Volume 25, Issue 1 (Spring 2023)
Advances in Cognitive Sciences 2023, 25(1): 90-107 |
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1- PhD Candidate in Cognitive Science-Modeling, Institute for Cognitive Science Studies, Tehran, Iran
2- Department of Clinical Psychology, Kharazmi University, Tehran, Iran
3- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
4- Department of Neuroscience, Shiraz University of Medical Sciences, Shiraz, Iran
2- Department of Clinical Psychology, Kharazmi University, Tehran, Iran
3- Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
4- Department of Neuroscience, Shiraz University of Medical Sciences, Shiraz, Iran
Abstract: (1205 Views)
Introduction
Sleep Deprivation (AD) is a common phenomenon in modern societies, but its long-term effects on cognitive brain function have been less scientifically studied. Despite studies measuring alertnessand attention, there are fewer consensuses on the effects of sleep deprivation on many high-level cognitive functions. This study can refer to the functions of perception, memory, and executive functions (1, 2). Sleep is an essential process for maintaining the balance of all body organs. Although each person spends an average of one-third of their life asleep, accurate information on the sleep mechanism has yet to be available (3, 5).
Since the studies carried out in this field have not provided detailed information on Inter-and-Intra network connections, additional detailed information on them, which was not available in preceding studies, can be acquired by conducting studies into the brain’s functional networks using methods including Independent Elements of Data Extraction. Therefore, this study, by making use of images obtained from Magnetic Resonance Imaging of the brain at rest and in a deep sleep, which Stockholm University conducted, reviews and compares the Intra and Inter networks in the seven major brain networks (N1-N7) in deep sleep and sleep deprivation. Accordingly, this study aims to investigate the effects of sleep deprivation on the brain’s involved networks and identify their connections. The purpose of this study is first to compare and evaluate the studies of network communication in SD at rest, which can be an accurate summary of the networks and areas of the brain that change in SD. Then, the effects of SDon the involved brain networks and their identification, the relationship of the identified networks with each other, and finally, the cognitive functions affected by the intervened networks on the Functional Brain Imaging Resonance data (fMRI) the extracted standard of the Stockholm University Sleepy Brain Project is addressed by the Independent Component Analysis (ICA) method.
Methods
In the Stockholm University Sleepy Brain Project, imaging was done for each person in two sessions: Once after normal sleep and once with partial SD. According to the random paradigm, participants should either experience full sleep or sleep only three hours a night (waking up at the usual time every day) (8).
Brain imaging scans were performed using GE's three-discoveryTesla MRI scanner, model 750, with the help of an 8-channel coil. Structural images of T1 and T2 were obtained to normalize fMRI images, as well as morphological processing. Adjustment parameters for anatomical imaging include the following: FOV 24, slice thickness 1 mm, sagittal data acquisition, cluttered data acquisition, and full head coverage. Resting data were obtained using an EPI sequence with a FOV of 28.8, slices were 3 mm thick, and no gaps were made between brain sections. Covers, taking data in a cluttered way, echo time (TE) equal to 30 milliseconds, repetition time (TR) equal to 2.5, seconds and Philip angle equal to 75 degrees.
In order to analyze the rest state data related to the study whose specifications were stated, FSL software and MATLAB toolbox were used. The present study also used free-surfer software to preprocess T1 anatomical data.
Due to the adverse effects of low-frequency displacement and head movement on the decomposition of data components, motion correction, and removal of displacements and other appropriate predicates before the main zinc analysis, data were processed using pre-stats preprocessors from the Melodic tool in FSL software. Because the rs-fMRI data were obtained in a cluttered manner, the slice-timing correction step was performed with the same constraint, and in order to correct the candidates' head movements, a head movement correction algorithm was applied to the data.
ICA analysis was used to process rs-fMRI images (9). To do this, the multisession temporal concatenation tool in MELODIC and the preprocessing and steps required for group data analysis in this tool were used. Spatial ICA analysis was performed using 20 independent component maps (IC maps) to detect resting state networks (RSN) from the control group.
The Yeo_7 networks atlas was used to extract the matrix of brain connections using the outputs of these analyzes. In order to determine and establish the correspondence between Yeo_7networks Atlas networks and 20 components of extraction ICA for each individual due to the limited number of independent components (20 components), as well as the number of Atlas networks (7 networks), this step is inspected. A careful eye was performed. Accordingly, one of the Yeo Atlas networks was assigned to each independent extraction component, according to Table 1.
