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Gao N, Chen H, Guo X, Hao X, Ma T. Geodesic shape regression based deep learning segmentation for assessing longitudinal hippocampal atrophy in dementia progression. Neuroimage Clin 2024; 43:103623. [PMID: 38821013 PMCID: PMC11179422 DOI: 10.1016/j.nicl.2024.103623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 04/12/2024] [Accepted: 05/25/2024] [Indexed: 06/02/2024]
Abstract
Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multiple independent scans. To accurately segment the hippocampal morphology from longitudinal 3T T1-weighted MR images, we propose a diffeomorphic geodesic guided deep learning method called the GeoLongSeg to mitigate the longitudinal variabilities that unrelated to diseases by enhancing intra-individual morphological consistency. Specifically, we integrate geodesic shape regression, an evolutional model that estimates smooth deformation process of anatomical shapes, into a two-stage segmentation network. We adopt a 3D U-Net in the first-stage network with an enhanced attention mechanism for independent segmentation. Then, a hippocampal shape evolutional trajectory is estimated by geodesic shape regression and fed into the second network to refine the independent segmentation. We verify that GeoLongSeg outperforms other four state-of-the-art segmentation pipelines in longitudinal morphological consistency evaluated by test-retest reliability, variance ratio and atrophy trajectories. When assessing hippocampal atrophy in longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), results based on GeoLongSeg exhibit spatial and temporal local atrophy in bilateral hippocampi of dementia patients. These features derived from GeoLongSeg segmentation exhibit the greatest discriminatory capability compared to the outcomes of other methods in distinguishing between patients and normal controls. Overall, GeoLongSeg provides an accurate and efficient segmentation network for extracting hippocampal morphology from longitudinal MR images, which assist precise atrophy measurement of the hippocampus in early stage of dementia.
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Affiliation(s)
- Na Gao
- School of Electronic & Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Hantao Chen
- School of Electronic & Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Xutao Guo
- School of Electronic & Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China
| | - Xingyu Hao
- School of Electronic & Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China
| | - Ting Ma
- School of Electronic & Information Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China; Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China.
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Gao N, Liu Z, Deng Y, Chen H, Ye C, Yang Q, Ma T. MR-based spatiotemporal anisotropic atrophy evaluation of hippocampus in Alzheimer's disease progression by multiscale skeletal representation. Hum Brain Mapp 2023; 44:5180-5197. [PMID: 37608620 PMCID: PMC10502645 DOI: 10.1002/hbm.26460] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/14/2023] [Accepted: 08/02/2023] [Indexed: 08/24/2023] Open
Abstract
Increasing evidence has shown a higher sensitivity of Alzheimer's disease (AD) progression by local hippocampal atrophy rather than the whole volume. However, existing morphological methods based on subfield-volume or surface in imaging studies are not capable to describe the comprehensive process of hippocampal atrophy as sensitive as histological findings. To map histological distinctive measurements onto medical magnetic resonance (MR) images, we propose a multiscale skeletal representation (m-s-rep) to quantify focal hippocampal atrophy during AD progression in longitudinal cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The m-s-rep captures large-to-small-scale hippocampal morphology by spoke interpolation over label projection on skeletal models. To enhance morphological correspondence within subjects, we align the longitudinal m-s-reps by surface-based transformations from baseline to subsequent timepoints. Cross-sectional and longitudinal measurements derived from m-s-rep are statistically analyzed to comprehensively evaluate the bilateral hippocampal atrophy. Our findings reveal that during the early AD progression, atrophy primarily affects the lateral-medial extent of the hippocampus, with a difference of 1.8 mm in lateral-medial width in 2 years preceding conversion (p < .001), and the medial head exhibits a maximum difference of 3.05%/year in local atrophy rate (p = .011) compared to controls. Moreover, progressive mild cognitive impairment (pMCI) exhibits more severe and widespread atrophy in the head and body compared to stable mild cognitive impairment (sMCI), with a maximum difference of 1.21 mm in thickness in the medial head 1 year preceding conversion (p = .012). In summary, our proposed method can quantitatively measure the hippocampal morphological changes on 3T MR images, potentially assisting the pre-diagnosis and prognosis of AD.
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Affiliation(s)
- Na Gao
- Department of Electronic & Information EngineeringHarbin Institute of Technology (Shenzhen)ShenzhenChina
| | - Zhiyuan Liu
- Department of Computer ScienceUniversity of North Carolina atChapel HillNorth CarolinaUSA
| | - Yuesheng Deng
- Department of Electronic & Information EngineeringHarbin Institute of Technology (Shenzhen)ShenzhenChina
| | - Hantao Chen
- Department of Electronic & Information EngineeringHarbin Institute of Technology (Shenzhen)ShenzhenChina
| | - Chenfei Ye
- International Research Institute for Artificial IntelligenceHarbin Institute of Technology at ShenzhenShenzhenChina
- Peng Cheng LaboratoryShenzhenChina
| | - Qi Yang
- Department of Radiology, Beijing Chaoyang HospitalCapital Medical UniversityBeijingChina
- Key Lab of Medical Engineering for Cardiovascular DiseaseMinistry of EducationBeijingChina
- Beijing Advanced Innovation Center for Big Data‐Based Precision MedicineBeijingChina
| | - Ting Ma
- Department of Electronic & Information EngineeringHarbin Institute of Technology (Shenzhen)ShenzhenChina
- International Research Institute for Artificial IntelligenceHarbin Institute of Technology at ShenzhenShenzhenChina
- Peng Cheng LaboratoryShenzhenChina
- Guangdong Provincial Key Laboratory of Aerospace Communication and Networking TechnologyHarbin Institute of Technology (Shenzhen)ShenzhenChina
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Qu H, Ge H, Wang L, Wang W, Hu C. Volume changes of hippocampal and amygdala subfields in patients with mild cognitive impairment and Alzheimer's disease. Acta Neurol Belg 2023:10.1007/s13760-023-02235-9. [PMID: 37043115 DOI: 10.1007/s13760-023-02235-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 03/06/2023] [Indexed: 04/13/2023]
Abstract
BACKGROUND Automated segmentation of hippocampal and amygdala subfields could improve classification accuracy of Mild Cognitive Impairments (MCI) and Alzheimer's Disease (AD) individuals. METHODS We applied T1-weighted magnetic resonance imaging (MRI) for 21 AD, 39 MCI and 32 normal control (NC) participants at 3-Tesla MRI. Twelve hippocampal subfields and 9 amygdala subfields in each hemisphere were analyzed using FreeSurfer 6.0. RESULTS Smaller volumes were observed in right/left whole hippocampus, right/left hippocampal tail, right/left subiculum, right Cornu ammonis 1(CA1), right/left molecular layer, right granule cell-molecular layer-dentate gyrus (GC-ML-DG), right CA4, right fimbria, right whole amygdala, right/left accessory basal, right anterior amygdala area, left central, left medial and right/left cortical nucleus of AD group compared to both MCI and NC groups (p < 0.001). The volumes of right presubiculum, right CA3, right hippocampus-amygdala-transition-area (HATA), right lateral, right basal, right central, right medial, right cortico-amygdaloid transition (CAT) and right paralaminar nucleus were significantly larger in NC than AD group (p ≤ 0.001), while the volumes of right subiculum, right CA1, right molecular layer, right whole hippocampus, right whole amygdala, right basal and right accessory basal were significantly larger in NC than MCI group (p ≤ 0.002). Trend analysis showed that most hippocampus and amygdala subfields have a trend of atrophy with the decline of cognitive function. Six core components were identified by the hierarchical clustering. The combined Receiver operating characteristic (ROC) analysis achieved the diagnostic performances (AUC: 0.81) in differentiating AD from MCI; (AUC: 0.79) in differentiating MCI from NC and (AUC: 0.97) in differentiating AD from NC. CONCLUSIONS Volumetric differences of hippocampus and amygdala were at a finer subfields scale, and the volumes of right basal nucleus, left parasubiculum, left medial nucleus, left GC-ML-DG, left hippocampal fissure, and right fimbria can be employed as neuroimaging biomarkers to assist the clinical diagnosis of MCI and AD.
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Affiliation(s)
- Hang Qu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou Jiangsu, China
- Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Haitao Ge
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Liping Wang
- Department of Biobank, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Wei Wang
- Department of Radiology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou Jiangsu, China.
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Zhang Z, Wu Y, Xiong D, Ibrahim JG, Srivastava A, Zhu H. Rejoinder: LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures. J Am Stat Assoc 2023. [DOI: 10.1080/01621459.2022.2139264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Affiliation(s)
- Zhengwu Zhang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Yuexuan Wu
- Department of Statistics, Florida State University, Tallahassee, FL
| | - Di Xiong
- Departments of Biostatistics University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Joseph G. Ibrahim
- Departments of Biostatistics University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Anuj Srivastava
- Department of Statistics, Florida State University, Tallahassee, FL
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Departments of Biostatistics University of North Carolina at Chapel Hill, Chapel Hill, NC
- Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
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Maikusa N, Shigemoto Y, Chiba E, Kimura Y, Matsuda H, Sato N. Harmonized Z-Scores Calculated from a Large-Scale Normal MRI Database to Evaluate Brain Atrophy in Neurodegenerative Disorders. J Pers Med 2022; 12:jpm12101555. [PMID: 36294692 PMCID: PMC9605567 DOI: 10.3390/jpm12101555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/16/2022] [Accepted: 09/16/2022] [Indexed: 12/05/2022] Open
Abstract
Alzheimer’s disease (AD), the most common type of dementia in elderly individuals, slowly and progressively diminishes the cognitive function. Mild cognitive impairment (MCI) is also a significant risk factor for the onset of AD. Magnetic resonance imaging (MRI) is widely used for the detection and understanding of the natural progression of AD and other neurodegenerative disorders. For proper assessment of these diseases, a reliable database of images from cognitively healthy participants is important. However, differences in magnetic field strength or the sex and age of participants between a normal database and an evaluation data set can affect the accuracy of the detection and evaluation of neurodegenerative disorders. We developed a brain segmentation procedure, based on 30 Japanese brain atlases, and suggest a harmonized Z-score to correct the differences in field strength and sex and age from a large data set (1235 cognitively healthy participants), including 1.5 T and 3 T T1-weighted brain images. We evaluated our harmonized Z-score for AD discriminative power and classification accuracy between stable MCI and progressive MCI. Our procedure can perform brain segmentation in approximately 30 min. The harmonized Z-score of the hippocampus achieved high accuracy (AUC = 0.96) for AD detection and moderate accuracy (AUC = 0.70) to classify stable or progressive MCI. These results show that our method can detect AD with high accuracy and high generalization capability. Moreover, it may discriminate between stable and progressive MCI. Our study has some limitations: the age groups in the 1.5 T data set and 3 T data set are significantly different. In this study, we focused on AD, which is primarily a disease of elderly patients. For other diseases in different age groups, the harmonized Z-score needs to be recalculated using different data sets.
