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Bao W, Bi H, Chao L, Jiang Y, Yu X, Ruan F, Wu D, Chen Z, Le K. Identifying the mediating role of brain atrophy on the relationship between DNA damage repair pathway and Alzheimer's disease: A Mendelian randomization analysis and mediation analysis. J Alzheimers Dis 2025:13872877251333811. [PMID: 40313062 DOI: 10.1177/13872877251333811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2025]
Abstract
BackgroundDNA damage and repair (DDR) and structural atrophies in different brain regions were recognized as critical factors in the onset of Alzheimer's disease (AD).ObjectiveWe utilized Mendelian randomization (MR) to examine the causal effects of the DDR-related molecular traits on AD and the potential mediating roles of different brain region volumes.MethodsIn primary analysis, we utilized public genome-wide association studies of AD and summary data from existing molecular traits datasets, including gene expression, DNA methylation, and protein levels quantitative trait loci (eQTL, mQTL, and pQTL) in both blood and brain to examine their causal associations by summary-data-based MR analysis and additional five two-sample MR methods. Subsequently, mediation analysis explored the potential mediate roles of 13 imaging-derived brain volume phenotypes in the associations between the DDR pathways and AD through a network MR design.ResultsWe found that the volumes of the right thalamus proper and global cerebral white matter mediated the causal pathways from EGFR to AD and relatively weak mediation effects of the right lateral ventricle volume in the causal pathways involving CHRNE, DNTT, and AD.ConclusionsWe identified causal relationships among DDR pathways, specific brain region volumes, and AD. Monitoring the molecular traits of these DDR-related genes and developing targeted drugs may help detect and interrupt the early progression of AD.
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Affiliation(s)
- Wei Bao
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
- Department of Pediatrics, The First Affiliated Hospital of Nanchang University, Jiangxi Medical College, Nanchang, Jiangxi Province, China
| | - Haidi Bi
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
| | - Lishuo Chao
- Department of Affective Disorders, The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, Guangdong Province, China
| | - Yaqing Jiang
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
| | - Xiaoping Yu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
| | - Fei Ruan
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
| | - Di Wu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
- Department of Pediatrics, The First Affiliated Hospital of Nanchang University, Jiangxi Medical College, Nanchang, Jiangxi Province, China
| | - Zhaoyan Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
| | - Kai Le
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
- Department of Rehabilitation Sciences, Faculty of Health and Social Sciences, Hong Kong Polytechnic University, Hong Kong S.A.R., China
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Weinstein SM, Tu D, Hu F, Pan R, Zhang R, Vandekar SN, Baller EB, Gur RC, Gur RE, Alexander-Bloch AF, Satterthwaite TD, Park JY. Mapping Individual Differences in Intermodal Coupling in Neurodevelopment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600817. [PMID: 38979274 PMCID: PMC11230458 DOI: 10.1101/2024.06.26.600817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure-function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling. We propose a new method, called CIDeR, which is designed to simultaneously perform hypothesis testing in a way that limits false positive results and improve detection of true positive results. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate the utility of CIDeR in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort.
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Affiliation(s)
- Sarah M. Weinstein
- Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA, USA
| | - Danni Tu
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Fengling Hu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruyi Pan
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Rongqian Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Simon N. Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Erica B. Baller
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
| | - Jun Young Park
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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Liao QM, Liu YL, Dou YK, Du Y, Wang M, Wei JX, Zhao LS, Yang X, Ma XH. Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion. Cereb Cortex 2024; 34:bhae208. [PMID: 38752981 DOI: 10.1093/cercor/bhae208] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/27/2024] [Accepted: 05/11/2024] [Indexed: 01/28/2025] Open
Abstract
Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.
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Affiliation(s)
- Qi-Meng Liao
- Mental Health Center and Laboratory of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yi-Lin Liu
- Mental Health Center and Laboratory of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yi-Kai Dou
- Mental Health Center and Laboratory of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yue Du
- Mental Health Center and Laboratory of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Min Wang
- Mental Health Center and Laboratory of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Jin-Xue Wei
- Mental Health Center and Laboratory of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Lian-Sheng Zhao
- Mental Health Center and Laboratory of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Xiao Yang
- Mental Health Center and Laboratory of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Xiao-Hong Ma
- Mental Health Center and Laboratory of Psychiatry, West China Hospital of Sichuan University, Chengdu 610041, China
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Saha R, Saha DK, Fu Z, Duda M, Silva RF, Calhoun VD. Analysis of Longitudinal Change Patterns in Developing Brain Using Functional and Structural Magnetic Resonance Imaging via Multimodal Fusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.07.588473. [PMID: 38645216 PMCID: PMC11030394 DOI: 10.1101/2024.04.07.588473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. Each individual modality may overlook crucial aspects of brain analysis. By combining them, we can uncover hidden brain connections and gain a more comprehensive understanding. In previous work, we identified multivariate patterns of change in whole-brain function during adolescence. In this work, we focus on linking functional change patterns (FCPs) to brain structure. We introduce two approaches and applied them to data from the Adolescent Brain and Cognitive Development (ABCD) dataset. First, we evaluate voxelwise sMRI-FCP coupling to identify structural patterns linked to our previously identified FCPs. Our approach revealed multiple interesting patterns in functional network connectivity (FNC) and gray matter volume (GMV) data that were linked to subject level variation. FCP components 2 and 4 exhibit extensive associations between their loadings and voxel-wise GMV data. Secondly, we leveraged a symmetric multimodal fusion technique called multiset canonical correlation analysis (mCCA) + joint independent component analysis (jICA). Using this approach, we identify structured FCPs such as one showing increased connectivity between visual and sensorimotor domains and decreased connectivity between sensorimotor and cognitive control domains, linked to structural change patterns (SCPs) including alterations in the bilateral sensorimotor cortex. Interestingly, females exhibit stronger coupling between brain functional and structural changes than males, highlighting sex-related differences. The combined results from both asymmetric and symmetric multimodal fusion methods underscore the intricate sex-specific nuances in neural dynamics. By utilizing two complementary multimodal approaches, our study enhances our understanding of the dynamic nature of brain connectivity and structure during the adolescent period, shedding light on the nuanced processes underlying adolescent brain development.
