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A framework for quantifying node-level community structure group differences in brain connectivity networks.
- Source :
-
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention [Med Image Comput Comput Assist Interv] 2012; Vol. 15 (Pt 2), pp. 196-203. - Publication Year :
- 2012
-
Abstract
- We propose a framework for quantifying node-level community structures between groups using anatomical brain networks derived from DTI-tractography. To construct communities, we computed hierarchical binary trees by maximizing two metrics: the well-known modularity metric (Q), and a novel metric that measures the difference between inter-community and intra-community path lengths. Changes in community structures on the nodal level were assessed between generated trees and a statistical framework was developed to detect local differences between two groups of community structures. We applied this framework to a sample of 42 subjects with major depression and 47 healthy controls. Results showed that several nodes (including the bilateral precuneus, which have been linked to self-awareness) within the default mode network exhibited significant differences between groups. These findings are consistent with those reported in previous literature, suggesting a higher degree of ruminative self-reflections in depression.
- Subjects :
- Female
Humans
Image Enhancement methods
Male
Middle Aged
Reproducibility of Results
Sensitivity and Specificity
Algorithms
Brain anatomy & histology
Connectome methods
Diffusion Tensor Imaging methods
Image Interpretation, Computer-Assisted methods
Imaging, Three-Dimensional methods
Nerve Net anatomy & histology
Subjects
Details
- Language :
- English
- Volume :
- 15
- Issue :
- Pt 2
- Database :
- MEDLINE
- Journal :
- Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
- Publication Type :
- Academic Journal
- Accession number :
- 23286049
- Full Text :
- https://doi.org/10.1007/978-3-642-33418-4_25