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Affiliation(s)
- Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo 113-8654, Japan
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
- Correspondence: ; Tel.: +81-42-341-2711
| | - Yoko Shigemoto
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Emiko Chiba
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Yukio Kimura
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Hiroshi Matsuda
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Noriko Sato
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
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Zhang Z, Wu Y, Xiong D, Ibrahim JG, Srivastava A, Zhu H. LESA: Longitudinal Elastic Shape Analysis of Brain Subcortical Structures. J Am Stat Assoc 2022; 118:3-17. [PMID: 37153845 PMCID: PMC10162479 DOI: 10.1080/01621459.2022.2102984] [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: 10/04/2021] [Revised: 07/01/2022] [Accepted: 07/09/2022] [Indexed: 10/17/2022]
Abstract
Over the past 30 years, magnetic resonance imaging has become a ubiquitous tool for accurately visualizing the change and development of the brain's subcortical structures (e.g., hippocampus). Although subcortical structures act as information hubs of the nervous system, their quantification is still in its infancy due to many challenges in shape extraction, representation, and modeling. Here, we develop a simple and efficient framework of longitudinal elastic shape analysis (LESA) for subcortical structures. Integrating ideas from elastic shape analysis of static surfaces and statistical modeling of sparse longitudinal data, LESA provides a set of tools for systematically quantifying changes of longitudinal subcortical surface shapes from raw structure MRI data. The key novelties of LESA include: (i) it can efficiently represent complex subcortical structures using a small number of basis functions and (ii) it can accurately delineate the spatiotemporal shape changes of the human subcortical structures. We applied LESA to analyze three longitudinal neuroimaging data sets and showcase its wide applications in estimating continuous shape trajectories, building life-span growth patterns, and comparing shape differences among different groups. In particular, with the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, we found that the Alzheimer's Disease (AD) can significantly speed the shape change of ventricle and hippocampus from 60 to 75 years old compared with normal aging.
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Affiliation(s)
- Zhengwu Zhang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill Chapel Hill, North Carolina
| | - Yuexuan Wu
- Department of Statistics, Florida State University, Tallahassee, Florida
| | - Di Xiong
- Departments of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Joseph G. Ibrahim
- Departments of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Anuj Srivastava
- Department of Statistics, Florida State University, Tallahassee, Florida
| | - Hongtu Zhu
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill Chapel Hill, North Carolina
- Departments of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Departments of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Departments of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Biomedical Research Imaging Center, University of North Carolina at Chapel, Hill Chapel Hill, North Carolina
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Relationships between the Fear of COVID-19 Scale and regional brain atrophy in mild cognitive impairment. Acta Neuropsychiatr 2022; 34:153-162. [PMID: 35156604 DOI: 10.1017/neu.2022.7] [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] [Indexed: 11/07/2022]
Abstract
BACKGROUND Several studies have reported that the pandemic of coronavirus disease 2019 (COVID-19) influenced cognitive function in the elderly. However, the effect of COVID-19-related fear on brain atrophy has not been evaluated. In this study, we evaluated the relation between brain atrophy and the effect of COVID-19-related fear by analysing changes in brain volume over time using magnetic resonance imaging (MRI). METHODS Participants were 25 Japanese patients with mild cognitive impairment (MCI) or subjective cognitive decline (SCD), who underwent 1.5-tesla MRI scan twice, once before and once after the pandemic outbreak of COVID-19, and the Fear of Coronavirus Disease 2019 Scale (FCV-19S) assessment during that period. We computed regional brain atrophy per day between the 1st and 2nd scan, and evaluated the relation between the FCV-19S scores and regional shrinkage. RESULTS There was significant positive correlation between the total FCV-19S score and volume reduction per day in the right posterior cingulate cortex. Regarding the subscales of FCV-19S, we found significant positive correlation between factor 2 of the FCV-19S and shrinkage of the right posterior cingulate cortex. CONCLUSIONS There was positive correlation between the FCV-19S score and regional brain atrophy per day. Although it is already known that the psychological effects surrounding the COVID-19 pandemic cause cognitive function decline, our results further suggest that anxiety and fear related to COVID-19 cause regional brain atrophy.
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Kagerer SM, Schroeder C, van Bergen JMG, Schreiner SJ, Meyer R, Steininger SC, Vionnet L, Gietl AF, Treyer V, Buck A, Pruessmann KP, Hock C, Unschuld PG. Low Subicular Volume as an Indicator of Dementia-Risk Susceptibility in Old Age. Front Aging Neurosci 2022; 14:811146. [PMID: 35309894 PMCID: PMC8926841 DOI: 10.3389/fnagi.2022.811146] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Hippocampal atrophy is an established Alzheimer’s Disease (AD) biomarker. Volume loss in specific subregions as measurable with ultra-high field magnetic resonance imaging (MRI) may reflect earliest pathological alterations. Methods Data from positron emission tomography (PET) for estimation of cortical amyloid β (Aβ) and high-resolution 7 Tesla T1 MRI for assessment of hippocampal subfield volumes were analyzed in 61 non-demented elderly individuals who were divided into risk-categories as defined by high levels of cortical Aβ and low performance in standardized episodic memory tasks. Results High cortical Aβ and low episodic memory interactively predicted subicular volume [F(3,57) = 5.90, p = 0.018]. The combination of high cortical Aβ and low episodic memory was associated with significantly lower subicular volumes, when compared to participants with high episodic memory (p = 0.004). Discussion Our results suggest that low subicular volume is linked to established indicators of AD risk, such as increased cortical Aβ and low episodic memory. Our data support subicular volume as a marker of dementia-risk susceptibility in old-aged non-demented persons.
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Affiliation(s)
- Sonja M. Kagerer
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Psychogeriatric Medicine, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Clemens Schroeder
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
| | | | - Simon J. Schreiner
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
| | - Rafael Meyer
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
| | - Stefanie C. Steininger
- Psychogeriatric Medicine, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Laetitia Vionnet
- Institute for Biomedical Engineering, University of Zurich and ETH Zürich, Zurich, Switzerland
| | - Anton F. Gietl
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Psychogeriatric Medicine, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Valerie Treyer
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Alfred Buck
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Klaas P. Pruessmann
- Institute for Biomedical Engineering, University of Zurich and ETH Zürich, Zurich, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Neurimmune, Schlieren, Switzerland
| | - Paul G. Unschuld
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Psychogeriatric Medicine, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, University of Zurich and ETH Zürich, Zurich, Switzerland
- Geriatric Psychiatry, Department of Psychiatry, University Hospitals of Geneva, University of Geneva, Geneva, Switzerland
- *Correspondence: Paul G. Unschuld,
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Li A, Yue L, Xiao S, Liu M. Cognitive Function Assessment and Prediction for Subjective Cognitive Decline and Mild Cognitive Impairment. Brain Imaging Behav 2021; 16:645-658. [PMID: 34491529 DOI: 10.1007/s11682-021-00545-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2021] [Indexed: 11/25/2022]
Abstract
Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative dementia. Recent studies found that subjective cognitive decline (SCD) may be the early clinical precursor that precedes mild cognitive impairment (MCI) for AD. SCD subjects with normal cognition may already have some medial temporal lobe atrophy. Although brain changes by AD have been widely studied in the literature, it is still challenging to investigate the anatomical subtle changes in SCD. This paper proposes a machine learning framework by combination of sparse coding and random forest (RF) to identify the informative imaging biomarkers for assessment and prediction of cognitive functions and their changes in individuals with MCI, SCD and normal control (NC) using magnetic resonance imaging (MRI). First, we compute the volumes from both the regions of interest from whole brain and the subregions of hippocampus and amygdala as the features of structural MRIs. Then, sparse coding is applied to identify the relevant features. Finally, the proximity-based RF is used to combine three sets of volumetric features and establish a regression model for predicting clinical scores. Our method has double feature selections to better explore the relevant features for prediction and is evaluated with the T1-weighted structural MR images from 36 MCI, 112 SCD, 78 NC subjects. The results demonstrate the effectiveness of proposed method. In addition to hippocampus and amygdala, we also found that the fimbria, basal nucleus and cortical nucleus subregions are more important than other regions for prediction of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores and their changes.
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Affiliation(s)
- Aojie Li
- Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, China
| | - Ling Yue
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Shifu Xiao
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Manhua Liu
- Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, Shanghai, China.
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Wei Y, Huang N, Liu Y, Zhang X, Wang S, Tang X. Hippocampal and Amygdalar Morphological Abnormalities in Alzheimer's Disease Based on Three Chinese MRI Datasets. Curr Alzheimer Res 2021; 17:1221-1231. [PMID: 33602087 DOI: 10.2174/1567205018666210218150223] [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] [Received: 08/19/2020] [Revised: 10/12/2020] [Accepted: 12/22/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Early detection of Alzheimer's disease (AD) and its early stage, the mild cognitive impairment (MCI), has important scientific, clinical and social significance. Magnetic resonance imaging (MRI) based statistical shape analysis provides an opportunity to detect regional structural abnormalities of brain structures caused by AD and MCI. OBJECTIVE In this work, we aimed to employ a well-established statistical shape analysis pipeline, in the framework of large deformation diffeomorphic metric mapping, to identify and quantify the regional shape abnormalities of the bilateral hippocampus and amygdala at different prodromal stages of AD, using three Chinese MRI datasets collected from different domestic hospitals. METHODS We analyzed the region-specific shape abnormalities at different stages of the neuropathology of AD by comparing the localized shape characteristics of the bilateral hippocampi and amygdalas between healthy controls and two disease groups (MCI and AD). In addition to group comparison analyses, we also investigated the association between the shape characteristics and the Mini Mental State Examination (MMSE) of each structure of interest in the disease group (MCI and AD combined) as well as the discriminative power of different morphometric biomarkers. RESULTS We found the strongest disease pathology (regional atrophy) at the subiculum and CA1 subregions of the hippocampus and the basolateral, basomedial as well as centromedial subregions of the amygdala. Furthermore, the shape characteristics of the hippocampal and amygdalar subregions exhibiting the strongest AD related atrophy were found to have the most significant positive associations with the MMSE. Employing the shape deformation marker of the hippocampus or the amygdala for automated MCI or AD detection yielded a significant accuracy boost over the corresponding volume measurement. CONCLUSION Our results suggested that the amygdalar and hippocampal morphometrics, especially those of shape morphometrics, can be used as auxiliary indicators for monitoring the disease status of an AD patient.
