhou
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
| | - Deping Kong
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Zongshuai Qu
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Maowen Ba
- Department of Neurology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Jinguang Hao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Tao Yao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Qunxi Dong
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Yi Su
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | | | - Kewei Chen
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
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11
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Wu J, Dong Q, Zhang J, Su Y, Wu T, Caselli RJ, Reiman EM, Ye J, Lepore N, Chen K, Thompson PM, Wang Y. Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology. Front Neurosci 2021; 15:762458. [PMID: 34899166 PMCID: PMC8655732 DOI: 10.3389/fnins.2021.762458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 11/01/2021] [Indexed: 12/03/2022] Open
Abstract
Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Teresa Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Richard J. Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, United States
| | - Eric M. Reiman
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
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12
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Wu J, Zhu W, Su Y, Gui J, Lepore N, Reiman EM, Caselli RJ, Thompson PM, Chen K, Wang Y. Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 12088:120880O. [PMID: 34961803 PMCID: PMC8710175 DOI: 10.1117/12.2607169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM).
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA
| | - Wenhui Zhu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA
| | - Yi Su
- Banner Alzheimer’s Institute, Phoenix, USA
| | - Jie Gui
- School of Cyber Science and Engineering, Southeast University, Nanjing, China
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology Children’s Hospital Los Angeles, Los Angeles, USA
| | | | | | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, USA
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, USA
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13
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An N, Fu Y, Shi J, Guo HN, Yang ZW, Li YC, Li S, Wang Y, Yao ZJ, Hu B. Synergistic Effects of APOE and CLU May Increase the Risk of Alzheimer's Disease: Acceleration of Atrophy in the Volumes and Shapes of the Hippocampus and Amygdala. J Alzheimers Dis 2021; 80:1311-1327. [PMID: 33682707 DOI: 10.3233/jad-201162] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The volume loss of the hippocampus and amygdala in non-demented individuals has been reported to increase the risk of developing Alzheimer's disease (AD). Many neuroimaging genetics studies mainly focused on the individual effects of APOE and CLU on neuroimaging to understand their neural mechanisms, whereas their synergistic effects have been rarely studied. OBJECTIVE To assess whether APOE and CLU have synergetic effects, we investigated the epistatic interaction and combined effects of the two genetic variants on morphological degeneration of hippocampus and amygdala in the non-demented elderly at baseline and 2-year follow-up. METHODS Besides the widely-used volume indicator, the surface-based morphometry method was also adopted in this study to evaluate shape alterations. RESULTS Our results showed a synergistic effect of homozygosity for the CLU risk allele C in rs11136000 and APOEɛ4 on the hippocampal and amygdalar volumes during a 2-year follow-up. Moreover, the combined effects of APOEɛ4 and CLU C were stronger than either of the individual effects in the atrophy progress of the amygdala. CONCLUSION These findings indicate that brain morphological changes are caused by more than one gene variant, which may help us to better understand the complex endogenous mechanism of AD.
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Affiliation(s)
- Na An
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yu Fu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Han-Ning Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zheng-Wu Yang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yong-Chao Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Shan Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yin Wang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zhi-Jun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.,Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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14
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Wu J, Dong Q, Gui J, Zhang J, Su Y, Chen K, Thompson PM, Caselli RJ, Reiman EM, Ye J, Wang Y. Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases. Front Neurosci 2021; 15:669595. [PMID: 34421510 PMCID: PMC8377280 DOI: 10.3389/fnins.2021.669595] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/15/2021] [Indexed: 01/04/2023] Open
Abstract
Biomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States.,Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
| | - Jie Gui
- School of Cyber Science and Engineering, Southeast University, Nanjing, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States
| | - Richard J Caselli
- Department of Neurology, Mayo Clinic Arizona, Scottsdale, AZ, United States
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, United States
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, United States
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15
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Stonnington CM, Wu J, Zhang J, Shi J, Bauer Iii RJ, Devadas V, Su Y, Locke DEC, Reiman EM, Caselli RJ, Chen K, Wang Y. Improved Prediction of Imminent Progression to Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics and Sparse Coding. J Alzheimers Dis 2021; 81:209-220. [PMID: 33749642 DOI: 10.3233/jad-200821] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Besides their other roles, brain imaging and other biomarkers of Alzheimer's disease (AD) have the potential to inform a cognitively unimpaired (CU) person's likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79%sensitivity/78%specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression, and leave-one-out procedures. OBJECTIVE To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI). METHODS Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years ("progressors") and 80 matched adults who remained CU for at least 4 years ("nonprogressors"). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI. RESULTS We achieved 92%prediction accuracy in the Arizona cohort, 92%prediction accuracy in the ADNI cohort, and 90%prediction accuracy when combining the two demographically distinct cohorts, as compared to 79%(Arizona) and 72%(ADNI) prediction accuracy using hippocampal volume. CONCLUSION Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers.
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Affiliation(s)
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | | | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Dona E C Locke
- Department of Psychiatry and Psychology, Mayo Clinic, Scottsdale, AZ, USA
| | | | | | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
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16
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Zhang Z, Wang X, Kong L, Zhu H. High-Dimensional Spatial Quantile Function-on-Scalar Regression. J Am Stat Assoc 2021; 117:1563-1578. [PMID: 37008532 PMCID: PMC10065478 DOI: 10.1080/01621459.2020.1870984] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
This article develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or image response on the whole spatial domain. Our method provides a comprehensive understanding of the effect of scalar covariates on functional responses across different quantile levels and also gives a practical way to generate new images for given covariate values. Theoretically, we establish the minimax rates of convergence for estimating coefficient functions under both fixed and random designs. We further develop an efficient primal-dual algorithm to handle high-dimensional image data. Simulations and real data analysis are conducted to examine the finite-sample performance.
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Affiliation(s)
- Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC
| | - Xiao Wang
- Department of Statistics, Purdue University, West Lafayette, IN
| | - Linglong Kong
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC
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17
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Fu Y, Zhang J, Li Y, Shi J, Zou Y, Guo H, Li Y, Yao Z, Wang Y, Hu B. A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 104:109989. [PMID: 32512131 PMCID: PMC9632410 DOI: 10.1016/j.pnpbp.2020.109989] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/19/2020] [Accepted: 05/30/2020] [Indexed: 10/24/2022]
Abstract
Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with >80% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.
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Affiliation(s)
- Yu Fu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yuan Li
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Ying Zou
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Hanning Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yongchao Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China.
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18
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Wang G, Dong Q, Wu J, Su Y, Chen K, Su Q, Zhang X, Hao J, Yao T, Liu L, Zhang C, Caselli RJ, Reiman EM, Wang Y. Developing univariate neurodegeneration biomarkers with low-rank and sparse subspace decomposition. Med Image Anal 2021; 67:101877. [PMID: 33166772 PMCID: PMC7725891 DOI: 10.1016/j.media.2020.101877] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 08/24/2020] [Accepted: 10/13/2020] [Indexed: 01/01/2023]
Abstract
Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of Aβ+AD and Aβ-cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between Aβ+AD and Aβ-CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25% reduction in the mean annual change with 80% power and two-tailed P=0.05are 116, 279 and 387 for the longitudinal Aβ+AD, Aβ+mild cognitive impairment (MCI) and Aβ+CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD (4.3, 95% CI = 2.3-8.2) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB.
