Ant Colony Clustering for ROI Identification in Functional Magnetic Resonance Imaging
Brain network analysis using functional magnetic resonance imaging (fMRI) is a widely used technique. The first step of brain network analysis in fMRI is to detect regions of interest (ROIs). The signals from these ROIs are then used to evaluate neural networks and quantify neuronal dynamics. The tw...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2019-01-01
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| Series: | Computational Intelligence and Neuroscience |
| Online Access: | http://dx.doi.org/10.1155/2019/5259643 |
| Summary: | Brain network analysis using functional magnetic resonance imaging (fMRI) is a widely used technique. The first step of brain network analysis in fMRI is to detect regions of interest (ROIs). The signals from these ROIs are then used to evaluate neural networks and quantify neuronal dynamics. The two main methods to identify ROIs are based on brain atlas registration and clustering. This work proposes a bioinspired method that combines both paradigms. The method, dubbed HAnt, consists of an anatomical clustering of the signal followed by an ant clustering step. The method is evaluated empirically in both in silico and in vivo experiments. The results show a significantly better performance of the proposed approach compared to other brain parcellations obtained using purely clustering-based strategies or atlas-based parcellations. |
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| ISSN: | 1687-5265 1687-5273 |