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Abstract
This paper develops a functional hybrid factor regression modelling framework to handle the heterogeneity of many large-scale imaging studies, such as the Alzheimer's disease neuroimaging initiative study. Despite the numerous successes of those imaging studies, such heterogeneity may be caused by the differences in study environment, population, design, protocols or other hidden factors, and it has posed major challenges in integrative analysis of imaging data collected from multicentres or multistudies. We propose both estimation and inference procedures for estimating unknown parameters and detecting unknown factors under our new model. The asymptotic properties of both estimation and inference procedures are systematically investigated. The finite-sample performance of our proposed procedures is assessed by using Monte Carlo simulations and a real data example on hippocampal surface data from the Alzheimer's disease study.
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Affiliation(s)
- C Huang
- Department of Statistics, Florida State University, 117 N. Woodward Ave., Tallahassee, Florida 32304, U.S.A
| | - H Zhu
- Department of Biostatistics, The University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, North Carolina 27599, U.S.A
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Ma Q, Zhang T, Zanetti MV, Shen H, Satterthwaite TD, Wolf DH, Gur RE, Fan Y, Hu D, Busatto GF, Davatzikos C. Classification of multi-site MR images in the presence of heterogeneity using multi-task learning. Neuroimage Clin 2018; 19:476-486. [PMID: 29984156 PMCID: PMC6029565 DOI: 10.1016/j.nicl.2018.04.037] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 04/09/2018] [Accepted: 04/28/2018] [Indexed: 12/21/2022]
Abstract
With the advent of Big Data Imaging Analytics applied to neuroimaging, datasets from multiple sites need to be pooled into larger samples. However, heterogeneity across different scanners, protocols and populations, renders the task of finding underlying disease signatures challenging. The current work investigates the value of multi-task learning in finding disease signatures that generalize across studies and populations. Herein, we present a multi-task learning type of formulation, in which different tasks are from different studies and populations being pooled together. We test this approach in an MRI study of the neuroanatomy of schizophrenia (SCZ) by pooling data from 3 different sites and populations: Philadelphia, Sao Paulo and Tianjin (50 controls and 50 patients from each site), which posed integration challenges due to variability in disease chronicity, treatment exposure, and data collection. Some existing methods are also tested for comparison purposes. Experiments show that classification accuracy of multi-site data outperformed that of single-site data and pooled data using multi-task feature learning, and also outperformed other comparison methods. Several anatomical regions were identified to be common discriminant features across sites. These included prefrontal, superior temporal, insular, anterior cingulate cortex, temporo-limbic and striatal regions consistently implicated in the pathophysiology of schizophrenia, as well as the cerebellum, precuneus, and fusiform, middle temporal, inferior parietal, postcentral, angular, lingual and middle occipital gyri. These results indicate that the proposed multi-task learning method is robust in finding consistent and reliable structural brain abnormalities associated with SCZ across different sites, in the presence of multiple sources of heterogeneity.
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Affiliation(s)
- Qiongmin Ma
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China; Center for Biomedical Image Computing and Analytics, and Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States; Beijing Institute of System Engineering, China.
| | - Tianhao Zhang
- Center for Biomedical Image Computing and Analytics, and Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Marcus V Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Hui Shen
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China
| | | | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, and Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Dewen Hu
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China
| | - Geraldo F Busatto
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, and Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States
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