Hierarchical multi-resolution mesh networks for brain decoding
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Human brain is supposed to process information in multiple frequency bands. Therefore, we can extract diverse information from functional Magnetic Resonance Imaging (fMRI) data by processing it at multiple resolutions. We propose a framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs), which establishes a set of brain networks at multiple resolutions of fMRI signal to represent the underlying cognitive process. Our framework, first, decomposes the fMRI signal into various frequency subbands using wavelet transform. Then, a brain network is formed at each subband by ensembling a set of local meshes. Arc weights of each local mesh are estimated by ridge regression. Finally, adjacency matrices of mesh networks obtained at different subbands are used to train classifiers in an ensemble learning architecture, called fuzzy stacked generalization (FSG). Our decoding performances on Human Connectome Project task-fMRI dataset reflect that HMMNs can successfully discriminate tasks with 99% accuracy, across 808 subjects. Diversity of information embedded in mesh networks of multiple subbands enables the ensemble of classifiers to collaborate with each other for brain decoding. The suggested HMMNs decode the cognitive tasks better than a single classifier applied to any subband. Also mesh networks have a better representation power compared to pairwise correlations or average voxel time series. Moreover, fusion of diverse information using FSG outperforms fusion with majority voting. We conclude that, fMRI data, recorded during a cognitive task, provide diverse information in multi-resolution mesh networks. Our framework fuses this complementary information and boosts the brain decoding performances obtained at individual subbands.
KeywordsfMRI data analysis Brain decoding Wavelet decomposition Mesh networks Hierarchical models Ensemble models
Compliance with Ethical Standards
Conflict of interests
Itir Onal Ertugrul, Mete Ozay and Fatos Yarman Vural declare that they have no conflicts of interest.
This article does not contain any studies with human participants or animals performed by any of the authors. Data used in this study were previously collected and archived in a data bank.
- Alkan, S., & Yarman-Vural, F.T. (2015). Ensembling brain regions for brain decoding. In 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 2948–2951).Google Scholar
- Barch, D.M., Burgess, G.C., Harms, M.P., Petersen, S.E., Schlaggar, B.L., Corbetta, M., Glasser, M.F., Curtiss, S., Dixit, S., Feldt, C., Nolan, D., Bryant, E., Hartley, T., Footer, O., Bjork, J.M., Poldrack, R., Smith, S., Johansen-Berg, H., Snyder, A.Z., & Essen, D.C.V. (2013). Function in the human connectome: task-fmri and individual differences in behavior. NeuroImage, 80, 169–189.CrossRefPubMedPubMedCentralGoogle Scholar
- Behroozi, M., Daliri, M.R., & Boyaci, H. (2011). Statistical analysis methods for the fmri data. Basic and Clinical Neuroscience, 2(4), 67–74.Google Scholar
- Binder, J.R., Gross, W.L., Allendorfer, J.B., Bonilha, L., Chapin, J., Edwards, J.C., Grabowski, T.J., Langfitt, J.T., Loring, D.W., Lowe, M.J., Koenig, K., Morgan, P.S., Ojemann, J.G., Rorden, C., Szaflarski, J.P., Tivarus, M.E., & Weaver, K.E. (2011). Mapping anterior temporal lobe language areas with fmri: a multicenter normative study. NeuroImage, 54(2), 1465–1475.CrossRefPubMedGoogle Scholar
- Kauppi, J.P., Jääskeläinen, I.P., Sams, M., & Tohka, J. (2010). Inter-subject correlation of brain hemodynamic responses during watching a movie: localization in space and frequency. Frontiers in Neuroinformatics, 4(5).Google Scholar
- Kauppi, J.P., Pajula, J., & Tohka, J. (2014). A versatile software package for inter-subject correlation based analyses of fmri. Frontiers in Neuroinformatics, 8(2).Google Scholar
- Kuncheva, L.I. (2004). Combining pattern classifiers: methods and algorithms. Wiley.Google Scholar
- Mandelbrot, B.B. (1977). The fractal geometry of nature. New York: Springer.Google Scholar
- Onal, I., Ozay, M., & Yarman Vural, F. (2015a). Modeling voxel connectivity for brain decoding. In PRNI, Stanford, CA, USA (pp. 5–8).Google Scholar
- Onal, I., Ozay, M., & Yarman Vural, F.T. (2015b). Functional mesh model with temporal measurements for brain decoding. In Engineering in medicine and biology society (EMBC), 2015 37th annual international conference of the IEEE (pp. 2624–2628): IEEE.Google Scholar
- Richiardi, J., Achard, S., Bunke, H., & De Ville, D.V. (2013). Machine learning with brain graphs: predictive modeling approaches for functional imaging in systems neuroscience. IEEE Signal Processing Magazine.Google Scholar