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

Neuroimage
|June 7, 2011
PubMed

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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