Objective Methods of Mimics and Emotion Assessment in Diagnostic of Cognitive Disorders
https://doi.org/10.31550/1727-2378-2024-23-7-82-85
Abstract
Aim. To present the data on impaired mimics and, indirectly, emotions in patients with cognitive disorders, obtained with the methods of mimics function assessment, which are used in scientific research and clinical practice.
Key points. Cognitive disorders are common in patients with neurological deficits. In diagnostics of neural diseases, including those manifesting with cognitive deficit, it is essential to identify impaired mimic functions. The analysed studies show the association between neurodegenerative processes in certain brain structures and impaired emotions (via mimics).
Conclusion. There are currently insufficient data on the objective characteristics of impaired mimics in patients with cognitive deficits.
A unified clinical method should be developed for the assessment of mimics and emotions in order to identify cognitive disorders.
About the Authors
M. A. KhramchenkoRussian Federation
Krasnoyarsk
E. S. Denisova
Russian Federation
Krasnoyarsk
Yu. N. Ashikhmina
Russian Federation
Krasnoyarsk
S. V. Prokopenko
Russian Federation
Krasnoyarsk
References
1. Izard C. Human Emotions. M.: MSU; 1980. (in Russian).
2. Dubinskaya A.D., Kukshina A.A., Yurova O.V., Kotelnikova A.V. et al. Modern views on the relationship between psychoemotional state and the bioelectrical activity of facial muscles. Problems of balneology, physiotherapy, and exercise therapy. 2019;96(6):61–7. (in Russian). DOI: 10.17116/kurort20199606161
3. Livingstone S.R., Vezer E., McGarry L.M., Lang A.E. et al. Deficits in the mimicry of facial expressions in Parkinson's disease. Front. Psychol. 2016;7:780. DOI: 10.3389/fpsyg.2016.00780
4. Mosaleva E.I., Zhumzhanov I.M., Alekseenko P.V., Ismailova S.B. et al. Cognitive fluctuations associated with therapy in patients with Parkinson diseases. Siberian Medical Review. 2020;6:63–7. (in Russian). DOI: 10.20333/2500136-2020-6-63-67
5. Burton K.W., Kaszniak A.W. Emotional experience and facial expression in Alzheimer's disease. Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 2006;13(3–4):636–51. DOI: 10.1080/13825580600735085
6. Liu X., Hildebrandt A., Recio G., Sommer W. et al. Individual differences in the speed of facial emotion recognition show little specificity but are strongly related with general mental speed: psychometric, neural and genetic evidence. Front. Behav. Neurosci. 2017;11:149. DOI: 10.3389/fnbeh.2017.00149
7. Lin H., Müller-Bardorff M., Gathmann B., Brieke J. et al. Stimulus arousal drives amygdalar responses to emotional expressions across sensory modalities. Sci. Rep. 2020;10(1):1898. DOI: 10.1038/s41598-020-58839-1
8. Ho M.W.-R., Chien S.H.-L., Lu M.-K., Chen J.-Ch. et al. Impairments in face discrimination and emotion recognition are related to aging and cognitive dysfunctions in Parkinson's disease with dementia. Sci. Rep. 2020;10(1):4367. DOI: 10.1038/s41598-020-61310-w
9. Heilman K.M., Nadeau S.E. Emotional and neuropsychiatric disorders associated with Alzheimer's disease. Neurotherapeutics. 2022;19(1):99–116. DOI: 10.1007/s13311-021-01172-w
10. Bono A.D., Twaite J.T., Krch D., McCabe D.L. et al. Mood and emotional disorders associated with parkinsonism, Huntington disease, and other movement disorders. Handb. Clin. Neurol. 2021:183:175–96. DOI: 10.1016/B978-0-12-822290-4.00015-3
11. Donaghy P.C., Barnett N., Olsen K., Taylor J.-P. et al. Symptoms associated with Lewy body disease in mild cognitive impairment. Int. J. Geriatr. Psychiatry. 2017;32(11):1163–71. DOI: 10.1002/gps.4742
12. Liu S., Ma R., Luo Ya., Liu P. et al. Facial expression recognition and ReHo analysis in major depressive disorder. Front. Psychol. 2021:12:688376. DOI: 10.3389/fpsyg.2021.688376
13. Grabowski K., Rynkiewicz A., Lassalle A., Baron-Cohen S. et al. Emotional expression in psychiatric conditions: New technology for clinicians. Psychiatry Clin. Neurosci. 2019;73(2):50–62. Epub. 2018 Dec. 25. DOI: 10.1111/pcn.12799
14. Jiang Z., Harati S., Crowell A., Mayberg H.S. et al. Classifying major depressive disorder and response to deep brain stimulation over time by analyzing facial expressions. IEEE Trans. Biomed. Eng. 2021;68(2):664–72. DOI: 10.1109/TBME.2020.3010472
15. Khomchenkova A.A., Prokopenko S.V., Ismailova S.B. Clinical aspects of hypomimia in Parkinson’s disease. Neurology Bulletin. 2022;LIV(1):45–53. (in Russian). DOI: 10.17816/nb89531
