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Wang Y, Zhu D, Zhao L, Wang X, Zhang Z, Hu B, Wu D, Zheng W. Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation. Neuroimage 2024; 295:120660. [PMID: 38815676 DOI: 10.1016/j.neuroimage.2024.120660] [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: 02/23/2024] [Revised: 05/17/2024] [Accepted: 05/28/2024] [Indexed: 06/01/2024] Open
Abstract
The topological organization of the macroscopic cortical networks important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex difference and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65 % accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.
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Affiliation(s)
- Ying Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Dalin Zhu
- Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Leilei Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhe Zhang
- Institute of Brain Science, Hangzhou Normal University, Hangzhou, China; School of Physics, Hangzhou Normal University, Hangzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; School of Medical Technology, Beijing Institute of Technology, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
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2
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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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Wilson S, Christiaens D, Yun H, Uus A, Cordero-Grande L, Karolis V, Price A, Deprez M, Tournier JD, Rutherford M, Grant E, Hajnal JV, Edwards AD, Arichi T, O'Muircheartaigh J, Im K. Dynamic changes in subplate and cortical plate microstructure at the onset of cortical folding in vivo. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.16.562524. [PMID: 38979235 PMCID: PMC11230247 DOI: 10.1101/2023.10.16.562524] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Cortical gyrification takes place predominantly during the second to third trimester, alongside other fundamental developmental processes, such as the development of white matter connections, lamination of the cortex and formation of neural circuits. The mechanistic biology that drives the formation cortical folding patterns remains an open question in neuroscience. In our previous work, we modelled the in utero diffusion signal to quantify the maturation of microstructure in transient fetal compartments, identifying patterns of change in diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester. In this work, we apply the same modelling approach to explore whether microstructural maturation of these compartments is correlated with the process of gyrification. We quantify the relationship between sulcal depth and tissue anisotropy within the cortical plate (CP) and underlying subplate (SP), key transient fetal compartments often implicated in mechanistic hypotheses about the onset of gyrification. Using in utero high angular resolution multi-shell diffusion-weighted imaging (HARDI) from the Developing Human Connectome Project (dHCP), our analysis reveals that the anisotropic, tissue component of the diffusion signal in the SP and CP decreases immediately prior to the formation of sulcal pits in the fetal brain. By back-projecting a map of folded brain regions onto the unfolded brain, we find evidence for cytoarchitectural differences between gyral and sulcal areas in the late second trimester, suggesting that regional variation in the microstructure of transient fetal compartments precedes, and thus may have a mechanistic function, in the onset of cortical folding in the developing human brain.
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Affiliation(s)
- Siân Wilson
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Division of Newborn Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Daan Christiaens
- Department of Electrical Engineering, Katholieke Universiteit Leuven, Belgium
| | - Hyukjin Yun
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Division of Newborn Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Alena Uus
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom
| | | | - Vyacheslav Karolis
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
| | - Anthony Price
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
| | - Maria Deprez
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom
| | - Jacques-Donald Tournier
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom
| | - Mary Rutherford
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
| | - Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Division of Newborn Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Joseph V Hajnal
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom
| | - A David Edwards
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
| | - Tomoki Arichi
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
- Department of Bioengineering, Imperial College London, United Kingdom
- Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, United Kingdom
| | - Jonathan O'Muircheartaigh
- Research Department of Early Life Imaging, Kings College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, King's College London, United Kingdom
| | - Kiho Im
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA
- Division of Newborn Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
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4
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Joo SW, Jo YT, Choi W, Kim SM, Yoo SY, Joe S, Lee J. Topological abnormalities of the morphometric similarity network of the cerebral cortex in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:57. [PMID: 38886369 PMCID: PMC11183129 DOI: 10.1038/s41537-024-00477-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/30/2024] [Indexed: 06/20/2024]
Abstract
A morphometric similarity (MS) network can be constructed using multiple magnetic resonance imaging parameters of each cortical region. An MS network can be used to assess the similarity between cortical regions. Although MS networks can detect microstructural alterations and capture connections between histologically similar cortical areas, the influence of schizophrenia on the topological characteristics of MS networks remains unclear. We obtained T1- and diffusion-weighted images of 239 healthy controls and 190 individuals with schizophrenia to construct the MS network. Group comparisons of the mean MS of the cortical regions and subnetworks were performed. The strengths of the connections between the cortical regions and the global and nodal network indices were compared between the groups. Clinical associations with the network indices were tested using Spearman's rho. Compared with healthy controls, individuals with schizophrenia had significant group differences in the mean MS of several cortical regions and subnetworks. Individuals with schizophrenia had both superior and inferior strengths of connections between cortical regions compared with those of healthy controls. We observed regional abnormalities of the MS network in individuals with schizophrenia regarding lower centrality values of the pars opercularis, superior frontal, and superior temporal areas. Specific nodal network measures of the right pars opercularis and left superior temporal areas were associated with illness duration in individuals with schizophrenia. We identified regional abnormalities of the MS network in schizophrenia with the left superior temporal area possibly being a key region in topological organization and cortical connections.
