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Updated: Jun 1, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
Published on: June 30, 2018
Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model
Jing Sui1, Godfrey Pearlson, Arvind Caprihan
1The Mind Research Network, Albuquerque, NM 87106, USA. jsui@mrn.org
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.
Insights
Schizophrenia and bipolar disorder share brain abnormalities in prefrontal cortex and white matter, but differ in specific functional and structural patterns. This study used multimodal imaging to differentiate these conditions.
Area of Science:
- Neuroscience
- Psychiatry
- Medical Imaging
Background:
- Schizophrenia and bipolar disorder exhibit overlapping yet distinct brain alterations.
- Previous studies often lack replicability and sufficient sample sizes.
- Clarifying neurobiological differences is crucial for diagnosis and treatment.
Purpose of the Study:
- To differentiate schizophrenia and bipolar disorder using combined functional MRI (fMRI) and diffusion tensor imaging (DTI).
- To introduce a novel "multimodal CCA+ joint ICA" fusion method for enhanced accuracy.
- To identify shared and distinct neurobiological markers between the disorders.
Main Methods:
- Collected fMRI (auditory oddball task) and DTI data from 164 participants (controls, schizophrenia, bipolar).
- Extracted features: fMRI contrast maps and DTI fractional anisotropy (FA).
- Employed a multimodal CCA+ joint ICA fusion technique for integrated analysis.
Main Results:
- Both patient groups showed prefrontal cortex and thalamus dysfunction, and reduced white matter integrity in specific tracts.
- Schizophrenia and bipolar disorder were distinguished by functional differences in medial frontal/visual cortex and specific white matter tracts.
- Shared abnormalities were observed in prefrontal-thalamic white matter integrity and frontal brain mechanisms.
Conclusions:
- Combined fMRI and DTI reveal both shared and distinct neurobiological patterns in schizophrenia and bipolar disorder.
- The multimodal fusion method provides a robust framework for analyzing complex brain data.
- Findings contribute to understanding the neurobiological underpinnings differentiating these psychiatric conditions.
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