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Neuroimage. 2013 Feb 1;66:119-32. doi: 10.1016/j.neuroimage.2012.10.051. Epub 2012 Oct 26.

Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia.

Author information

1
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA. Electronic address: jsui@mrn.org.
2
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA; Dept. of ECE, University of New Mexico, Albuquerque, NM 87131, USA.
3
Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA; Depts. of Psychiatry and Neurobiology, Yale University, New Haven, CT, 06519 USA.
4
Dept. of CSEE, University of Maryland, Baltimore County, Baltimore, MD, 21250 USA.
5
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA; Dept. of Psychology, University of New Mexico, Albuquerque, NM, 87131 USA.
6
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA.
7
Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454 USA; Department of Child and Adolescent Psychiatry, Erasmus University, 3000 CB Rotterdam, The Netherlands.
8
Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454 USA.
9
Department of Psychiatry, University of Iowa, Iowa City, IA, 52242 USA.
10
The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM 87106, USA; Dept. of ECE, University of New Mexico, Albuquerque, NM 87131, USA; Dept. of CSEE, University of Maryland, Baltimore County, Baltimore, MD, 21250 USA.

Abstract

Multimodal fusion is an effective approach to better understand brain diseases. However, most such instances have been limited to pair-wise fusion; because there are often more than two imaging modalities available per subject, there is a need for approaches that can combine multiple datasets optimally. In this paper, we extended our previous two-way fusion model called "multimodal CCA+joint ICA", to three or N-way fusion, that enables robust identification of correspondence among N data types and allows one to investigate the important question of whether certain disease risk factors are shared or distinct across multiple modalities. We compared "mCCA+jICA" with its alternatives in a 3-way fusion simulation and verified its advantages in both decomposition accuracy and modal linkage detection. We also applied it to real functional Magnetic Resonance Imaging (fMRI)-Diffusion Tensor Imaging (DTI) and structural MRI fusion to elucidate the abnormal architecture underlying schizophrenia (n=97) relative to healthy controls (n=116). Both modality-common and modality-unique abnormal regions were identified in schizophrenia. Specifically, the visual cortex in fMRI, the anterior thalamic radiation (ATR) and forceps minor in DTI, and the parietal lobule, cuneus and thalamus in sMRI were linked and discriminated between patients and controls. One fMRI component with regions of activity in motor cortex and superior temporal gyrus individually discriminated schizophrenia from controls. Finally, three components showed significant correlation with duration of illness (DOI), suggesting that lower gray matter volumes in parietal, frontal, and temporal lobes and cerebellum are associated with increased DOI, along with white matter disruption in ATR and cortico-spinal tracts. Findings suggest that the identified fractional anisotropy changes may relate to the corresponding functional/structural changes in the brain that are thought to play a role in the clinical expression of schizophrenia. The proposed "mCCA+jICA" method showed promise for elucidating the joint or coupled neuronal abnormalities underlying mental illnesses and improves our understanding of the disease process.

KEYWORDS:

DTI; Multimodal fusion; N-way fusion; Schizophrenia; fMRI; mCCA+jICA; sMRI

PMID:
23108278
PMCID:
PMC3897558
DOI:
10.1016/j.neuroimage.2012.10.051
[Indexed for MEDLINE]
Free PMC Article

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