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Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Woohyeok Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sun Min Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - So Young Yoo
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Soohyun Joe
- Brain Laboratory, Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, California, USA
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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5
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Dong D, Chen X, Li W, Gao X, Wang Y, Zhou F, Eickhoff SB, Chen H. Opposite changes in morphometric similarity of medial reward and lateral non-reward orbitofrontal cortex circuits in obesity. Neuroimage 2024; 290:120574. [PMID: 38467346 DOI: 10.1016/j.neuroimage.2024.120574] [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: 12/09/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 03/13/2024] Open
Abstract
Obesity has a profound impact on metabolic health thereby adversely affecting brain structure and function. However, the majority of previous studies used a single structural index to investigate the link between brain structure and body mass index (BMI), which hinders our understanding of structural covariance between regions in obesity. This study aimed to examine the relationship between macroscale cortical organization and BMI using novel morphometric similarity networks (MSNs). The individual MSNs were first constructed from individual eight multimodal cortical morphometric features between brain regions. Then the relationship between BMI and MSNs within the discovery sample of 434 participants was assessed. The key findings were further validated in an independent sample of 192 participants. We observed that the lateral non-reward orbitofrontal cortex (lOFC) exhibited decoupling (i.e., reduction in integration) in obesity, which was mainly manifested by its decoupling with the cognitive systems (i.e., DMN and FPN) while the medial reward orbitofrontal cortex (mOFC) showed de-differentiation (i.e., decrease in distinctiveness) in obesity, which was mainly represented by its de-differentiation with the cognitive and attention systems (i.e., DMN and VAN). Additionally, the lOFC showed de-differentiation with the visual system in obesity, while the mOFC showed decoupling with the visual system and hyper-coupling with the sensory-motor system in obesity. As an important first step in revealing the role of underlying structural covariance in body mass variability, the present study presents a novel mechanism that underlies the reward-control interaction imbalance in obesity, thus can inform future weight-management approaches.
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Affiliation(s)
- Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Ximei Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Wei Li
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Yulin Wang
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China; Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Feng Zhou
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing 400715, China; Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing 400715, China.