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Affiliation(s)
- Yuanyuan Wei
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Nianwei Huang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Zhang
- Department of Neurology, Nanlou Division, Chinese PLA General Hospital; National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Silun Wang
- YIWEI Medical Technology Co., Ltd, Shenzhen, Guangdong, China
| | - Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
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Maikusa N, Fukami T, Matsuda H. Longitudinal analysis of brain structure using existence probability. Brain Behav 2020; 10:e01869. [PMID: 33034427 PMCID: PMC7749599 DOI: 10.1002/brb3.1869] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 09/04/2020] [Accepted: 09/08/2020] [Indexed: 11/28/2022] Open
Abstract
INTRODUCTION We propose a method to evaluate quantitatively the longitudinal structural changes in brain atrophy to provide early detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI). METHODS We used existence probabilities obtained by segmenting magnetic resonance (MR) images at two different time points into four regions: gray matter, white matter, cerebrospinal fluid, and background. This method was applied to T1-weighted MR images of 110 participants with normal cognition (NL), 165 with MCI, and 82 with AD, obtained from the Japanese Alzheimer's Disease Neuroimaging Initiative database. RESULTS We obtained the coefficients of probability change (CPC) for each dataset. We found high area under the receiver operating characteristic curve (ROC) values (up to 0.908 of the difference of ROCs) for some CPC regions that are considered indicators of atrophy. Additionally, we attempted to establish a machine-learning algorithm to classify participants as NL or AD. The maximum accuracy was 92.1% for NL-AD classification and 81.2% for NL-MCI classification using CPC values between images acquired at first and sixth months, respectively. CONCLUSION These results showed that the proposed method is effective for the early detection of AD and MCI.
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Affiliation(s)
- Norihide Maikusa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Tadanori Fukami
- Department of Informatics, Faculty of Engineering, Yamagata University, Yamagata, Japan
| | - Hiroshi Matsuda
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo, Japan
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Martí-Juan G, Sanroma-Guell G, Piella G. A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105348. [PMID: 31995745 DOI: 10.1016/j.cmpb.2020.105348] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 01/10/2020] [Accepted: 01/18/2020] [Indexed: 05/02/2023]
Abstract
BACKGROUND AND OBJECTIVES Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer's disease using longitudinal neuroimaging. METHODS We search for papers using longitudinal imaging data, focused on Alzheimer's Disease and published between 2007 and 2019 on four different search engines. RESULTS After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions. CONCLUSIONS Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.
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Affiliation(s)
- Gerard Martí-Juan
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | | | - Gemma Piella
- BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
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13
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Liu Y, Nacewicz BM, Zhao G, Adluru N, Kirk GR, Ferrazzano PA, Styner MA, Alexander AL. A 3D Fully Convolutional Neural Network With Top-Down Attention-Guided Refinement for Accurate and Robust Automatic Segmentation of Amygdala and Its Subnuclei. Front Neurosci 2020; 14:260. [PMID: 32508558 PMCID: PMC7253589 DOI: 10.3389/fnins.2020.00260] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Accepted: 03/09/2020] [Indexed: 12/17/2022] Open
Abstract
Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most existing deep learning based approaches in neuroimaging do not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the subnuclei of the amygdala. To tackle this challenging task, we developed a dual-branch dilated residual 3D fully convolutional network with parallel convolutions to extract more global context and alleviate the class imbalance issue by maintaining a small receptive field that is just the size of the regions of interest (ROIs). We also conduct multi-scale feature fusion in both parallel and series to compensate the potential information loss during convolutions, which has been shown to be important for small objects. The serial feature fusion enabled by residual connections is further enhanced by a proposed top-down attention-guided refinement unit, where the high-resolution low-level spatial details are selectively integrated to complement the high-level but coarse semantic information, enriching the final feature representations. As a result, the segmentations resulting from our method are more accurate both volumetrically and morphologically, compared with other deep learning based approaches. To the best of our knowledge, this work is the first deep learning-based approach that targets the subregions of the amygdala. We also demonstrated the feasibility of using a cycle-consistent generative adversarial network (CycleGAN) to harmonize multi-site MRI data, and show that our method generalizes well to challenging traumatic brain injury (TBI) datasets collected from multiple centers. This appears to be a promising strategy for image segmentation for multiple site studies and increased morphological variability from significant brain pathology.
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Affiliation(s)
- Yilin Liu
- Waisman Brain Imaging Laboratory, University of Wisconsin-Madison, Madison, WI, United States
| | - Brendon M. Nacewicz
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Gengyan Zhao
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
| | - Nagesh Adluru
- Waisman Brain Imaging Laboratory, University of Wisconsin-Madison, Madison, WI, United States
| | - Gregory R. Kirk
- Waisman Brain Imaging Laboratory, University of Wisconsin-Madison, Madison, WI, United States
| | - Peter A. Ferrazzano
- Waisman Brain Imaging Laboratory, University of Wisconsin-Madison, Madison, WI, United States
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, United States
| | - Martin A. Styner
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
- Department of Computer Science, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Andrew L. Alexander
- Waisman Brain Imaging Laboratory, University of Wisconsin-Madison, Madison, WI, United States
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
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14
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Tang X, Lyu G, Chen M, Huang W, Lin Y. Amygdalar and Hippocampal Morphometry Abnormalities in First-Episode Schizophrenia Using Deformation-Based Shape Analysis. Front Psychiatry 2020; 11:677. [PMID: 32765318 PMCID: PMC7379331 DOI: 10.3389/fpsyt.2020.00677] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 06/29/2020] [Indexed: 11/14/2022] Open
Abstract
In this study, we investigated and quantified the amygdalar and hippocampal morphometry abnormalities exerted by first-episode schizophrenia using a total of 92 patients and 106 healthy control participants. Magnetic resonance imaging (MRI) based automated segmentation was conducted to obtain the amygdalar and hippocampal segmentations. Disease-versus-control volume differences of the bilateral amygdalas and hippocampi were quantified. In addition, deformation-based statistical shape analysis was employed to quantify the region-specific shape abnormalities of each structure of interest. To better identify the key relevant areas in the pathology of first-episode schizophrenia, each structure was divided into four subregions; CA1, CA2, CA3 combined with dentate gyrus for the hippocampus in each hemisphere and basolateral, basomedial, centromedial, and lateral nucleus for the amygdala in each hemisphere. We observed significant global volume reduction and localized shape atrophy in each of the four structures of interest. The amygdalar shape abnormalities mainly occurred at the basolateral and centromedial subregions, whereas the hippocampal shape abnormalities mainly concentrated on the CA1 and CA2 subregions. For the same structure, the one on the right hemisphere was affected more by the disease pathology than that on the left hemisphere. To conclude, we have successfully quantified the global and local morphometric abnormalities of the bilateral amygdalas and hippocampi using a sophisticated statistical analysis pipeline and high-field subregion segmentations, with MRI data of a considerable sample size. This study is one of the very first of such kind in first-episode schizophrenia analyses.
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Affiliation(s)
- Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Guiwen Lyu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Minhua Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.,Department of Electrical and Electronic Engineering, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
| | - Weikai Huang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yin Lin
- Department of Psychology, Shenzhen Children's Hospital, Shenzhen, China
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15
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Wang L, Heywood A, Stocks J, Bae J, Ma D, Popuri K, Toga AW, Kantarci K, Younes L, Mackenzie IR, Zhang F, Beg MF, Rosen H. Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2019; 4:e190017. [PMID: 31754634 PMCID: PMC6868780 DOI: 10.20900/jpbs.20190017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
We report on the ongoing project "PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis" describing completed and future work supported by this grant. This project is a multi-site, multi-study collaboration effort with research spanning seven sites across the US and Canada. The overall goal of the project is to study neurodegeneration within Alzheimer's Disease, Frontotemporal Dementia, and related neurodegenerative disorders, using a variety of brain imaging and computational techniques to develop methods for the early and accurate prediction of disease and its course. The overarching goal of the project is to develop the earliest and most accurate biomarker that can differentiate clinical diagnoses to inform clinical trials and patient care. In its third year, this project has already completed several projects to achieve this goal, focusing on (1) structural MRI (2) machine learning and (3) FDG-PET and multimodal imaging. Studies utilizing structural MRI have identified key features of underlying pathology by studying hippocampal deformation that is unique to clinical diagnosis and also post-mortem confirmed neuropathology. Several machine learning experiments have shown high classification accuracy in the prediction of disease based on Convolutional Neural Networks utilizing MRI images as input. In addition, we have also achieved high accuracy in predicting conversion to DAT up to five years in the future. Further, we evaluated multimodal models that combine structural and FDG-PET imaging, in order to compare the predictive power of multimodal to unimodal models. Studies utilizing FDG-PET have shown significant predictive ability in the prediction and progression of disease.
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Affiliation(s)
- Lei Wang
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Ashley Heywood
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Jane Stocks
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Jinhyeong Bae
- Northwestern University Feinberg School of Medicine, Chicago, 60611 IL, USA
| | - Da Ma
- School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada
| | - Karteek Popuri
- School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada
| | - Arthur W. Toga
- Keck School of Medicine of University of Southern California, Los Angeles, 90033 CA, USA
| | - Kejal Kantarci
- Departments of Neurology and Radiology, Mayo Clinic, Rochester, 55905 MN, USA
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, 21218 MD, USA
| | - Ian R. Mackenzie
- Department of Pathology and Lab Medicine, University of British Columbia, Vancouver, B6T1Z4 BC, Canada
| | - Fengqing Zhang
- Department of Psychology, Drexel University, Philadelphia, 19104 PA, USA
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, V6A1S6 BC, Canada
| | - Howard Rosen
- Department of Neurology, University of California, San Francisco, 94143 CA, USA
| | - Alzheimer’s Disease Neuroimaging Initiative
- Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNIAcknowledgement_List.pdf
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16
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Liu G, Tan X, Dang C, Tan S, Xing S, Huang N, Peng K, Xie C, Tang X, Zeng J. Regional Shape Abnormalities in Thalamus and Verbal Memory Impairment After Subcortical Infarction. Neurorehabil Neural Repair 2019; 33:476-485. [PMID: 31081462 DOI: 10.1177/1545968319846121] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background. Subcortical infarcts can result in verbal memory impairment, but the potential underlying mechanisms remain unknown. Objective. We investigated the spatiotemporal deterioration patterns of brain structures in patients with subcortical infarction and identified the regions that contributed to verbal memory impairment. Methods. Cognitive assessment and structural magnetic resonance imaging were performed 1, 4, and 12 weeks after stroke onset in 28 left-hemisphere and 22 right-hemisphere stroke patients with subcortical infarction. Whole-brain volumetric analysis combined with a further-refined shape analysis was conducted to analyze longitudinal morphometric changes in brain structures and their relationship to verbal memory performance. Results. Between weeks 1 and 12, significant volume decreases in the ipsilesional basal ganglia, inferior white matter, and thalamus were found in the left-hemisphere stroke group. Among those 3 structures, only the change rate of the thalamus volume was significantly correlated with that in immediate recall. For the right-hemisphere stroke group, only the ipsilesional basal ganglia survived the week 1 to week 12 group comparison, but its change rate was not significantly correlated with the verbal memory change rate. Shape analysis of the thalamus revealed atrophies of the ipsilesional thalamic subregions connected to the prefrontal, temporal, and premotor cortices in the left-hemisphere stroke group and positive correlations between the rates of those atrophies and the change rate in immediate recall. Conclusions. Secondary damage to the thalamus, especially to the left subregions connected to specific cortices, may be associated with early verbal memory impairment following an acute subcortical infarct.
