Fuzzy-based Description of Computational Complexity of Central Nervous Systems

• Autorzy: Prokopowicz Piotr , Mikołajewski Dariusz , Tyburek Krzysztof , Kotlarz Piotr

Identyfikatory

DOI

10.26636/jtit.2020.145620

• Języki publikacji: EN

Abstrakty

EN

Computational intelligence algorithms are currently capable of dealing with simple cognitive processes, but still remain inefficient compared with the human brain’s ability to learn from few exemplars or to analyze problems that have not been defined in an explicit manner. Generalization and decision-making processes typically require an uncertainty model that is applied to the decision options while relying on the probability approach. Thus, models of such cognitive functions usually interact with reinforcement-based learning to simplify complex problems. Decision-makers are needed to choose from the decision options that are available, in order to ensure that the decision-makers’ choices are rational. They maximize the subjective overall utility expected, given by the outcomes in different states and weighted with subjective beliefs about the occurrence of those states. Beliefs are captured by probabilities and new information is incorporated using the Bayes’ law. Fuzzy-based models described in this paper propose a different – they may serve as a point of departure for a family of novel methods enabling more effective and neurobiologically reliable brain simulation that is based on fuzzy logic techniques and that turns out to be useful in both basic and applied sciences. The approach presented provides a valuable insight into understanding the aforementioned processes, doing that in a descriptive, fuzzy-based manner, without presenting a complex analysis.

Słowa kluczowe

EN

cognitive deficit; cognitive functions; computational simulation; fuzzy descriptors

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2020
• Tom: nr 3
• Strony: 57--66
• Opis fizyczny: Bibliogr. 47 poz., fot.

Twórcy

autor

Prokopowicz Piotr

• Institute of Computer Science, Kazimierz Wielki University, Kopernika 1, 85-074 Bydgoszcz, Poland

autor

Mikołajewski Dariusz

• Institute of Computer Science, Kazimierz Wielki University, Kopernika 1, 85-074 Bydgoszcz, Poland

autor

Tyburek Krzysztof

• Institute of Computer Science, Kazimierz Wielki University, Kopernika 1, 85-074 Bydgoszcz, Poland

autor

Kotlarz Piotr

• Institute of Computer Science, Kazimierz Wielki University, Kopernika 1, 85-074 Bydgoszcz, Poland

