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Connectivity-Based Brain Parcellation: A Connectivity-Based Atlas for Schizophrenia Research
Qi Wang1, Rong Chen2, Joseph JaJa3
1Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742, USA. qiwang321@gmail.com.
Neuroinformatics
|October 5, 2015
Summary
This study introduces a novel connectivity-based brain parcellation method, outperforming standard atlases in classifying schizophrenia. The new approach reveals significant connectivity differences between schizophrenic and normal subjects.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Brain Connectivity Analysis
Background:
- Standard brain atlases use manual delineations or predefined regions, which may not be optimal for connectivity analysis.
- Existing methods often overlook voxel-wise connectivity patterns crucial for understanding brain networks.
Purpose of the Study:
- To develop a novel method for parcellating the brain into regions of interest based on functional connectivity.
- To evaluate the efficacy of this connectivity-based atlas compared to a standard anatomical atlas in distinguishing between schizophrenic and normal subjects.
Main Methods:
- Formulated brain parcellation as a graph-cut problem, solved using a novel multi-class Hopfield network algorithm.
- Applied the connectivity-based parcellation method to diffusion tensor imaging data from schizophrenia patients and healthy controls.
Main Results:
- The connectivity-based atlas demonstrated superior classification performance in distinguishing schizophrenic from normal subjects compared to a standard anatomical atlas.
- Significant systematic differences in connectivity patterns were identified between the two groups using the developed atlas.
Conclusions:
- Connectivity-based brain parcellation offers a more effective approach for neuroimaging analysis, particularly in psychiatric research.
- This method provides a more refined understanding of brain network alterations in conditions like schizophrenia.
