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

Jing Sui, Hao He, Godfrey D. Pearlson, Tülay Adali, Kent A. Kiehl, Qingbao Yu, Vince P. Clark, Eduardo Castro, Tonya White, Bryon A. Mueller, Beng C. Ho, Nancy C. Andreasen, Vince Daniel Calhoun

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)119-132
Number of pages14
JournalNeuroImage
Volume66
DOIs
StatePublished - Feb 1 2013
Externally publishedYes

Fingerprint

Neuroimaging
Schizophrenia
Parietal Lobe
Diffusion Tensor Imaging
Magnetic Resonance Imaging
Temporal Lobe
Joints
Radiation
Occipital Lobe
Anisotropy
Motor Cortex
Brain Diseases
Frontal Lobe
Visual Cortex
Thalamus
Surgical Instruments
Cerebellum
Brain

Keywords

  • DTI
  • FMRI
  • MCCA+jICA
  • Multimodal fusion
  • N-way fusion
  • Schizophrenia
  • SMRI

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology
  • Medicine(all)

Cite this

Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia. / Sui, Jing; He, Hao; Pearlson, Godfrey D.; Adali, Tülay; Kiehl, Kent A.; Yu, Qingbao; Clark, Vince P.; Castro, Eduardo; White, Tonya; Mueller, Bryon A.; Ho, Beng C.; Andreasen, Nancy C.; Calhoun, Vince Daniel.

In: NeuroImage, Vol. 66, 01.02.2013, p. 119-132.

Research output: Contribution to journalArticle

Sui, J, He, H, Pearlson, GD, Adali, T, Kiehl, KA, Yu, Q, Clark, VP, Castro, E, White, T, Mueller, BA, Ho, BC, Andreasen, NC & Calhoun, VD 2013, 'Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia', NeuroImage, vol. 66, pp. 119-132. https://doi.org/10.1016/j.neuroimage.2012.10.051
Sui, Jing ; He, Hao ; Pearlson, Godfrey D. ; Adali, Tülay ; Kiehl, Kent A. ; Yu, Qingbao ; Clark, Vince P. ; Castro, Eduardo ; White, Tonya ; Mueller, Bryon A. ; Ho, Beng C. ; Andreasen, Nancy C. ; Calhoun, Vince Daniel. / Three-way (N-way) fusion of brain imaging data based on mCCA+jICA and its application to discriminating schizophrenia. In: NeuroImage. 2013 ; Vol. 66. pp. 119-132.
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