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6
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Jiang L, Eickhoff SB, Genon S, Wang G, Yi C, He R, Huang X, Yao D, Dong D, Li F, Xu P. Multimodal Covariance Network Reflects Individual Cognitive Flexibility. Int J Neural Syst 2024; 34:2450018. [PMID: 38372035 DOI: 10.1142/s0129065724500187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Cognitive flexibility refers to the capacity to shift between patterns of mental function and relies on functional activity supported by anatomical structures. However, how the brain's structural-functional covarying is preconfigured in the resting state to facilitate cognitive flexibility under tasks remains unrevealed. Herein, we investigated the potential relationship between individual cognitive flexibility performance during the trail-making test (TMT) and structural-functional covariation of the large-scale multimodal covariance network (MCN) using magnetic resonance imaging (MRI) and electroencephalograph (EEG) datasets of 182 healthy participants. Results show that cognitive flexibility correlated significantly with the intra-subnetwork covariation of the visual network (VN) and somatomotor network (SMN) of MCN. Meanwhile, inter-subnetwork interactions across SMN and VN/default mode network/frontoparietal network (FPN), as well as across VN and ventral attention network (VAN)/dorsal attention network (DAN) were also found to be closely related to individual cognitive flexibility. After using resting-state MCN connectivity as representative features to train a multi-layer perceptron prediction model, we achieved a reliable prediction of individual cognitive flexibility performance. Collectively, this work offers new perspectives on the structural-functional coordination of cognitive flexibility and also provides neurobiological markers to predict individual cognitive flexibility.
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Affiliation(s)
- Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Guangying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Runyang He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Xunan Huang
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- School of Foreign Languages, University of Electronic Science and Technology of China, Sichuan, Chengdu 611731, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Debo Dong
- Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Center Jülich, Jülich, Germany
- Faculty of Psychology, Southwest University, Chongqing 400715, P. R. China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, P. R. China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, ChengDu 610041, P. R. China
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan 250012, P. R. China
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7
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Tremblay SA, Alasmar Z, Pirhadi A, Carbonell F, Iturria-Medina Y, Gauthier CJ, Steele CJ. MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582381. [PMID: 38463982 PMCID: PMC10925263 DOI: 10.1101/2024.02.27.582381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.
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Affiliation(s)
- Stefanie A Tremblay
- Department of Physics, Concordia University, Montreal, Canada
- School of Health, Concordia University, Montreal, Canada
- EPIC Centre, Montreal Heart Institute, Montreal, Canada
| | - Zaki Alasmar
- School of Health, Concordia University, Montreal, Canada
- Department of Psychology, Concordia University, Montreal, Canada
| | - Amir Pirhadi
- Department of Electrical Engineering, Concordia University, Montreal, Canada
- ViTAA medical solutions, Montreal, Canada
| | | | - Yasser Iturria-Medina
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada
- Ludmer Center for NeuroInformatics and Mental Health, Montreal, Canada
| | - Claudine J Gauthier
- Department of Physics, Concordia University, Montreal, Canada
- School of Health, Concordia University, Montreal, Canada
- EPIC Centre, Montreal Heart Institute, Montreal, Canada
| | - Christopher J Steele
- School of Health, Concordia University, Montreal, Canada
- Department of Psychology, Concordia University, Montreal, Canada
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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8
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Chen AA, Weinstein SM, Adebimpe A, Gur RC, Gur RE, Merikangas KR, Satterthwaite TD, Shinohara RT, Shou H. Similarity-based multimodal regression. Biostatistics 2023:kxad033. [PMID: 38058018 DOI: 10.1093/biostatistics/kxad033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 10/07/2023] [Accepted: 11/06/2023] [Indexed: 12/08/2023] Open
Abstract
To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.
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Affiliation(s)
- Andrew A Chen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Sarah M Weinstein
- Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA 19122, USA
| | - Azeez Adebimpe
- Penn Lifespan Informatics & Neuroimaging Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute Penn Medicine and CHOP, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathleen R Merikangas
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics & Neuroimaging Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
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9
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Weinstein SM, Vandekar SN, Baller EB, Tu D, Adebimpe A, Tapera TM, Gur RC, Gur RE, Detre JA, Raznahan A, Alexander-Bloch AF, Satterthwaite TD, Shinohara RT, Park JY. Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence. Neuroimage 2022; 264:119712. [PMID: 36309332 PMCID: PMC10062374 DOI: 10.1016/j.neuroimage.2022.119712] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/16/2022] [Accepted: 10/25/2022] [Indexed: 11/05/2022] Open
Abstract
With the increasing availability of neuroimaging data from multiple modalities-each providing a different lens through which to study brain structure or function-new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.
