Abstract
Exploring information processing mechanisms in the human brain is of significant importance to the development of artificial intelligence and translational study. In particular, essential functions of the brain, ranging from perception to thinking, are studied, with the evolution of analytical strategies from a single aspect such as a single cognitive function or experiment to the increasing demands on the multi-aspect integration. Here we introduce a systematic approach to realize an integrated understanding of the brain mechanisms with respect to cognitive functions and brain activity patterns. Our approach is driven by a conceptual brain model, performs systematic experimental design and evidential type inference that are further integrated into the method of evidence combination and fusion computing, and realizes never-ending learning. It allows comparisons among various mechanisms on a specific brain-related disease by means of machine learning. We evaluate its ability from the brain functional connectivity perspective, which has become an analytical tool for exploring information processing of connected nodes between different functional interacting brain regions, and for revealing hidden relationships that link connectivity abnormalities to mental disorders. Results show that the potential relationships on clinical signs–cognitive functions–brain activity patterns have important implications for both cognitive assessment and personalized rehabilitation.
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References
Cuijpers, P.: Targets and outcomes of psychotherapies for mental disorders: an overview. World Psychiatry 18(3), 276–285 (2019). https://doi.org/10.1002/wps.20661
Zhong, N., Bradshaw, J.M., Liu, J., Taylor, J.G.: Brain informatics. IEEE Intell. Syst. 26(5), 16–21 (2011). https://doi.org/10.1109/MIS.2011.83
Zhong, N., Chen, J.: Constructing a new-style conceptual model of brain data for systematic brain informatics. IEEE Trans. Knowl. Data Eng. 24(12), 2127–2142 (2011). https://doi.org/10.1109/TKDE.2011.139
Kuai, H., Zhong, N.: The extensible data-brain model: architecture, applications and directions. J. Comput. Sci. 101103 (2020). https://doi.org/10.1016/j.jocs.2020.101103
Kuai, H., et al.: Multi-source brain computing with systematic fusion for smart health. Inf. Fusion 75, 150–167 (2021). https://doi.org/10.1016/j.inffus.2021.03.009
Kuai, H., Zhang, X., Yang, Y., Chen, J., Shi, B., Zhong, N.: Thinking-loop: the semantic vector driven closed-loop model for brain computing. IEEE Access 8, 4273–4288 (2020). https://doi.org/10.1109/ACCESS.2019.2963070
Liang, P., Jia, X., Taatgen, N.A., Borst, J.P., Li, K.: Activity in the fronto-parietal network indicates numerical inductive reasoning beyond calculation: An fMRI study combined with a cognitive model. Sci. Rep. 6(1), 1–10 (2016). https://doi.org/10.1038/srep25976
Liang, P., Jia, X., Taatgen, N.A., Zhong, N., Li, K.: Different strategies in solving series completion inductive reasoning problems: an fMRI and computational study. Int. J. Psychophysiol. 93(2), 253–260 (2014). https://doi.org/10.1016/j.ijpsycho.2014.05.006
Yang, Y., et al.: The functional architectures of addition and subtraction: network discovery using fMRI and DCM. Hum. Brain Mapp. 38(6), 3210–3225 (2017). https://doi.org/10.1002/hbm.23585
Yang, Y., et al.: Task and resting-state fMRI reveal altered salience responses to positive stimuli in patients with major depressive disorder. PLoS ONE 11(5), e0155092 (2016). https://doi.org/10.1371/journal.pone.0155092
Zhong, N., Liang, P.P., Qin, Y.L., Lu, S.F., Yang, Y.H., Li, K.C.: Neural substrates of data-driven scientific discovery: an fMRI study during performance of number series completion task. Sci. China Life Sci. 54(5), 466–473 (2011). https://doi.org/10.1007/s11427-011-4166-x
Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002). https://doi.org/10.1006/nimg.2001.0978
Zuo, X.N., et al.: Network centrality in the human functional connectome. Cereb. Cortex 22(8), 1862–1875 (2011). https://doi.org/10.1093/cercor/bhr269
Crescentini, C., Seyed-Allaei, S., De Pisapia, N., Jovicich, J., Amati, D., Shallice, T.: Mechanisms of rule acquisition and rule following in inductive reasoning. J. Neurosci. 31(21), 7763–7774 (2011). https://doi.org/10.1523/JNEUROSCI.4579-10.2011
Bubb, E.J., Metzler-Baddeley, C., Aggleton, J.P.: The cingulum bundle: anatomy, function, and dysfunction. Neurosci. Biobehav. Rev. 92, 104–127 (2018). https://doi.org/10.1016/j.neubiorev.2018.05.008
Balsters, J.H., Laird, A.R., Fox, P.T., Eickhoff, S.B.: Bridging the gap between functional and anatomical features of cortico-cerebellar circuits using meta-analytic connectivity modeling. Hum. Brain Mapp. 35(7), 3152–3169 (2014). https://doi.org/10.1002/hbm.22392
Cole, M.W., Reynolds, J.R., Power, J.D., Repovs, G., Anticevic, A., Braver, T.S.: Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16(9), 1348–1355 (2013). https://doi.org/10.1038/nn.3470
Cao, H., et al.: Cross-paradigm connectivity: reliability, stability, and utility. Brain Imaging Behav. 15(2), 614–629 (2020). https://doi.org/10.1007/s11682-020-00272-z
Mitchell, T., et al.: Never-ending learning. Commun. ACM 61(5), 103–115 (2018). https://doi.org/10.1145/3191513
Acknowledgements
This work is partially supported by grants from the JSPS Grants-in-Aid for Scientific Research of Japan (19K12123), the National Natural Science Foundation of China (61420106005), the National Basic Research Program of China (2014CB744600), the National Key Research and Development Project of China (2020YFC2007302) and the Key Research Project of Academy for Multi-disciplinary Studies of Capital Normal University (JCKXYJY2019019).
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Kuai, H., Chen, J., Tao, X., Imamura, K., Liang, P., Zhong, N. (2021). Exploring the Brain Information Processing Mechanisms from Functional Connectivity to Translational Applications. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds) Brain Informatics. BI 2021. Lecture Notes in Computer Science(), vol 12960. Springer, Cham. https://doi.org/10.1007/978-3-030-86993-9_10
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