BrainParcel: A Brain Parcellation Algorithm for Cognitive State Classification
In this study, we propose a novel brain parcellation algorithm, called BrainParcel. BrainParcel defines a set of supervoxels by partitioning a voxel level brain graph into a number of subgraphs, which are assumed to represent “homogeneous” brain regions with respect to a predefined criteria. Aforementioned brain graph is constructed by a set of local meshes, called mesh networks. Then, the supervoxels are obtained using a graph partitioning algorithm. The supervoxels form partitions of brain as an alternative to anatomical regions (AAL). Compared to AAL, supervoxels gather functionally and spatially close voxels. This study shows that BrainParcel can achieve higher accuracies in cognitive state classification compared to AAL. It has a better representation power compared to similar brain segmentation methods, reported the literature.
KeywordsfMRI Brain partitioning Mesh model
This project is supported by TUBITAK under grant number 116E091. We thank UMRAM Center of Bilkent University for opening their facilities to collect fMRI dataset. We also thank to Dr. Itir Onal Ertugrul and Dr. Orhan Firat for their contribution and effort of data collection.
- 1.Alkan, S., Yarman-Vural, F.T.: Ensembling brain regions for brain decoding. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2948–2951. IEEE (2015)Google Scholar
- 6.Flandin, G., Kherif, F., Pennec, X., Malandain, G., Ayache, N., Poline, J.-B.: Improved detection sensitivity in functional MRI data using a brain parcelling technique. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 467–474. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45786-0_58CrossRefGoogle Scholar
- 7.Flandin, G., Kherif, F., Pennec, X., Riviere, D., Ayache, N., Poline, J.B.: Parcellation of brain images with anatomical and functional constraints for fmri data analysis, pp. 907–910 (2002)Google Scholar
- 10.Moğultay, H., Alkan, S., Yarman-Vural, F.T.: Classification of fMRI data by using clustering. In: 23th Signal Processing and Communications Applications Conference, SIU, pp. 2381–2383. IEEE (2015)Google Scholar
- 13.Onal, I., Ozay, M., Yarman-Vural, F.T.: Functional mesh model with temporal measurements for brain decoding. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2624–2628. IEEE (2015)Google Scholar
- 14.Onal, I., Ozay, M., Yarman-Vural, F.T.: Modeling voxel connectivity for brain decoding. In: International Workshop on Pattern Recognition in NeuroImaging (PRNI), pp. 5–8. IEEE (2015)Google Scholar
- 15.Ozay, M., Öztekin, I., Öztekin, U., Yarman-Vural, F.T.: Mesh learning for classifying cognitive processes (2012). arXiv preprint arXiv:1205.2382
- 18.Thirion, B., Varoquaux, G., Dohmatob, E., Poline, J.B.: Which fMRI clustering gives good brain parcellations? Front. Neurosci. 8 (2014)Google Scholar
- 21.Wang, J., Wang, H.: A supervoxel-based method for groupwise whole brain parcellation with resting-state fMRI data. Front. Hum. Neurosci. 10 (2016)Google Scholar