Multi-Objective Cognitive Model: a Supervised Approach for Multi-subject fMRI Analysis
- 185 Downloads
In order to decode human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with independent optimization approach, where the best solution of each step may not be optimum for the next steps. For tackling the mentioned issue, this paper introduces Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective function for MVP analysis rather than just using those disjoint steps. For solving the integrated problem, we proposed a customized multi-objective optimization approach, where all possible solutions are firstly generated, and then our method ranks and selects the robust solutions as the final results. Empirical studies confirm that the proposed method can generate superior performance in comparison with other techniques.
KeywordsMulti-objective cognitive model fMRI analysis Multivariate pattern Multi-objective optimization
This work was supported in part by the National Natural Science Foundation of China (61422204 and 61473149), and NUAA Fundamental Research Funds (NE2013105).
Compliance with Ethical Standards
Conflict of interests
Muhammad Yousefnezhad and Daoqiang Zhang declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Bennett, C.M., Baird, A., Miller, M.B., Wolfrod, G.L. (2009). Neural correlates of interspieces perspective taking in the post-mortem atlantic salmon: an argument for multiple comparisons correction. Human Brain Mapping, 1, 1995.Google Scholar
- Bradley, P.S., & Mangasarian, O.L. (1998). Feature selection via concave minimization and support vector machines. In 15th international conference on machine learning (ICML) (Vol. 98, pp. 82–90). Association for computing machinery (ACM). Madison, Wisconsin, USA.Google Scholar
- Cai, M.B., Schuck, N.W., Pillow, J.W., Niv, Y. (2016). A bayesian method for reducing bias in neural representational similarity analysis. In Advances in neural information processing systems (NIPS) (pp. 4951–4959).Google Scholar
- Chen, P.H., Chen, J., Yeshurun, Y., Hasson, U., Haxby, J., Ramadge, P.J. (2015). A reduced-dimension fmri shared response model. In 28th advances in neural information processing systems (NIPS-15) (pp. 460–468). Advances in neural information processing systems (NIPS). Montréal, Canada.Google Scholar
- Chen, P.H., Guntupalli, J.S., Haxby, J.V., Ramadge, P.J. (2014). Joint svd-hyperalignment for multi-subject fmri data alignment. In 24th international workshop on machine learning for signal processing (MLSP) (pp. 1–6). Reims, France: IEEE.Google Scholar
- Chen, P.H., Zhu, X., Zhang, H., Turek, J.S., Chen, J., Willke, T.L., Hasson, U., Ramadge, P.J. (2016). A convolutional autoencoder for multi-subject fmri data aggregation. In 29th workshop of representation learning in artificial and biological neural networks. Advances in neural information processing systems (NIPS). Barcelona, Spain.Google Scholar
- Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.Google Scholar
- Lorbert, A., & Ramadge, P.J. (2012). Kernel hyperalignment. In 25th advances in neural information processing systems (NIPS-12). Advances in neural Information Processing Systems (NIPS) (pp. 1790–1798). Harveys, Lake Tahoe.Google Scholar
- Penny, W.D., Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E. (2011). Statistical parametric mapping: the analysis of functional brain images. Academic Press. ISBN: 978-0-12-372560-8.Google Scholar
- Xu, H., Lorbert, A., Ramadge, P.J., Guntupalli, J.S., Haxby, J.V. (2012). Regularized hyperalignment of multi-set fmri data. In Statistical signal processing workshop (SSP) (pp. 229–232). Ann Arbor, USA: IEEE.Google Scholar
- Yousefnezhad, M., & Zhang, D. (2016). Decoding visual stimuli in human brain by using anatomical pattern analysis on fmri images. In 8th international conference on brain inspired cognitive systems (BICS’16) (pp. 47–57). Beijing: Springer.Google Scholar
- Yousefnezhad, M., & Zhang, D. (2017). Local discriminant hyperalignment for multi-subject fmri data alignment. In 34th AAAI conference on artificial intelligence (AAAI-17). Association for the advancement of artificial intelligence (AAAI). San Francisco, California, USA.Google Scholar
- Yousefnezhad, M., & Zhang, D. (2017). Multi-region neural representation: a novel model for decoding visual stimuli in human brains. In 17th SIAM international conference on data mininig (SDM-17). Society for industrial and applied mathematics (SIAM). Houston, Texas, USA.Google Scholar
- Zitzler, E., & Künzli, S. (2004). Indicator-based selection in multiobjective search. In International conference on parallel problem solving from nature (pp. 832–842). Birmingham: Springer.Google Scholar