Peculiarities of the Activity of the Brain Structures of People with Schizophrenia During the Categorization of Objects of Animate and Inanimate Nature

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One of the features of the work of the brain of people suffering from schizophrenia is changes in the activity of their brain during visual categorization of animate and inanimate objects. The purpose of this study was to analyze the brain activity of people with schizophrenia and their visual categorization of objects with different semantic and physical characteristics. It was assumed that the patterns of brain activity in individuals with schizophrenia would differ from the group of healthy individuals both in the early and late stages of visual processing. Using the method of visual evoked potentials, we studied the features of brain activity in 25 people suffering from schizophrenia from 1 to 7 years old, when they categorized images of animate and inanimate nature, low and high spatial frequency. It was found that the amplitudes of P170 (N170) in the left and right posterior and central leads, as well as the amplitudes of P300 in the central lead in people with schizophrenia do not differ during categorization of animate and inanimate objects, which does not correspond to the data obtained earlier from the people without mental health abnormalities. The revealed result is important for a better understanding of the restructuring of the brain during visual perception of objects of different categories, which occurs during the development of schizophrenia.

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作者简介

O. Shchemeleva

Pavlov Institute of Physiology, Russian Academy of Sciences

Email: psy.journ@yandex.ru

Researcher, Laboratory of Physiology of Vision

俄罗斯联邦, St. Petersburg

S. Murav'eva

Pavlov Institute of Physiology, Russian Academy of Sciences

Email: psy.journ@yandex.ru

Researcher, Laboratory of Physiology of Vision

俄罗斯联邦, St. Petersburg

V. Lebedev

Pavlov Institute of Physiology, Russian Academy of Sciences

Email: psy.journ@yandex.ru

Graduate Student, Laboratory of Physiology of Vision

俄罗斯联邦, St. Petersburg

E. Vershinina

Pavlov Institute of Physiology, Russian Academy of Sciences

编辑信件的主要联系方式.
Email: psy.journ@yandex.ru

Senior researcher, laboratory of information technologies and mathematical simulation