Results
The results of in-network comparisons of networks corresponding to the Yeo Atlas and, in the next step, the results of standard comparisons and statistical analyses related to cross-network analyzes were evaluated. Considering that the Yeo standard atlas of seven networks was used to study brain networks, a total of twenty components extracted from ICA analysis (16 components after removal of non-brain components) on this subset of seven networks were distributed, the details of which are given in Table 1. The relationship between the quantity components was examined by identifying the networks between which in-network analyzes are possible (N1, N2, N5, N6, and N7). Calculation of this quantity, i.e., the quantity of intra-network communication was calculated from the communication matrix obtained from time series of 16 independent components arranged in order and accordance with the N1 to N7 networks.
Table 1. The relationship between the components of the ICA analysis and the networks defined in the Yeo_7Networks Atlas
As shown in Table 2, considering the threshold of 0.05 as a significant level of difference between the two groups, the inter-network communication for the network pairs N1-N7, N2-N6, N3-N7 and finally, N4-N6 have a significant difference in SD and normal.
It should be noted that the results and quantitative studies between the 16 direct components of ICA showed no significant difference between the two groups (two different imaging sessions under normal conditions and SD).
Table 2. Inter-network communication between the sleepy brain and the brain with normal sleep
Conclusion
The lack of a significant correlation between the 16 components extracted from the ICA is in line with one of the articles published by the primary team of the Stockholm University Sleepy Brain Project (8). According to the in-network comparison, the N1 and N5 networks were significantly different, which can be interpreted as related to the visual function networks and the limbic network.
In general, based on the neuroimaging studies obtained from this study and previous studies, SD outcomes can be categorized into three main networks related to negative and positive emotions, memory, and attention, which with the results, the result of brain network communication is also associated with significant differences. Consistent with the results of this study, fMRI studies that showed that acute SD affects network communication function and different areas of the brain showed that the density of prominent sensorimotor network modules decreased. Liu et al. also reported changes in the sensory-motor areas during their 2014 reports (22). These results indicate the sensitivity of the sensorimotor network to SD, which may be associated with a decrease in the degree of nodules in the middle region of the thalamus. In general, the thalamus plays a significant role in the sensory system and is the main link between the central and peripheral nervous systems.
Ethical Considerations
Compliance with ethical guidelines
The present study included data analysis and was approved by the Institute of Higher Education of Cognitive Sciences.
Author’s contributions
This research is under the research cluster of sleep studies with a focus on Alireza Moradi and member professors, such as Habibollah Khazaei, Mohammad Nami, and Kamran Kazemi, in line with the objectives of this research. Mohammad Naseh Talebi and Alireza Moradi were in charge of the idea of this research and the design of the experiment. Mohammad Naseh Talebi and Kamran Kazemi have designed the model and computational framework. Mohammad Naseh Talebi has been in charge of implementing and performing calculations.
Funding
This project has been done at the expense of the project "Identification of sleep patterns and Intervention" at the Institute of Higher Education of Cognitive Sciences.
Acknowledgments
The present article is related to one of the researches of the first author of his doctoral dissertation; for this reason, the authors appreciate the cooperation of all those who contributed to the advancement of this study, as well as the esteemed President of the Institute of Higher Education of Cognitive Sciences for leading this research.
Conflicts of interest
The authors declared that no known financial interests or personal relationships affect the work reported in this article.
Sleep Deprivation (AD) is a common phenomenon in modern societies, but its long-term effects on cognitive brain function have been less scientifically studied. Despite studies measuring alertnessand attention, there are fewer consensuses on the effects of sleep deprivation on many high-level cognitive functions. This study can refer to the functions of perception, memory, and executive functions (1, 2). Sleep is an essential process for maintaining the balance of all body organs. Although each person spends an average of one-third of their life asleep, accurate information on the sleep mechanism has yet to be available (3, 5).
