Classifying Phenotypes Based on the Community Structure of Human Brain Networks
| Autor: | Paul M. Thompson, Joshua Faskowitz, Anvar Kurmukov, Neda Jahanshad, Boris A. Gutman, Marina Ananyeva, Leonid Zhukov, Yulia Dodonova |
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| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science 05 social sciences Community structure Human brain Modular design Phenotype 050105 experimental psychology 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure Neuroimaging medicine Connectome 0501 psychology and cognitive sciences business Cognitive impairment Neuroscience Classifier (UML) 030217 neurology & neurosurgery |
| Zdroj: | Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics ISBN: 9783319676746 GRAIL/MFCA/MICGen@MICCAI |
| DOI: | 10.1007/978-3-319-67675-3_1 |
| Popis: | Human anatomical brain networks derived from the analysis of neuroimaging data are known to demonstrate modular organization. Modules, or communities, of cortical brain regions capture information about the structure of connections in the entire network. Hence, anatomical changes in network connectivity (e.g., caused by a certain disease) should translate into changes in the community structure of brain regions. This means that essential structural differences between phenotypes (e.g., healthy and diseased) should be reflected in how brain networks cluster into communities. To test this hypothesis, we propose a pipeline to classify brain networks based on their underlying community structure. We consider network partitionings into both non-overlapping and overlapping communities and introduce a distance between connectomes based on whether or not they cluster into modules similarly. We next construct a classifier that uses partitioning-based kernels to predict a phenotype from brain networks. We demonstrate the performance of the proposed approach in a task of classifying structural connectomes of healthy subjects and those with mild cognitive impairment and Alzheimer’s disease. |
| Databáze: | OpenAIRE |
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