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Affiliation(s)
- Rekha Saha
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Debbrata K. Saha
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Marlena Duda
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Rogers F. Silva
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University 55 Park Pl NE, Atlanta, GA 30303, USA
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Liu C, Peng Y, Yang Y, Li P, Chen D, Nie D, Liu H, Liu P. Structure of brain grey and white matter in infants with spastic cerebral palsy and periventricular white matter injury. Dev Med Child Neurol 2024; 66:514-522. [PMID: 37635344 DOI: 10.1111/dmcn.15739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 07/21/2023] [Accepted: 07/27/2023] [Indexed: 08/29/2023]
Abstract
AIM To investigate the possible covariation of grey matter volume (GMV) and white matter fractional anisotropy in infants with spastic cerebral palsy (CP) and periventricular white matter injury. METHOD Thirty-nine infants with spastic CP and 25 typically developing controls underwent structural magnetic resonance imaging and diffusion tensor imaging. Multimodal canonical correlation analysis with joint independent component analysis were used to capture differences in GMV and fractional anisotropy between groups. Correlation analysis was performed between imaging findings and clinical features. RESULTS Infants with spastic CP showed one joint group-discriminating component (i.e. GMV-fractional anisotropy) associated with regions in the cortico-basal ganglia-thalamo-cortical loop and in the corpus callosum compared to typically developing controls and one modality-specific group-discriminating component (i.e. GMV). Significant negative correlations were found between loadings in certain regions and the motor function score in spastic CP. INTERPRETATION In infants with spastic CP, covarying GMV-fractional anisotropy and altered GMV in specific regions were implicated in motor dysfunction, which confirmed that simultaneous GMV and fractional anisotropy changes underly motor deficits, but might also extend to sensory, cognitive, or visual dysfunction. These findings also suggest that multimodal fusion analysis allows for a more comprehensive understanding of the relevance between grey and white matter structures and its crucial role in the neuropathological mechanisms of spastic CP.
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Affiliation(s)
- Chengxiang Liu
- Life Science Research Center, School of Life Science and Technology, Xidian University, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, China
| | - Ying Peng
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yanli Yang
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Pengyu Li
- Life Science Research Center, School of Life Science and Technology, Xidian University, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, China
| | - Duoli Chen
- Life Science Research Center, School of Life Science and Technology, Xidian University, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, China
| | - Dingxin Nie
- Life Science Research Center, School of Life Science and Technology, Xidian University, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, China
| | - Heng Liu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Peng Liu
- Life Science Research Center, School of Life Science and Technology, Xidian University, China
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, China
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Sui J, Zhi D, Calhoun VD. Data-driven multimodal fusion: approaches and applications in psychiatric research. PSYCHORADIOLOGY 2023; 3:kkad026. [PMID: 38143530 PMCID: PMC10734907 DOI: 10.1093/psyrad/kkad026] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 12/26/2023]
Abstract
In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.
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Affiliation(s)
- Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, United States
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Cui F, Zhao L, Lu M, Liu R, Lv Q, Lin D, Li K, Zhang Y, Wang Y, Wang Y, Wang L, Tan Z, Tu Y, Zou Y. Functional and structural brain reorganization in patients with ischemic stroke: a multimodality MRI fusion study. Cereb Cortex 2023; 33:10453-10462. [PMID: 37566914 DOI: 10.1093/cercor/bhad295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 08/13/2023] Open
Abstract
Understanding how structural and functional reorganization occurs is crucial for stroke diagnosis and prognosis. Previous magnetic resonance imaging (MRI) studies focused on the analyses of a single modality and demonstrated abnormalities in both lesion regions and their associated distal regions. However, the relationships of multimodality alterations and their associations with poststroke motor deficits are still unclear. In this study, 71 hemiplegia patients and 41 matched healthy controls (HCs) were recruited and underwent MRI examination at baseline and at 2-week follow-up sessions. A multimodal fusion approach (multimodal canonical correlation analysis + joint independent component analysis), with amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV) as features, was used to extract the co-altered patterns of brain structure and function. Then compared the changes in patients' brain structure and function between baseline and follow-up sessions. Compared with HCs, the brain structure and function of stroke patients decreased synchronously in the local lesions and their associated distal regions. Damage to structure and function in the local lesion regions was associated with motor function. After 2 weeks, ALFF in the local lesion regions was increased, while GMV did not improve. Taken together, the brain structure and function in the local lesions and their associated distal regions were damaged synchronously after ischemic stroke, while during motor recovery, the 2 modalities were changed separately.
