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Lewandowska M, Tołpa K, Rogala J, Piotrowski T, Dreszer J. Multivariate multiscale entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender-related differences. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2023; 19:18. [PMID: 37798774 PMCID: PMC10552392 DOI: 10.1186/s12993-023-00218-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 09/18/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND The study aimed to determine how the resting-state EEG (rsEEG) complexity changes both over time and space (channels). The complexity of rsEEG and its sex/gender differences were examined using the multivariate Multiscale Entropy (mMSE) in 95 healthy adults. Following the probability maps (Giacometti et al. in J Neurosci Methods 229:84-96, 2014), channel sets have been identified that correspond to the functional networks. For each channel set the area under curve (AUC), which represents the total complexity, MaxSlope-the maximum complexity change of the EEG signal at thefine scales (1:4 timescales), and AvgEnt-to the average entropy level at coarse-grained scales (9:12 timescales), respectively, were extracted. To check dynamic changes between the entropy level at the fine and coarse-grained scales, the difference in mMSE between the #9 and #4 timescale (DiffEnt) was also calculated. RESULTS We found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlope and DiffEnt but lower AvgEnt in all channel sets. CONCLUSIONS Novel results of the present study are: (1) an identification of the mMSE features that capture entropy at the fine and coarse timescales in the channel sets corresponding to the main resting-state networks; (2) the sex/gender differences in these features.
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Affiliation(s)
- Monika Lewandowska
- Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100, Torun, Poland
| | - Krzysztof Tołpa
- Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100, Torun, Poland
| | - Jacek Rogala
- Faculty of Physics, University of Warsaw, Pasteur 5 Street, 02-093, Warsaw, Poland
| | - Tomasz Piotrowski
- Institute of Engineering and Technology, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Torun, Grudziądzka 5 Street, 87-100, Torun, Poland
| | - Joanna Dreszer
- Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Faculty of Philosophy and Social Sciences, Nicolaus Copernicus University in Torun, Gagarina 39 Street, 87-100, Torun, Poland.
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Jia G, Hubbard CS, Hu Z, Xu J, Dong Q, Niu H, Liu H. Intrinsic brain activity is increasingly complex and develops asymmetrically during childhood and early adolescence. Neuroimage 2023:120225. [PMID: 37336421 DOI: 10.1016/j.neuroimage.2023.120225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/18/2023] [Accepted: 06/11/2023] [Indexed: 06/21/2023] Open
Abstract
A large body of evidence suggests that brain signal complexity (BSC) may be an important indicator of healthy brain functioning or alternately, a harbinger of disease and dysfunction. However, despite recent progress our current understanding of how BSC emerges and evolves in large-scale networks, and the factors that shape these dynamics, remains limited. Here, we utilized resting-state functional near-infrared spectroscopy (rs-fNIRS) to capture and characterize the nature and time course of BSC dynamics within large-scale functional networks in 107 healthy participants ranging from 6-13 years of age. Age-dependent increases in spontaneous BSC were observed predominantly in higher-order association areas including the default mode (DMN) and attentional (ATN) networks. Our results also revealed asymmetrical developmental patterns in BSC that were specific to the dorsal and ventral ATN networks, with the former showing a left-lateralized and the latter demonstrating a right-lateralized increase in BSC. These age-dependent laterality shifts appeared to be more pronounced in females compared to males. Lastly, using a machine-learning model, we showed that BSC is a reliable predictor of chronological age. Higher-order association networks such as the DMN and dorsal ATN demonstrated the most robust prognostic power for predicting ages of previously unseen individuals. Taken together, our findings offer new insights into the spatiotemporal patterns of BSC dynamics in large-scale intrinsic networks that evolve over the course of childhood and adolescence, suggesting that a network-based measure of BSC represents a promising approach for tracking normative brain development and may potentially aid in the early detection of atypical developmental trajectories.
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Affiliation(s)
- Gaoding Jia
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
| | - Catherine S Hubbard
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Zhenyan Hu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
| | - Jingping Xu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
| | - Haijing Niu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China.
| | - Hesheng Liu
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
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Lord B, Allen JJB. Evaluating EEG complexity metrics as biomarkers for depression. Psychophysiology 2023:e14274. [PMID: 36811526 DOI: 10.1111/psyp.14274] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 12/23/2022] [Accepted: 01/13/2023] [Indexed: 02/24/2023]
Abstract
Nonlinear EEG analysis offers the potential for both increased diagnostic accuracy and deeper mechanistic understanding of psychopathology. EEG complexity measures have previously been shown to positively correlate with clinical depression. In this study, resting state EEG recordings were taken across multiple sessions and days with both eyes open and eyes closed conditions from a total of 306 subjects, 62 of which were in a current depressive episode, and 81 of which had a history of diagnosed depression but were not currently depressed. Three different EEG montages (mastoids, average, and Laplacian) were also computed. Higuchi fractal dimension (HFD) and sample entropy (SampEn) were calculated for each unique condition. The complexity metrics showed high internal consistency within session and high stability across days. Higher complexity was found in open-eye recordings compared to closed eyes. The predicted correlation between complexity and depression was not found. However, an unexpected sex effect was observed, in which males and females exhibited different topographic patterns of complexity.