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Affiliation(s)
- Sarah M Weinstein
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Simon N Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Erica B Baller
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Danni Tu
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Azeez Adebimpe
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Strategy Innovation & Deployment Section, Johnson and Johnson, Raritan, NJ, 08869, USA
| | - Tinashe M Tapera
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health Intramural Research Program, Bethesda, MD 20892, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jun Young Park
- Department of Statistical Sciences and Department of Psychology, University of Toronto, Toronto, ON, M5G 1Z5, Canada.
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10
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Hu F, Weinstein SM, Baller EB, Valcarcel AM, Adebimpe A, Raznahan A, Roalf DR, Robert‐Fitzgerald TE, Gonzenbach V, Gur RC, Gur RE, Vandekar S, Detre JA, Linn KA, Alexander‐Bloch A, Satterthwaite TD, Shinohara RT. Voxel-wise intermodal coupling analysis of two or more modalities using local covariance decomposition. Hum Brain Mapp 2022; 43:4650-4663. [PMID: 35730989 PMCID: PMC9491276 DOI: 10.1002/hbm.25980] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/05/2022] [Accepted: 05/31/2022] [Indexed: 12/28/2022] Open
Abstract
When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities - that is, in how modalities covary at the voxel level. Previous studies have shown that local covariance structures between modalities, or intermodal coupling (IMCo), can be summarized for two modalities, and that two-modality IMCo reveals otherwise undiscovered patterns in neurodevelopment and certain diseases. However, previous IMCo methods are based on the slopes of local weighted linear regression lines, which are inherently asymmetric and limited to the two-modality setting. Here, we present a generalization of IMCo estimation which uses local covariance decompositions to define a symmetric, voxel-wise coupling coefficient that is valid for two or more modalities. We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi-modal data continues to increase, principal-component-based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github.com/hufengling/pIMCo.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sarah M. Weinstein
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Erica B. Baller
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- The Penn Lifespan Informatics and Neuroimaging Center, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alessandra M. Valcarcel
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Azeez Adebimpe
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- The Penn Lifespan Informatics and Neuroimaging Center, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Armin Raznahan
- National Institute of Mental Health, Intramural Research ProgramNational Institute of HealthBethesdaMarylandUSA
| | - David R. Roalf
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Timothy E. Robert‐Fitzgerald
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Virgilio Gonzenbach
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of RadiologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Raquel E. Gur
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of RadiologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Simon Vandekar
- Department of BiostatisticsVanderbilt UniversityNashvilleTennesseeUSA
| | - John A. Detre
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kristin A. Linn
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Aaron Alexander‐Bloch
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- The Penn Lifespan Informatics and Neuroimaging Center, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and Analytics (CBICA)Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
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11
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Developmental coupling of cerebral blood flow and fMRI fluctuations in youth. Cell Rep 2022; 38:110576. [PMID: 35354053 PMCID: PMC9006592 DOI: 10.1016/j.celrep.2022.110576] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/03/2022] [Accepted: 03/04/2022] [Indexed: 12/16/2022] Open
Abstract
The functions of the human brain are metabolically expensive and reliant on coupling between cerebral blood flow (CBF) and neural activity, yet how this coupling evolves over development remains unexplored. Here, we examine the relationship between CBF, measured by arterial spin labeling, and the amplitude of low-frequency fluctuations (ALFF) from resting-state magnetic resonance imaging across a sample of 831 children (478 females, aged 8-22 years) from the Philadelphia Neurodevelopmental Cohort. We first use locally weighted regressions on the cortical surface to quantify CBF-ALFF coupling. We relate coupling to age, sex, and executive functioning with generalized additive models and assess network enrichment via spin testing. We demonstrate regionally specific changes in coupling over age and show that variations in coupling are related to biological sex and executive function. Our results highlight the importance of CBF-ALFF coupling throughout development; we discuss its potential as a future target for the study of neuropsychiatric diseases.