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Affiliation(s)
- Fangyuan Cui
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700, China
| | - Lei Zhao
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, No.16 Lincui Road, Chaoyang District, Beijing 100101, China
| | - Mengxin Lu
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700, China
- Department of Traditional Chinese Medicine, Beijing Chaoyang Hospital, Capital Medical University, No.8 South Gongti Road, Chaoyang District, Beijing 100020, China
| | - Ruoyi Liu
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700, China
- Department of Traditional Chinese Medicine, Cangzhou Central Hospital, No.16 Xinhua West Road, Cangzhou, Hebei 061000, China
| | - Qiuyi Lv
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700, China
| | - Dan Lin
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700, China
| | - Kuangshi Li
- 5Department of Rehabilitation, Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700, China
| | - Yong Zhang
- 5Department of Rehabilitation, Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700, China
| | - Yahui Wang
- Department of Rehabilitation Medicine, Beijing Tsinghua Changgung Hospital, No.168 Litang Road, Changping District, Beijing 102218, China
| | - Yue Wang
- Department of Protology, China-Japan Friendship Hospital, No.2 East Yinghua Road, Chaoyang District, Beijing 100029, China
| | - Liping Wang
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700, China
| | - Zhongjian Tan
- Department of Radiology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700, China
| | - Yiheng Tu
- Department of Psychology, University of Chinese Academy of Sciences, No.19 Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Yihuai Zou
- Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No.5 Haiyuncang, Dongcheng District, Beijing 100700, China
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Rapid Interactions of Widespread Brain Networks Characterize Semantic Cognition. J Neurosci 2023; 43:142-154. [PMID: 36384679 PMCID: PMC9838707 DOI: 10.1523/jneurosci.0529-21.2022] [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: 03/12/2021] [Revised: 10/25/2022] [Accepted: 10/31/2022] [Indexed: 11/17/2022] Open
Abstract
Language comprehension requires the rapid retrieval and integration of contextually appropriate concepts ("semantic cognition"). Current neurobiological models of semantic cognition are limited by the spatial and temporal restrictions of single-modality neuroimaging and lesion approaches. This is a major impediment given the rapid sequence of processing steps that have to be coordinated to accurately comprehend language. Through the use of fused functional magnetic resonance imaging and electroencephalography analysis in humans (n = 26 adults; 15 females), we elucidate a temporally and spatially specific neurobiological model for real-time semantic cognition. We find that semantic cognition in the context of language comprehension is supported by trade-offs between widespread neural networks over the course of milliseconds. Incorporation of spatial and temporal characteristics, as well as behavioral measures, provide convergent evidence for the following progression: a hippocampal/anterior temporal phonological semantic retrieval network (peaking at ∼300 ms after the sentence final word); a frontotemporal thematic semantic network (∼400 ms); a hippocampal memory update network (∼500 ms); an inferior frontal semantic syntactic reappraisal network (∼600 ms); and nodes of the default mode network associated with conceptual coherence (∼750 ms). Additionally, in typical adults, mediatory relationships among these networks are significantly predictive of language comprehension ability. These findings provide a conceptual and methodological framework for the examination of speech and language disorders, with additional implications for the characterization of cognitive processes and clinical populations in other cognitive domains.SIGNIFICANCE STATEMENT The present study identifies a real-time neurobiological model of the meaning processes required during language comprehension (i.e., "semantic cognition"). Using a novel application of fused magnetic resonance imaging and electroencephalography in humans, we found that semantic cognition during language comprehension is supported by a rapid progression of widespread neural networks related to meaning, meaning integration, memory, reappraisal, and conceptual cohesion. Relationships among these systems were predictive of individuals' language comprehension efficiency. Our findings are the first to use fused neuroimaging analysis to elucidate language processes. In so doing, this study provides a new conceptual and methodological framework in which to characterize language processes and guide the treatment of speech and language deficits/disorders.
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Liu K, Li Q, Yao L, Guo X. The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification. Front Neurosci 2022; 16:902528. [PMID: 35720713 PMCID: PMC9205193 DOI: 10.3389/fnins.2022.902528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 04/25/2022] [Indexed: 11/15/2022] Open
Abstract
Structural magnetic resonance imaging (MRI) features have played an increasingly crucial role in discriminating patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from normal controls (NC). However, the large number of structural MRI studies only extracted low-level neuroimaging features or simply concatenated multitudinous features while ignoring the interregional covariate information. The appropriate representation and integration of multilevel features will be preferable for the precise discrimination in the progression of AD. In this study, we proposed a novel inter-coupled feature representation method and built an integration model considering the two-level (the regions of interest (ROI) level and the network level) coupled features based on structural MRI data. For the intra-coupled interactions about the network-level features, we performed the ROI-level (intra- and inter-) coupled interaction within each network by feature expansion and coupling learning. For the inter-coupled interaction of the network-level features, we measured the coupled relationships among different networks via Canonical correlation analysis. We evaluated the classification performance using coupled feature representations on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results showed that the coupled integration model with hierarchical features achieved the optimal classification performance with an accuracy of 90.44% for AD and NC groups, with an accuracy of 87.72% for the MCI converter (MCI-c) and MCI non-converter (MCI-nc) groups. These findings suggested that our two-level coupled interaction representation of hierarchical features has been the effective means for the precise discrimination of MCI-c from MCI-nc groups and, therefore, helpful in the characterization of different AD courses.
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Affiliation(s)
- Ke Liu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Engineering Research Center of Intelligent Technology and Educational Application, Beijing Normal University, Beijing, China
| | - Qing Li
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Li Yao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Engineering Research Center of Intelligent Technology and Educational Application, Beijing Normal University, Beijing, China
| | - Xiaojuan Guo
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
- Engineering Research Center of Intelligent Technology and Educational Application, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer. PERSONALITY NEUROSCIENCE 2021; 4:e6. [PMID: 34909565 PMCID: PMC8640675 DOI: 10.1017/pen.2021.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/30/2021] [Accepted: 04/12/2021] [Indexed: 12/13/2022]
Abstract
By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.
