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Artificial Intelligence Applied to Spatial Cognition Assessment

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Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (IWINAC 2022)

Abstract

Spatial cognition is a function that strongly affects adaptation. This is particularly evident when it is impaired, as often happens after brain injury.

Neglect, or hemispatial visual neglect, is a dramatic consequence of right hemisphere damage that leads patient to ignore the left, controlateral part of the space. It is assessed with tasks and tests that require to direct attention on the whole visual field, both on left and right. Also in healthy people, spatial exploration is not perfectly symmetrical, as witnessed by the phenomenon called pseudo-neglect.

In recent years, these tools have been enhanced by new technological solutions, producing new data.

In this paper, we describe our attempt to use Artificial Intelligence for the assessment of spatial cognition starting from the enhanced version of the Baking Tray Task, the e-BTT.

Results indicate that Artificial Intelligence can be an effective method to analyze these new data thus leading to a more comprehensive assessment.

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References

  1. Abdi, H., Valentin, D., Edelman, B.: Neural Networks (No. 124). Sage, Thousand Oaks (1999)

    Google Scholar 

  2. Allen, G.L.: Spatial abilities, cognitive maps, and wayfinding. In: Wayfinding Behavior: Cognitive Mapping and Other Spatial Processes, p. 4680 (1999)

    Google Scholar 

  3. Alpaydin, E.: Machine Learning. MIT Press, Cambridge (2021)

    Google Scholar 

  4. Argiuolo, A., Ponticorvo, M.: E-TAN platform and E-baking tray task potentialities: new ways to solve old problems. In: PSYCHOBIT, September 2020

    Google Scholar 

  5. Avola, D., Cinque, L., Foresti, G.L., Mercuri, C., Pannone, D.: A practical framework for the development of augmented reality applications by using ArUco markers. In: International Conference on Pattern Recognition Applications and Methods, vol. 2, pp. 645–654. SciTePress, February 2016

    Google Scholar 

  6. Bartolomeo, P.: Visual neglect. Curr. Opin. Neurol. 20(4), 381–386 (2007)

    Article  Google Scholar 

  7. Bartolomeo, P., Thiebaut de Schotten, M., Doricchi, F.: Left unilateral neglect as a disconnection syndrome. Cereb. Cortex 17(11), 2479–2490 (2007)

    Article  Google Scholar 

  8. Becker, S.: Unsupervised learning procedures for neural networks. Int. J. Neural Syst. 2(01n02), 17–33 (1991)

    Article  Google Scholar 

  9. Bro, R., Smilde, A.K.: Principal component analysis. Anal. Methods 6(9), 2812–2831 (2014)

    Article  CAS  Google Scholar 

  10. Cerrato, A., Ponticorvo, M., Gigliotta, O., Bartolomeo, P., Miglino, O.: BTT-scan: uno strumento per la valutazione della negligenza spaziale unilaterale. Sistemi intelligenti 31(2), 253–270 (2019)

    Google Scholar 

  11. Cerrato, A., Ponticorvo, M.: Enhancing neuropsychological testing with gamification and tangible interfaces: the baking tray task. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds.) IWINAC 2017. LNCS, vol. 10338, pp. 147–156. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59773-7_16

    Chapter  Google Scholar 

  12. Cerrato, A., Ponticorvo, M., Gigliotta, O., Bartolomeo, P., Miglino, O.: The assessment of visuospatial abilities with tangible interfaces and machine learning. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds.) IWINAC 2019. LNCS, vol. 11486, pp. 78–87. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19591-5_9

    Chapter  Google Scholar 

  13. Cerrato, A., et al.: E-TAN, a technology-enhanced platform with tangible objects for the assessment of visual neglect: a multiple single-case study. Neuropsychol. Rehabil. 31(7), 1130–1144 (2021)

    Article  Google Scholar 

  14. Chollet, F., et al.: Keras. GitHub (2015). https://github.com/fchollet/keras

  15. Committeri, G., et al.: Neural bases of personal and extrapersonal neglect in humans. Brain 130(2), 431–441 (2007)

    Google Scholar 

  16. Facchin, A., Beschin, N., Daini, R.: Rehabilitation of right (personal) neglect by prism adaptation: a case report. Ann. Phys. Rehabil. Med. 60(3), 220–222 (2016)

    Article  Google Scholar 

  17. Gigliotta, O., Malkinson, T.S., Miglino, O., Bartolomeo, P.: Pseudoneglect in visual search: behavioral evidence and connectional constraints in simulated neural circuitry. Eneuro 4(6) (2017)

