Automatic Recognition of Resting State fMRI Networks with Dictionary Learning
Resting state functional magnetic resonance imaging (rs-fMRI) is a functional neuroimaging technique that investigates the spatially remote yet functionally linked neuronal coactivation patterns of the brain at rest. Non-invasiveness and task-free characteristics of rs-fMRI make it particularly suitable for aging, pediatric and clinical population. Researchers typically follow a source separation strategy to efficiently reconstruct the concurrent interacting resting state networks (RSN) from a myriad of whole brain fMRI signals. RSNs are currently identified by visual inspection with prior knowledge of spatial clustering of RSNs, as the variability and spatial overlapping nature of RSNs combined with presence of various sources of noise make automatic identification of RSNs a challenging task. In this study, we have developed an automated recognition algorithm to classify all the distinct RSNs. First, in contrast to traditional single level decomposition, a multi-level deep sparse matrix factorization-based dictionary leaning strategy was used to extract hierarchical features from the data at each level. Then we used maximum likelihood estimates of these spatial features using Kullback-Leibler divergence to perform the recognition of RSNs. Experimental results confirmed the effectiveness of our proposed approach in accurately classifying all the RSNs.
KeywordsResting state networks Dictionary learning fMRI KL divergence
This study was supported by the University of Texas System Brain Research grant and the National Institutes of Health (NIH) under award number R03 DC013990. We thank Kanish Goel and Beiming Cao for their valuable inputs. We also thank Dr. Bart Rypma and the volunteering participants for being of help in data collection.
- 12.Eavani, H., et al.: Sparse dictionary learning of resting state fMRI networks. In: International Workshop on Pattern Recognition in NeuroImaging International Workshop on Pattern Recognition in Neuroimaging, pp. 73–76 (2012)Google Scholar
- 13.Dash, D., Sao, A. K., Wang, J., Biswal, B.: How many fMRI scans are necessary and sufficient for resting brain connectivity analysis? In: IEEE 6th Global Conference on Signal and Information Processing (GlobalSIP) (2018)Google Scholar
- 15.Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML, pp. 689–696. ACM, New York (2009)Google Scholar
- 16.Dash, D., Abrol, V., Sao, A.K., Biswal, B.: The model order limit: deep sparse factorization for resting brain. In: IEEE 15th International Symposium on Biomedical Imaging (ISBI), pp. 1244–1247 (2018)Google Scholar
- 18.Zhao, Y., et al.: Automatic recognition of fMRI-derived functional networks using 3D convolutional neural networks. IEEE Trans. Biomed. Eng. 65, 1975–1984 (2017)Google Scholar
- 21.The ADHD-200 Consortium. The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2018)Google Scholar
- 22.Dash, D., Abrol, V., Sao, A. K., Biswal, B.: Spatial sparsification and low rank projection for fast analysis of multi-subject resting state fMRI data. In: IEEE 15th International Symposium on Biomedical Imaging (ISBI), pp. 1280–1283 (2018)Google Scholar
- 23.SPM Homepage. https://www.fil.ion.ucl.ac.uk/spm. Accessed 29 June 2018
- 24.Abrol, V., Sharma, P., Sao, A.K.: Fast exemplar selection algorithm for matrix approximation and representation: a variant oasis algorithm. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4436–4440 (2017)Google Scholar