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12
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Li X, Zhang S, Jiang X, Zhang S, Han J, Guo L, Zhang T. Cortical development coupling between surface area and sulcal depth on macaque brains. Brain Struct Funct 2022; 227:1013-1029. [PMID: 34989870 DOI: 10.1007/s00429-021-02444-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/15/2021] [Indexed: 02/06/2023]
Abstract
Postnatal development of cerebral cortex is associated with a variety of neuronal processes and is thus critical to development of brain function and cognition. Longitudinal changes of cortical morphology and topology, such as postnatal cortical thinning and flattening have been widely studied. However, thorough and systematic investigation of such cortical change, including how to quantify it from multiple spatial directions and how to relate it to surface topology, is rarely found. In this work, based on a longitudinal macaque neuroimaging dataset, we quantified local changes in gyral white matter's surface area and sulcal depth during early development. We also investigated how these two metrics are coupled and how this coupling is linked to cortical surface topology, underlying white matter, and positions of functional areas. Semi-parametric generalized additive models were adopted to quantify the longitudinal changes of surface area (A) and sulcal depth (D), and the coupling patterns between them. This resulted in four classes of regions, according to how they change compared with global change throughout early development: slower surface area change and slower sulcal depth change (slowA_slowD), slower surface area change and faster sulcal depth change (slowA_fastD), faster surface area change and slower sulcal depth change (fastA_slowD), and faster surface area change and faster sulcal depth change (fastA_fastD). We found that cortex-related metrics, including folding pattern and cortical thickness, vary along slowA_fastD-fastA_slowD axis, and structural connection-related metrics vary along fastA_fastD-slowA_slowD axis, with which brain functional sites align better. It is also found that cortical landmarks, including sulcal pits and gyral hinges, spatially reside on the borders of the four patterns. These findings shed new lights on the relationship between cortex development, surface topology, axonal wiring pattern and brain functions.
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Affiliation(s)
- Xiao Li
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
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13
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Weinstein SM, Vandekar SN, Adebimpe A, Tapera TM, Robert‐Fitzgerald T, Gur RC, Gur RE, Raznahan A, Satterthwaite TD, Alexander‐Bloch AF, Shinohara RT. A simple permutation-based test of intermodal correspondence. Hum Brain Mapp 2021; 42:5175-5187. [PMID: 34519385 PMCID: PMC8519855 DOI: 10.1002/hbm.25577] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 05/25/2021] [Accepted: 06/10/2021] [Indexed: 12/14/2022] Open
Abstract
Many key findings in neuroimaging studies involve similarities between brain maps, but statistical methods used to measure these findings have varied. Current state-of-the-art methods involve comparing observed group-level brain maps (after averaging intensities at each image location across multiple subjects) against spatial null models of these group-level maps. However, these methods typically make strong and potentially unrealistic statistical assumptions, such as covariance stationarity. To address these issues, in this article we propose using subject-level data and a classical permutation testing framework to test and assess similarities between brain maps. Our method is comparable to traditional permutation tests in that it involves randomly permuting subjects to generate a null distribution of intermodal correspondence statistics, which we compare to an observed statistic to estimate a p-value. We apply and compare our method in simulated and real neuroimaging data from the Philadelphia Neurodevelopmental Cohort. We show that our method performs well for detecting relationships between modalities known to be strongly related (cortical thickness and sulcal depth), and it is conservative when an association would not be expected (cortical thickness and activation on the n-back working memory task). Notably, our method is the most flexible and reliable for localizing intermodal relationships within subregions of the brain and allows for generalizable statistical inference.