Bibliografia

• [1] J. Olszak, M. Radom, and P. Formanowicz, „Some aspects of modeling and analysis of complex biological systems using time Petri nets", Bull. of the Polish Acad. of Sci.: Tech. Sci., vol. 66, no. 1, pp. 67-78, 2018 (DOI: 10.24425/119060).
• [2] D. He, Z. Zheng, and L. Stone, „Detecting generalized synchrony: An improved approach", Phys. Rev. E, vol. 67, no. 2, 026223, 2003 (DOI: 10.1103/PhysRevE.67.026223).
• [3] E. W. Lang, A.M. Toma, I. R. Keck, J. M. Gerriz-Scez, and C. G. Puntonet, „Brain connectivity analysis: A short survey", Comput. Intell. and Neurosci., vol. 2012, article ID 412512, pp. 1-21, 2012 (DOI: 10.1155/2012/412512).
• [4] M. Krumin and S. Shoham, „Multivariate autoregressive modeling and granger causality analysis of multiple spike trains", Comput. Intell. and Neurosci., vol. 2010, article ID 752428, pp. 1-9, 2010 (DOI: 10.1155/2010/752428).
• [5] Human Connectome Project [Online]. Available: www.humanconnectomeproject.org
• [6] W. de Haan, E. C. W. van Straaten, A. A. Gouw, and C. J. Stam, „Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease", PLOS Comput. Biol., vol. 13, no. 9, e1005707, 2017 (DOI: 10.1371/journal.pcbi.1005707).
• [7] H. Niu, I. A lvarez-A lvarez, F. Guillen-Grima, and I. Aguinaga-Ontoso, „Prevalencia e incidencia de la enfermedad de Alzheimer en Europa: metaanalisis", Neurologia, vol. 32, no. 8, pp. 523-532, 2017 (DOI: 10.1016/j.nrl.2016.02.016).
• [8] J. R. Petrella, W. Hao, A. Rao, and P. M. Doraiswamy, „Computational causal modeling of the dynamic biomarker cascade in Alzheimer's disease", Comput. and Mathem. Methods in Med., vol. 2019, pp. 1-8, 2019 (DOI: 10.1155/2019/6216530).
• [9] P. D. Roberts, A. Spiros, and H. Geerts, „Simulations of symptomatic treatments for Alzheimer's disease: Computational analysis of pathology and mechanisms of drug action", Alzheimer's Res. & Ther., vol. 4, no. 6, 2012 (DOI: 10.1186/alzrt153).
• [10] S. J. B. Vos et al., „Prevalence and prognosis of Alzheimer's disease at the mild cognitive impairment stage", Brain, vol. 138, no. 5, pp. 1327-1338, 2015 (DOI: 10.1093/brain/awv029).
• [11] J. Weller and A. Budson, „Current understanding of Alzheimer's disease diagnosis and treatment", F1000Res., vol. 7, 1161, 2018 (DOI: 10.12688/f1000research.14506.1).
• [12] F. Zhu et al., „COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease", Scient. Rep., vol. 6, no. 1, 2016 (DOI: 10.1038/srep34567).
• [13] Z. Hu, L.Wu, J. Jia, and Y. Han, „Advances in longitudinal studies of amnestic mild cognitive impairment and Alzheimer's disease based on multi-modal MRI techniques", Neurosci. Bull., vol. 30, no. 2, pp. 198-206, 2014 (DOI: 10.1007/s12264-013-1407-y).
• [14] V. Valkanova, K. P. Ebmeier, and C. Allan, „Depression is linked to dementia in older adults", Practitioner, vol. 261, no. 1800, pp. 11-15, 2017 [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/29023080/
• [15] B. S. Diniz, M. A. Butters, S. M. Albert, M. A. Dew, and C. F. Reynolds, „Late-life depression and risk of vascular dementia and Alzheimer's disease: systematic review and meta-analysis of community-based cohort studies", British J. of Psychiatry, vol. 202, no. 5, pp. 329-335, 2013 (DOI: 10.1192/bjp.bp.112.118307).
• [16] W. Liao et al., „The characteristic of cognitive dysfunction in remitted late life depression and amnestic mild cognitive impairment", Psychiatry Res., vol. 251, pp. 168-175, 2017 (DOI: 10.1016/j.psychres.2017.01.024).
• [17] G. M. McKhann et al., „The diagnosis of dementia due to Alzheimer's disease: Recommendations from the national institute on aging-Alzheimer's association workgroups on diagnostic guidelines for Alzheimer's disease". Alzheimer's & Dement., vol. 7, no. 3, pp. 263-269, 2011 (DOI: 10.1016/j.jalz.2011.03.005).