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Affiliation(s)
- Gang Liu
- 1 The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Xiaoqing Tan
- 2 Southern University of Science and Technology, Shenzhen, Guangdong, China.,3 Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chao Dang
- 1 The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Shuangquan Tan
- 1 The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Shihui Xing
- 1 The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Nianwei Huang
- 2 Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Kangqiang Peng
- 4 Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
| | - Chuanmiao Xie
- 4 Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China
| | - Xiaoying Tang
- 2 Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Jinsheng Zeng
- 1 The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China
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17
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18
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Dong Y, Wang Q, Yao H, Xiao Y, Wei J, Xie P, Hu J, Chen W, Tang Y, Zhou H, Liu J. A promising structural magnetic resonance imaging assessment in patients with preclinical cognitive decline and diabetes mellitus. J Cell Physiol 2019; 234:16838-16846. [PMID: 30786010 DOI: 10.1002/jcp.28359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 01/30/2019] [Accepted: 02/01/2019] [Indexed: 01/18/2023]
Abstract
Subjective cognitive decline (SCD) is frequently reported in diabetic patients. Diabetes mellitus (DM) is associated with changes in the microstructure of the brain arise in diabetic patients, including changes in gray matter volume (GMV). However, the underlying mechanisms of changes in GMV in DM patients with cognitive impairment remain uncertain. Here, we present an overview of amyloid-β-dependent cognitive impairment in DM patients with SCD. Moreover, we review the evolving insights from studies on the GMV changes in GMV and cognitive dysfunction to which provide the mechanisms of cognitive impairment in T2DM. Ultimately, the novel structural magnetic resonance imaging (MRI) protocol was used for detecting neuroimaging biomarkers that can predict the clinical outcomes in diabetic patients with SCD. A reliable MRI protocol would be helpful to detect neurobiomarkers, and to understand the pathological mechanisms of preclinical cognitive impairment in diabetic patients.
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Affiliation(s)
- Yulan Dong
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Qi Wang
- Department of Radiology, the Hunan Province Hospital, Changsha, China
| | - Hailun Yao
- Institute of Pharmacy and Medical Technology, Hunan Polytechnic of Environment and Biology, Hengyang, Hunan, China
| | - Yawen Xiao
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Jiaohong Wei
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Peihan Xie
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Jun Hu
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Wen Chen
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Yan Tang
- Department of Ultrasound, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Hong Zhou
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China.,Hengyang Medical College, University of South China, Hengyang, China
| | - Jincai Liu
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
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19
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Makkinejad N, Schneider JA, Yu J, Leurgans SE, Kotrotsou A, Evia AM, Bennett DA, Arfanakis K. Associations of amygdala volume and shape with transactive response DNA-binding protein 43 (TDP-43) pathology in a community cohort of older adults. Neurobiol Aging 2019; 77:104-111. [PMID: 30784812 DOI: 10.1016/j.neurobiolaging.2019.01.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 01/18/2019] [Accepted: 01/21/2019] [Indexed: 01/17/2023]
Abstract
Transactive response DNA-binding protein 43 (TDP-43) pathology is common in old age and is strongly associated with cognitive decline and dementia above and beyond contributions from other neuropathologies. TDP-43 pathology in aging typically originates in the amygdala, a brain region also affected by other age-related neuropathologies such as Alzheimer's pathology. The purpose of this study was two-fold: to determine the independent effects of TDP-43 pathology on the volume, as well as shape, of the amygdala in a community cohort of older adults, and to determine the contribution of amygdala volume to the variance of the rate of cognitive decline after accounting for the contributions of neuropathologies and demographics. Cerebral hemispheres from 198 participants of the Rush Memory and Aging Project and the Religious Orders Study were imaged with MRI ex vivo and underwent neuropathologic examination. Measures of amygdala volume and shape were extracted for all participants. Regression models controlling for neuropathologies and demographics showed an independent negative association of TDP-43 with the volume of the amygdala. Shape analysis revealed a unique pattern of amygdala deformation associated with TDP-43 pathology. Finally, mixed-effects models showed that amygdala volume explained an additional portion of the variance of the rate of decline in global cognition, episodic memory, semantic memory, and perceptual speed, above and beyond what was explained by demographics and neuropathologies.
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Affiliation(s)
- Nazanin Makkinejad
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Pathology, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Junxiao Yu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sue E Leurgans
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Aikaterini Kotrotsou
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Arnold M Evia
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA; Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL, USA.
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20
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Tang X, Ross CA, Johnson H, Paulsen JS, Younes L, Albin RL, Ratnanather JT, Miller MI. Regional subcortical shape analysis in premanifest Huntington's disease. Hum Brain Mapp 2018; 40:1419-1433. [PMID: 30376191 DOI: 10.1002/hbm.24456] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 10/18/2018] [Accepted: 10/23/2018] [Indexed: 11/11/2022] Open
Abstract
Huntington's disease (HD) involves preferential and progressive degeneration of striatum and other subcortical regions as well as regional cortical atrophy. It is caused by a CAG repeat expansion in the Huntingtin gene, and the longer the expansion the earlier the age of onset. Atrophy begins prior to manifest clinical signs and symptoms, and brain atrophy in premanifest expansion carriers can be studied. We employed a diffeomorphometric pipeline to contrast subcortical structures' morphological properties in a control group with three disease groups representing different phases of premanifest HD (far, intermediate, and near to onset) as defined by the length of the CAG expansion and the participant's age (CAG-Age-Product). A total of 1,428 magnetic resonance image scans from 694 participants from the PREDICT-HD cohort were used. We found significant region-specific atrophies in all subcortical structures studied, with the estimated abnormality onset time varying from structure to structure. Heterogeneous shape abnormalities of caudate nuclei were present in premanifest HD participants estimated furthest from onset and putaminal shape abnormalities were present in participants intermediate to onset. Thalamic, hippocampal, and amygdalar shape abnormalities were present in participants nearest to onset. We assessed whether the estimated progression of subcortical pathology in premanifest HD tracked specific pathways. This is plausible for changes in basal ganglia circuits but probably not for changes in hippocampus and amygdala. The regional shape analyses conducted in this study provide useful insights into the effects of HD pathology in subcortical structures.
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Affiliation(s)
- Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Christopher A Ross
- Division of Neurobiology, Departments of Psychiatry, Neurology, Neuroscience and Pharmacology, and Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Hans Johnson
- Departments of Neurology and Psychiatry, The University of Iowa Carver College of Medicine, Iowa City, Iowa
| | - Jane S Paulsen
- Departments of Neurology and Psychiatry, The University of Iowa Carver College of Medicine, Iowa City, Iowa
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland.,Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Roger L Albin
- Neurology Service and GRECC, VAAAHS, Ann Arbor, Michigan.,Department of Neurology, University of Michigan Medical School, Ann Arbor, Michigan
| | - J Tilak Ratnanather
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
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21
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Li H, Jia X, Qi Z, Fan X, Ma T, Pang R, Ni H, Li CSR, Lu J, Li K. Disrupted Functional Connectivity of Cornu Ammonis Subregions in Amnestic Mild Cognitive Impairment: A Longitudinal Resting-State fMRI Study. Front Hum Neurosci 2018; 12:413. [PMID: 30420801 PMCID: PMC6216144 DOI: 10.3389/fnhum.2018.00413] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 09/24/2018] [Indexed: 12/16/2022] Open
Abstract
Background: The cornu ammonis (CA), as part of the hippocampal formation, represents a primary target region of neural degeneration in amnestic mild cognitive impairment (aMCI). Previous studies have revealed subtle structural deficits of the CA subregions (CA1-CA3, bilateral) in aMCI; however, it is not clear how the network function is impacted by aMCI. The present study examined longitudinal changes in resting state functional connectivity (FC) of each CA subregion and how these changes relate to neuropsychological profiles in aMCI. Methods: Twenty aMCI and 20 healthy control (HC) participants underwent longitudinal cognitive assessment and resting state functional MRI scans at baseline and 15 months afterward. Imaging data were processed with published routines in SPM8 and CONN software. Two-way analysis of covariance was performed with covariates of age, gender, education level, follow up interval, gray matter volume, mean FD, as well as global correlation (GCOR). Pearson’s correlation was conducted to evaluate the relationship between the longitudinal changes in CA subregional FC and neuropsychological performance in aMCI subjects. Results: Resting state FC between the right CA1 and right middle temporal gyrus (MTG) as well as between the left CA2 and bilateral cuneal cortex (CC) were decreased in aMCI subjects as compared to HC. Longitudinal decrease in FC between the right CA1 and right MTG was correlated with reduced capacity of episodic memory in aMCI subjects. Conclusion: The current findings suggest functional alterations in the CA subregions. CA1 connectivity with the middle temporal cortex may represent an important neural marker of memory dysfunction in aMCI.