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11
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Liang L, Chen Z, Wei Y, Tang F, Nong X, Li C, Yu B, Duan G, Su J, Mai W, Zhao L, Zhang Z, Deng D. Fusion analysis of gray matter and white matter in subjective cognitive decline and mild cognitive impairment by multimodal CCA-joint ICA. Neuroimage Clin 2021; 32:102874. [PMID: 34911186 PMCID: PMC8605254 DOI: 10.1016/j.nicl.2021.102874] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/30/2021] [Accepted: 11/01/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Previous multimodal neuroimaging studies analyzed each dataset independently in subjective cognitive decline (SCD) and mild cognitive impairment (MCI), missing the cross-information. Multi-modal fusion analysis can provide more integral and comprehensive information regarding the brain. There has been a paucity of research on fusion analysis of sMRI and DTI in SCD and MCI. MATERIALS AND METHODS In the present study, we conducted fusion analysis of structural MRI and DTI by applying multimodal canonical correlation analysis with joint independent component analysis (mCCA-jICA) to capture the cross-information of gray matter (GM) and white matter (WM) in 62 SCD patients, 99 MCI patients, and 70 healthy controls (HCs). We further analyzed correlations between the mixing coefficients of mCCA-jICA and neuropsychological scores among the three groups. RESULTS A set of joint-discriminative independent components of GM and fractional anisotropy (FA) exhibited significant links between SCD and HCs, as well as between MCI and HCs. The covariant abnormalities primarily involved the frontal lobe/middle temporal gyrus/calcarine sulcus-anterior thalamic radiation/superior longitudinal fasciculus in SCD, and middle temporal gyrus/ fusiform gyrus/caudate necleus-forceps minor/anterior thalamic radiation in MCI. There was no significant difference between SCD and MCI groups. CONCLUSIONS The covariant GM-WM abnormalities in SCD and MCI were found in specific brain regions involved in cognitive processing, which confirms the simultaneous GM and WM changes underlying cognitive decline. These findings suggest that multimodal fusion analysis allows for a more comprehensive understanding of the association among different types of brain tissues and its crucial role in the neuropathological mechanism of SCD and MCI.
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Affiliation(s)
- Lingyan Liang
- The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, Guangxi, China
| | - Zaili Chen
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China; Department of Medical Instrument Measurement, Shenzhen Academy of Metrology and Quality Inspection, Shenzhen 518055, China.
| | - Yichen Wei
- Department of Radiology, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Fei Tang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China; Department of Medical Instrument Measurement, Shenzhen Academy of Metrology and Quality Inspection, Shenzhen 518055, China.
| | - Xiucheng Nong
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Chong Li
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Bihan Yu
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Gaoxiong Duan
- The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, Guangxi, China
| | - Jiahui Su
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Wei Mai
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Lihua Zhao
- Department of Acupuncture, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi, China
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, China; Peng Cheng Laboratory, Shenzhen 518055, China.
| | - Demao Deng
- The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021, Guangxi, China.
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12
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Levy HC, Poppe A, Hiser J, Wootton BM, Hallion LS, Tolin DF, Stevens MC. An Examination of the Association Between Subjective Distress and Functional Connectivity During Discarding Decisions in Hoarding Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:1013-1022. [PMID: 33771533 DOI: 10.1016/j.bpsc.2020.12.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/17/2020] [Accepted: 12/23/2020] [Indexed: 01/16/2023]
Abstract
BACKGROUND Individuals with hoarding disorder (HD) demonstrate exaggerated subjective distress and hyperactivation of cingulate and insular cortex regions when discarding personal possessions. No prior study has sought to determine whether this subjective distress is associated with specific profiles of abnormal brain function in individuals with HD. METHODS We used multimodal canonical correlation analysis plus joint independent component analysis to test whether five hoarding-relevant domains of subjective distress when deciding to discard possessions (anxiety, sadness, monetary value, importance, and sentimental attachment) are associated with functional magnetic resonance imaging-measured whole-brain functional connectivity in 72 participants with HD and 44 healthy controls. RESULTS Three extracted components differed between HD participants and healthy control subjects. Each of these components depicted an abnormal profile of functional connectivity in HD participants relative to control subjects during discarding decisions, and a specific distress response profile. One component pair showed a relationship between anxiety ratings during discarding decisions and connectivity among the pallidum, perirhinal ectorhinal cortex, and dorsolateral prefrontal cortex. Another component comprised sadness ratings during discarding decisions and connectivity in the pallidum, nucleus accumbens, amygdala, and dorsolateral prefrontal cortex. The third component linked HD brain connectivity in several dorsolateral prefrontal cortex regions with perceived importance ratings during discarding decisions. CONCLUSIONS The findings indicate that in patients with HD, the subjective intensity of anxiety, sadness, and perceived possession importance is related to abnormal functional connectivity in key frontal and emotional processing brain regions. The findings are discussed in terms of emerging neurobiological models of HD.
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Affiliation(s)
- Hannah C Levy
- Anxiety Disorders Center, Institute of Living, Hartford, Connecticut.
| | - Andrew Poppe
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, Connecticut
| | - Jaryd Hiser
- Anxiety Disorders Center, Institute of Living, Hartford, Connecticut
| | - Bethany M Wootton
- Anxiety Disorders Center, Institute of Living, Hartford, Connecticut; Discipline of Clinical Psychology, Graduate School of Health, University of Technology Sydney, Sydney, South Wales, Australia
| | - Lauren S Hallion
- Anxiety Disorders Center, Institute of Living, Hartford, Connecticut; Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - David F Tolin
- Anxiety Disorders Center, Institute of Living, Hartford, Connecticut; Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
| | - Michael C Stevens
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, Connecticut; Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut
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13
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Zhuang X, Yang Z, Cordes D. A technical review of canonical correlation analysis for neuroscience applications. Hum Brain Mapp 2020; 41:3807-3833. [PMID: 32592530 PMCID: PMC7416047 DOI: 10.1002/hbm.25090] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 05/23/2020] [Indexed: 12/11/2022] Open
Abstract
Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA-variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well-known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA-related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA-related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.