Since the studies carried out in this field have not provided detailed information on Inter-and-Intra network connections, additional detailed information on them, which was not available in preceding studies, can be acquired by conducting studies into the brain’s functional networks using methods including Independent Elements of Data Extraction. Therefore, this study, by making use of images obtained from Magnetic Resonance Imaging of the brain at rest and in a deep sleep, which Stockholm University conducted, reviews and compares the Intra and Inter networks in the seven major brain networks (N1-N7) in deep sleep and sleep deprivation. Accordingly, this study aims to investigate the effects of sleep deprivation on the brain’s involved networks and identify their connections. The purpose of this study is first to compare and evaluate the studies of network communication in SD at rest, which can be an accurate summary of the networks and areas of the brain that change in SD. Then, the effects of SDon the involved brain networks and their identification, the relationship of the identified networks with each other, and finally, the cognitive functions affected by the intervened networks on the Functional Brain Imaging Resonance data (fMRI) the extracted standard of the Stockholm University Sleepy Brain Project is addressed by the Independent Component Analysis (ICA) method.
Methods
In the Stockholm University Sleepy Brain Project, imaging was done for each person in two sessions: Once after normal sleep and once with partial SD. According to the random paradigm, participants should either experience full sleep or sleep only three hours a night (waking up at the usual time every day) (8).
Brain imaging scans were performed using GE's three-discoveryTesla MRI scanner, model 750, with the help of an 8-channel coil. Structural images of T1 and T2 were obtained to normalize fMRI images, as well as morphological processing. Adjustment parameters for anatomical imaging include the following: FOV 24, slice thickness 1 mm, sagittal data acquisition, cluttered data acquisition, and full head coverage. Resting data were obtained using an EPI sequence with a FOV of 28.8, slices were 3 mm thick, and no gaps were made between brain sections. Covers, taking data in a cluttered way, echo time (TE) equal to 30 milliseconds, repetition time (TR) equal to 2.5, seconds and Philip angle equal to 75 degrees.
In order to analyze the rest state data related to the study whose specifications were stated, FSL software and MATLAB toolbox were used. The present study also used free-surfer software to preprocess T1 anatomical data.
Due to the adverse effects of low-frequency displacement and head movement on the decomposition of data components, motion correction, and removal of displacements and other appropriate predicates before the main zinc analysis, data were processed using pre-stats preprocessors from the Melodic tool in FSL software. Because the rs-fMRI data were obtained in a cluttered manner, the slice-timing correction step was performed with the same constraint, and in order to correct the candidates' head movements, a head movement correction algorithm was applied to the data.
ICA analysis was used to process rs-fMRI images (9). To do this, the multisession temporal concatenation tool in MELODIC and the preprocessing and steps required for group data analysis in this tool were used. Spatial ICA analysis was performed using 20 independent component maps (IC maps) to detect resting state networks (RSN) from the control group.
The Yeo_7 networks atlas was used to extract the matrix of brain connections using the outputs of these analyzes. In order to determine and establish the correspondence between Yeo_7networks Atlas networks and 20 components of extraction ICA for each individual due to the limited number of independent components (20 components), as well as the number of Atlas networks (7 networks), this step is inspected. A careful eye was performed. Accordingly, one of the Yeo Atlas networks was assigned to each independent extraction component, according to Table 1.
Results
The results of in-network comparisons of networks corresponding to the Yeo Atlas and, in the next step, the results of standard comparisons and statistical analyses related to cross-network analyzes were evaluated. Considering that the Yeo standard atlas of seven networks was used to study brain networks, a total of twenty components extracted from ICA analysis (16 components after removal of non-brain components) on this subset of seven networks were distributed, the details of which are given in Table 1. The relationship between the quantity components was examined by identifying the networks between which in-network analyzes are possible (N1, N2, N5, N6, and N7). Calculation of this quantity, i.e., the quantity of intra-network communication was calculated from the communication matrix obtained from time series of 16 independent components arranged in order and accordance with the N1 to N7 networks.
Table 1. The relationship between the components of the ICA analysis and the networks defined in the Yeo_7Networks Atlas
| IC | Network |
| IC_01 | Yeo_7Networks_1 |
| IC_05 | |
| IC_09 | |
| IC_02 | Yeo_7Networks_2 |
| IC_07 | |
| IC_06 | Yeo_7Networks_3 |
| IC_20 | Yeo_7Networks_4 |
| IC_14 | Yeo_7Networks_5 |
| IC_17 | |
| IC_13 | Yeo_7Networks_6 |
| IC_15 | |
| IC_04 | Yeo_7Networks_7 |
| IC_08 | |
| IC_12 | |
| IC_16 | |
| IC_19 | |
| IC_10 | Cerebellum |
| IC_03 | White Matter |
| IC_11 | CSF |
| IC_18 | Artifact |
As shown in Table 2, considering the threshold of 0.05 as a significant level of difference between the two groups, the inter-network communication for the network pairs N1-N7, N2-N6, N3-N7 and finally, N4-N6 have a significant difference in SD and normal.
