Original Article

Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study

Abstract

Objective: Schizophrenia is a complex neurodevelopmental illness that is associated with different deficits in the cerebral cortex and neural networks, resulting in irregularity of brain waves. Various neuropathological hypotheses have been proposed for this irregularity that we intend to examine in this computational study.

Method: We used a mathematical model of a neuronal population based on cellular automata to examine two hypotheses about the neuropathology of schizophrenia: first, reducing neuronal stimulation thresholds to increase neuronal excitability; and second, increasing the percentage of excitatory neurons and decreasing the percentage of inhibitory neurons to increase the excitation to inhibition ratio in the neuronal population. Then, we compare the complexity of the output signals produced by the model in both cases with real healthy resting-state electroencephalogram (EEG) signals using the Lempel-Ziv complexity measure and see if these changes alter (increase or decrease) the complexity of the neuronal population dynamics.

Results: By lowering the neuronal stimulation threshold (i.e., the first hypothesis), no significant change in the pattern and amplitude of the network complexity was observed, and the model complexity was very similar to the complexity of real EEG signals (P > 0.05). However, increasing the excitation to inhibition ratio (i.e., the second hypothesis) led to significant changes in the complexity pattern of the designed network (P < 0.05). More interestingly, in this case, the complexity of the output signals of the model increased significantly compared to real healthy EEGs (P = 0.002) and the model output of the unchanged condition (P = 0.028) and the first hypothesis (P = 0.001).

Conclusion: Our computational model suggests that imbalances in the excitation to inhibition ratio in the neural network are probably the source of abnormal neuronal firing patterns and thus the cause of increased complexity of brain electrical activity in schizophrenia.