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Affiliation(s)
- Sarah M. Weinstein
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | | | - Azeez Adebimpe
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Tinashe M. Tapera
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Timothy Robert‐Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Ruben C. Gur
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Raquel E. Gur
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Armin Raznahan
- Section on Developmental NeurogenomicsNational Institute of Mental Health Intramural Research ProgramBethesdaMaryland
| | - Theodore D. Satterthwaite
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Lifespan Informatics and Neuroimaging CenterUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Psychiatry, Brain Behavior Laboratory and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Center for Biomedical Image Computing and Analytics, Department of RadiologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
| | - Aaron F. Alexander‐Bloch
- Department of Psychiatry, Neurodevelopment and Psychosis Section and Penn‐CHOP Lifespan Brain InstituteUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of PhiladelphiaPhiladelphiaPennsylvania
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
- Center for Biomedical Image Computing and Analytics, Department of RadiologyUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvania
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14
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Alexander-Bloch AF, Raznahan A, Vandekar SN, Seidlitz J, Lu Z, Mathias SR, Knowles E, Mollon J, Rodrigue A, Curran JE, Görring HHH, Satterthwaite TD, Gur RE, Bassett DS, Hoftman GD, Pearlson G, Shinohara RT, Liu S, Fox PT, Almasy L, Blangero J, Glahn DC. Imaging local genetic influences on cortical folding. Proc Natl Acad Sci U S A 2020; 117:7430-7436. [PMID: 32170019 PMCID: PMC7132284 DOI: 10.1073/pnas.1912064117] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Recent progress in deciphering mechanisms of human brain cortical folding leave unexplained whether spatially patterned genetic influences contribute to this folding. High-resolution in vivo brain MRI can be used to estimate genetic correlations (covariability due to shared genetic factors) in interregional cortical thickness, and biomechanical studies predict an influence of cortical thickness on folding patterns. However, progress has been hampered because shared genetic influences related to folding patterns likely operate at a scale that is much more local (<1 cm) than that addressed in prior imaging studies. Here, we develop methodological approaches to examine local genetic influences on cortical thickness and apply these methods to two large, independent samples. We find that such influences are markedly heterogeneous in strength, and in some cortical areas are notably stronger in specific orientations relative to gyri or sulci. The overall, phenotypic local correlation has a significant basis in shared genetic factors and is highly symmetric between left and right cortical hemispheres. Furthermore, the degree of local cortical folding relates systematically with the strength of local correlations, which tends to be higher in gyral crests and lower in sulcal fundi. The relationship between folding and local correlations is stronger in primary sensorimotor areas and weaker in association areas such as prefrontal cortex, consistent with reduced genetic constraints on the structural topology of association cortex. Collectively, our results suggest that patterned genetic influences on cortical thickness, measurable at the scale of in vivo MRI, may be a causal factor in the development of cortical folding.
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Affiliation(s)
- Aaron F Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104;
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
| | | | - Simon N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37203
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
| | - Zhixin Lu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
| | - Samuel R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Emma Knowles
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Josephine Mollon
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Amanda Rodrigue
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Joanne E Curran
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | - Harald H H Görring
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | | | - Raquel E Gur
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle S Bassett
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104
| | - Gil D Hoftman
- Department of Psychiatry, University of California, Los Angeles, CA 90095
| | - Godfrey Pearlson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford, CT 06102
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, University of Pennsylvania, Philadelphia, PA 19104
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104
| | - Siyuan Liu
- National Institute of Mental Health, Bethesda, MD 20814
| | - Peter T Fox
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford, CT 06102
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15
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Dworkin JD, Linn KA, Solomon AJ, Satterthwaite TD, Raznahan A, Bakshi R, Shinohara RT. A local group differences test for subject-level multivariate density neuroimaging outcomes. Biostatistics 2019; 22:646-661. [PMID: 31875881 DOI: 10.1093/biostatistics/kxz058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 11/24/2019] [Accepted: 11/29/2019] [Indexed: 11/14/2022] Open
Abstract
A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes can be quantified using summary statistics of voxel intensities. However, the manifestation of a disease process in imaging data is often unknown, or appears as a complex and nonlinear relationship between the voxel intensities on various modalities. When the relevant pattern is unknown, summary statistics are often unable to capture differences between disease groups, and their use may encourage post hoc searches for the optimal summary measure. In this study, we introduce the multi-modal density testing (MMDT) framework for the naive discovery of group differences in voxel intensity profiles. MMDT operationalizes multi-modal magnetic resonance imaging (MRI) data as multivariate subject-level densities of voxel intensities and utilizes kernel density estimation to develop a local two-sample test for individual points within the density space. Through simulations, we show that this method controls type I error and recovers relevant differences when applied to a specified point. Additionally, we demonstrate the ability to maintain power while controlling the family-wise error rate and false discovery rate when applying the test over a grid of points within the density space. Finally, we apply this method to a study of subjects with either multiple sclerosis (MS) or conditions that tend to mimic MS on MRI, and find significant differences between the two groups in their voxel intensity profiles within the thalamus.