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Affiliation(s)
- Xiaowei Zhuang
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
| | - Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
- University of ColoradoBoulderColoradoUSA
- Department of Brain HealthUniversity of NevadaLas VegasNevadaUSA
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14
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Sánchez SM, Duarte-Abritta B, Abulafia C, De Pino G, Bocaccio H, Castro MN, Sevlever GE, Fonzo GA, Nemeroff CB, Gustafson DR, Guinjoan SM, Villarreal MF. White matter fiber density abnormalities in cognitively normal adults at risk for late-onset Alzheimer's disease. J Psychiatr Res 2020; 122:79-87. [PMID: 31931231 DOI: 10.1016/j.jpsychires.2019.12.019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/30/2019] [Indexed: 12/13/2022]
Abstract
Tau accumulation affecting white matter tracts is an early neuropathological feature of late-onset Alzheimer's disease (LOAD). There is a need to ascertain methods for the detection of early LOAD features to help with disease prevention efforts. The microstructure of these tracts and anatomical brain connectivity can be assessed by analyzing diffusion MRI (dMRI) data. Considering that family history increases the risk of developing LOAD, we explored the microstructure of white matter through dMRI in 23 cognitively normal adults who are offspring of patients with Late-Onset Alzheimer's Disease (O-LOAD) and 22 control subjects (CS) without family history of AD. We also evaluated the relation of white matter microstructure metrics with cortical thickness, volumetry, in vivo amyloid deposition (with the help of PiB positron emission tomography -PiB-PET) and regional brain metabolism (as FDG-PET) measures. Finally we studied the association between cognitive performance and white matter microstructure metrics. O-LOAD exhibited lower fiber density and fractional anisotropy in the posterior portion of the corpus callosum and right fornix when compared to CS. Among O-LOAD, reduced fiber density was associated with lower amyloid deposition in the right hippocampus, and greater cortical thickness in the left precuneus, while higher mean diffusivity was related with greater cortical thickness of the right superior temporal gyrus. Additionally, compromised white matter microstructure was associated with poorer semantic fluency. In conclusion, white matter microstructure metrics may reveal early differences in O-LOAD by virtue of parental history of the disorder, when compared to CS without a family history of LOAD. We demonstrate that these differences are associated with lower fiber density in the posterior portion of the corpus callosum and the right fornix.
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Affiliation(s)
- Stella M Sánchez
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Departamento de Física, Facultad de Cs. Exactas y Naturales, Universidad de Buenos Aires, Argentina
| | - Bárbara Duarte-Abritta
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Argentina
| | - Carolina Abulafia
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Institute for Biomedical Research (BIOMED), Pontifical Catholic University of Argentina, Argentina
| | - Gabriela De Pino
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Argentina; Escuela de Ciencia y Tecnología, Universidad Nacional de San Martín, Argentina; Laboratorio de Neuroimágenes, Departamento de Imágenes, Fundación FLENI, Argentina
| | - Hernan Bocaccio
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Departamento de Física, Facultad de Cs. Exactas y Naturales, Universidad de Buenos Aires, Argentina
| | - Mariana N Castro
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Departamento de Fisiología, Facultad de Medicina, Universidad de Buenos Aires, Argentina; Departamento de Salud Mental, Facultad de Medicina, Universidad de Buenos Aires, Argentina
| | - Gustavo E Sevlever
- Departamento de Neuropatología y Biología Molecular, Fundación FLENI, Argentina
| | - Greg A Fonzo
- Institute of Early Life Adversity Research, Department of Psychiatry, University of Texas at Austin, United States
| | - Charles B Nemeroff
- Institute of Early Life Adversity Research, Department of Psychiatry, University of Texas at Austin, United States
| | - Deborah R Gustafson
- Department of Neurology, State University of New York University Downstate Medical Center, United States; Department of Health and Education, University of Skövde, Sweden
| | - Salvador M Guinjoan
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Departamento de Fisiología, Facultad de Medicina, Universidad de Buenos Aires, Argentina; Departamento de Salud Mental, Facultad de Medicina, Universidad de Buenos Aires, Argentina; Servicio de Psiquiatría, Fundación FLENI, Argentina; Neurofisiología I, Facultad de Psicología, Universidad de Buenos Aires, Argentina.
| | - Mirta F Villarreal
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; Departamento de Física, Facultad de Cs. Exactas y Naturales, Universidad de Buenos Aires, Argentina
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15
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Tang F, Yang H, Li L, Ji E, Fu Z, Zhang Z. Fusion analysis of gray matter and white matter in bipolar disorder by multimodal CCA-joint ICA. J Affect Disord 2020; 263:80-88. [PMID: 31818800 DOI: 10.1016/j.jad.2019.11.119] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Revised: 09/24/2019] [Accepted: 11/28/2019] [Indexed: 01/15/2023]
Abstract
BACKGROUND Bipolar disorder (BD) patients show morphological abnormalities in gray matter (GM) and white matter (WM), which can be revealed by structure MRI (sMRI) and diffusion tensor imaging (DTI) respectively. However, previous studies on BD mainly relied on separated analysis of single neuroimaging modality, and it remains unclear how GM and WM covary to the abnormal brain structures of BD patients. METHODS We recorded multimodal sMRI-DTI data of 35 BD patients and 30 healthy controls (HC) and used multimodal canonical component analysis and joint independent component analysis (mCCA-jICA) to identify altered covariant structures in GM and WM of BD. Group-discriminative and joint group-discriminative independent components (ICs) were identified between BD and HC. Correlation analysis was performed between the mixing coefficients and behavioral index. RESULTS For BD patients, experiments results revealed that the GM atrophy in inferior frontal gyrus, right anterior cingulate gyrus and left superior frontal gyrus are associated with the WM integrity reduction in corticospinal tract and superior longitudinal fasciculus. Further, compared with HC, different correlation between mixing coefficients of ICs and age was observed for BD patients. LIMITATIONS The number of participants needs to be increased to more rigorously validate the results of this study, ideally from multiple sites. Functional imaging data could be utilized to explore structural-functional covariant pattern in BD. Possible confounding effect of medication. CONCLUSIONS We performed fusion analysis of sMRI and DTI and revealed covariant (GM-WM) structural patterns of BD patients. This study could be useful for developing more reliable neural biomarkers of BD.