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Affiliation(s)
- Gang Wang
- Ulsan Ship and Ocean College, Ludong University, Yantai, China.
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809 Tempe, AZ 85287, USA
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809 Tempe, AZ 85287, USA
| | - Yi Su
- Banner Alzheimer's Institute and Banner Good Samaritan Pet Center, Phoenix, AZ, USA
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan Pet Center, Phoenix, AZ, USA
| | - Qingtang Su
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Xiaofeng Zhang
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Jinguang Hao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Tao Yao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Li Liu
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Caiming Zhang
- Shandong Province Key Lab of Digital Media Technology, Shandong University of Finance and Economics, Jinan, China
| | | | - Eric M Reiman
- Banner Alzheimer's Institute and Banner Good Samaritan Pet Center, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809 Tempe, AZ 85287, USA.
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19
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Wu J, Zhang J, Li Q, Su Y, Chen K, Reiman EM, Wang J, Lepore N, Ye J, Thompson PM, Wang Y. Patch-Based Surface Morphometry Feature Selection with Federated Group Lasso Regression. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11583. [PMID: 33250550 DOI: 10.1117/12.2575984] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Collectively, vast quantities of brain imaging data exist across hospitals and research institutions, providing valuable resources to study brain disorders such as Alzheimer's disease (AD). However, in practice, putting all these distributed datasets into a centralized platform is infeasible due to patient privacy concerns, data restrictions and legal regulations. In this study, we propose a novel federated feature selection framework that can analyze the data at each individual institution without data-sharing or accessing private patient information. In this framework, we first propose a federated group lasso optimization method based on block coordinate descent. We employ stability selection to determine statistically significant features, by solving the group lasso problem with a sequence of regularization parameters. To accelerate the stability selection, we further propose a federated screening rule, which can identify and exclude the irrelevant features before solving the group lasso. Here, we use this framework for patch based feature selection on hippocampal morphometry. Shape is characterized through two different kinds of local measures, the radial distance and the surface area determined via tensor-based morphometry (TBM). The method is tested on 1,127 T1-weighted brain magnetic resonance images (MRI) of AD, mild cognitive impairment (MCI) and elderly control subjects, randomly assigned to five independent hypothetical institutions for testing purpose. We examine the association of MRI-based anatomical measures with general cognitive assessment and amyloid burden to identify the morphometry changes related to AD deterioration and plaque accumulation. Finally, we visualize the significance of the association on the hippocampal surfaces. Our experimental results successfully demonstrate the efficiency and effectiveness of our method.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA
| | - Qingyang Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA
| | - Yi Su
- Banner Alzheimer's Institute, 100 Washtenaw Avenue, Phoenix, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, 100 Washtenaw Avenue, Phoenix, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute, 100 Washtenaw Avenue, Phoenix, USA
| | - Jie Wang
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, 1129 Huizhou Ave, Baohe District, Hefei, China
| | - Natasha Lepore
- CIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, 4650 Sunset Blvd. MS 81, Los Angeles, USA
| | - Jieping Ye
- Department of Computational Medicine and Bioinformatics, University of Michigan, 1301 Beal Avenue, Ann Arbor, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, 4676 Admiralty Way, Los Angeles, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA
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20
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Dong Q, Zhang W, Stonnington CM, Wu J, Gutman BA, Chen K, Su Y, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline. NEUROIMAGE-CLINICAL 2020; 27:102338. [PMID: 32683323 PMCID: PMC7371915 DOI: 10.1016/j.nicl.2020.102338] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/15/2020] [Accepted: 07/02/2020] [Indexed: 12/31/2022]
Abstract
A completely automated surface-based ventricular morphometry system. Generate a whole connected 3D ventricular shape model. Test-retest the system in two independent CU subject cohorts. Subregional ventricular abnormalities prior to clinically memory decline.
Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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21
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Yao Z, Fu Y, Wu J, Zhang W, Yu Y, Zhang Z, Wu X, Wang Y, Hu B. Morphological changes in subregions of hippocampus and amygdala in major depressive disorder patients. Brain Imaging Behav 2020; 14:653-667. [PMID: 30519998 PMCID: PMC6551316 DOI: 10.1007/s11682-018-0003-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Despite many neuroimaging studies in the past years, the neuroanatomical substrates of major depressive disorder (MDD) subcortical structures are still not well understood. Since hippocampus and amygdala are the two vital subcortical structures that most susceptible to MDD, finding the evidence of morphological changes in their subregions may bring some new insights for MDD research. Combining structural magnetic resonance imaging (MRI) with novel morphometry analysis methods, we recruited 25 MDD patients and 28 healthy controls (HC), and investigated their volume and morphological differences in hippocampus and amygdala. Relative to volumetric method, our methods detected more significant global morphological atrophies (p<0.05). More precisely, subiculum and cornu ammonis (CA) 1 subregions of bilateral hippocampus, lateral (LA) and basolateral ventromedial (BLVM) of left amygdala and LA, BLVM, central (CE), amygdalostriatal transition area (ASTR), anterior cortical (ACO) and anterior amygdaloid area (AAA) of right amygdala were demonstrated prone to atrophy. Correlation analyses between each subject's surface eigenvalues and Hamilton Depression Scale (HAMD) were then performed. Correlation results showed that atrophy areas in hippocampus and amygdala have slight tendencies of expanding into other subregions with the development of MDD. Finally, we performed group morphometric analysis and drew the atrophy and expansion areas between MDD-Medicated group (only 19 medicated subjects in MDD group were included) and HC group, found some preliminary evidence about subregional morphological resilience of hippocampus and amygdala. These findings revealed new pathophysiologic patterns in the subregions of hippocampus and amygdala, which can help with subsequent smaller-scale MDD research.
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Affiliation(s)
- Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, P.O. Box 730000, Lanzhou, China
| | - Yu Fu
- School of Information Science and Engineering, Lanzhou University, P.O. Box 730000, Lanzhou, China
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ, 85287, USA
| | - Wenwen Zhang
- Department of Radiology, Gansu Provincial Hospital, Lanzhou, China
| | - Yue Yu
- School of Information Science and Engineering, Lanzhou University, P.O. Box 730000, Lanzhou, China
| | - Zicheng Zhang
- School of Information Science and Engineering, Lanzhou University, P.O. Box 730000, Lanzhou, China
| | - Xia Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
- College of Information Science and Technology, Beijing Normal University, P.O. Box 100000, Beijing, China.
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P.O. Box 878809, Tempe, AZ, 85287, USA.