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Affiliation(s)
- Jordan D Dworkin
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Kristin A Linn
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Andrew J Solomon
- Department of Neurological Sciences, Larner College of Medicine at The University of Vermont, 149 Beaumont Avenue, Burlington, VT 05405, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, National Institutes of Health, 10 Center Drive, Bethesda, MD 20892, USA
| | - Rohit Bakshi
- Departments of Neurology and Radiology, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Road, Boston, MA 02115, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
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16
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Abstract
The anatomical structure of the human brain varies widely, as does individual cognitive behavior. It is important and interesting to study the relationship between brain structure and cognitive behavior. There has however been little previous work on the relationship between inhibitory control and brain structure. The goal of this study was to elucidate possible cortical markers related to inhibitory control using structural magnetic resonance imaging (sMRI) data. In this study, we analyzed sMRI data and inhibitory control behavior measurement values from 361 healthy adults from the Human Connectome Project (HCP). The data of all participants were divided into two datasets. In the first dataset, we first constructed individual brain morphometric similarity networks by calculating the inter-regional statistical similarity relationship of nine cortical characteristic measures (such as volume) for each brain area obtained from sMRI data. Areas that covary in their morphology are termed 'connected'. After that, we used a brain connectome-based predictive model (CPM) to search for 'connected' brain areas that were significantly related to inhibitory control. This is a purely data-driven method with built-in cross-validation. Two different 'connected' patterns were observed for high and low inhibitory control networks. The high inhibitory control network comprised 25 'connections' (edges between nodes), mostly involving nodes in the prefrontal and especially orbitofrontal cortex and inferior frontal gyrus. In the low inhibitory control network, nodes were scattered between parietal, occipital and limbic areas. Furthermore, these 'connections' were verified as reliable and generalizable in a cross-validation dataset. Two regions of interest, the right ventromedial prefrontal cortex including a part of medial area 10 (R.OFCmed) and left middle temporal gyrus (L.MTG) were crucial nodes in the two networks, respectively, which suggests that these two regions may be fundamentally involved in inhibitory control. Our findings potentially help to understand the relationship between areas with a correlated cortical structure and inhibitory control, and further help to reveal the brain systems related to inhibition and its disorders.