• [18] P. Yu et al., Enriching amnestic mild cognitive impairment populations for clinical trials: optimal combination of biomarkers to predict conversion to dementia", J. of Alzheimer's Dis., vol. 32, no. 2, pp. 373-385, 2012 (DOI: 10.3233/JAD-2012-120832).
• [19] Y. Chow et al., „Limbic brain structures and burnout-A systematic review", Adv. in Medical Sci., vol. 63, no. 1, pp. 192-198 2018 (DOI: 10.1016/j.advms.2017.11.004).
• [20] G. Q. Chen et al., „Neuroimaging basis in the conversion of aMCI patients with APOE-e4 to AD: study protocol of a prospective diagnostic trial", BMC Neurol., vol. 16, no. 1, 2016 (DOI: 10.1186/s12883-016-0587-2).
• [21] J. Chen, Z. Zhang, and S. Li, „Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?", Neurosci. Bull., vol. 31, no. 1, pp. 128-140 2015 (DOI: 10.1007/s12264-014-1490-8).
• [22] J. Haworth, M. Phillips, M. Newson, P. J. Rogers, A. Torrens-Burton, and A. Tales, „Measuring information processing speed in mild cognitive impairment: clinical versus research dichotomy", J. of Alzheimer's Dis., vol. 51, no. 1, pp. 263-275, 2016 (DOI: 10.3233/JAD-150791).
• [23] Y. Yu, W. Zhao, S. Li, and C. Yin, MRI-based comparative study of different mild cognitive impairment subtypes: protocol for an observational case-control study", BMJ Open, vol. 7, no. 3, 2017 (DOI:10.1136/bmjopen-2016-013432).
• [24] L. C. Lowe, C. Gaser, and K. Franke, „The effect of the apoe genotype on individual brainage in normal aging, mild cognitive impairment, and Alzheimer's disease", PLoS ONE, vol. 11, no. 7, e0157514, pp. 1-25, 2016 (DOI: 10.1371/journal.pone.0157514).
• [25] C. A. Frantzidis et al., „Functional disorganization of small-world brain networks in mild Alzheimer's disease and amnestic mild cognitive impairment: an EEG study using relative wavelet entropy (RWE)", Front. in Aging Neurosci., vol. 6, 2014 (DOI: 10.3389/fnagi.2014.00224).
• [26] V. I. Zilidou et al., „Functional re-organization of cortical networks of senior citizens after a 24-week traditional dance program", Front. in Aging Neurosci., vol. 10, 2018 (DOI: 10.3389/fnagi.2018.00422).
• [27] H. J. Li et al., „Toward systems neuroscience in mild cognitive impairment and Alzheimer's disease: A meta-analysis of 75 FMRI studies", Human Brain Mapp., vol. 36, no. 3, pp. 1217-1232, 2015 (DOI: 10.1002/hbm.22689).
• [28] K. Zeng, Y. Wang, G. Ouyang, Z. Bian, L. Wang, and X. Li, „Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes", Front. in Comput. Neurosci., vol. 9, p. 133, 2015 (DOI: 10.3389/fncom.2015.00133).
• [29] F. Miraglia, F. Vecchio, P. Bramanti, and P. M. Rossini, „EEG characteristics in „eyes-open" versus „eyes-closed" conditions: Smallworld network architecture in healthy aging and age-related brain degeneration", Clin. Neurophysiol., vol. 127, no.2, pp. 1261-1268, 2016 (DOI: 10.1016/j.clinph.2015.07.040).
• [30] B. Zhang et al., „Characterizing topological patterns in amnestic mild cognitive impairment by quantitative water diffusivity", J. of Alzheimer's Dis., vol. 43, no. 2, pp. 687-697, 2014 (DOI: 10.3233/JAD-140882).
• [31] J. M. Czerniak, H. Zarzycki, and D. Ewald, „AAO as a new strategy in modeling and simulation of constructional problems optimization", Simul. Modell. Pract. and Theory, vol. 76, pp. 22-33, 2017 (DOI: 10.1016/j.simpat.2017.04.001).
• [32] X. Li and Z. J. Zhang, „Neuropsychological and neuroimaging characteristics of amnestic mild cognitive impairment subtypes: A selective overview", CNS Neurosci. & Ther., vol. 21, no. 10, pp. 776-783, 2015 (DOI: 10.1111/cns.12391).