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, P.O. Box 730000, Lanzhou, China.
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22
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Shi J, Wang Y. Hyperbolic Wasserstein Distance for Shape Indexing. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2020; 42:1362-1376. [PMID: 30763239 PMCID: PMC6687563 DOI: 10.1109/tpami.2019.2898400] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Shape space is an active research topic in computer vision and medical imaging fields. The distance defined in a shape space may provide a simple and refined index to represent a unique shape. This work studies the Wasserstein space and proposes a novel framework to compute the Wasserstein distance between general topological surfaces by integrating hyperbolic Ricci flow, hyperbolic harmonic map, and hyperbolic power Voronoi diagram algorithms. The resulting hyperbolic Wasserstein distance can intrinsically measure the similarity between general topological surfaces. Our proposed algorithms are theoretically rigorous and practically efficient. It has the potential to be a powerful tool for 3D shape indexing research. We tested our algorithm with human face classification and Alzheimer's disease (AD) progression tracking studies. Experimental results demonstrated that our work may provide a succinct and effective shape index.
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23
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Cerrolaza JJ, Picazo ML, Humbert L, Sato Y, Rueckert D, Ballester MÁG, Linguraru MG. Computational anatomy for multi-organ analysis in medical imaging: A review. Med Image Anal 2019; 56:44-67. [DOI: 10.1016/j.media.2019.04.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 02/05/2019] [Accepted: 04/13/2019] [Indexed: 12/19/2022]
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24
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Dong Q, Zhang W, Wu J, Li B, Schron EH, McMahon T, Shi J, Gutman BA, Chen K, Baxter LC, Thompson PM, Reiman EM, Caselli RJ, Wang Y. Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects. NEUROIMAGE-CLINICAL 2019; 22:101744. [PMID: 30852398 PMCID: PMC6411498 DOI: 10.1016/j.nicl.2019.101744] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 02/02/2019] [Accepted: 03/02/2019] [Indexed: 11/30/2022]
Abstract
Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals. Applied surface-based hippocampal morphometry on cognitively unimpaired subjects. Our study identified APOE-e4 dose effects on cognitively unimpaired subjects. Surface-based hippocampal morphometry outperformed the hippocampal volume measure. Surface-based hippocampal morphometry may be a potential preclinical AD biomarker.
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Affiliation(s)
- Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Bolun Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Travis McMahon
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Boris A Gutman
- Armour College of Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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25
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Saha PK, Jin D, Liu Y, Christensen GE, Chen C. Fuzzy Object Skeletonization: Theory, Algorithms, and Applications. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2018; 24:2298-2314. [PMID: 28809701 DOI: 10.1109/tvcg.2017.2738023] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Skeletonization offers a compact representation of an object while preserving important topological and geometrical features. Literature on skeletonization of binary objects is quite mature. However, challenges involved with skeletonization of fuzzy objects are mostly unanswered. This paper presents a new theory and algorithm of skeletonization for fuzzy objects, evaluates its performance, and demonstrates its applications. A formulation of fuzzy grassfire propagation is introduced; its relationships with fuzzy distance functions, level sets, and geodesics are discussed; and several new theoretical results are presented in the continuous space. A notion of collision-impact of fire-fronts at skeletal points is introduced, and its role in filtering noisy skeletal points is demonstrated. A fuzzy object skeletonization algorithm is developed using new notions of surface- and curve-skeletal voxels, digital collision-impact, filtering of noisy skeletal voxels, and continuity of skeletal surfaces. A skeletal noise pruning algorithm is presented using branch-level significance. Accuracy and robustness of the new algorithm are examined on computer-generated phantoms and micro- and conventional CT imaging of trabecular bone specimens. An application of fuzzy object skeletonization to compute structure-width at a low image resolution is demonstrated, and its ability to predict bone strength is examined. Finally, the performance of the new fuzzy object skeletonization algorithm is compared with two binary object skeletonization methods.
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26
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Wu J, Zhang J, Shi J, Chen K, Caselli RJ, Reiman EM, Wang Y. HIPPOCAMPUS MORPHOMETRY STUDY ON PATHOLOGY-CONFIRMED ALZHEIMER'S DISEASE PATIENTS WITH SURFACE MULTIVARIATE MORPHOMETRY STATISTICS. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:1555-1559. [PMID: 30123414 DOI: 10.1109/isbi.2018.8363870] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases in elderly and the incidence of this disease is increasing with older ages. One of the hallmarks of AD is the accumulation of beta-amyloid plaques (Aβ) in human brains. Most of prior brain imaging researchers used the clinical symptom based diagnosis without the confirmation of imaging or fluid Aβ information. In this work, we study hippocampus morphometry on a cohort consisting of Aβ positive AD (N = 151) and matched Aβ negative cognitively unimpaired subjects (N = 271) with Aβ positivity determined via florbetapir PET. The brain images are obtained from publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI). We compute our surface multivariate morphometry statistics from segmented hippocampus structure in structural MR images. With these features, we find statistically significant difference by using Hotelling's T2 tests. Meanwhile, we apply a patch-based analysis of sparse coding system for binary group classification and achieve an accuracy rate of 90.48%. Our results demonstrate that our surface multivariate morphometry statistics (MMS) perform better than traditional hippocampal volume measures in classification and it may be applied as a potential biomarker for distinguishing dementia due to AD from age matched normal aging individuals.
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Affiliation(s)
- Jianfeng Wu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
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27
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Tu L, Styner M, Vicory J, Elhabian S, Wang R, Hong J, Paniagua B, Prieto JC, Yang D, Whitaker R, Pizer SM. Skeletal Shape Correspondence Through Entropy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:1-11. [PMID: 28945591 PMCID: PMC5943061 DOI: 10.1109/tmi.2017.2755550] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
We present a novel approach for improving the shape statistics of medical image objects by generating correspondence of skeletal points. Each object's interior is modeled by an s-rep, i.e., by a sampled, folded, two-sided skeletal sheet with spoke vectors proceeding from the skeletal sheet to the boundary. The skeleton is divided into three parts: the up side, the down side, and the fold curve. The spokes on each part are treated separately and, using spoke interpolation, are shifted along that skeleton in each training sample so as to tighten the probability distribution on those spokes' geometric properties while sampling the object interior regularly. As with the surface/boundary-based correspondence method of Cates et al., entropy is used to measure both the probability distribution tightness and the sampling regularity, here of the spokes' geometric properties. Evaluation on synthetic and real world lateral ventricle and hippocampus data sets demonstrate improvement in the performance of statistics using the resulting probability distributions. This improvement is greater than that achieved by an entropy-based correspondence method on the boundary points.
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28
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Zhang M, Wells WM, Golland P. Probabilistic modeling of anatomical variability using a low dimensional parameterization of diffeomorphisms. Med Image Anal 2017; 41:55-62. [PMID: 28732595 DOI: 10.1016/j.media.2017.06.013] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 06/19/2017] [Accepted: 06/28/2017] [Indexed: 11/29/2022]
Abstract
We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space.