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Valcarcel AM, Linn KA, Khalid F, Vandekar SN, Tauhid S, Satterthwaite TD, Muschelli J, Martin ML, Bakshi R, Shinohara RT. A dual modeling approach to automatic segmentation of cerebral T2 hyperintensities and T1 black holes in multiple sclerosis. Neuroimage Clin 2018; 20:1211-1221. [PMID: 30391859 PMCID: PMC6224321 DOI: 10.1016/j.nicl.2018.10.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 08/26/2018] [Accepted: 10/15/2018] [Indexed: 11/25/2022]
Abstract
BACKGROUND AND PURPOSE Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis (MS). The most widely established MRI outcome measure is the volume of hyperintense lesions on T2-weighted images (T2L). Unfortunately, T2L are non-specific for the level of tissue destruction and show a weak relationship to clinical status. Interest in lesions that appear hypointense on T1-weighted images (T1L) ("black holes") has grown because T1L provide more specificity for axonal loss and a closer link to neurologic disability. The technical difficulty of T1L segmentation has led investigators to rely on time-consuming manual assessments prone to inter- and intra-rater variability. This study aims to develop an automatic T1L segmentation approach, adapted from a T2L segmentation algorithm. MATERIALS AND METHODS T1, T2, and fluid-attenuated inversion recovery (FLAIR) sequences were acquired from 40 MS subjects at 3 Tesla (3 T). T2L and T1L were manually segmented. A Method for Inter-Modal Segmentation Analysis (MIMoSA) was then employed. RESULTS Using cross-validation, MIMoSA proved to be robust for segmenting both T2L and T1L. For T2L, a Sørensen-Dice coefficient (DSC) of 0.66 and partial AUC (pAUC) up to 1% false positive rate of 0.70 were achieved. For T1L, 0.53 DSC and 0.64 pAUC were achieved. Manual and MIMoSA segmented volumes were correlated and resulted in 0.88 for T1L and 0.95 for T2L. The correlation between Expanded Disability Status Scale (EDSS) scores and manual versus automatic volumes were similar for T1L (0.32 manual vs. 0.34 MIMoSA), T2L (0.33 vs. 0.32), and the T1L/T2L ratio (0.33 vs 0.33). CONCLUSIONS Though originally designed to segment T2L, MIMoSA performs well for segmenting T1 black holes in patients with MS.
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Affiliation(s)
- Alessandra M Valcarcel
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Kristin A Linn
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fariha Khalid
- Laboratory for Neuroimaging Research, Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases, Boston, MA, USA; Departments of Neurology and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon N Vandekar
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Shahamat Tauhid
- Laboratory for Neuroimaging Research, Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases, Boston, MA, USA; Departments of Neurology and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA
| | - Melissa Lynne Martin
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rohit Bakshi
- Laboratory for Neuroimaging Research, Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases, Boston, MA, USA; Departments of Neurology and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Valcarcel AM, Linn KA, Vandekar SN, Satterthwaite TD, Muschelli J, Calabresi PA, Pham DL, Martin ML, Shinohara RT. MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions. J Neuroimaging 2018. [PMID: 29516669 DOI: 10.1111/jon.12506] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND AND PURPOSE Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions. METHODS Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking. RESULTS In the Johns Hopkins study, MIMoSA yielded average Sørensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset. CONCLUSION MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study.
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Affiliation(s)
- Alessandra M Valcarcel
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kristin A Linn
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Simon N Vandekar
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - John Muschelli
- Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - Peter A Calabresi
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD
| | - Dzung L Pham
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD
| | - Melissa Lynne Martin
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Seidlitz J, Váša F, Shinn M, Romero-Garcia R, Whitaker KJ, Vértes PE, Wagstyl K, Kirkpatrick Reardon P, Clasen L, Liu S, Messinger A, Leopold DA, Fonagy P, Dolan RJ, Jones PB, Goodyer IM, Raznahan A, Bullmore ET. Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron 2017; 97:231-247.e7. [PMID: 29276055 DOI: 10.1016/j.neuron.2017.11.039] [Citation(s) in RCA: 239] [Impact Index Per Article: 34.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 10/05/2017] [Accepted: 11/22/2017] [Indexed: 12/22/2022]
Abstract
Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organization comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs with tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust, and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.
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Affiliation(s)
- Jakob Seidlitz
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA.
| | - František Váša
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Maxwell Shinn
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | | | - Kirstie J Whitaker
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Petra E Vértes
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Konrad Wagstyl
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | | | - Liv Clasen
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Siyuan Liu
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - David A Leopold
- Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, MD 20892, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD 20892, USA
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, UK
| | - Raymond J Dolan
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London WC1N 3BG, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK
| | - Peter B Jones
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | - Ian M Goodyer
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | | | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Edward T Bullmore
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK; ImmunoPsychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK
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