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Affiliation(s)
- Fei Tang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen 518060, China
| | - Haichen Yang
- Department for Affective Disorders, Shenzhen Mental Health Centre, Shenzhen Key Lab for Psychological Healthcare, Shenzhen 518020, China
| | - Linling Li
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen 518060, China
| | - Erni Ji
- Department for Affective Disorders, Shenzhen Mental Health Centre, Shenzhen Key Lab for Psychological Healthcare, Shenzhen 518020, China
| | - Zening Fu
- The Mind Research Network, University of New Mexico, Albuquerque, NM 87106, USA
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong, China; Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen 518060, China; Peng Cheng Laboratory, Shenzhen 518055, China.
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16
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Development of a transcallosal tractography template and its application to dementia. Neuroimage 2019; 200:302-312. [PMID: 31260838 DOI: 10.1016/j.neuroimage.2019.06.065] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 06/12/2019] [Accepted: 06/27/2019] [Indexed: 11/23/2022] Open
Abstract
Understanding the architecture of transcallosal connections would allow for more specific assessments of neurodegeneration across many fields of neuroscience, neurology, and psychiatry. To map these connections, we conducted probabilistic tractography in 100 Human Connectome Project subjects in 32 cortical areas using novel post-processing algorithms to create a spatially precise Trancallosal Tract Template (TCATT). We found robust transcallosal tracts in all 32 regions, and a topographical analysis in the corpus callosum largely agreed with well-established subdivisions of the corpus callosum. We then obtained diffusion MRI data from a cohort of patients with Alzheimer's disease (AD) and another with progressive supranuclear palsy (PSP) and used a two-compartment model to calculate free-water corrected fractional anisotropy (FAT) and free-water (FW) within the TCATT. These metrics were used to determine between-group differences and to determine which subset of tracts was best associated with cognitive function (Montreal Cognitive Assessment (MoCA)). In AD, we found robust between-group differences in FW (31/32 TCATT tracts) in the absence of between-group differences in FAT. FW in the inferior temporal gyrus TCATT tract was most associated with MoCA scores in AD. In PSP, there were widespread differences in both FAT and FW, and MoCA was predicted by FAT in the inferior frontal pars triangularis, preSMA, and medial frontal gyrus TCATT tracts as well as FW in the inferior frontal pars opercularis TCATT tract. The TCATT improves spatial localization of corpus callosum measurements to enhance the evaluation of treatment effects, as well as the monitoring of brain microstructure in relation to cognitive dysfunction and disease progression. Here, we have shown its direct relevance in capturing between-group differences and associating it with the MoCA in AD and PSP.
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17
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Shao W, Li X, Zhang J, Yang C, Tao W, Zhang S, Zhang Z, Peng D. White matter integrity disruption in the pre-dementia stages of Alzheimer's disease: from subjective memory impairment to amnestic mild cognitive impairment. Eur J Neurol 2019; 26:800-807. [PMID: 30584694 DOI: 10.1111/ene.13892] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 12/18/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND PURPOSE Subjective memory impairment (SMI) and amnestic mild cognitive impairment (aMCI) are thought to represent the pre-dementia stages of Alzheimer's disease (AD). SMI is considered a more advanced pre-clinical status prior to aMCI. Understanding the neuromechanism of SMI will have great benefits for monitoring the disease progression of AD. The study aims to explore whether SMI shows alterations of white matter (WM) integrity similar to the patterns of aMCI. METHODS The atlas-based analyses were performed to investigate the diffusion changes in the major WM tracts amongst 22 individuals with normal cognition (NC), 22 SMI patients and 25 aMCI patients. The correlations between the altered diffusion metrics and cognitive performance in the SMI and aMCI groups were assessed. RESULTS The diffusion tensor metrics of SMI were intermediate between the NC and aMCI groups. The aMCI group presented disrupted integrity in multiple WM tracts, including the left anterior thalamic radiation, right corticospinal tract and left cingulum of the hippocampus (CgH), compared to the NC group. The left CgH showed diffusion alterations in the SMI group. In the aMCI group, the mean diffusivity of the left CgH was negatively correlated with episodic memory, whilst the radial diffusivity of the right corticospinal tract was negatively correlated with executive function. No significant relationship was found in the SMI group. CONCLUSION The study suggested that SMI patients might present detectable WM integrity changes in the left CgH before exhibiting objective cognitive dysfunction, which may provide novel insights into the pathological mechanisms of AD.
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Affiliation(s)
- W Shao
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Beijing, China
| | - X Li
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - J Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - C Yang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - W Tao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - S Zhang
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China.,Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Z Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - D Peng
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Beijing, China.,Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
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Riederer I, Bohn KP, Preibisch C, Wiedemann E, Zimmer C, Alexopoulos P, Förster S. Alzheimer Disease and Mild Cognitive Impairment: Integrated Pulsed Arterial Spin-Labeling MRI and 18F-FDG PET. Radiology 2018; 288:198-206. [DOI: 10.1148/radiol.2018170575] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Isabelle Riederer
- From the Department of Diagnostic and Interventional Neuroradiology (I.R., C.P., C.Z.), Department of Diagnostic and Interventional Radiology (I.R.), Department of Nuclear Medicine (K.P.B., E.W., S.F.), TUM Neuroimaging Center (TUM-NIC) (C.P., S.F.), and Departments of Psychiatry and Psychotherapy (P.A.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany
| | - Karl Peter Bohn
