Cargando…
Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study
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...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Psychiatry & Psychology Research Center, Tehran University of Medical Sciences
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10293699/ https://www.ncbi.nlm.nih.gov/pubmed/37383967 http://dx.doi.org/10.18502/ijps.v18i2.12363 |
_version_ | 1785063045807996928 |
---|---|
author | Khaleghi, Ali Mohammadi, Mohammad Reza Shahi, Kian Motie Nasrabadi, Ali |
author_facet | Khaleghi, Ali Mohammadi, Mohammad Reza Shahi, Kian Motie Nasrabadi, Ali |
author_sort | Khaleghi, Ali |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10293699 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Psychiatry & Psychology Research Center, Tehran University of Medical Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-102936992023-06-28 Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study Khaleghi, Ali Mohammadi, Mohammad Reza Shahi, Kian Motie Nasrabadi, Ali Iran J Psychiatry Original Article 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. Psychiatry & Psychology Research Center, Tehran University of Medical Sciences 2023-04 /pmc/articles/PMC10293699/ /pubmed/37383967 http://dx.doi.org/10.18502/ijps.v18i2.12363 Text en Copyright © 2023 Tehran University of Medical Sciences. https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International license (https://creativecommons.org/licenses/by-nc/4.0/). Non-commercial uses of the work are permitted, provided the original work is properly cited. |
spellingShingle | Original Article Khaleghi, Ali Mohammadi, Mohammad Reza Shahi, Kian Motie Nasrabadi, Ali Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study |
title | Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study |
title_full | Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study |
title_fullStr | Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study |
title_full_unstemmed | Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study |
title_short | Possible Neuropathological Mechanisms Underlying the Increased Complexity of Brain Electrical Activity in Schizophrenia: A Computational Study |
title_sort | possible neuropathological mechanisms underlying the increased complexity of brain electrical activity in schizophrenia: a computational study |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10293699/ https://www.ncbi.nlm.nih.gov/pubmed/37383967 http://dx.doi.org/10.18502/ijps.v18i2.12363 |
work_keys_str_mv | AT khaleghiali possibleneuropathologicalmechanismsunderlyingtheincreasedcomplexityofbrainelectricalactivityinschizophreniaacomputationalstudy AT mohammadimohammadreza possibleneuropathologicalmechanismsunderlyingtheincreasedcomplexityofbrainelectricalactivityinschizophreniaacomputationalstudy AT shahikian possibleneuropathologicalmechanismsunderlyingtheincreasedcomplexityofbrainelectricalactivityinschizophreniaacomputationalstudy AT motienasrabadiali possibleneuropathologicalmechanismsunderlyingtheincreasedcomplexityofbrainelectricalactivityinschizophreniaacomputationalstudy |