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Neuroimage Clin. 2018 May 9;19:476-486. doi: 10.1016/j.nicl.2018.04.037. eCollection 2018.

Classification of multi-site MR images in the presence of heterogeneity using multi-task learning.

Author information

1
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. Electronic address: qiongmin.ma@nudt.edu.cn.
2
Center for Biomedical Image Computing and Analytics, and Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States.
3
Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.
4
College of Mechatronics and Automation, National University of Defense Technology, Changsha, Hunan 410073, China.
5
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, United States.

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.

KEYWORDS:

Imaging heterogeneity; MRI; Multi-site classification; Multi-task learning; Schizophrenia; Sparsity

PMID:
29984156
PMCID:
PMC6029565
DOI:
10.1016/j.nicl.2018.04.037
[Indexed for MEDLINE]
Free PMC Article

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