- From the Department of Diagnostic and Interventional Neuroradiology (I.R., C.P., C.Z.), Department of Diagnostic and Interventional Radiology (I.R.), Department of Nuclear Medicine (K.P.B., E.W., S.F.), TUM Neuroimaging Center (TUM-NIC) (C.P., S.F.), and Departments of Psychiatry and Psychotherapy (P.A.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany
| | - Christine Preibisch
- From the Department of Diagnostic and Interventional Neuroradiology (I.R., C.P., C.Z.), Department of Diagnostic and Interventional Radiology (I.R.), Department of Nuclear Medicine (K.P.B., E.W., S.F.), TUM Neuroimaging Center (TUM-NIC) (C.P., S.F.), and Departments of Psychiatry and Psychotherapy (P.A.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany
| | - Eva Wiedemann
- From the Department of Diagnostic and Interventional Neuroradiology (I.R., C.P., C.Z.), Department of Diagnostic and Interventional Radiology (I.R.), Department of Nuclear Medicine (K.P.B., E.W., S.F.), TUM Neuroimaging Center (TUM-NIC) (C.P., S.F.), and Departments of Psychiatry and Psychotherapy (P.A.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany
| | - Claus Zimmer
- From the Department of Diagnostic and Interventional Neuroradiology (I.R., C.P., C.Z.), Department of Diagnostic and Interventional Radiology (I.R.), Department of Nuclear Medicine (K.P.B., E.W., S.F.), TUM Neuroimaging Center (TUM-NIC) (C.P., S.F.), and Departments of Psychiatry and Psychotherapy (P.A.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany
| | - Panagiotis Alexopoulos
- From the Department of Diagnostic and Interventional Neuroradiology (I.R., C.P., C.Z.), Department of Diagnostic and Interventional Radiology (I.R.), Department of Nuclear Medicine (K.P.B., E.W., S.F.), TUM Neuroimaging Center (TUM-NIC) (C.P., S.F.), and Departments of Psychiatry and Psychotherapy (P.A.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany
| | - Stefan Förster
- From the Department of Diagnostic and Interventional Neuroradiology (I.R., C.P., C.Z.), Department of Diagnostic and Interventional Radiology (I.R.), Department of Nuclear Medicine (K.P.B., E.W., S.F.), TUM Neuroimaging Center (TUM-NIC) (C.P., S.F.), and Departments of Psychiatry and Psychotherapy (P.A.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany
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Wang B, Niu Y, Miao L, Cao R, Yan P, Guo H, Li D, Guo Y, Yan T, Wu J, Xiang J, Zhang H. Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping. Front Aging Neurosci 2017; 9:378. [PMID: 29209199 PMCID: PMC5701971 DOI: 10.3389/fnagi.2017.00378] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 11/03/2017] [Indexed: 01/05/2023] Open
Abstract
Alzheimer's disease (AD) is a frequently observed, irreversible brain function disorder among elderly individuals. Resting-state functional magnetic resonance imaging (rs-fMRI) has been introduced as an alternative approach to assessing brain functional abnormalities in AD patients. However, alterations in the brain rs-fMRI signal complexities in mild cognitive impairment (MCI) and AD patients remain unclear. Here, we described the novel application of permutation entropy (PE) to investigate the abnormal complexity of rs-fMRI signals in MCI and AD patients. The rs-fMRI signals of 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. After preprocessing, whole-brain entropy maps of the four groups were extracted and subjected to Gaussian smoothing. We performed a one-way analysis of variance (ANOVA) on the brain entropy maps of the four groups. The results after adjusting for age and sex differences together revealed that the patients with AD exhibited lower complexity than did the MCI and NC controls. We found five clusters that exhibited significant differences and were distributed primarily in the occipital, frontal, and temporal lobes. The average PE of the five clusters exhibited a decreasing trend from MCI to AD. The AD group exhibited the least complexity. Additionally, the average PE of the five clusters was significantly positively correlated with the Mini-Mental State Examination (MMSE) scores and significantly negatively correlated with Functional Assessment Questionnaire (FAQ) scores and global Clinical Dementia Rating (CDR) scores in the patient groups. Significant correlations were also found between the PE and regional homogeneity (ReHo) in the patient groups. These results indicated that declines in PE might be related to changes in regional functional homogeneity in AD. These findings suggested that complexity analyses using PE in rs-fMRI signals can provide important information about the fMRI characteristics of cognitive impairments in MCI and AD.
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Affiliation(s)
- Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.,Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Yan Niu
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Liwen Miao
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Rui Cao
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Pengfei Yan
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Dandan Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yuxiang Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China.,Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing, China
| | - Jinglong Wu
- Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing Institute of Technology, Beijing, China.,Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Hui Zhang
- Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, China
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20
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Teipel SJ, Cavedo E, Weschke S, Grothe MJ, Rojkova K, Fontaine G, Dauphinot L, Gonzalez-Escamilla G, Potier MC, Bertin H, Habert MO, Dubois B, Hampel H. Cortical amyloid accumulation is associated with alterations of structural integrity in older people with subjective memory complaints. Neurobiol Aging 2017. [PMID: 28646687 DOI: 10.1016/j.neurobiolaging.2017.05.016] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We determined the effect of cortical amyloid load using 18F-florbetapir PET on cognitive performance and gray matter structural integrity derived from MRI in 318 cognitively normally performing older people with subjective memory impairment from the INSIGHT-preAD cohort using multivariate partial least squares regression. Amyloid uptake was associated with reduced gray matter structural integrity in hippocampus, entorhinal and cingulate cortex, middle temporal gyrus, prefrontal cortex, and lentiform nucleus (p < 0.01, permutation test). Higher amyloid load was associated with poorer global cognitive performance, delayed recall and attention (p < 0.05), independently of its effects on gray matter connectivity. These findings agree with the assumption of a two-stage effect of amyloid on cognition, (1) an early direct effect in the preclinical stages of Alzheimer's disease and (2) a delayed effect mediated by downstream effects of amyloid accumulation, such as gray matter connectivity decline.