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Affiliation(s)
- Brian Lord
- Department of Psychology, University of Arizona, Tucson, Arizona, USA
| | - John J B Allen
- Department of Psychology, University of Arizona, Tucson, Arizona, USA
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He H, Lin W, Yang J, Chen Y, Tan S, Guan Q. Age-related intrinsic functional connectivity underlying emotion utilization. Cereb Cortex 2023:7033308. [PMID: 36758953 DOI: 10.1093/cercor/bhad023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 02/11/2023] Open
Abstract
Previous studies investigated the age-related positivity effect in terms of emotion perception and management, whereas little is known about whether the positivity effect is shown in emotion utilization (EU). If yes, the EU-related intrinsic functional connectivity and its age-associated alterations remain to be elucidated. In this study, we collected resting-state functional magnetic resonance imaging data from 62 healthy older adults and 72 undergraduates as well as their self-ratings of EU. By using the connectome-based predictive modeling (CPM) method, we constructed a predictive model of the positive relationship between EU self-ratings and resting-state functional connectivity. Lesion simulation analyses revealed that the medial-frontal network, default mode network, frontoparietal network, and subcortical regions played key roles in the EU-related CPM. Older subjects showed significantly higher EU self-ratings than undergraduates, which was associated with strengthened connectivity between the left dorsolateral prefrontal cortex and bilateral frontal poles, and between the left frontal pole and thalamus. A mediation analysis indicated that the age-related EU network mediated the age effect on EU self-ratings. Our findings extend previous research on the age-related "positivity effect" to the EU domain, suggesting that the positivity effect on the self-evaluation of EU is probably associated with emotion knowledge which accumulates with age.
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Affiliation(s)
- Hao He
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Wenyi Lin
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China
| | - Jiawang Yang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China
| | - Yiqi Chen
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Department of Psychology, University of Mannheim, Mannheim, Germany
| | - Siping Tan
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
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Thiele JA, Richter A, Hilger K. Multimodal Brain Signal Complexity Predicts Human Intelligence. eNeuro 2023; 10:ENEURO.0345-22.2022. [PMID: 36657966 PMCID: PMC9910576 DOI: 10.1523/eneuro.0345-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 12/01/2022] [Accepted: 12/13/2022] [Indexed: 01/20/2023] Open
Abstract
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven's Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
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Affiliation(s)
- Jonas A Thiele
- Department of Psychology I, University of Würzburg, Würzburg 97070, Germany
| | - Aylin Richter
- Department of Biology, University of Würzburg, Würzburg 97074, Germany
| | - Kirsten Hilger
- Department of Psychology I, University of Würzburg, Würzburg 97070, Germany
- Department of Psychology, Frankfurt University, Frankfurt am Main 60629, Germany
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Dreszer J, Grochowski M, Lewandowska M, Nikadon J, Gorgol J, Bałaj B, Finc K, Duch W, Kałamała P, Chuderski A, Piotrowski T. Spatiotemporal complexity patterns of resting-state bioelectrical activity explain fluid intelligence: Sex matters. Hum Brain Mapp 2020; 41:4846-4865. [PMID: 32808732 PMCID: PMC7643359 DOI: 10.1002/hbm.25162] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Revised: 07/12/2020] [Accepted: 07/27/2020] [Indexed: 11/11/2022] Open
Abstract
Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting-state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6-min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo-parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro-temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing.
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Affiliation(s)
- Joanna Dreszer
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Faculty of Philosophy and Social SciencesInstitute of Psychology, Nicolaus Copernicus UniversityToruńPoland
| | - Marek Grochowski
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Department of Informatics, Faculty of Physics, Astronomy, and InformaticsNicolaus Copernicus UniversityToruńPoland
| | - Monika Lewandowska
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Faculty of Philosophy and Social SciencesInstitute of Psychology, Nicolaus Copernicus UniversityToruńPoland
| | - Jan Nikadon
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
| | - Joanna Gorgol
- Faculty of PsychologyUniversity of WarsawWarsawPoland
| | - Bibianna Bałaj
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Faculty of Philosophy and Social SciencesInstitute of Psychology, Nicolaus Copernicus UniversityToruńPoland
| | - Karolina Finc
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
| | - Włodzisław Duch
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Department of Informatics, Faculty of Physics, Astronomy, and InformaticsNicolaus Copernicus UniversityToruńPoland
| | - Patrycja Kałamała
- Department of Cognitive ScienceInstitute of Philosophy, Jagiellonian UniversityKrakowPoland
| | - Adam Chuderski
- Department of Cognitive ScienceInstitute of Philosophy, Jagiellonian UniversityKrakowPoland
| | - Tomasz Piotrowski
- Centre for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland
- Department of Informatics, Faculty of Physics, Astronomy, and InformaticsNicolaus Copernicus UniversityToruńPoland
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