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Affiliation(s)
- Miaomiao Zhang
- Computer Science and Artificial Intelligence Laboratory, MIT, Massachusetts.
| | - William M Wells
- Computer Science and Artificial Intelligence Laboratory, MIT, Massachusetts; Brigham and Women's Hospital, Harvard Medical School, Massachusetts
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, MIT, Massachusetts
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29
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Ghayoor A, Vaidya JG, Johnson HJ. Robust automated constellation-based landmark detection in human brain imaging. Neuroimage 2017; 170:471-481. [PMID: 28392490 DOI: 10.1016/j.neuroimage.2017.04.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Revised: 02/04/2017] [Accepted: 04/05/2017] [Indexed: 10/19/2022] Open
Abstract
A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.
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Affiliation(s)
- Ali Ghayoor
- Department of Electrical and Computer Engineering, 1402 Seamans Center for the Engineering Arts and Science, The University of Iowa, Iowa City, IA 52240, USA; Department of Psychiatry, University of Iowa Hospitals & Clinics, Iowa City, IA 52242, USA
| | - Jatin G Vaidya
- Department of Psychiatry, University of Iowa Hospitals & Clinics, Iowa City, IA 52242, USA
| | - Hans J Johnson
- Department of Electrical and Computer Engineering, 1402 Seamans Center for the Engineering Arts and Science, The University of Iowa, Iowa City, IA 52240, USA; Department of Psychiatry, University of Iowa Hospitals & Clinics, Iowa City, IA 52242, USA.
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30
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Shi J, Zhang W, Wang Y. Shape Analysis with Hyperbolic Wasserstein Distance. PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2016; 2016:5051-5061. [PMID: 28392672 DOI: 10.1109/cvpr.2016.546] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Shape space is an active research field in computer vision study. The shape distance defined in a shape space may provide a simple and refined index to represent a unique shape. Wasserstein distance defines a Riemannian metric for the Wasserstein space. It intrinsically measures the similarities between shapes and is robust to image noise. Thus it has the potential for the 3D shape indexing and classification research. While the algorithms for computing Wasserstein distance have been extensively studied, most of them only work for genus-0 surfaces. This paper proposes a novel framework to compute Wasserstein distance between general topological surfaces with hyperbolic metric. The computational algorithms are based on Ricci flow, hyperbolic harmonic map, and hyperbolic power Voronoi diagram and the method is general and robust. We apply our method to study human facial expression, longitudinal brain cortical morphometry with normal aging, and cortical shape classification in Alzheimer's disease (AD). Experimental results demonstrate that our method may be used as an effective shape index, which outperforms some other standard shape measures in our AD versus healthy control classification study.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering Arizona State University
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering Arizona State University
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering Arizona State University
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31
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Zhang M, Wells WM, Golland P. Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2016; 9902:166-173. [PMID: 28664199 PMCID: PMC5484008 DOI: 10.1007/978-3-319-46726-9_20] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Using image-based descriptors to investigate clinical hypotheses and therapeutic implications is challenging due to the notorious "curse of dimensionality" coupled with a small sample size. In this paper, we present a low-dimensional analysis of anatomical shape variability in the space of diffeomorphisms and demonstrate its benefits for clinical studies. To combat the high dimensionality of the deformation descriptors, we develop a probabilistic model of principal geodesic analysis in a bandlimited low-dimensional space that still captures the underlying variability of image data. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than models based on the high-dimensional state-of-the-art approaches such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA).
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Affiliation(s)
- Miaomiao Zhang
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA
| | - William M Wells
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA
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32
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Shi J, Zhang W, Tang M, Caselli RJ, Wang Y. Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis. Med Image Anal 2016; 35:517-529. [PMID: 27639215 DOI: 10.1016/j.media.2016.09.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Revised: 09/02/2016] [Accepted: 09/02/2016] [Indexed: 01/01/2023]
Abstract
Landmark curves were widely adopted in neuroimaging research for surface correspondence computation and quantified morphometry analysis. However, most of the landmark based morphometry studies only focused on landmark curve shape difference. Here we propose to compute a set of conformal invariant-based shape indices, which are associated with the landmark curve induced boundary lengths in the hyperbolic parameter domain. Such shape indices may be used to identify which surfaces are conformally equivalent and further quantitatively measure surface deformation. With the surface Ricci flow method, we can conformally map a multiply connected surface to the Poincaré disk. Our algorithm provides a stable method to compute the shape index values in the 2D (Poincaré Disk) parameter domain. The proposed shape indices are succinct, intrinsic and informative. Experimental results with synthetic data and 3D MRI data demonstrate that our method is invariant under isometric transformations and able to detect brain surface abnormalities. We also applied the new shape indices to analyze brain morphometry abnormalities associated with Alzheimer' s disease (AD). We studied the baseline MRI scans of a set of healthy control and AD patients from the Alzheimer' s Disease Neuroimaging Initiative (ADNI: 30 healthy control subjects vs. 30 AD patients). Although the lengths of the landmarks in Euclidean space, cortical surface area, and volume features did not differ between the two groups, our conformal invariant based shape indices revealed significant differences by Hotelling' s T2 test. The novel conformal invariant shape indices may offer a new sensitive biomarker and enrich our brain imaging analysis toolset for studying diagnosis and prognosis of AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, P.O. Box 878809, USA
| | - Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, P.O. Box 878809, USA
| | - Miao Tang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, P.O. Box 878809, USA
| | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85287, P.O. Box 878809, USA.
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33
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Kong D, Giovanello KS, Wang Y, Lin W, Lee E, Fan Y, Murali Doraiswamy P, Zhu H. Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data. J Alzheimers Dis 2016; 46:695-702. [PMID: 25869783 DOI: 10.3233/jad-150164] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The growing public threat of Alzheimer's disease (AD) has raised the urgency to discover and validate prognostic biomarkers in order to predicting time to onset of AD. It is anticipated that both whole genome single nucleotide polymorphism (SNP) data and high dimensional whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. The aim of this paper is to test whether both whole genome SNP data and whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. In 343 subjects with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI-1), we extracted high dimensional MR imaging (volumetric data on 93 brain regions plus a surface fluid registration based hippocampal subregion and surface data), and whole genome data (504,095 SNPs from GWAS), as well as routine neurocognitive and clinical data at baseline. MCI patients were then followed over 48 months, with 150 participants progressing to AD. Combining information from whole brain MR imaging and whole genome data was substantially superior to the standard model for predicting time to onset of AD in a 48-month national study of subjects at risk. Our findings demonstrate the promise of combined imaging-whole genome prognostic markers in people with mild memory impairment.