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Affiliation(s)
- Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE) - Rostock/Greifswald, Rostock, Germany; Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany.
| | - Enrica Cavedo
- AXA Research Fund & UPMC Chair, Paris, France; Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France; IRCCS Istituto Centro San Giovanni di Dio-Fatebenefratelli, Brescia, Italy
| | - Sarah Weschke
- German Center for Neurodegenerative Diseases (DZNE) - Rostock/Greifswald, Rostock, Germany; Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Michel J Grothe
- German Center for Neurodegenerative Diseases (DZNE) - Rostock/Greifswald, Rostock, Germany; Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Katrine Rojkova
- AXA Research Fund & UPMC Chair, Paris, France; Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - Gaëlle Fontaine
- ICM Institut du Cerveau et de la Moelle épinière, CNRS UMR7225, INSERM U1127, UPMC, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Luce Dauphinot
- ICM Institut du Cerveau et de la Moelle épinière, CNRS UMR7225, INSERM U1127, UPMC, Hôpital de la Pitié-Salpêtrière, Paris, France
| | | | - Marie-Claude Potier
- ICM Institut du Cerveau et de la Moelle épinière, CNRS UMR7225, INSERM U1127, UPMC, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Hugo Bertin
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Paris, France
| | - Marie-Odile Habert
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, Paris, France; Centre pour l'Acquisition et le Traitement des Images, Paris, France; AP-HP, Hôpital Pitié-Salpêtrière, Département de Médecine Nucléaire, Paris, France
| | - Bruno Dubois
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM) - Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - Harald Hampel
- AXA Research Fund & UPMC Chair, Paris, France; Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
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21
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Kantarci K, Murray ME, Schwarz CG, Reid RI, Przybelski SA, Lesnick T, Zuk SM, Raman MR, Senjem ML, Gunter JL, Boeve BF, Knopman DS, Parisi JE, Petersen RC, Jack CR, Dickson DW. White-matter integrity on DTI and the pathologic staging of Alzheimer's disease. Neurobiol Aging 2017; 56:172-179. [PMID: 28552181 DOI: 10.1016/j.neurobiolaging.2017.04.024] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 04/07/2017] [Accepted: 04/25/2017] [Indexed: 11/16/2022]
Abstract
Pattern of diffusion tensor MRI (DTI) alterations were investigated in pathologically-staged Alzheimer's disease (AD) patients (n = 46). Patients with antemortem DTI studies and a range of AD pathology at autopsy were included. Patients with a high neurofibrillary tangle (NFT) stage (Braak IV-VI) had significantly elevated mean diffusivity (MD) in the crus of fornix and ventral cingulum tracts, precuneus, and entorhinal white matter on voxel-based analysis after adjusting for age and time from MRI to death (p < 0.001). Higher MD and lower fractional anisotropy in the ventral cingulum tract, entorhinal, and precuneus white matter was associated with higher Braak NFT stage and clinical disease severity. There were no MD and fractional anisotropy differences among the low (none and sparse) and high (moderate and frequent) β-amyloid neuritic plaque groups. The NFT pathology of AD is associated with DTI alterations involving the medial temporal limbic connections and medial parietal white matter. This pattern of diffusion abnormalities is also associated with clinical disease severity.
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Affiliation(s)
- Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| | - Melissa E Murray
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Jacksonville, FL, USA
| | | | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | - Timothy Lesnick
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Samantha M Zuk
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Mekala R Raman
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Jeffrey L Gunter
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Joseph E Parisi
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Dennis W Dickson
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Jacksonville, FL, USA
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22
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 PMCID: PMC6818723 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 182] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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23
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Vandekar SN, Shinohara RT, Raznahan A, Hopson RD, Roalf DR, Ruparel K, Gur RC, Gur RE, Satterthwaite TD. Subject-level measurement of local cortical coupling. Neuroimage 2016; 133:88-97. [PMID: 26956908 PMCID: PMC4889557 DOI: 10.1016/j.neuroimage.2016.03.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Revised: 02/16/2016] [Accepted: 03/01/2016] [Indexed: 01/08/2023] Open
Abstract
The human cortex is highly folded to allow for a massive expansion of surface area. Notably, the thickness of the cortex strongly depends on cortical topology, with gyral cortex sometimes twice as thick as sulcal cortex. We recently demonstrated that global differences in thickness between gyral and sulcal cortex continue to evolve throughout adolescence. However, human cortical development is spatially heterogeneous, and global comparisons lack power to detect localized differences in development or psychopathology. Here we extend previous work by proposing a new measure - local cortical coupling - that is sensitive to differences in the localized topological relationship between cortical thickness and sulcal depth. After estimation, subject-level coupling maps can be analyzed using standard neuroimaging analysis tools. Capitalizing on a large cross-sectional sample (n=932) of youth imaged as part of the Philadelphia Neurodevelopmental Cohort, we demonstrate that local coupling is spatially heterogeneous and exhibits nonlinear development-related trajectories. Moreover, we uncover sex differences in coupling that indicate divergent patterns of cortical topology. Developmental changes and sex differences in coupling support its potential as a neuroimaging phenotype for investigating neuropsychiatric disorders that are increasingly conceptualized as disorders of brain development. R code to estimate subject-level coupling maps from any two cortical surfaces generated by FreeSurfer is made publicly available along with this manuscript.
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Affiliation(s)
- Simon N Vandekar
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Russell T Shinohara
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Armin Raznahan
- Child Psychiatry Branch, National Institutes of Mental Health, Bethesda, MD 20892, USA
| | - Ryan D Hopson
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kosha Ruparel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA; Philadelphia Veterans Administration Medical Center, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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24
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Konukoglu E, Coutu JP, Salat DH, Fischl B. Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease. Neuroimage 2016; 134:573-586. [PMID: 27103138 DOI: 10.1016/j.neuroimage.2016.04.038] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 03/26/2016] [Accepted: 04/15/2016] [Indexed: 11/25/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer's and Huntington's diseases (Salat et al., 2010; Rosas et al., 2006). The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as diffusion tensor imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer's disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of different conditions in the same region as well as uncover spatial variations of effects across the white matter. The proposed procedures were able to answer questions on structural variations such as: "are there regions in the white matter where Alzheimer's disease has a different effect than aging or similar effect as aging?" and "are there regions in the white matter that are affected by both mild cognitive impairment and Alzheimer's disease but with differing multivariate effects?"
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Affiliation(s)
- Ender Konukoglu
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
| | - Jean-Philippe Coutu
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David H Salat
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
| | - Bruce Fischl
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, MIT, USA
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