• [33] D. V. Nguyen, J. R. Li, D. Grebenkov, and D. Le Bihan, „A finite elements method to solve the Bloch-Torrey equation applied to diffusion magnetic resonance imaging", J. of Comput. Phys., vol. 263, pp. 283-302, 2014 (DOI: 10.1016/j.jcp.2014.01.009).
• [34] D. Zhang et al., „Multimodal classification of Alzheimer's disease and mild cognitive impairment", NeuroImage, vol. 55, no. 3, pp. 856-867, 2011 (DOI: 10.1016/j.neuroimage.2011.01.008).
• [35] P. Prokopowicz and D. Mikołajewski, „Fuzzy-based computational simulations of brain functions - preliminary concept", Bio-Algorith. and Med-Syst., vol. 12, no. 3, 2016
• [36] P. Prokopowicz and D. Ślęzak, „Ordered fuzzy numbers: Sources and intuitions", in Theory and Applications of Ordered Fuzzy Numbers: A Tribute to Professor Witold Kosiński, P. Prokopowicz, J. Czerniak, D. Mikołajewski, Ł. Apiecionek, and D. Ślęzak, Eds. Springer, 2017, pp. 47-56 (DOI: 10.1007/978-3-319-59614-3 3).
• [37] P. Prokopowicz and D. Ślęzak, „Ordered fuzzy numbers: Definitions and operations", in Theory and Applications of Ordered Fuzzy Numbers: A Tribute to Professor Witold Kosiński, P. Prokopowicz, J. Czerniak, D. Mikołajewski, Ł. Apiecionek, and D. Ślęzak, Eds. Springer, 2017, pp. 57-79 (DOI: 10.1007/978-3-319-59614-3 4).
• [38] P. Prokopowicz, „Processing direction with ordered fuzzy numbers", in Theory and Applications of Ordered Fuzzy Numbers: A Tribute to Professor Witold Kosiński, P. Prokopowicz, J. Czerniak, D. Mikołajewski, Ł. Apiecionek, and D. Ślęzak, Eds. Springer, 2017, pp. 81-98 (DOI: 10.1007/978-3-319-59614-3 5).
• [39] P. Prokopowicz, „The use of ordered fuzzy numbers for modeling changes in dynamic processes", Inform. Sciences, vol. 470, pp. 1-14, 2019 (DOI: 10.1016/j.ins.2018.08.0)
• [40] J. M. Czerniak and H. Zarzycki, „Artificial acari optimization as a new strategy for global optimization of multimodal functions", J. of Comput. Sci., vol. 22, pp. 209-227 2017 (DOI: 10.1016/j.jocs.2017.05.028).
• [41] J. Masiak, M. Kuspit, W. Surtel, and M. J. Jarosz, „Stress, coping styles and personality tendencies of medical students of urban and rural origin", Ann. of Agricul. and Environ. Med., vol. 21, no. 1, pp. 189-193, 2014 [Online]. Available: http://www.aaem.pl/pdf-72083-9310?filename=Stress_%20coping%20styles%20and.pdf
• [42] G. M. Wojcik et al., „Mapping the human brain in frequency band analysis of brain cortex electroencephalographic activity for selected psychiatric disorders", Front. in Neuroinform., vol. 12, 2018 (DOI: 10.3389/fninf.2018.00073).
• [43] G. M. Wojcik et al., „New protocol for quantitative analysis of brain cortex electroencephalographic activity in patients with psychiatric disorders", Front. in Neuroinform., vol. 12, 2018 (DOI: 10.3389/fninf.2018.00027).
• [44] P. Prokopowicz and D. Mikołajewski, „OFN-based brain function modeling", in Theory and Applications of Ordered Fuzzy Numbers: A Tribute to Professor Witold Kosiński, P. Prokopowicz, J. Czerniak, D. Mikołajewski, .. Apiecionek, and D. Ślęzak, Eds. Springer, 2017, pp. 303-322 (DOI: 10.1007/978-3-319-59614-3_18).
• [45] P. Serkies, „A novel predictive fuzzy adaptive controller for a twomass drive system", Bull. of the Polish Acad. of Sci.: Tech. Sci., vol. 66, no. 1, pp. 37-47, 2018 (DOI: 10.24425/119057).
• [46] A. Piegat and M. Plucinski, „Fuzzy number division and the multigranularity phenomenon", Bull. of the Polish Acad. of Sci., Tech. Sci., vol. 65, no. 4, pp. 497-511, 2017 (DOI: 10.1515/bpasts-2017-0055).
• [47] M. Jasiński, P. Majtczak, and A. Malinowski, „Fuzzy logic in decision support system as a simple Human/Internet of Things interface for shunt active power filter", Bull. of the Polish Acad. of Sci., Tech. Sci., vol. 64, no. 4, pp. 877-886, 2016 (DOI: 10.1515/bpasts-2016-0096).