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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
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Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with
Matthew J Hoptman1,2, Umit Tural1, Kelvin O Lim3
1Clinical Research Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA.
Brain Sciences
|February 25, 2022
Summary
This study integrated structural and functional brain connectivity in schizophrenia, finding links between multimodal connectivity scores and patient behaviors. These findings highlight the importance of examining combined connectivity for understanding schizophrenia network mechanisms.
Area of Science:
- Neuroscience
- Psychiatry
- Medical Imaging
Background:
- Schizophrenia is characterized by disrupted brain connectivity.
- Previous neuroimaging studies often examined structural or functional connectivity separately.
- Direct integration of multimodal neuroimaging data in schizophrenia remains underexplored.
Purpose of the Study:
- To integrate structural and functional connectivity data in the default mode network of schizophrenia patients.
- To investigate correlations between structural and functional connectivity measures.
- To explore the relationship between multimodal connectivity and clinical symptoms in schizophrenia.
Main Methods:
- Acquired resting-state fMRI and diffusion tensor imaging data from 29 schizophrenia patients and 25 healthy controls.
- Utilized the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) to estimate connectivity.
- Created multimodal connectivity scores (MCS) using principal component analysis and examined correlations with clinical measures.
Main Results:
- Identified consistent structural tracts between specific brain regions in the default mode network.
- Found significant correlations between structural and functional connectivity in schizophrenia patients, particularly involving frontotemporoparietal regions.
- Multimodal connectivity scores correlated with clinical symptoms, with higher connectivity linked to externalizing behaviors and lower connectivity to psychosis severity.
Conclusions:
- The FATCAT toolbox effectively integrates multimodal connectivity data.
- Conjoint analysis of structural and functional connectivity provides valuable insights into schizophrenia's network mechanisms.
- Integrated connectivity measures show promise for understanding the neurobiological underpinnings of diverse schizophrenia symptoms.