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Affiliation(s)
- Hui Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Xiuqin Jia
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.,Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Zhigang Qi
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Xiang Fan
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tian Ma
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Ran Pang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Hong Ni
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, United States.,Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, CT, United States.,Beijing Huilongguan Hospital, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of MRI and Brain Informatics, Beijing, China
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22
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Kang K, Kwak K, Yoon U, Lee JM. Lateral Ventricle Enlargement and Cortical Thinning in Idiopathic Normal-pressure Hydrocephalus Patients. Sci Rep 2018; 8:13306. [PMID: 30190599 PMCID: PMC6127145 DOI: 10.1038/s41598-018-31399-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 08/14/2018] [Indexed: 01/26/2023] Open
Abstract
We utilized three-dimensional, surface-based, morphometric analysis to investigate ventricle shape between 2 groups: (1) idiopathic normal-pressure hydrocephalus (INPH) patients who had a positive response to the cerebrospinal fluid tap test (CSFTT) and (2) healthy controls. The aims were (1) to evaluate the location of INPH-related structural abnormalities of the lateral ventricles and (2) to investigate relationships between lateral ventricular enlargement and cortical thinning in INPH patients. Thirty-three INPH patients and 23 healthy controls were included in this study. We used sparse canonical correlation analysis to show correlated regions of ventricular surface expansion and cortical thinning. Significant surface expansion in the INPH group was observed mainly in clusters bilaterally located in the superior portion of the lateral ventricles, adjacent to the high convexity of the frontal and parietal regions. INPH patients showed a significant bilateral expansion of both the temporal horns of the lateral ventricles and the medial aspects of the frontal horns of the lateral ventricles to surrounding brain regions, including the medial frontal lobe. Ventricular surface expansion was associated with cortical thinning in the bilateral orbitofrontal cortex, bilateral rostral anterior cingulate cortex, left parahippocampal cortex, left temporal pole, right insula, right inferior temporal cortex, and right fusiform gyrus. These results suggest that patients with INPH have unique patterns of ventricular surface expansion. Our findings encourage future studies to elucidate the underlying mechanism of lateral ventricular morphometric abnormalities in INPH patients.
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Affiliation(s)
- Kyunghun Kang
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.,Department of Neurology, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Kichang Kwak
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Uicheul Yoon
- Department of Biomedical Engineering, Daegu Catholic University, Gyeongsan-si, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
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Campbell KM, Fletcher PT. Nonparametric Aggregation of Geodesic Trends for Longitudinal Data Analysis. SHAPE IN MEDICAL IMAGING : INTERNATIONAL WORKSHOP, SHAPEMI 2018, HELD IN CONJUNCTION WITH MICCAI 2018, GRANADA, SPAIN, SEPTEMBER 20, 2018 : PROCEEDINGS. SHAPEMI (WORKSHOP) (2018 : GRANADA, SPAIN) 2018; 11167:232-243. [PMID: 33345261 PMCID: PMC7749520 DOI: 10.1007/978-3-030-04747-4_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We propose a technique for analyzing longitudinal imaging data that models individual changes with diffeomorphic geodesic regression and aggregates these geodesics into a nonparametric group average trend. Our model is specifically tailored to the unbalanced and sparse characteristics of longitudinal imaging studies. That is, each individual has few data points measured over a short period of time, while the study population as a whole spans a wide age range. We use geodesic regression to estimate individual trends, which is an appropriate model for capturing shape changes over a short time window, as is typically found within an individual. Geodesics are also adept at handling the low sample sizes found within individuals, and can model the change between as few as two timepoints. However, geodesics are limited for modeling longer-term trends, where constant velocity may not be appropriate. Therefore, we develop a novel nonparametric regression to aggregate individual trends into an average group trend. We demonstrate the power of our method to capture non-geodesic group trends on hippocampal volume (real-valued data) and diffeomorphic registration of full 3D MRI from the longitudinal OASIS data.
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Affiliation(s)
- Kristen M Campbell
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT
| | - P Thomas Fletcher
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT
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Kelly DA, Seidenberg M, Reiter K, Nielson KA, Woodard JL, Smith JC, Durgerian S, Rao SM. Differential 5-year brain atrophy rates in cognitively declining and stable APOE-ε4 elders. Neuropsychology 2018; 32:647-653. [PMID: 29911873 DOI: 10.1037/neu0000444] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE The apolipoprotein E (APOE) ε4 allele is the most important genetic risk factor for late-onset Alzheimer's disease. Many ε4 carriers, however, never develop Alzheimer's disease. The purpose of this study is to characterize the variability in phenotypic expression of the ε4 allele, as measured by the longitudinal trajectory of cognitive test scores and MRI brain volumes, in cognitively intact elders. METHOD Healthy older adults, ages 65-85, participated in a 5-year longitudinal study that included structural MRI and cognitive testing administered at baseline and at 1.5 and 5 years postenrollment. Participants included 22 ε4 noncarriers, 15 ε4 carriers who experienced a decline in cognition over the 5-year interval, and 11 ε4 carriers who remained cognitively stable. RESULTS No baseline cognitive or volumetric group differences were observed. Compared to noncarriers, declining ε4 carriers had significantly greater rates of atrophy in left (p = .001, Cohen's d = .691) and right (p = .003, d = .622) cortical gray matter, left (p = .003, d = .625) and right (p = .020, d = .492) hippocampi, and greater expansion of the right inferior lateral ventricle (p < .001, d = .751) over 5 years. CONCLUSIONS This study illustrates the variability in phenotypic expression of the ε4 allele related to neurodegeneration. Specifically, only those individuals who exhibited longitudinal declines in cognitive function experienced concomitant changes in brain volume. Future research is needed to better understand the biological and lifestyle factors that may influence the expression of the ε4 allele. (PsycINFO Database Record
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Affiliation(s)
- Dana A Kelly
- Department of Psychology, Rosalind Franklin University of Medicine and Science
| | - Michael Seidenberg
- Department of Psychology, Rosalind Franklin University of Medicine and Science
| | | | | | | | - J Carson Smith
- Department of Kinesiology, School of Public Health, University of Maryland
| | | | - Stephen M Rao
- Schey Center for Cognitive Neuroimaging, Cleveland Clinic
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Tang X, Luo Y, Chen Z, Huang N, Johnson HJ, Paulsen JS, Miller MI. A Fully-Automated Subcortical and Ventricular Shape Generation Pipeline Preserving Smoothness and Anatomical Topology. Front Neurosci 2018; 12:321. [PMID: 29867332 PMCID: PMC5966575 DOI: 10.3389/fnins.2018.00321] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Accepted: 04/25/2018] [Indexed: 11/13/2022] Open
Abstract
In this paper, we present a fully-automated subcortical and ventricular shape generation pipeline that acts on structural magnetic resonance images (MRIs) of the human brain. Principally, the proposed pipeline consists of three steps: (1) automated structure segmentation using the diffeomorphic multi-atlas likelihood-fusion algorithm; (2) study-specific shape template creation based on the Delaunay triangulation; (3) deformation-based shape filtering using the large deformation diffeomorphic metric mapping for surfaces. The proposed pipeline is shown to provide high accuracy, sufficient smoothness, and accurate anatomical topology. Two datasets focused upon Huntington's disease (HD) were used for evaluating the performance of the proposed pipeline. The first of these contains a total of 16 MRI scans, each with a gold standard available, on which the proposed pipeline's outputs were observed to be highly accurate and smooth when compared with the gold standard. Visual examinations and outlier analyses on the second dataset, which contains a total of 1,445 MRI scans, revealed 100% success rates for the putamen, the thalamus, the globus pallidus, the amygdala, and the lateral ventricle in both hemispheres and rates no smaller than 97% for the bilateral hippocampus and caudate. Another independent dataset, consisting of 15 atlas images and 20 testing images, was also used to quantitatively evaluate the proposed pipeline, with high accuracy having been obtained. In short, the proposed pipeline is herein demonstrated to be effective, both quantitatively and qualitatively, using a large collection of MRI scans.
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Affiliation(s)
- Xiaoying Tang
- Sun Yat-sen University-Carnegie Mellon University Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, China.,Sun Yat-sen University-Carnegie Mellon University Shunde International Joint Research Institute, Shunde, China.,School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Yuan Luo
- Sun Yat-sen University-Carnegie Mellon University Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, China.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Zhibin Chen
- Sun Yat-sen University-Carnegie Mellon University Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, China.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Nianwei Huang
- Sun Yat-sen University-Carnegie Mellon University Shunde International Joint Research Institute, Shunde, China
| | - Hans J Johnson
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, United States
| | - Jane S Paulsen
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, United States
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
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26
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Oishi K, Chang L, Huang H. Baby brain atlases. Neuroimage 2018; 185:865-880. [PMID: 29625234 DOI: 10.1016/j.neuroimage.2018.04.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 02/27/2018] [Accepted: 04/02/2018] [Indexed: 01/23/2023] Open
Abstract
The baby brain is constantly changing due to its active neurodevelopment, and research into the baby brain is one of the frontiers in neuroscience. To help guide neuroscientists and clinicians in their investigation of this frontier, maps of the baby brain, which contain a priori knowledge about neurodevelopment and anatomy, are essential. "Brain atlas" in this review refers to a 3D-brain image with a set of reference labels, such as a parcellation map, as the anatomical reference that guides the mapping of the brain. Recent advancements in scanners, sequences, and motion control methodologies enable the creation of various types of high-resolution baby brain atlases. What is becoming clear is that one atlas is not sufficient to characterize the existing knowledge about the anatomical variations, disease-related anatomical alterations, and the variations in time-dependent changes. In this review, the types and roles of the human baby brain MRI atlases that are currently available are described and discussed, and future directions in the field of developmental neuroscience and its clinical applications are proposed. The potential use of disease-based atlases to characterize clinically relevant information, such as clinical labels, in addition to conventional anatomical labels, is also discussed.
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Affiliation(s)
- Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Linda Chang
- Departments of Diagnostic Radiology and Nuclear Medicine, and Neurology, University of Maryland School of Medicine, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Hao Huang
- Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
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Bourgin J, Guyader N, Chauvin A, Juphard A, Sauvée M, Moreaud O, Silvert L, Hot P. Early Emotional Attention is Impacted in Alzheimer's Disease: An Eye-Tracking Study. J Alzheimers Dis 2018; 63:1445-1458. [PMID: 29782325 DOI: 10.3233/jad-180170] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Emotional deficits have been repetitively reported in Alzheimer's disease (AD) without clearly identifying how emotional processing is impaired in this pathology. This paper describes an investigation of early emotional processing, as measured by the effects of emotional visual stimuli on a saccadic task involving both pro (PS) and anti (AS) saccades. Sixteen patients with AD and 25 age-matched healthy controls were eye-tracked while they had to quickly move their gaze toward a positive, negative, or neutral image presented on a computer screen (in the PS condition) or away from the image (in the AS condition). The age-matched controls made more AS mistakes for negative stimuli than for other stimuli, and triggered PSs toward negative stimuli more quickly than toward other stimuli. In contrast, patients with AD showed no difference with regard to the emotional category in any of the tasks. The present study is the first to highlight a lack of early emotional attention in patients with AD. These results should be taken into account in the care provided to patients with AD, since this early impairment might seriously degrade their overall emotional functioning.