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Affiliation(s)
- Dehan Kong
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Kelly S Giovanello
- Department of Psychology, University of North Carolina, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.,Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Eunjee Lee
- Department of Statistics, University of North Carolina, Chapel Hill, NC, USA
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - P Murali Doraiswamy
- Departments of Psychiatry and Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.,Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
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34
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Statistical shape analysis: From landmarks to diffeomorphisms. Med Image Anal 2016; 33:155-158. [PMID: 27377332 DOI: 10.1016/j.media.2016.06.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 06/13/2016] [Accepted: 06/15/2016] [Indexed: 11/22/2022]
Abstract
We offer a blazingly brief review of evolution of shape analysis methods in medical imaging. As the representations and the statistical models grew more sophisticated, the problem of shape analysis has been gradually redefined to accept images rather than binary segmentations as a starting point. This transformation enabled shape analysis to take its rightful place in the arsenal of tools for extracting and understanding patterns in large clinical image sets. We speculate on the future developments in shape analysis and potential applications that would bring this mathematically rich area to bear on clinical practice.
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36
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Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study. PLoS One 2016; 11:e0152901. [PMID: 27065111 PMCID: PMC4827849 DOI: 10.1371/journal.pone.0152901] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Accepted: 03/21/2016] [Indexed: 11/25/2022] Open
Abstract
The apolipoprotein E (APOE) e4 genotype is a powerful risk factor for late-onset Alzheimer’s disease (AD). In the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, we previously reported significant baseline structural differences in APOE e4 carriers relative to non-carriers, involving the left hippocampus more than the right—a difference more pronounced in e4 homozygotes than heterozygotes. We now examine the longitudinal effects of APOE genotype on hippocampal morphometry at 6-, 12- and 24-months, in the ADNI cohort. We employed a new automated surface registration system based on conformal geometry and tensor-based morphometry. Among different hippocampal surfaces, we computed high-order correspondences, using a novel inverse-consistent surface-based fluid registration method and multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance. At each time point, using Hotelling’s T2 test, we found significant morphological deformation in APOE e4 carriers relative to non-carriers in the full cohort as well as in the non-demented (pooled MCI and control) subjects at each follow-up interval. In the complete ADNI cohort, we found greater atrophy of the left hippocampus than the right, and this asymmetry was more pronounced in e4 homozygotes than heterozygotes. These findings, combined with our earlier investigations, demonstrate an e4 dose effect on accelerated hippocampal atrophy, and support the enrichment of prevention trial cohorts with e4 carriers.
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37
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Zhang W, Shi J, Stonnington C, Bauer RJ, Gutman BA, Chen K, Thompson PM, Reiman EM, Caselli RJ, Wang Y. MORPHOMETRIC ANALYSIS OF HIPPOCAMPUS AND LATERAL VENTRICLE REVEALS REGIONAL DIFFERENCE BETWEEN COGNITIVELY STABLE AND DECLINING PERSONS. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2016; 2016:14-18. [PMID: 27499828 PMCID: PMC4974021 DOI: 10.1109/isbi.2016.7493200] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Alzheimers disease (AD) is a progressive neurodegenerative disease most prevalent in the elderly. Distinguishing disease-related memory decline from normal age-related memory decline has been clinically difficult due to the subtlety of cognitive change during the preclinical stage of AD. In contrast, sensitive biomarkers derived from in vivo neuroimaging data could improve the early identification of AD. In this study, we employed a morphometric analysis in the hippocampus and lateral ventricle. A novel group-wise template-based segmentation algorithm was developed for ventricular segmentation. Further, surface multivariate tensor-based morphometry and radial distance on each surface point were computed. Using Hotellings T2 test, we found significant morphometric differences in both hippocampus and lateral ventricle between stable and clinically declining subjects. The left hemisphere was more severely affected than the right during this early disease stage. Hippocampal and ventricular morphometry has significant potential as an imaging biomarker for onset prediction and early diagnosis of AD.
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Affiliation(s)
- Wen Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | | | | | - Boris A Gutman
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Univ. of Southern California, Marina del Rey, CA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ
| | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Univ. of Southern California, Marina del Rey, CA
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
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Zhang J, Stonnington C, Li Q, Shi J, Bauer RJ, Gutman BA, Chen K, Reiman EM, Thompson PM, Ye J, Wang Y. APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2016; 2016:646-650. [PMID: 27499829 DOI: 10.1109/isbi.2016.7493350] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Alzheimer's disease (AD) is a progressive brain disease. Accurate diagnosis of AD and its prodromal stage, mild cognitive impairment, is crucial for clinical trial design. There is also growing interests in identifying brain imaging biomarkers that help evaluate AD risk presymptomatically. Here, we applied a recently developed multivariate tensor-based morphometry (mTBM) method to extract features from hippocampal surfaces, derived from anatomical brain MRI. For such surface-based features, the feature dimension is usually much larger than the number of subjects. We used dictionary learning and sparse coding to effectively reduce the feature dimensions. With the new features, an Adaboost classifier was employed for binary group classification. In tests on publicly available data from the Alzheimers Disease Neuroimaging Initiative, the new framework outperformed several standard imaging measures in classifying different stages of AD. The new approach combines the efficiency of sparse coding with the sensitivity of surface mTBM, and boosts classification performance.
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Affiliation(s)
- Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | | | - Qingyang Li
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
| | | | - Boris A Gutman
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Univ.of Southern California, Marina del Rey, CA
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ
| | | | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, Univ.of Southern California, Marina del Rey, CA
| | - Jieping Ye
- Dept. of Computational Medicine and Bioinformatics, Univ. of Michigan, Ann Arbor, MI
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ
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Sun F, Choi YK, Yu Y, Wang W. Medial Meshes - A Compact and Accurate Representation of Medial Axis Transform. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2016; 22:1278-1290. [PMID: 26829240 DOI: 10.1109/tvcg.2015.2448080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The medial axis transform has long been known as an intrinsic shape representation supporting a variety of shape analysis and synthesis tasks. However, for a given shape, it is hard to obtain its faithful, concise and stable medial axis, which hinders the application of the medial axis. In this paper, we introduce the medial mesh, a new discrete representation of the medial axis. A medial mesh is a 2D simplicial complex coupled with a radius function that provides a piecewise linear approximation to the medial axis. We further present an effective algorithm for computing a concise and stable medial mesh for a given shape. Our algorithm is quantitatively driven by a shape approximation error metric, and progressively simplifies an initial medial mesh by iteratively contracting edges until the approximation error reaches a predefined threshold. We further demonstrate the superior efficiency and accuracy of our method over existing methods for medial axis simplification.