It should be noted that the results and quantitative studies between the 16 direct components of ICA showed no significant difference between the two groups (two different imaging sessions under normal conditions and SD).
Table 2. Inter-network communication between the sleepy brain and the brain with normal sleep
| N7 | N6 | N5 | N4 | N3 | N1 | ||
| 0.120601 | -0.05988 | -0.01044 | -0.18049 | 0.252824 | 0.057348 | N1 | Mean_sleepy |
| 0.090767 | -0.03559 | -0.03318 | -0.16854 | 0.245587 | 0.048776 | Mean_Normal | |
| 0.014076 | 0.092777 | 0.063801 | 0.316714 | 0.379162 | 0.345787 | P-value | |
| -0.09487 | -0.00493 | -0.11061 | 0.182571 | 0.157416 | N2 | Mean_sleepy | |
| -0.12506 | -0.04711 | -0.11129 | 0.137647 | 0.145839 | Mean_Normal | ||
| 0.067147 | 0.01773 | 0.483531 | 0.052288 | 0.349363 | P-value | ||
| 0.080088 | 0.125171 | -0.1369 | -0.27903 | N3 | Mean_sleepy | ||
| 0.023843 | 0.152754 | -0.14778 | -0.31545 | Mean_Normal | |||
| 0.011188 | 0.124014 | 0.305441 | 0.158307 | P-value | |||
| -0.20806 | -0.20871 | 0.049985 | N4 | Mean_sleepy | |||
| -0.18534 | -0.24536 | 0.06937 | Mean_Normal | ||||
| 0.111569 | 0.040618 | 0.158307 | P-value | ||||
| 0.030669 | -0.05383 | N5 | Mean_sleepy | ||||
| 0.047542 | -0.03762 | Mean_Normal | |||||
| 0.089608 | 0.198278 | P-value | |||||
| 0.827037 | N6 | Mean_sleepy | |||||
| 0.80689 | Mean_Normal | ||||||
| 0.051968 | P-value |
Conclusion
The lack of a significant correlation between the 16 components extracted from the ICA is in line with one of the articles published by the primary team of the Stockholm University Sleepy Brain Project (8). According to the in-network comparison, the N1 and N5 networks were significantly different, which can be interpreted as related to the visual function networks and the limbic network.
In general, based on the neuroimaging studies obtained from this study and previous studies, SD outcomes can be categorized into three main networks related to negative and positive emotions, memory, and attention, which with the results, the result of brain network communication is also associated with significant differences. Consistent with the results of this study, fMRI studies that showed that acute SD affects network communication function and different areas of the brain showed that the density of prominent sensorimotor network modules decreased. Liu et al. also reported changes in the sensory-motor areas during their 2014 reports (22). These results indicate the sensitivity of the sensorimotor network to SD, which may be associated with a decrease in the degree of nodules in the middle region of the thalamus. In general, the thalamus plays a significant role in the sensory system and is the main link between the central and peripheral nervous systems.
Ethical Considerations
Compliance with ethical guidelines
The present study included data analysis and was approved by the Institute of Higher Education of Cognitive Sciences.
Author’s contributions
This research is under the research cluster of sleep studies with a focus on Alireza Moradi and member professors, such as Habibollah Khazaei, Mohammad Nami, and Kamran Kazemi, in line with the objectives of this research. Mohammad Naseh Talebi and Alireza Moradi were in charge of the idea of this research and the design of the experiment. Mohammad Naseh Talebi and Kamran Kazemi have designed the model and computational framework. Mohammad Naseh Talebi has been in charge of implementing and performing calculations.
Funding
This project has been done at the expense of the project "Identification of sleep patterns and Intervention" at the Institute of Higher Education of Cognitive Sciences.
Acknowledgments
The present article is related to one of the researches of the first author of his doctoral dissertation; for this reason, the authors appreciate the cooperation of all those who contributed to the advancement of this study, as well as the esteemed President of the Institute of Higher Education of Cognitive Sciences for leading this research.
Conflicts of interest
The authors declared that no known financial interests or personal relationships affect the work reported in this article.
Type of Study: Research |
Received: 2021/09/11 | Accepted: 2022/09/5 | Published: 2023/07/10
Received: 2021/09/11 | Accepted: 2022/09/5 | Published: 2023/07/10
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