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Affiliation(s)
- Jessica Bourgin
- Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS UMR 5105, Laboratoire de Psychologie et Neurocognition (LPNC), Grenoble, France
| | - Nathalie Guyader
- Université Grenoble Alpes, CNRS UMR 5216, Laboratoire Grenoble Images Parole Signal Automatique (GIPSA-lab), Grenoble, France
| | - Alan Chauvin
- Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS UMR 5105, Laboratoire de Psychologie et Neurocognition (LPNC), Grenoble, France
| | | | - Mathilde Sauvée
- Pôle de Psychiatrie et Neurologie, CHU Grenoble, Grenoble, France
- Centre Mémoire de Ressources et de Recherche, Pôle de Psychiatrie et Neurologie, CHU Grenoble, Grenoble, France
| | - Olivier Moreaud
- Pôle de Psychiatrie et Neurologie, CHU Grenoble, Grenoble, France
- Centre Mémoire de Ressources et de Recherche, Pôle de Psychiatrie et Neurologie, CHU Grenoble, Grenoble, France
| | - Laetitia Silvert
- Université Clermont Auvergne, UCA-CNRS UMR 6024, Laboratoire de Psychologie Sociale et Cognitive (LAPSCO), Clermont-Ferrand, France
| | - Pascal Hot
- Université Grenoble Alpes, Université Savoie Mont Blanc, CNRS UMR 5105, Laboratoire de Psychologie et Neurocognition (LPNC), Grenoble, France
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28
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Tang X, Chen N, Zhang S, Jones JA, Zhang B, Li J, Liu P, Liu H. Predicting auditory feedback control of speech production from subregional shape of subcortical structures. Hum Brain Mapp 2017; 39:459-471. [PMID: 29058356 DOI: 10.1002/hbm.23855] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 09/27/2017] [Accepted: 10/11/2017] [Indexed: 11/06/2022] Open
Abstract
Although a growing body of research has focused on the cortical sensorimotor mechanisms that support auditory feedback control of speech production, much less is known about the subcortical contributions to this control process. This study examined whether subregional anatomy of subcortical structures assessed by statistical shape analysis is associated with vocal compensations and cortical event-related potentials in response to pitch feedback errors. The results revealed significant negative correlations between the magnitudes of vocal compensations and subregional shape of the right thalamus, between the latencies of vocal compensations and subregional shape of the left caudate and pallidum, and between the latencies of cortical N1 responses and subregional shape of the left putamen. These associations indicate that smaller local volumes of the basal ganglia and thalamus are predictive of slower and larger neurobehavioral responses to vocal pitch errors. Furthermore, increased local volumes of the left hippocampus and right amygdala were predictive of larger vocal compensations, suggesting that there is an interplay between the memory-related subcortical structures and auditory-vocal integration. These results, for the first time, provide evidence for differential associations of subregional morphology of the basal ganglia, thalamus, hippocampus, and amygdala with neurobehavioral processing of vocal pitch errors, suggesting that subregional shape measures of subcortical structures can predict behavioral outcome of auditory-vocal integration and associated neural features. Hum Brain Mapp 39:459-471, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Xiaoying Tang
- Sun Yat-sen University-Carnegie Melon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,Sun Yat-sen University-Carnegie Melon University (SYSU-CMU) Shunde International Joint Research Institute, Shunde, 528300, China.,School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006, China.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania
| | - Na Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Siyun Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Jeffery A Jones
- Psychology Department and Laurier Centre for Cognitive Neuroscience, Wilfrid Laurier University, Waterloo, Ontario, N2L 3C5, Canada
| | - Baofeng Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Jingyuan Li
- Sun Yat-sen University-Carnegie Melon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, 510006, China.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, 15213, Pennsylvania
| | - Peng Liu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Hanjun Liu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.,Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
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29
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Li J, Gong Y, Tang X. Hierarchical Subcortical Sub-Regional Shape Network Analysis in Alzheimer's Disease. Neuroscience 2017; 366:70-83. [PMID: 29037598 DOI: 10.1016/j.neuroscience.2017.10.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 09/11/2017] [Accepted: 10/07/2017] [Indexed: 01/06/2023]
Abstract
In this paper, by utilizing surface diffeomorphic deformations, we constructed and analyzed subcortical shape morphometric networks in 210 healthy control (HC) subjects and 175 subjects with Alzheimer's disease (AD), aiming to identify AD-induced abnormalities in the subcortical shape network. We quantitatively analyzed pertinent network attributes of the entire network and each node. Further to this, hierarchical analyses were performed; group comparisons were conducted at the structure level first and then the sub-region level. The bilateral amygdalae, hippocampi, as well as the thalamus were all divided into multiple functionally distinct sub-regions. From the structure level analysis, we found significant HC-vs-AD group differences in the average local efficiency and average global efficiency. In addition, the local nodal efficiencies between the right thalamus and all three of the right hippocampus, right amygdala, and left thalamus, as well as that between the left amygdala and left hippocampus, decreased significantly in AD. According to the sub-regional network analyses, we observed significant AD-induced local efficiency decreases between different sub-regions within the right hippocampus itself and between the subiculum of the right hippocampus and the sub-region of the right thalamus connecting to the temporal lobe, indicating a degradation of circuit between the hippocampus, thalamus, and temporal lobe. Statistical comparisons were performed using 40,000 non-parametric permutation tests, with false discovery rate correction employed for multiple comparison correction.
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Affiliation(s)
- Jingyuan Li
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Yujing Gong
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Xiaoying Tang
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA; School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, China; Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Shunde International Joint Research Institute, Shunde, Guangdong, China.
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Tang X, Miller MI, Younes L. BIOMARKER CHANGE-POINT ESTIMATION WITH RIGHT CENSORING IN LONGITUDINAL STUDIES. Ann Appl Stat 2017; 11:1738-1762. [PMID: 30271520 DOI: 10.1214/17-aoas1056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
We consider in this paper a statistical two-phase regression model in which the change point of a disease biomarker is measured relative to another point in time, such as the manifestation of the disease, which is subject to right-censoring (i.e., possibly unobserved over the entire course of the study). We develop point estimation methods for this model, based on maximum likelihood, and bootstrap validation methods. The effectiveness of our approach is illustrated by numerical simulations, and by the estimation of a change point for amygdalar atrophy in the context of Alzheimer's disease, wherein it is related to the cognitive manifestation of the disease.
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Affiliation(s)
- Xiaoying Tang
- SYSU-CMU Joint Institute of Engineering, Sun Yat-Sen University, No. 132, East Waihuan Road, Guangzhou Higher Education Mega Center, Guangzhou, 510006, P.R. China
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, 3400 N. Charles St. Baltimore, Maryland 21218 USA
| | - Laurent Younes
- Center for Imaging Science, Johns Hopkins University, 3400 N. Charles St. Baltimore, Maryland 21218 USA
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Nie X, Sun Y, Wan S, Zhao H, Liu R, Li X, Wu S, Nedelska Z, Hort J, Qing Z, Xu Y, Zhang B. Subregional Structural Alterations in Hippocampus and Nucleus Accumbens Correlate with the Clinical Impairment in Patients with Alzheimer's Disease Clinical Spectrum: Parallel Combining Volume and Vertex-Based Approach. Front Neurol 2017; 8:399. [PMID: 28861033 PMCID: PMC5559429 DOI: 10.3389/fneur.2017.00399] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 07/25/2017] [Indexed: 11/13/2022] Open
Abstract
Deep gray matter structures are associated with memory and other important functions that are impaired in Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, systematic characterization of the subregional atrophy and deformations in these structures in AD and MCI still need more investigations. In this article, we combined complex volumetry- and vertex-based analysis to investigate the pattern of subregional structural alterations in deep gray matter structures and its association with global clinical scores in AD (n = 30) and MCI patients (n = 30), compared to normal controls (NCs, n = 30). Among all seven pairs of structures, the bilateral hippocampi and nucleus accumbens showed significant atrophy in AD compared with NCs (p < 0.05). But only the subregional atrophy in the dorsal-medial part of the left hippocampus, the ventral part of right hippocampus, and the left nucleus accumbens, the posterior part of the right nucleus accumbens correlated with the worse clinical scores of MMSE and MOCA (p < 0.05). Furthermore, the medial-ventral part of right thalamus significantly shrank and correlated with clinical scores without decreasing in its whole volume (p > 0.05). In conclusion, the atrophy of these four subregions in bilateral hippocampi and nucleus accumbens was associated with cognitive impairment of patients, which might be potential target regions of treatment in AD. The surface analysis could provide additional information to volume comparison in finding the early pathological progress in deep gray matter structures.
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Affiliation(s)
- Xiuling Nie
- State Key laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
| | - Yu Sun
- State Key laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
- Institute of Cancer and Genetic Science, University of Birmingham, Birmingham, United Kingdom
| | - Suiren Wan
- State Key laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Renyuan Liu
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xueping Li
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Sichu Wu
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zuzana Nedelska
- Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University in Prague, Motol University Hospital, Prague, Czechia
- International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech Republic
| | - Jakub Hort
- Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University in Prague, Motol University Hospital, Prague, Czechia
- International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, Czech Republic
| | - Zhao Qing
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yun Xu
- Department of Neurology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
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Principal component analysis of the shape deformations of the hippocampus in Alzheimer's disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:4013-4016. [PMID: 28269165 DOI: 10.1109/embc.2016.7591607] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this paper, we present the principal component analysis (PCA) of shape deformations of bilateral hippocampi in Alzheimer's disease (AD) as derived in the large deformation diffeomorphic metric mapping setting. We investigated the PCA patterns (the scores and loadings) of the bilateral hippocampi for 51 subjects, 28 of which had AD while 23 were normal aging. Student's t-tests were used to select the components displaying significant group difference for a more purposed analysis. Our findings revealed that the head part of the hippocampus in each hemisphere, to be specific the CA1 and subiculum subregions, contributed the most to the significant group differences. To further our analysis, we examined the classification accuracy yielded by these shape deformation patterns when either solely PCA or PCA followed by a Student's t-test was utilized as the approach to dimension reduction. Linear discriminant analysis (LDA) and support vector machine (SVM) were used as candidates for the classification technique. According to our leave-one-out cross-validation experiments, SVM had a much higher accuracy than LDA, with the best performance (overall accuracy: 94.1% (48/51); sensitivity: 92.9% (26/28); and specificity: 95.7% (22/23)) achieved by SVM when using both the left and the right hippocampal shape deformation patterns and employing solely PCA for dimension reduction.