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40
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Pouch AM, Tian S, Takebe M, Yuan J, Gorman R, Cheung AT, Wang H, Jackson BM, Gorman JH, Gorman RC, Yushkevich PA. Medially constrained deformable modeling for segmentation of branching medial structures: Application to aortic valve segmentation and morphometry. Med Image Anal 2015; 26:217-31. [PMID: 26462232 PMCID: PMC4679439 DOI: 10.1016/j.media.2015.09.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Revised: 09/08/2015] [Accepted: 09/16/2015] [Indexed: 11/28/2022]
Abstract
Deformable modeling with medial axis representation is a useful means of segmenting and parametrically describing the shape of anatomical structures in medical images. Continuous medial representation (cm-rep) is a "skeleton-first" approach to deformable medial modeling that explicitly parameterizes an object's medial axis and derives the object's boundary algorithmically. Although cm-rep has effectively been used to segment and model a number of anatomical structures with non-branching medial topologies, the framework is challenging to apply to objects with branching medial geometries since branch curves in the medial axis are difficult to parameterize. In this work, we demonstrate the first clinical application of a new "boundary-first" deformable medial modeling paradigm, wherein an object's boundary is explicitly described and constraints are imposed on boundary geometry to preserve the branching configuration of the medial axis during model deformation. This "boundary-first" framework is leveraged to segment and morphologically analyze the aortic valve apparatus in 3D echocardiographic images. Relative to manual tracing, segmentation with deformable medial modeling achieves a mean boundary error of 0.41 ± 0.10 mm (approximately one voxel) in 22 3DE images of normal aortic valves at systole. Deformable medial modeling is additionally demonstrated on pathological cases, including aortic stenosis, Marfan syndrome, and bicuspid aortic valve disease. This study demonstrates a promising approach for quantitative 3DE analysis of aortic valve morphology.
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Affiliation(s)
- Alison M Pouch
- Deparment of Surgery, University of Pennsylvania, Philadelphia, PA, United States ; Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States .
| | - Sijie Tian
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States
| | - Manabu Takebe
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States
| | - Jiefu Yuan
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States
| | - Robert Gorman
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States
| | - Albert T Cheung
- Deparment of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, United States
| | - Hongzhi Wang
- IBM Almaden Research Center, San Jose, CA, United States
| | - Benjamin M Jackson
- Deparment of Surgery, University of Pennsylvania, Philadelphia, PA, United States
| | - Joseph H Gorman
- Deparment of Surgery, University of Pennsylvania, Philadelphia, PA, United States ; Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States
| | - Robert C Gorman
- Deparment of Surgery, University of Pennsylvania, Philadelphia, PA, United States ; Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
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Nitzken MJ, Casanova MF, Gimelfarb G, Inanc T, Zurada JM, El-Baz A. Shape analysis of the human brain: a brief survey. IEEE J Biomed Health Inform 2015; 18:1337-54. [PMID: 25014938 DOI: 10.1109/jbhi.2014.2298139] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The survey outlines and compares popular computational techniques for quantitative description of shapes of major structural parts of the human brain, including medial axis and skeletal analysis, geodesic distances, Procrustes analysis, deformable models, spherical harmonics, and deformation morphometry, as well as other less widely used techniques. Their advantages, drawbacks, and emerging trends, as well as results of applications, in particular, for computer-aided diagnostics, are discussed.
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42
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Peng H, Wang X, Duan Y, Frey SH, Gu X. Brain Morphometry on Congenital Hand Deformities based on Teichmüller Space Theory. COMPUTER AIDED DESIGN 2015; 58:84-91. [PMID: 27158152 PMCID: PMC4855530 DOI: 10.1016/j.cad.2014.08.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Congenital Hand Deformities (CHD) are usually occurred between fourth and eighth week after the embryo is formed. Failure of the transformation from arm bud cells to upper limb can lead to an abnormal appearing/functioning upper extremity which is presented at birth. Some causes are linked to genetics while others are affected by the environment, and the rest have remained unknown. CHD patients develop prehension through the use of their hands, which affect the brain as time passes. In recent years, CHD have gain increasing attention and researches have been conducted on CHD, both surgically and psychologically. However, the impacts of CHD on brain structure are not well-understood so far. Here, we propose a novel approach to apply Teichmüller space theory and conformal welding method to study brain morphometry in CHD patients. Conformal welding signature reflects the geometric relations among different functional areas on the cortex surface, which is intrinsic to the Riemannian metric, invariant under conformal deformation, and encodes complete information of the functional area boundaries. The computational algorithm is based on discrete surface Ricci flow, which has theoretic guarantees for the existence and uniqueness of the solutions. In practice, discrete Ricci flow is equivalent to a convex optimization problem, therefore has high numerically stability. In this paper, we compute the signatures of contours on general 3D surfaces with surface Ricci flow method, which encodes both global and local surface contour information. Then we evaluated the signatures of pre-central and post-central gyrus on healthy control and CHD subjects for analyzing brain cortical morphometry. Preliminary experimental results from 3D MRI data of CHD/control data demonstrate the effectiveness of our method. The statistical comparison between left and right brain gives us a better understanding on brain morphometry of subjects with Congenital Hand Deformities, in particular, missing the distal part of the upper limb.
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Affiliation(s)
- Hao Peng
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794
| | - Xu Wang
- Department of Computer Science, University of Missouri, Columbia, MO 65211
| | - Ye Duan
- Department of Computer Science, University of Missouri, Columbia, MO 65211
| | - Scott H. Frey
- Department of Psychological Sciences, Brain Imaging Center, University of Missouri, Columbia, MO 65211
| | - Xianfeng Gu
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794
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Pouch AM, Tian S, Takabe M, Wang H, Yuan J, Cheung AT, Jackson BM, Gorman JH, Gorman RC, Yushkevich PA. Segmentation of the Aortic Valve Apparatus in 3D Echocardiographic Images: Deformable Modeling of a Branching Medial Structure. STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. STACOM (WORKSHOP) 2015; 8896:196-203. [PMID: 26247062 PMCID: PMC4523230 DOI: 10.1007/978-3-319-14678-2_20] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
3D echocardiographic (3DE) imaging is a useful tool for assessing the complex geometry of the aortic valve apparatus. Segmentation of this structure in 3DE images is a challenging task that benefits from shape-guided deformable modeling methods, which enable inter-subject statistical shape comparison. Prior work demonstrates the efficacy of using continuous medial representation (cm-rep) as a shape descriptor for valve leaflets. However, its application to the entire aortic valve apparatus is limited since the structure has a branching medial geometry that cannot be explicitly parameterized in the original cm-rep framework. In this work, we show that the aortic valve apparatus can be accurately segmented using a new branching medial modeling paradigm. The segmentation method achieves a mean boundary displacement of 0.6 ± 0.1 mm (approximately one voxel) relative to manual segmentation on 11 3DE images of normal open aortic valves. This study demonstrates a promising approach for quantitative 3DE analysis of aortic valve morphology.