    Google Scholar 

  18. Guariglia, C., Antonucci, G.: Personal and extrapersonal space: a case of neglect dissociation. Neuropsychologia 30(11), 1001–1009 (1992)

    Article  CAS  Google Scholar 

  19. Halligan, P.W., Marshall, J.C.: Spatial compression in visual neglect: a case study. Cortex 27(4), 623–629 (1991)

    Article  CAS  Google Scholar 

  20. Halligan, P.W., Robertson, I.: Spatial Neglect: A Clinical Handbook for Diagnosis and Treatment. Psychology Press, Hove (2014)

    Book  Google Scholar 

  21. Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25566-3_40

    Chapter  Google Scholar 

  22. Jewell, G., McCourt, M.E.: Pseudoneglect: a review and meta-analysis of performance factors in line bisection tasks. Neuropsychologia 38(1), 93–110 (2000)

    Article  CAS  Google Scholar 

  23. McIntosh, R.D., Brodie, E.E., Beschin, N., Robertson, I.H.: Improving the clinical diagnosis of personal neglect: a reformulated comb and razor test. Cortex 36(2), 289–292 (2000)

    Article  CAS  Google Scholar 

  24. Menon, A., Mehrotra, K., Mohan, C.K., Ranka, S.: Characterization of a class of sigmoid functions with applications to neural networks. Neural Netw. 9(5), 819–835 (1996)

    Article  Google Scholar 

  25. Miglino, O., Ponticorvo, M., Bartolomeo, P.: Place cognition and active perception: a study with evolved robots. Connect. Sci. 21(1), 3–14 (2009)

    Article  Google Scholar 

  26. Pizzamiglio, L., et al.: Visual neglect for far and near extra-personal space in humans. Cortex 25(3), 471–477 (1989)

    Article  CAS  Google Scholar 

  27. Plummer, P., Morris, M.E., Dunai, J.: Assessment of unilateral neglect. Phys. Ther. 83(8), 732–740 (2003)

    Article  Google Scholar 

  28. Pitzalis, S., Spinelli, D., Zoccolotti, P.: Vertical neglect: behavioral and electrophysiological data. Cortex 33(4), 679–688 (1997)

    Article  CAS  Google Scholar 

  29. Purves, D., et al.: Cognitive Neuroscience, vol. 6, no. 4. Sinauer Associates Inc., Sunderland (2008)

    Google Scholar 

  30. Shelton, P.A., Bowers, D., Heilman, K.M.: Peripersonal and vertical neglect. Brain 113(1), 191–205 (1990)

    Article  Google Scholar 

  31. Sinaga, K.P., Yang, M.S.: Unsupervised K-means clustering algorithm. IEEE Access 8, 80716–80727 (2020)

    Article  Google Scholar 

  32. Somma, F., et al.: Further to the left: stress-induced increase of spatial pseudoneglect during the COVID-19 lockdown. Front. Psychol. 12 (2021)

    Google Scholar 

  33. Somma, F., et al.: Valutazione dello pseudoneglect mediante strumenti tangibili e digitali. Sistemi intelligenti 32(3), 533–549 (2020)

    Google Scholar 

  34. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    Google Scholar 

  35. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: KDD (2019)

    Google Scholar 

  36. Tham, K., Tegner, R.: The baking tray task: a test of spatial neglect. Neuropsychol. Rehabil. 6(1), 19–26 (1996)

    Article  CAS  Google Scholar 

  37. Urbanski, M., et al.: Négligence spatiale unilatérale: une conséquence dramatique mais souvent négligée des lésions de l’hémisphère droit. Revue Neurologique, 16 (2007)

    Google Scholar 

  38. Van Rossum, G., Drake, F.L., Jr.: Python tutorial. Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands (1995)

    Google Scholar 

  39. Wang, W., Huang, Y., Wang, Y., Wang, L.: Generalized autoencoder: a neural network framework for dimensionality reduction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 490–497 (2014)

    Google Scholar 

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Acknowledgements

Authors would like to thank Antonietta Argiulo and Federica Somma who were involved in data collection.

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More details about the model and the related code can be provided to whom is interested by emailing the authors.

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Correspondence to Michela Ponticorvo .

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Ponticorvo, M., Coccorese, M., Gigliotta, O., Bartolomeo, P., Marocco, D. (2022). Artificial Intelligence Applied to Spatial Cognition Assessment. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_40

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_40

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