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33
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The TPS Direct Transport: A New Method for Transporting Deformations in the Size-and-Shape Space. Int J Comput Vis 2017. [DOI: 10.1007/s11263-017-1031-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Qin Y, Zhang S, Jiang R, Gao F, Tang X, Zhu W. Region-specific atrophy of precentral gyrus in patients with amyotrophic lateral sclerosis. J Magn Reson Imaging 2017; 47:115-122. [PMID: 28591490 DOI: 10.1002/jmri.25765] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Accepted: 05/01/2017] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To assess the region-specific atrophy of precentral gyrus (PrCG) and its correlation to clinical function score in amyotrophic lateral sclerosis (ALS). MATERIALS AND METHODS Twenty-eight patients with sporadic ALS and 28 healthy controls underwent high-resolution 3D T1-BRAVO magnetic resonance imaging at 3T. The bilateral PrCG segmentations were automatically obtained from a validated segmentation pipeline based on diffeomorphic multi-atlas likelihood fusion. Patients with ALS were further subclassified into early-stage (ALS-e, n = 22) and late-stage (ALS-l, n = 6) groups, with 12 months as a disease duration cutoff. Vertex-based shape analysis was performed to quantify the region-specific abnormalities of PrCG in ALS subgroups as compared to controls. In addition, we tested the statistical association between altered PrCG morphometry and clinical disability in ALS as evaluated by the revised ALS Functional Rating Scale (ALSFRS-r). RESULTS Compared to controls, vertex-wise analysis showed that both ALS-e and ALS-l had significant atrophy of the dorsal-lateral part of PrCG (P < 0.05, uncorrected). Importantly, atrophy in ALS-e was not as widespread as that in ALS-l; while atrophy in ALS-e was mostly confined to the dorsal-lateral region (P < 0.05, uncorrected, surface areas exhibiting significant difference at a level of P = 0.05: left 613.88 mm2 , right 937.80 mm2 ), atrophy in ALS-l occurred at the dorsal-medial and ventral region as well (P < 0.05, uncorrected, surface areas exhibiting significant difference at a level of P = 0.05: left 1465.98 mm2 , right 1253.89 mm2 ). Partial correlation analysis showed that the significant surface area atrophy of PrCG in ALS, especially that of the dorsal-lateral portion, was found to link tightly with ALSFRS-r (P < 0.05, uncorrected, surface areas exhibiting significant correlation: left 723.08 mm2 , right 474.24 mm2 ). CONCLUSION Our findings suggest that an altered PrCG morphometry, especially atrophy of the surface area in the dorsal-lateral portion, may be associated with the dysfunction that characterizes ALS. This study is an initial attempt to apply a validated statistical shape analysis pipeline to cortical gray matter structure like PrCG. LEVEL OF EVIDENCE 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2018;47:115-122.
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Affiliation(s)
- Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
| | - Rifeng Jiang
- Department of Radiology, Xiehe Hospital, Fujian Medical University, Fuzhou, Fujian, P.R. China
| | - Fei Gao
- Shandong Medical Imaging Research Institute, Shandong University, Jinan, P.R. China
| | - Xiaoying Tang
- Sun Yat-sen University Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, P.R. China.,SYSU-CMU Shunde International Joint Research Institute, Shunde, Guangdong, P.R. China.,School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, P.R. China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
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35
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Piras P, Teresi L, Traversetti L, Varano V, Gabriele S, Kotsakis T, Raia P, Puddu PE, Scalici M. The conceptual framework of ontogenetic trajectories: parallel transport allows the recognition and visualization of pure deformation patterns. Evol Dev 2017; 18:182-200. [PMID: 27161949 DOI: 10.1111/ede.12186] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Ontogeny is usually studied by analyzing a deformation series spanning over juvenile to adult shapes. In geometric morphometrics, this approach implies applying generalized Procrustes analysis coupled with principal component analysis on multiple individuals or multiple species datasets. The trouble with such a procedure is that it mixes intra- and inter-group variation. While MANCOVA models are relevant statistical/mathematical tools to draw inferences about the similarities of trajectories, if one wants to observe and interpret the morphological deformation alone by filtering inter-group variability, a particular tool, namely parallel transport, is necessary. In the context of ontogenetic trajectories, one should firstly perform separate multivariate regressions between shape and size, using regression predictions to estimate within-group deformations relative to the smallest individuals. These deformations are then applied to a common reference (the mean of per-group smallest individuals). The estimation of deformations can be performed on the Riemannian manifold by using sophisticated connection metrics. Nevertheless, parallel transport can be effectively achieved by estimating deformations in the Euclidean space via ordinary Procrustes analysis. This approach proved very useful in comparing ontogenetic trajectories of species presenting large morphological differences at early developmental stages.
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Affiliation(s)
- P Piras
- Dipartimento di Scienze, Università Roma Tre, Rome, Italy.,Center for Evolutionary Ecology, Università Roma Tre, Rome, Italy.,Dipartimento di Ingegneria Strutturale e Geotecnica "Sapienza", Università di Roma, Rome, Italy.,Dipartimento di Scienze Cardiovascolari, Respiratorie, Nefrologiche, Anestesiologiche e Geriatriche, Sapienza, Università di Roma, Rome, Italy
| | - L Teresi
- Dipartimento di Matematica e Fisica, Università Roma Tre, Rome, Italy
| | - L Traversetti
- Dipartimento di Scienze, Università Roma Tre, Rome, Italy
| | - V Varano
- Dipartimento di Architettura, Università Roma Tre, Rome, Italy
| | - S Gabriele
- Dipartimento di Architettura, Università Roma Tre, Rome, Italy
| | - T Kotsakis
- Dipartimento di Scienze, Università Roma Tre, Rome, Italy.,Center for Evolutionary Ecology, Università Roma Tre, Rome, Italy
| | - P Raia
- Dipartimento di Scienze della Terra, dell'Ambiente e delle Risorse (DiSTAR), Università degli Studi di Napoli Federico II, Naples, Italy
| | - P E Puddu
- Dipartimento di Scienze Cardiovascolari, Respiratorie, Nefrologiche, Anestesiologiche e Geriatriche, Sapienza, Università di Roma, Rome, Italy
| | - M Scalici
- Dipartimento di Scienze, Università Roma Tre, Rome, Italy
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36
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Seymour KE, Tang X, Crocetti D, Mostofsky SH, Miller MI, Rosch KS. Anomalous subcortical morphology in boys, but not girls, with ADHD compared to typically developing controls and correlates with emotion dysregulation. Psychiatry Res 2017; 261:20-28. [PMID: 28104573 PMCID: PMC5335909 DOI: 10.1016/j.pscychresns.2017.01.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Revised: 01/04/2017] [Accepted: 01/05/2017] [Indexed: 10/20/2022]
Abstract
There has been limited investigation of volume and shape difference in subcortical structures in children with ADHD and a paucity of examination of the influence of sex on these findings. The objective of this study was to examine morphology (volume and shape) of subcortical structures and their association with emotion dysregulation (ED) in girls and boys with ADHD as compared to their typically-developing (TD) counterparts. Participants included 218 children ages 8-12 years old with and without DSM-IV ADHD. Structural magnetic resonance images were obtained, and shape analyses were conducted using large deformation diffeomorphic metric mapping (LDDMM). Compared to TD boys, boys with ADHD showed reduced volumes in the bilateral globus pallidus and amygdala. There were no volumetric differences in any structure between ADHD and TD girls. Shape analysis revealed localized compressions within the globus pallidus, putamen and amygdala in ADHD boys relative to TD boys, as well as significant correlations between increased ED and unique subregion expansion in right globus pallidus, putamen, and right amygdala. Our findings suggest a sexually dimorphic pattern of differences in subcortical structures in children with ADHD compared to TD children, and a possible neurobiological mechanism by which boys with ADHD demonstrate increased difficulties with ED.
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Affiliation(s)
- Karen E. Seymour
- Division of Child and Adolescent Psychiatry, Johns Hopkins University School of Medicine
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute
| | - Xiaoying Tang
- Sun Yat-sen University Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, China
- SYSU-CMU International Joint Research Institute, Shunde, Guangdong, China
| | - Deana Crocetti
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute
| | - Stewart H. Mostofsky
- Division of Child and Adolescent Psychiatry, Johns Hopkins University School of Medicine
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute
- Department of Neurology, Johns Hopkins University School of Medicine
| | | | - Keri S. Rosch
- Division of Child and Adolescent Psychiatry, Johns Hopkins University School of Medicine
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute
- Department of Neuropsychology, Kennedy Krieger Institute
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37
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Geodesic distance on a Grassmannian for monitoring the progression of Alzheimer's disease. Neuroimage 2017; 146:1016-1024. [DOI: 10.1016/j.neuroimage.2016.10.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2016] [Revised: 09/17/2016] [Accepted: 10/14/2016] [Indexed: 02/01/2023] Open
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38
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Tang X, Qin Y, Zhu W, Miller MI. Surface-based vertexwise analysis of morphometry and microstructural integrity for white matter tracts in diffusion tensor imaging: With application to the corpus callosum in Alzheimer's disease. Hum Brain Mapp 2017; 38:1875-1893. [PMID: 28083895 DOI: 10.1002/hbm.23491] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Revised: 11/14/2016] [Accepted: 11/30/2016] [Indexed: 11/08/2022] Open
Abstract
In this article, we present a unified statistical pipeline for analyzing the white matter (WM) tracts morphometry and microstructural integrity, both globally and locally within the same WM tract, from diffusion tensor imaging. Morphometry is quantified globally by the volumetric measurement and locally by the vertexwise surface areas. Meanwhile, microstructural integrity is quantified globally by the mean fractional anisotropy (FA) and trace values within the specific WM tract and locally by the FA and trace values defined at each vertex of its bounding surface. The proposed pipeline consists of four steps: (1) fully automated segmentation of WM tracts in a multi-contrast multi-atlas framework; (2) generation of the smooth surface representations for the WM tracts of interest; (3) common template surface generation on which the localized morphometric and microstructural statistics are defined and a variety of statistical analyses can be conducted; (4) multiple comparison correction to determine the significance of the statistical analysis results. Detailed herein, this pipeline has been applied to the corpus callosum in Alzheimer's disease (AD) with significantly decreased FA values and increased trace values, both globally and locally, being detected in patients with AD when compared to normal aging populations. A subdivision of the corpus callosum in both hemispheres revealed that the AD pathology primarily affects the body and splenium of the corpus callosum. Validation analyses and two multiple comparison correction strategies are provided. Hum Brain Mapp 38:1875-1893, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Xiaoying Tang
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.,Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Shunde International Joint Research Institute, Shunde, Guangdong, China.,School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
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39
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Ma X, Li Z, Jing B, Liu H, Li D, Li H. Identify the Atrophy of Alzheimer's Disease, Mild Cognitive Impairment and Normal Aging Using Morphometric MRI Analysis. Front Aging Neurosci 2016; 8:243. [PMID: 27803665 PMCID: PMC5067377 DOI: 10.3389/fnagi.2016.00243] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2016] [Accepted: 10/03/2016] [Indexed: 11/17/2022] Open
Abstract
Quantitatively assessing the medial temporal lobe (MTL) structures atrophy is vital for early diagnosis of Alzheimer's disease (AD) and accurately tracking of the disease progression. Morphometry characteristics such as gray matter volume (GMV) and cortical thickness have been proved to be valuable measurements of brain atrophy. In this study, we proposed a morphometric MRI analysis based method to explore the cross-sectional differences and longitudinal changes of GMV and cortical thickness in patients with AD, MCI (mild cognitive impairment) and the normal elderly. High resolution 3D MRI data was obtained from ADNI database. SPM8 plus DARTEL was carried out for data preprocessing. Two kinds of z-score map were calculated to, respectively, reflect the GMV and cortical thickness decline compared with age-matched normal control database. A volume of interest (VOI) covering MTL structures was defined by group comparison. Within this VOI, GMV, and cortical thickness decline indicators were, respectively, defined as the mean of the negative z-scores and the sum of the normalized negative z-scores of the corresponding z-score map. Kruskal-Wallis test was applied to statistically identify group wise differences of the indicators. Support vector machines (SVM) based prediction was performed with a leave-one-out cross-validation design to evaluate the predictive accuracies of the indicators. Linear least squares estimation was utilized to assess the changing rate of the indicators for the three groups. Cross-sectional comparison of the baseline decline indicators revealed that the GMV and cortical thickness decline were more serious from NC, MCI to AD, with statistic significance. Using a multi-region based SVM model with the two indicators, the discrimination accuracy between AD and NC, MCI and NC, AD and MCI was 92.7, 91.7, and 78.4%, respectively. For three-way prediction, the accuracy was 74.6%. Furthermore, the proposed two indicators could also identify the atrophy rate differences among the three groups in longitudinal analysis. The proposed method could serve as an automatic and time-sparing approach for early diagnosis and tracking the progression of AD.
