Статья

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

Raymond PomponioCenter for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, USA. Electronic address: [email protected]Güray ErusCenter for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, USAMohamad HabesCenter for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, USA; Department of Neurology, University of Pennsylvania, USAJimit DoshiCenter for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, USADhivya SrinivasanCenter for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, USAElizabeth MamourianCenter for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, USAVishnu BashyamCenter for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, USAIlya M. NasrallahCenter for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, USA; Department of Radiology, University of Pennsylvania, USATheodore D. SatterthwaiteDepartment of Psychiatry, University of Pennsylvania, USAYong FanCenter for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, USALenore J. LaunerLaboratory of Epidemiology and Population Sciences, National Institute on Aging, USAColin L. MastersFlorey Institute of Neuroscience and Mental Health, University of Melbourne, AustraliaPaul MaruffFlorey Institute of Neuroscience and Mental Health, University of Melbourne, AustraliaChuanjun ZhuoTianjin Mental Health Center, Nankai University Affiliated Tianjin Anding Hospital, Tianjin, China; Department of Psychiatry, Tianjin Medical University, Tianjin, ChinaHenry VölzkeInstitute for Community Medicine, University of Greifswald, GermanySterling C. JohnsonJürgen FrippCSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, AustraliaNikolaos KoutsoulerisDepartment of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, GermanyDaniel H. WolfDepartment of Psychiatry, University of Pennsylvania, USARaquel E. GurDepartment of Radiology, University of Pennsylvania, USA; Department of Psychiatry, University of Pennsylvania, USARuben C. GurDepartment of Radiology, University of Pennsylvania, USA; Department of Psychiatry, University of Pennsylvania, USAJohn C. MorrisDepartment of Neurology, Washington University in St. Louis, USAMarilyn S. AlbertDepartment of Neurology, Johns Hopkins University School of Medicine, USAHans J. GrabeSusan M. ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging, USAR. Nick BryanDepartment of Diagnostic Medicine, University of Texas at Austin, USADavid A. WolkDepartment of Neurology, University of Pennsylvania, USARussell T. ShinoharaHaochang ShouChristos Davatzikos

2019en

ABI

Аннотация

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

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Идентификаторы

DOI: 10.1016/j.neuroimage.2019.116450

Цитирования и источники

Цитирований: 2Использованных источников: 0

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