俄罗斯联邦, St. Petersburg

参考

  1. Vershinina E.A., Safarova G.L. O primenenii metodov matematicheskoj statistiki v klinicheskih i jeksperimental'nyh issledovanijah. Uspehi gerontologii. 2019. V. 32. № 6. P. 1052–1062. (In Russian)
  2. Kropotov Ju.D., Pronina M.V., Poljakov Ju.I., Ponomarev V.A. Funkcional'nye biomarkery v diagnostike psihicheskih zabolevanij: kognitivnye vyzvannye potencialy. Fiziologija cheloveka. 2013. V. 39. № 1. P. 14–25. (In Russian)
  3. Moiseenko G.A., i dr. Klassifikacija i raspoznavanie izobrazhenij zhivoj i nezhivoj prirody. Opticheskij zhurnal. 2015. V. 82. № 10. P. 53–64. (In Russian)
  4. Murav'eva S.V., i dr. Stimuljacija raboty zritel'noj sistemy s pomoshh'ju kognitivnoj zadachi v uslovijah virtual'noj sredy u pacientov s shizofreniej i depressiej. Fiziologija cheloveka. 2020. V. 46. № 5. P. 27–36. (In Russian)
  5. Murav'eva S.V., i dr. Issledovanie zritel'nyh kognitivnyh vyzvannyh potencialov pri shizofrenii na rannih stadijah zabolevanija i ih korrekcija pri pomoshhi interaktivnyh virtual'nyh sred. Fiziologija cheloveka. 2017. V. 43. № 6. P. 24–36. (In Russian)
  6. Shelepin Ju.E., i dr. Metody ikoniki i metody kartirovanija mozga v ocenke funkcional'nogo sostojanija zritel'noj sistemy. Sensornye sistemy. 2014. V. 28. № 2. P. 61–75. (In Russian)
  7. Shhemeleva O.V., i dr. Jelektrofiziologicheskie pokazateli dejatel'nosti mozga v processe verbal'nogo i neverbal'nogo vzaimodejstvija sobesednikov. Fiziologija cheloveka. 2019. V. 45. № 6. P. 16–26. (In Russian)
  8. Abhishek P., et al. Lower P300 amplitudes for internally-generated events in patients with schizophrenia. Asian Journal of Psychiatry. 2018. V. 35. P. 67–71.
  9. Andrade G.N., et al. Atypical visual and somatosensory adaptation in schizophrenia-spectrum disorders. Translational Psychiatry. 2016. V. 6. (5). № e804.
  10. Behroozi M., Daliri M.R., Shekarchi B. EEG phase patterns reflect the representation of semantic categories of objects. Medical & biological engineering & computing. 2016. V. 54. № 1. P. 205–221.
  11. Bodatsch M., Brockhaus-Dumke A., Klosterkötter J., Ruhrmann S. Forecasting psychosis by event-related potentials—systematic review and specific meta-analysis. Biological psychiatry. 2015. V. 77. № 11. P. 951–958.
  12. Bosworth R.G., Dobkins K.R. Effects of prematurity on the development of contrast sensitivity: testing the visual experience hypothesis. Vision Research. 2013. V. 82. P. 31–41.
  13. Carlson T., Tovar D.A., Alink A., Kriegeskorte N. Representational dynamics of object vision: The first 1000 ms. Journal of vision. 2013. V. 13 № 10. P. 1. doi: https:/doi.org/10.1167/13.10.1.
  14. Cerino R., Vergara S. How objects categorize the human brain: EEG and fMRI as analysis point. Res. Comput. Sci. 2020. V. 149. № 4. P. 43–55.
  15. Clarke A., Devereux B.J., Randall B., Tyler L.K. Predicting the time course of individual objects with MEG. Cerebral Cortex. 2015. V. 25. № 10. P. 3602–3612.
  16. Contini E.W., Wardle S.G., Carlson T.A. Decoding the time-course of object recognition in the human brain: From visual features to categorical decisions. Neuropsychologia. 2017. V. 105. P. 165–176.
  17. Devia C., et al. EEG classification during scene free-viewing for schizophrenia detection. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2019. V. 27. № 6. P. 1193–1199.
  18. Grootswagers T., Robinson A.K., Shatek S.M., Carlson T.A. Untangling featural and conceptual object representations. NeuroImage. 2019. V. 202:116083.
  19. Karimi H., et al. Temporal dynamics of animacy categorization in the brain of patients with mild cognitive impairment. PloS One. 2022. V. 17. № 2. P. e0264058.
  20. Khaligh-Razavi S.M., Cichy R.M., Pantazis D., Oliva A. Tracking the spatiotemporal neural dynamics of real-world object size and animacy in the human brain. Journ. of Cognitive Neuroscience. 2018. V. 30. № 11. P. 1559–1576.
  21. Kiang M., Gerritsen C.J. The N400 event-related brain potential response: A window on deficits in predicting meaning in schizophrenia. International Journ. of Psychophysiology. 2019. V. 145. P. 65–69.
  22. Li F., et al. The Time-Varying Networks in P300:_newline A Task-Evoked EEG Study. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2016. V. 24. № 7. P. 725–733.
  23. Maher S., et al. Deficient cortical face-sensitive N170 responses and basic visual processing in schizophrenia. Schizophr. Res. 2016. V. 170. № 1. P. 87–94.
  24. Martínez A., et al. Neural oscillatory deficits in schizophrenia predict behavioral and neurocognitive impairments. Frontiers in Human Neuroscience. 2015. V. 9. https:/doi.org/10.3389/fnhum.2015.00371
  25. Mudar R.A., et al. The effects of amnestic mild cognitive impairment on Go/No Go semantic categorization task performance and event-related potentials. Journal of Alzheimer's Disease. 2016. V. 50. № 2. P. 577–590.
  26. Oribe N., et al. Progressive reduction of visual P300 amplitude in patients with first-episode schizophrenia: an ERP study. Schizophrenia bulletin. 2015. V. 41. № 2. P. 460–470.
  27. Oribe N., et al. Early and late stages of visual processing in individuals in prodromal state and first episode schizophrenia: An ERP study. Schizophrenia Research. 2013. V. 146. P. 95–102.
  28. Ozaki T, Toyomaki A, Hashimoto N, Kusumi I. Quantitative resting state electroencephalography in patients with schizophrenia spectrum disorders treated with strict monotherapy using atypical antipsychotics. Clin Psychopharmacol Neurosci. 2021. V. 19. № 2. P. 313–322.
  29. Perrottelli A., et al. EEG-based measures in at-risk mental state and early stages of schizophrenia: a systematic review. Frontiers in Psychiatry. 2021. V. 12. https:/doi.org/10.3389/fpsyt.2021.653642
  30. Pokorny V.J., et al. Aberrant cortical connectivity during ambiguous object recognition is associated with schizophrenia. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2021. V. 6. № 12. P. 1193–1201.
  31. Salisbury D.F., et al. Neutral face and complex object neurophysiological processing deficits in long-term schizophrenia and in first hospitalized schizophrenia-spectrum individuals. International Journal of Psychophysiology. 2019. № 145. P. 57–64.
  32. Sklar A.L., Coffman B.A., Salisbury D.F. Localization of early-stage visual processing deficits at schizophrenia spectrum illness onset using magnetoencephalography. Schizophrenia Bulletin. 2020. V. 46. №. 4. P. 955–963.
  33. Tremblay E., et al. Delayed early primary visual pathway development in premature infants: high density electrophysiological evidence. PLoS One. 2014. V. 9 № 9. e107992.
  34. Vaziri-Pashkam M., Taylor J., Xu Y. Spatial frequency tolerant visual object representations in the human ventral and dorsal visual processing pathways. Journ. of Cognitive Neuroscience. 2019. V. 31. № 1. P. 49–63.

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1. JATS XML
2. Fig. 1. An example of a black-and-white image - an inanimate object subjected to digital filtering by convolution with a DoG function in the region of high (a) and low spatial frequencies (b).

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3. Fig. 2. Averaged evoked potentials recorded in people with schizophrenia in the central lead (Cz), in the occipital-temporal lead on the left (T5) and on the right (T6) when categorizing images of objects of living (black line) and inanimate (gray line) nature, filtered for low (A) and high spatial frequencies (B). Asterisks show significant differences depending on the level of significance when comparing the ratio of the amplitudes of one component within each group at low and high spatial frequencies. Significance level: * — p < 0.05; ** — p < 0.01

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