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Affiliation(s)
- Xiangyu Ma
- School of Biomedical Engineering, Capital Medical UniversityBeijing, China
| | - Zhaoxia Li
- School of Chinese Medicine, Capital Medical UniversityBeijing, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical UniversityBeijing, China
| | - Han Liu
- School of Biomedical Engineering, Capital Medical UniversityBeijing, China
| | - Dan Li
- College of Software Engineering, Beijing University of TechnologyBeijing, China
| | - Haiyun Li
- School of Biomedical Engineering, Capital Medical UniversityBeijing, China
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40
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Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, Galluzzi S, Marizzoni M, Frisoni GB. Brain atrophy in Alzheimer's Disease and aging. Ageing Res Rev 2016; 30:25-48. [PMID: 26827786 DOI: 10.1016/j.arr.2016.01.002] [Citation(s) in RCA: 438] [Impact Index Per Article: 54.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 01/15/2016] [Accepted: 01/20/2016] [Indexed: 01/22/2023]
Abstract
Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.
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Affiliation(s)
- Lorenzo Pini
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Michela Pievani
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Martina Bocchetta
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
| | - Daniele Altomare
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Paolo Bosco
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Enrica Cavedo
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) Hôpital de la Pitié-Salpétrière & Institut du Cerveau et de la Moelle épinière (ICM), UMR S 1127, Hôpital de la Pitié-Salpétrière Paris & CATI Multicenter Neuroimaging Platform, France
| | - Samantha Galluzzi
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Moira Marizzoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Giovanni B Frisoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland.
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41
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Education is associated with sub-regions of the hippocampus and the amygdala vulnerable to neuropathologies of Alzheimer's disease. Brain Struct Funct 2016; 222:1469-1479. [PMID: 27535407 DOI: 10.1007/s00429-016-1287-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Accepted: 08/11/2016] [Indexed: 01/18/2023]
Abstract
We evaluated the correlation of educational attainment with structural volume and shape morphometry of the bilateral hippocampi and amygdalae in a sample of 110 non-demented, older adults at elevated sociodemographic risk for cognitive and functional declines. In both men and women, no significant education-volume correlation was detected for either structure. However, when performing shape analysis, we observed regionally specific associations with education after adjusting for age, intracranial volume, and race. By sub-dividing the hippocampus and the amygdala into compatible subregions, we found that education was positively associated with size variations in the CA1 and subiculum subregions of the hippocampus and the basolateral subregion of the amygdala (p < 0.05). In addition, we detected a greater left versus right asymmetric pattern in the shape-education correlation for the hippocampus but not the amygdala. This asymmetric association was largely observed in men versus women. These findings suggest that education in youth may exert direct and indirect influences on brain reserve in regions that are most vulnerable to the neuropathologies of aging, dementia, and specifically, Alzheimer disease.
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42
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Tang X, Holland D, Dale AM, Miller MI. APOE Affects the Volume and Shape of the Amygdala and the Hippocampus in Mild Cognitive Impairment and Alzheimer's Disease: Age Matters. J Alzheimers Dis 2016; 47:645-60. [PMID: 26401700 DOI: 10.3233/jad-150262] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper examines how age intervenes in the effects of APOE ɛ4 allele on the volume and shape morphometrics of the hippocampus and the amygdala in mild cognitive impairment (MCI) and Alzheimer's disease. We evaluate the structural morphological differences between ɛ4 carriers and non-carriers in two age-dependent subgroups; younger than 75 years (Young-Old) and older than 80 years (Very-Old). While we show that the four structures of interest atrophy significantly in the ɛ4 carriers, relative to the non-carriers, of the Young-Old group, this effect is not observed in their Very-Old counterparts. The structures in the right hemisphere are found to be more affected by the APOE genotype than those in the left hemisphere and we identify the relevant regions in which significant atrophy occurs to be parts of the basolateral, centromedial, and lateral nucleus subregions of the amygdala and the CA1 and subiculum subregions of the hippocampus. We also observe that the APOE genotype only affects MCI patients that deteriorated to dementia within 3 years while leaving their "non-converting" counterparts unaffected.
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Affiliation(s)
- Xiaoying Tang
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Dominic Holland
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Anders M Dale
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA.,Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.,Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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43
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Tang X, Qin Y, Wu J, Zhang M, Zhu W, Miller MI. Shape and diffusion tensor imaging based integrative analysis of the hippocampus and the amygdala in Alzheimer's disease. Magn Reson Imaging 2016; 34:1087-99. [PMID: 27211255 DOI: 10.1016/j.mri.2016.05.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Accepted: 05/11/2016] [Indexed: 01/18/2023]
Abstract
We analyzed, in an integrative fashion, the morphometry and structural integrity of the bilateral hippocampi and amygdalas in Alzheimer's disease (AD) using T1-weighted images and diffusion tensor images (DTIs). We detected significant hippocampal and amygdalar volumetric atrophies in AD relative to healthy controls (HCs). Shape analysis revealed significant region-specific atrophies with the hippocampal atrophy mainly being concentrated on the CA1 and CA2 while the amygdalar atrophy was concentrated on the basolateral and basomedial. In all structures, the structural integrity displayed a significantly decreased mean fractional anisotropy (FA) value and an increased mean trace value in AD. In addition to the inter-group comparisons, we systematically evaluated the discriminative power of our three types of features (volume, shape, and DTI), both individually and in their possible combinations, when differentiating between AD and HCs. We found the volume features to be redundant when the more sophisticated shape features were available. A combination of the shape and DTI features of the right hippocampus, with classification automatically performed by support vector machine, yielded the strongest classification result (overall accuracy, 94.6%; sensitivity, 95.5%; specificity, 93.3%).
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Affiliation(s)
- Xiaoying Tang
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Shunde International Joint Research Institute, Shunde, Guangdong, China.
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiong Wu
- Sun Yat-sen University-Carnegie Mellon University (SYSU-CMU) Joint Institute of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Min Zhang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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44
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Lin N, Xiong LL, Zhang RP, Zheng H, Wang L, Qian ZY, Zhang P, Chen ZW, Gao FB, Wang TH. Injection of Aβ1-40 into hippocampus induced cognitive lesion associated with neuronal apoptosis and multiple gene expressions in the tree shrew. Apoptosis 2016; 21:621-40. [DOI: 10.1007/s10495-016-1227-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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45
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Geng X, Mori M. Monosynaptic excitatory transmission from the hippocampal CA1 region to the subiculum. Neurosci Lett 2015; 604:42-6. [DOI: 10.1016/j.neulet.2015.07.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 06/24/2015] [Accepted: 07/27/2015] [Indexed: 11/29/2022]
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46
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Atrophic Patterns of the Frontal-Subcortical Circuits in Patients with Mild Cognitive Impairment and Alzheimer's Disease. PLoS One 2015; 10:e0130017. [PMID: 26066658 PMCID: PMC4466229 DOI: 10.1371/journal.pone.0130017] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 05/15/2015] [Indexed: 11/25/2022] Open
Abstract
Atrophy of the cortical thickness and gray matter volume are regarded as sensitive markers for the early clinical diagnosis of Alzheimer’s disease (AD). This study aimed to investigate differences in atrophy patterns in the frontal-subcortical circuits between MCI and AD, assess whether these differences were essential for the pathologic basis of cognitive impairment. A total of 131 individuals were recruited, including 45 with cognitively normal controls (CN), 46 with MCI, and 40 with AD. FreeSurfer software was used to perform volumetric measurements of the frontal-subcortical circuits from 3.0T magnetic resonance (MR) scans. Data revealed that both MCI and AD subjects had a thinner cortex in the left caudal middle frontal gyrus and the left lateral orbitofrontal gyrus compared with CN individuals. The left lateral orbitofrontal gyrus was also thinner in AD compared with MCI patients. There were no statistically significant differences in the cortical mean curvature among the three groups. Both MCI and AD subjects exhibited smaller bilateral hippocampus volumes compared with CN individuals. The volumes of the bilateral hippocampus and the right putamen were also smaller in AD compared with MCI patients. Logistic regression analyses revealed that the left lateral orbitofrontal gyrus and bilateral hippocampus were risk factors for cognitive impairment. These current results suggest that atrophy was heterogeneous in subregions of the frontal-subcortical circuits in MCI and AD patients. Among these subregions, the reduced thickness of the left lateral orbitofrontal and the smaller volume of the bilateral hippocampus seemed to be markers for predicting cognitive impairment.
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