1. Alavi SS, Mohammadi MR, Hooshyari Z, Mohammadi Kalhori S, Salehi M, Salmanian M, et al. Epidemiology of Psychotic Disorders Based on Demographic Variables in Iranian Children and Adolescents. Iran J Psychiatry. 2021;16(1):1-12.
2. Mohammadi MR, Ahmadi N, Khaleghi A, Mostafavi SA, Kamali K, Rahgozar M, et al. Prevalence and Correlates of Psychiatric Disorders in a National Survey of Iranian Children and Adolescents. Iran J Psychiatry. 2019;14(1):1-15.
3. Janoutova J, Janackova P, Sery O, Zeman T, Ambroz P, Kovalova M, et al. Epidemiology and risk factors of schizophrenia. Neuro Endocrinol Lett. 2016;37(1):1-8.
4. Modai S, Shomron N. Molecular Risk Factors for Schizophrenia. Trends Mol Med. 2016;22(3):242-53.
5. Duchatel RJ, Shannon Weickert C, Tooney PA. White matter neuron biology and neuropathology in schizophrenia. NPJ Schizophr. 2019;5(1):10.
6. Obi-Nagata K, Temma Y, Hayashi-Takagi A. Synaptic functions and their disruption in schizophrenia: From clinical evidence to synaptic optogenetics in an animal model. Proc Jpn Acad Ser B Phys Biol Sci. 2019;95(5):179-97.
7. Fernández A, Gómez C, Hornero R, López-Ibor JJ. Complexity and schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry. 2013;45:267-76.
8. Ibanez-Molina AJ, Lozano V, Soriano MF, Aznarte JI, Gomez-Ariza CJ, Bajo MT. EEG Multiscale Complexity in Schizophrenia During Picture Naming. Front Physiol. 2018;9:1213.
9. Yang AC, Hong CJ, Liou YJ, Huang KL, Huang CC, Liu ME, et al. Decreased resting-state brain activity complexity in schizophrenia characterized by both increased regularity and randomness. Hum Brain Mapp. 2015;36(6):2174-86.
10. Breakspear M. The nonlinear theory of schizophrenia. Aust N Z J Psychiatry. 2006;40(1):20-35.
11. Akar SA, Kara S, Latifoglu F, Bilgic V. Analysis of the Complexity Measures in the EEG of Schizophrenia Patients. Int J Neural Syst. 2016;26(2):1650008.
12. Kutepov IE, Dobriyan VV, Zhigalov MV, Stepanov MF, Krysko AV, Yakovleva TV, et al. EEG analysis in patients with schizophrenia based on Lyapunov exponents. Inform Med Unlocked. 2020;18:100289.
13. Namazi H, Aghasian E, Ala TS. Fractal-based classification of electroencephalography (EEG) signals in healthy adolescents and adolescents with symptoms of schizophrenia. Technol Health Care. 2019;27(3):233-41.
14. Bai D, Yao W, Lv Z, Yan W, Wang J. Multiscale multidimensional recurrence quantitative analysis for analysing MEG signals in patients with schizophrenia. Biomed Signal Process Control. 2021;68:102586.
15. Rojas DC. Review of schizophrenia research using MEG. Magnetoencephalography: From Signals to Dynamic Cortical Networks. 2019:1121-46.
16. Bassett DS, Sporns O. Network neuroscience. Nat Neurosci. 2017;20(3):353-64.
17. Aine CJ, Bockholt HJ, Bustillo JR, Cañive JM, Caprihan A, Gasparovic C, et al. Multimodal Neuroimaging in Schizophrenia: Description and Dissemination. Neuroinformatics. 2017;15(4):343-64.
18. Čiháková D, Eaton WW, Talor MV, Harkus UH, Demyanovich H, Rodriguez K, et al. Gut permeability and mimicry of the Glutamate Ionotropic Receptor NMDA type Subunit Associated with protein 1 (GRINA) as potential mechanisms related to a subgroup of people with schizophrenia with elevated antigliadin antibodies (AGA IgG). Schizophr Res. 2019;208:414-9.
19. Egerton A, Modinos G, Ferrera D, McGuire P. Neuroimaging studies of GABA in schizophrenia: a systematic review with meta-analysis. Transl Psychiatry. 2017;7(6):e1147.
20. Khaleghi A, Mohammadi MR, Shahi K, Motie Nasrabadi A. A neuronal population model based on cellular automata to simulate the electrical waves of the brain. Waves Random Complex Media. 2021:1-20.
21. Zyma I, Tukaev S, Seleznov I, Kiyono K, Popov A, Chernykh M, et al. Electroencephalograms during mental arithmetic task performance. Data. 2019;4(1):14.
22. Zarafshan H, Khaleghi A, Mohammadi MR, Moeini M, Malmir N. Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task. J Clin Exp Neuropsychol. 2016;38(3):361-9.
23. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H. EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett. 2016;6(2):66-73.
24. Barreira L. Lyapunov exponents: Springer; 2017.
25. Cao L. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena. 1997;110(1-2):43-50.
26. Huys QJ, Maia TV, Frank MJ. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci. 2016;19(3):404-13.
27. Khaleghi A, Mohammadi MR, Shahi K, Nasrabadi AM. Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches. Clin Psychopharmacol Neurosci. 2022;20(1):26-36.
28. Khaleghi A, Sheikhani A, Mohammadi MR, Nasrabadi AM, Vand SR, Zarafshan H, et al. EEG classification of adolescents with type I and type II of bipolar disorder. Australas Phys Eng Sci Med. 2015;38(4):551-9.
29. Moeini M, Khaleghi A, Mohammadi MR. Characteristics of Alpha Band Frequency in Adolescents with Bipolar II Disorder: A Resting-State QEEG Study. Iran J Psychiatry. 2015;10(1):8-12.
30. Khaleghi A, Zarafshan H, Mohammadi MR. Visual and auditory steady-state responses in attention-deficit/hyperactivity disorder. Eur Arch Psychiatry Clin Neurosci. 2019;269(6):645-55.
31. Torrey EF, Barci BM, Webster MJ, Bartko JJ, Meador-Woodruff JH, Knable MB. Neurochemical markers for schizophrenia, bipolar disorder, and major depression in postmortem brains. Biol Psychiatry. 2005;57(3):252-60.
32. Lewis DA, Hashimoto T, Volk DW. Cortical inhibitory neurons and schizophrenia. Nat Rev Neurosci. 2005;6(4):312-24.
33. Bartos M, Vida I, Jonas P. Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks. Nat Rev Neurosci. 2007;8(1):45-56.
34. Fries P. Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu Rev Neurosci. 2009;32:209-24.
35. Chatterjee I, Kumar V, Sharma S, Dhingra D, Rana B, Agarwal M, et al. Identification of brain regions associated with working memory deficit in schizophrenia. F1000Res. 2019;8:124.
36. Wu G, Palaniyappan L, Zhang M, Yang J, Xi C, Liu Z, et al. Imbalance Between Prefronto-Thalamic and Sensorimotor-Thalamic Circuitries Associated with Working Memory Deficit in Schizophrenia. Schizophr Bull. 2022;48(1):251-61.
37. Lewis DA, Curley AA, Glausier JR, Volk DW. Cortical parvalbumin interneurons and cognitive dysfunction in schizophrenia. Trends Neurosci. 2012;35(1):57-67.
38. Murray AJ, Woloszynowska-Fraser MU, Ansel-Bollepalli L, Cole KL, Foggetti A, Crouch B, et al. Parvalbumin-positive interneurons of the prefrontal cortex support working memory and cognitive flexibility. Sci Rep. 2015;5:16778.
39. Chung DW, Fish KN, Lewis DA. Pathological Basis for Deficient Excitatory Drive to Cortical Parvalbumin Interneurons in Schizophrenia. Am J Psychiatry. 2016;173(11):1131-9.
Files
IssueVol 18 No 2 (2023) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/ijps.v18i2.12363
Keywords
Computational Modelling Neurophysiology Neuropathology Schizophrenia
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Khaleghi A, Mohammadi MR, Shahi K, Motie Nasrabadi A. Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study. Iran J Psychiatry. 2023;18(2):127-133.