Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model
Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 57; no. 3; pp. 839 - 855 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.08.2011
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 1095-9572 |
| DOI | 10.1016/j.neuroimage.2011.05.055 |
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| Abstract | Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, “multimodal CCA+ joint ICA”, which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into “features” (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting that brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function–structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms.
► fMRI–DTI data fusion across 3 groups (HC, SZ, BP) for 164 subjects. ► Propose a novel general, flexible fusion model based on multimodal CCA+ joint ICA. ► Identify group-discriminating aspects in multiple facets. ► Visualize the potential function–structure network by evaluating the joint ICs. |
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| AbstractList | Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, "multimodal CCA+ joint ICA", which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into "features" (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting that brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function-structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms.Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, "multimodal CCA+ joint ICA", which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into "features" (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting that brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function-structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms. Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, “multimodal CCA+ joint ICA”, which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into “features” (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting that brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function–structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms. ► fMRI–DTI data fusion across 3 groups (HC, SZ, BP) for 164 subjects. ► Propose a novel general, flexible fusion model based on multimodal CCA+ joint ICA. ► Identify group-discriminating aspects in multiple facets. ► Visualize the potential function–structure network by evaluating the joint ICs. Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, "multimodal CCA+ joint ICA", which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into "features" (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting that brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function-structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms. Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, “multimodal CCA+ joint ICA’, which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into “features” (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function-structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms. Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, "multimodal CCA+ joint ICu, which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into "features" (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting that brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function-structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms. |
| Author | Sui, Jing Yamamoto, Jeremy Kiehl, Kent A. Liu, Jingyu Caprihan, Arvind Adali, Tülay Calhoun, Vince D. Pearlson, Godfrey |
| AuthorAffiliation | 3 Dept. of Psychiatry, Yale University, New Haven, CT, USA, 06519 1 The Mind Research Network, Albuquerque, NM, USA, 87106 4 Dept. of CSEE, University of Maryland, Baltimore County, Baltimore, MD, USA, 21250 5 Dept. of Psychology, University of New Mexico, Albuquerque, NM, USA, 87131 2 Olin Neuropsychiatry Research Center, Hartford, CT, USA, 06106 6 Dept. of ECE, University of New Mexico, Albuquerque, NM, USA, 87131 |
| AuthorAffiliation_xml | – name: 4 Dept. of CSEE, University of Maryland, Baltimore County, Baltimore, MD, USA, 21250 – name: 3 Dept. of Psychiatry, Yale University, New Haven, CT, USA, 06519 – name: 6 Dept. of ECE, University of New Mexico, Albuquerque, NM, USA, 87131 – name: 1 The Mind Research Network, Albuquerque, NM, USA, 87106 – name: 2 Olin Neuropsychiatry Research Center, Hartford, CT, USA, 06106 – name: 5 Dept. of Psychology, University of New Mexico, Albuquerque, NM, USA, 87131 |
| Author_xml | – sequence: 1 givenname: Jing surname: Sui fullname: Sui, Jing email: jsui@mrn.org organization: The Mind Research Network, Albuquerque, NM 87106, USA – sequence: 2 givenname: Godfrey surname: Pearlson fullname: Pearlson, Godfrey organization: Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA – sequence: 3 givenname: Arvind surname: Caprihan fullname: Caprihan, Arvind organization: The Mind Research Network, Albuquerque, NM 87106, USA – sequence: 4 givenname: Tülay surname: Adali fullname: Adali, Tülay organization: Dept. of CSEE, University of Maryland, Baltimore County, Baltimore, MD 21250, USA – sequence: 5 givenname: Kent A. surname: Kiehl fullname: Kiehl, Kent A. organization: The Mind Research Network, Albuquerque, NM 87106, USA – sequence: 6 givenname: Jingyu surname: Liu fullname: Liu, Jingyu organization: The Mind Research Network, Albuquerque, NM 87106, USA – sequence: 7 givenname: Jeremy surname: Yamamoto fullname: Yamamoto, Jeremy organization: The Mind Research Network, Albuquerque, NM 87106, USA – sequence: 8 givenname: Vince D. surname: Calhoun fullname: Calhoun, Vince D. email: vcalhoun@unm.edu organization: The Mind Research Network, Albuquerque, NM 87106, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21640835$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adult Anisotropy Bipolar disorder Bipolar Disorder - diagnosis Brain - physiopathology Brain Mapping Confidence intervals Diagnosis, Differential Diffusion tensor imaging (DTI) Diffusion Tensor Imaging - methods Female Fractional anisotropy (FA) Functional MRI (fMRI) Humans Image Interpretation, Computer-Assisted - methods Independent component analysis (ICA) Magnetic Resonance Imaging - methods Male Medical imaging Multimodal canonical correlation analysis (mCCA) Schizophrenia Schizophrenia - diagnosis |
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