16. Moreira H.S., Costa A.S., Machado A., Castro S.L. et al. Impaired recognition of facial and vocal emotions in mild cognitive impairment. J. Int. Neuropsychol. Soc. 2022;28(1):48–61. Epub. 2021 Mar. 4. DOI: 10.1017/S135561772100014X
17. Jiskoot L.C., Poos J.M., Vollebergh M.E., Franzen S. et al. Emotion recognition of morphed facial expressions in presymptomatic and symptomatic frontotemporal dementia, and Alzheimer's dementia. J. Neurol. 2021;268(1):102–13. Epub. 2020 Jul. 29. DOI: 10.1007/s00415-020-10096-y
18. Chen L.-Yu., Tsai T.-H., Ho A., Li Ch.-H. et al. Predicting neuropsychiatric symptoms of persons with dementia in a day care center using a facial expression recognition system. Aging (Albany NY). 2022;14(3):1280–91. DOI: 10.18632/aging.203869
19. Chen K.-H., Lwi S.J., Hua A.Y., Haase C.M. et al. Increased subjective experience of non-target emotions in patients with frontotemporal dementia and Alzheimer's disease. Curr. Opin. Behav. Sci. 2017:15:77–84. DOI: 10.1016/j.cobeha.2017.05.017
20. Haque R.U., Manzanares C.M., Brown L.N., Pongos A.L. et al. VisMET: a passive, efficient, and sensitive assessment of visuospatial memory in healthy aging, mild cognitive impairment, and Alzheimer's disease. Learn. Mem. 2019;26(3):93–100. DOI: 10.1101/lm.048124.118
21. Haque R.U., Pongos A.L., Manzanares C.M., Lah J.J. et al. Deep convolutional neural networks and transfer learning for measuring cognitive impairment using eye-tracking in a distributed tablet-based environment. IEEE Trans. Biomed. Eng. 2021;68(1):11–8. Epub. 2020 Dec. 21. DOI: 10.1109/TBME.2020.2990734
22. Mollahosseini A., Hasani B., Mahoor M.H. AffectNet: a database for facial expression, valence, and arousal computing in the wild. IEEE Trans. Affect. Comput. 2017;10(1):18–31. DOI: 10.1109/TAFFC.2017.2740923
23. Jiang Z., Seyedi S., Haque R.U., Pongos A.L. et al. Automated analysis of facial emotions in subjects with cognitive impairment. PLoS One. 2022;17(1):e0262527. DOI: 10.1371/journal.pone.0262527
24. Gerłowska J., Dmitruk K., Rejdak K. Facial emotion mimicry in older adults with and without cognitive impairments due to Alzheimer's disease. AIMS Neurosci. 2021;8(2):226–38. DOI: 10.3934/Neuroscience.2021012
25. Ma H.-I., Gunnery S.D., Stevenson M.T., Saint-Hilaire M. et al. Experienced facial masking indirectly compromises quality of life through stigmatization of women and men with Parkinson's disease. Stigma Health. 2019;4(4):462–72. DOI: 10.1037/sah0000168
26. Zarbakhsh P., Demirel H. Low-rank sparse coding and region of interest pooling for dynamic 3D facial expression recognition. Signal Image Video Process. 2018;12(5):1611–8. DOI: 10.1007/s11760-018-1318-5
27. Liu Yi., Wang Z., Yu G. The effectiveness of facial expression recognition in detecting emotional responses to sound interventions in older adults with dementia. Front. Psychol. 2021:12:707809. DOI: 10.3389/fpsyg.2021.707809
28. Borgomaneri S., Bolloni C., Sessa P., Avenanti A. Blocking facial mimicry affects recognition of facial and body expressions. PLoS One. 2020;15(2):e0229364. DOI: 10.1371/journal.pone.0229364
29. Perusquía-Hernández M., Ayabe-Kanamura S., Suzuki K. Human perception and biosignal-based identification of posed and spontaneous smiles. PLoS One. 2019;14(12):e0226328. DOI: 10.1371/journal.pone.0226328
30. Rozaliev V.L., Zaboleeva-Zotova A.V., Orlova Yu.A., Gusynin O.S. et al. Determination of emotional state of human: analysis of directions and possibilities of complex use of instrumental methods. Caspian Journal: Control and High Technologies. 2018;2(42):162–72. (in Russian).
31. Patel S., Oishi K., Wright A., Sutherland-Foggio H. et al. Right hemisphere regions critical for expression of emotion through prosody. Front. Neurol. 2018;6(9):224. DOI: 10.3389/fneur.2018.00224
32. Bastiaansen M., Oosterholt M., Mitas O., Han D. et al. An emotion al roller coaster: electrophysiological evidence of emotional engagement during a roller-coaster ride with virtual reality add-on. J. Hospitality Tourism Res. 2022; 46(1):29–54. Epub. 2020 Jul. 29. DOI: 10.1177/1096348020944436
Review
For citations:
Khramchenko M.A., Denisova E.S., Ashikhmina Yu.N., Prokopenko S.V. Objective Methods of Mimics and Emotion Assessment in Diagnostic of Cognitive Disorders. Title. 2024;23(7):82-85. (In Russ.) https://doi.org/10.31550/1727-2378-2024-23-7-82-85