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Affiliation(s)
- Alison M. Pouch
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA,
| | - Sijie Tian
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Manabu Takabe
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jiefu Yuan
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA
| | - Albert T. Cheung
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Benjamin M. Jackson
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA,Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph H. Gorman
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA,Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert C. Gorman
- Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, USA,Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
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Lao Y, Wang Y, Shi J, Ceschin R, Nelson MD, Panigrahy A, Leporé N. Thalamic alterations in preterm neonates and their relation to ventral striatum disturbances revealed by a combined shape and pose analysis. Brain Struct Funct 2014; 221:487-506. [PMID: 25366970 DOI: 10.1007/s00429-014-0921-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2014] [Accepted: 10/15/2014] [Indexed: 10/24/2022]
Abstract
Finding the neuroanatomical correlates of prematurity is vital to understanding which structures are affected, and to designing efficient prevention and treatment strategies. Converging results reveal that thalamic abnormalities are important indicators of prematurity. However, little is known about the localization of the abnormalities within the subnuclei of the thalamus, or on the association of altered thalamic development with other deep gray matter disturbances. Here, we aim to investigate the effect of prematurity on the thalamus and the putamen in the neonatal brain, and further investigate the associated abnormalities between these two structures. Using brain structural magnetic resonance imaging, we perform a novel combined shape and pose analysis of the thalamus and putamen between 17 preterm (41.12 ± 5.08 weeks) and 19 term-born (45.51 ± 5.40 weeks) neonates at term equivalent age. We also perform a set of correlation analyses between the thalamus and the putamen, based on the surface and pose results. We locate significant alterations on specific surface regions such as the anterior and ventral anterior (VA) thalamic nuclei, and significant relative pose changes of the left thalamus and the right putamen. In addition, we detect significant association between the thalamus and the putamen for both surface and pose parameters. The regions that are significantly associated include the VA, and the anterior and inferior putamen. We detect statistically significant surface deformations and pose changes on the thalamus and putamen, and for the first time, demonstrate the feasibility of using relative pose parameters as indicators for prematurity in neonates. Our methods show that regional abnormalities of the thalamus are associated with alterations of the putamen, possibly due to disturbed development of shared pre-frontal connectivity. More specifically, the significantly correlated regions in these two structures point to frontal-subcortical pathways including the dorsolateral prefrontal-subcortical circuit, the lateral orbitofrontal-subcortical circuit, the motor circuit, and the oculomotor circuit. These findings reveal new insight into potential subcortical structural covariates for poor neurodevelopmental outcomes in the preterm population.
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Affiliation(s)
- Yi Lao
- Department of Radiology, University of Southern California and Children's Hospital, 4650 Sunset Blvd, MS#81, Los Angeles, CA, 90027, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA
| | - Rafael Ceschin
- Department of Radiology, Children's Hospital of Pittsburgh UPMC, Pittsburgh, PA, USA
| | - Marvin D Nelson
- Department of Radiology, University of Southern California and Children's Hospital, 4650 Sunset Blvd, MS#81, Los Angeles, CA, 90027, USA
| | - Ashok Panigrahy
- Department of Radiology, University of Southern California and Children's Hospital, 4650 Sunset Blvd, MS#81, Los Angeles, CA, 90027, USA.,Department of Radiology, Children's Hospital of Pittsburgh UPMC, Pittsburgh, PA, USA
| | - Natasha Leporé
- Department of Radiology, University of Southern California and Children's Hospital, 4650 Sunset Blvd, MS#81, Los Angeles, CA, 90027, USA.
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Bompard L, Xu S, Styner M, Paniagua B, Ahn M, Yuan Y, Jewells V, Gao W, Shen D, Zhu H, Lin W. Multivariate longitudinal shape analysis of human lateral ventricles during the first twenty-four months of life. PLoS One 2014; 9:e108306. [PMID: 25265017 PMCID: PMC4180454 DOI: 10.1371/journal.pone.0108306] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2014] [Accepted: 08/28/2014] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Little is known about the temporospatial shape characteristics of human lateral ventricles (LVs) during the first two years of life. This study aimed to delineate the morphological growth characteristics of LVs during early infancy using longitudinally acquired MR images in normal healthy infants. METHODS 24 healthy infants were MR imaged starting from 2 weeks old every 3 months during the first and every 6 months during the second year. Bilateral LVs were segmented and longitudinal morphological and shape analysis were conducted using longitudinal mixed effect models. RESULTS A significant bilateral ventricular volume increase (p<0.0001) is observed in year one (Left: 126±51% and Right: 145±62%), followed by a significant reduction (p<0.02) during the second year of life (Left: -24±27% and Right: -20±18%) despite the continuing increase of intracranial volume. Morphological analysis reveals that the ventricular growth is spatially non-uniform, and that the most significant growth occurs during the first 6 months. The first 3 months of life exhibit a significant (p<0.01) bilateral lengthening of the anterior lateral ventricle and a significant increase of radius (p<0.01) and area (p<0.01) at the posterior portion of the ventricle. Shape analysis shows that the horns exhibit a faster growth rate than the mid-body. Finally, bilateral significant age effects (p<0.01) are observed for the growth of LVs whereas gender effects are more subtle and significant effects (p<0.01) only present at the left anterior and posterior horns. More importantly, both the age and gender effects are growth directionally dependent. CONCLUSIONS We have demonstrated the temporospatial shape growth characteristics of human LVs during the first two years of life using a unique longitudinal MR data set. A temporally and spatially non-uniform growth pattern was reported. These normative results could provide invaluable information to discern abnormal growth patterns in patients with neurodevelopmental disorders.
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Affiliation(s)
- Lucile Bompard
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Shun Xu
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Martin Styner
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Beatriz Paniagua
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Mihye Ahn
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ying Yuan
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, United States of America
| | - Valerie Jewells
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Wei Gao
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Dinggang Shen
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Hongtu Zhu
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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Shi J, Leporé N, Gutman BA, Thompson PM, Baxter LC, Caselli RJ, Wang Y. Genetic influence of apolipoprotein E4 genotype on hippocampal morphometry: An N = 725 surface-based Alzheimer's disease neuroimaging initiative study. Hum Brain Mapp 2014; 35:3903-18. [PMID: 24453132 PMCID: PMC4269525 DOI: 10.1002/hbm.22447] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 11/23/2013] [Accepted: 11/26/2013] [Indexed: 01/12/2023] Open
Abstract
The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database-the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High-order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T(2) test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State UniversityTempeArizona
| | - Natasha Leporé
- Department of RadiologyChildren's Hospital Los AngelesLos AngelesCalifornia
| | - Boris A. Gutman
- Imaging Genetics CenterInstitute for Neuroimaging and InformaticsUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Paul M. Thompson
- Department of NeurologyImaging Genetics CenterLaboratory of Neuro ImagingUCLA School of MedicineLos AngelesCalifornia
- Department of Psychiatry and Biobehavioral SciencesSemel Institute, UCLA School of MedicineLos AngelesCalifornia
| | - Leslie C. Baxter
- Human Brain Imaging Laboratory, Barrow Neurological InstitutePhoenixArizona
| | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State UniversityTempeArizona
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Sparks R, Madabhushi A. Explicit shape descriptors: novel morphologic features for histopathology classification. Med Image Anal 2013; 17:997-1009. [PMID: 23850744 PMCID: PMC3811112 DOI: 10.1016/j.media.2013.06.002] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2011] [Revised: 05/31/2013] [Accepted: 06/03/2013] [Indexed: 11/25/2022]
Abstract
Object morphology, defined as shape and size characteristics, observed on medical imagery is often an important marker for disease presence and/or aggressiveness. In the context of prostate cancer histopathology, gland morphology is an integral component of the Gleason grading system which enables discrimination between low and high grade disease. However, clinicians are often unable to distinguish between subtle differences in object morphology, as evidenced by high inter-observer variability in Gleason grading. Boundary-based morphologic descriptors, such as the variance in the distance from points on the boundary of an object to its center, may not have the requisite discriminability to separate objects with subtle shape differences. In this paper, we present a set of novel explicit shape descriptors (ESDs) which are capable of distinguishing subtle shape differences between prostate glands of intermediate Gleason grades (grades 3 and 4) on prostate cancer histopathology. Calculation of ESDs involves: (1) representing object morphology using an explicit shape model (e.g. medial axis); (2) aligning the shape models via a non-rigid registration scheme with a diffeomorphic constraint and quantifying shape model dissimilarity; and (3) applying a non-linear dimensionality reduction scheme (e.g. Graph Embedding) to learn a low dimensional projection encoding the shape differences between objects. ESDs are hence the principal eigenvectors in the reduced embedding space. In this work we demonstrate that ESDs in conjunction with a Support Vector Machine classifier are able to correctly distinguish between 888 prostate glands corresponding to different Gleason grades (benign, grade 3, or grade 4) of prostate cancer from 58 needle biopsy specimens with a maximum accuracy of 0.89 and corresponding area under the receiver operating characteristic curve of 0.78.
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Affiliation(s)
- Rachel Sparks
- Rutgers University, Department of Biomedical Engineering, 599 Taylor Road, Piscataway, NJ, USA
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, 10900 Euclid Ave, Cleveland, OH, USA
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48
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Shi J, Thompson PM, Gutman B, Wang Y. Surface fluid registration of conformal representation: application to detect disease burden and genetic influence on hippocampus. Neuroimage 2013; 78:111-34. [PMID: 23587689 PMCID: PMC3683848 DOI: 10.1016/j.neuroimage.2013.04.018] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2012] [Revised: 03/06/2013] [Accepted: 04/05/2013] [Indexed: 11/23/2022] Open
Abstract
In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E[element of]4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, UCLA Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Boris Gutman
- Laboratory of Neuro Imaging, UCLA Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
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Shi J, Wang Y, Ceschin R, An X, Lao Y, Vanderbilt D, Nelson MD, Thompson PM, Panigrahy A, Leporé N. A multivariate surface-based analysis of the putamen in premature newborns: regional differences within the ventral striatum. PLoS One 2013; 8:e66736. [PMID: 23843961 PMCID: PMC3700976 DOI: 10.1371/journal.pone.0066736] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Accepted: 05/09/2013] [Indexed: 11/20/2022] Open
Abstract
Many children born preterm exhibit frontal executive dysfunction, behavioral problems including attentional deficit/hyperactivity disorder and attention related learning disabilities. Anomalies in regional specificity of cortico-striato-thalamo-cortical circuits may underlie deficits in these disorders. Nonspecific volumetric deficits of striatal structures have been documented in these subjects, but little is known about surface deformation in these structures. For the first time, here we found regional surface morphological differences in the preterm neonatal ventral striatum. We performed regional group comparisons of the surface anatomy of the striatum (putamen and globus pallidus) between 17 preterm and 19 term-born neonates at term-equivalent age. We reconstructed striatal surfaces from manually segmented brain magnetic resonance images and analyzed them using our in-house conformal mapping program. All surfaces were registered to a template with a new surface fluid registration method. Vertex-based statistical comparisons between the two groups were performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We found statistically significant differences in regional morphology between the two groups that are consistent across statistics, but more extensive for multivariate measures. Differences were localized to the ventral aspect of the striatum. In particular, we found abnormalities in the preterm anterior/inferior putamen, which is interconnected with the medial orbital/prefrontal cortex and the midline thalamic nuclei including the medial dorsal nucleus and pulvinar. These findings support the hypothesis that the ventral striatum is vulnerable, within the cortico-stiato-thalamo-cortical neural circuitry, which may underlie the risk for long-term development of frontal executive dysfunction, attention deficit hyperactivity disorder and attention-related learning disabilities in preterm neonates.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, Decision Systems and Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Yalin Wang
- School of Computing, Informatics, Decision Systems and Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Rafael Ceschin
- Department of Radiology, Children’s Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America
| | - Xing An
- School of Computing, Informatics, Decision Systems and Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Yi Lao
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
| | - Douglas Vanderbilt
- Department of Pediatrics, University of Southern California, Los Angeles, California, United States of America
- Developmental-Behavioral Pediatrics Fellowship Program, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
| | - Marvin D. Nelson
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
- Department of Radiology, University of Southern California, Los Angeles, California, United States of America
| | - Paul M. Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, University of California Los Angeles School of Medicine, Los Angeles, California, United States of America
| | - Ashok Panigrahy
- Department of Radiology, Children’s Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
| | - Natasha Leporé
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
- Department of Radiology, University of Southern California, Los Angeles, California, United States of America
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50
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McIntosh C, Svistoun I, Purdie TG. Groupwise conditional random forests for automatic shape classification and contour quality assessment in radiotherapy planning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:1043-1057. [PMID: 23475352 DOI: 10.1109/tmi.2013.2251421] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Radiation therapy is used to treat cancer patients around the world. High quality treatment plans maximally radiate the targets while minimally radiating healthy organs at risk. In order to judge plan quality and safety, segmentations of the targets and organs at risk are created, and the amount of radiation that will be delivered to each structure is estimated prior to treatment. If the targets or organs at risk are mislabelled, or the segmentations are of poor quality, the safety of the radiation doses will be erroneously reviewed and an unsafe plan could proceed. We propose a technique to automatically label groups of segmentations of different structures from a radiation therapy plan for the joint purposes of providing quality assurance and data mining. Given one or more segmentations and an associated image we seek to assign medically meaningful labels to each segmentation and report the confidence of that label. Our method uses random forests to learn joint distributions over the training features, and then exploits a set of learned potential group configurations to build a conditional random field (CRF) that ensures the assignment of labels is consistent across the group of segmentations. The CRF is then solved via a constrained assignment problem. We validate our method on 1574 plans, consisting of 17[Formula: see text] 579 segmentations, demonstrating an overall classification accuracy of 91.58%. Our results also demonstrate the stability of RF with respect to tree depth and the number of splitting variables in large data sets.
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Affiliation(s)
- Chris McIntosh
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 2M9 Canada.
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