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Donoghue T, Hammonds R, Lybrand E, Washcke L, Gao R, Voytek B. Evaluating and Comparing Measures of Aperiodic Neural Activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.15.613114. [PMID: 39314334 PMCID: PMC11419150 DOI: 10.1101/2024.09.15.613114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Neuro-electrophysiological recordings contain prominent aperiodic activity - meaning irregular activity, with no characteristic frequency - which has variously been referred to as 1/f (or 1/f-like activity), fractal, or 'scale-free' activity. Previous work has established that aperiodic features of neural activity is dynamic and variable, relating (between subjects) to healthy aging and to clinical diagnoses, and also (within subjects) tracking conscious states and behavioral performance. There are, however, a wide variety of conceptual frameworks and associated methods for the analyses and interpretation of aperiodic activity - for example, time domain measures such as the autocorrelation, fractal measures, and/or various complexity and entropy measures, as well as measures of the aperiodic exponent in the frequency domain. There is a lack of clear understanding of how these different measures relate to each other and to what extent they reflect the same or different properties of the data, which makes it difficult to synthesize results across approaches and complicates our overall understanding of the properties, biological significance, and demographic, clinical, and behavioral correlates of aperiodic neural activity. To address this problem, in this project we systematically survey the different approaches for measuring aperiodic neural activity, starting with an automated literature analysis to curate a collection of the most common methods. We then evaluate and compare these methods, using statistically representative time series simulations. In doing so, we establish consistent relationships between the measures, showing that much of what they capture reflects shared variance - though with some notable idiosyncrasies. Broadly, frequency domain methods are more specific to aperiodic features of the data, whereas time domain measures are more impacted by oscillatory activity. We extend this analysis by applying the measures to a series of empirical EEG and iEEG datasets, replicating the simulation results. We conclude by summarizing the relationships between the multiple methods, emphasizing opportunities for re-examining previous findings and for future work.
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Cai T, Zhao G, Zang J, Zong C, Zhang Z, Xue C. Quantifying instability in neurological disorders EEG based on phase space DTM function. Comput Biol Med 2024; 180:108951. [PMID: 39094326 DOI: 10.1016/j.compbiomed.2024.108951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 07/25/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024]
Abstract
Classifying individuals with neurological disorders and healthy subjects using EEG is a crucial area of research. The current feature extraction approach focuses on the frequency domain features in each of the EEG frequency bands and functional brain networks. In recent years, researchers have discovered and extensively studied stability differences in the electroencephalograms (EEG) of patients with neurological disorders. Based on this, this paper proposes a feature descriptor to characterize EEG instability. The proposed method starts by forming a signal point cloud through Phase Space Reconstruction (PSR). Subsequently, a pseudo-metric space is constructed, and pseudo-distances are calculated based on the consistent measure of the point cloud. Finally, Distance to Measure (DTM) Function are generated to replace the distance function in the original metric space. We calculated the relative distances in the point cloud by measuring signal similarity and, based on this, summarized the point cloud structures formed by EEG with different stabilities after PSR. This process demonstrated that Multivariate Kernel Density Estimation (MKDE) based on a Gaussian kernel can effectively separate the mappings of different stable components within the signal in the phase space. The two average DTM values are then proposed as feature descriptors for EEG instability.In the validation phase, the proposed feature descriptor is tested on three typical neurological disorders: epilepsy, Alzheimer's disease, and Parkinson's disease, using the Bonn dataset, CHB-MIT, the Florida State University dataset, and the Iowa State University dataset. DTM values are used as feature inputs for four different machine learning classifiers, and The results show that the best classification accuracy of the proposed method reaches 98.00 %, 96.25 %, 96.71 % and 95.34 % respectively, outperforming commonly used nonlinear descriptors. Finally, the proposed method is tested and analyzed using noisy signals, demonstrating its robustness compared to other methods.
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Affiliation(s)
- Tianming Cai
- Shanxi College of Technology, No.11 Changning Street, Development Zone, Shuozhou, Shanxi, 036000, China; North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
| | - Guoying Zhao
- Shanxi College of Technology, No.11 Changning Street, Development Zone, Shuozhou, Shanxi, 036000, China; North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
| | - Junbin Zang
- Shanxi College of Technology, No.11 Changning Street, Development Zone, Shuozhou, Shanxi, 036000, China; North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China.
| | - Chen Zong
- The Second Hospital of Shanxi Medical University, No.382 Wuyi Road, Taiyuan, Shanxi, 030001, China
| | - Zhidong Zhang
- North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
| | - Chenyang Xue
- North University of China, School of Instrument and Electronics, No.3 College Road, Jiancaoping District, Taiyuan, Shanxi, 030051, China
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Shi LJ, Li CC, Zhang XT, Lin YC, Wang YP, Zhang JC. Application of HFO and scaling analysis of neuronal oscillations in the presurgical evaluation of focal epilepsy. Brain Res Bull 2024; 215:111018. [PMID: 38908759 DOI: 10.1016/j.brainresbull.2024.111018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/07/2024] [Accepted: 06/19/2024] [Indexed: 06/24/2024]
Abstract
PURPOSE To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy. METHODS MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results. RESULTS Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ. CONCLUSION HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.
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Affiliation(s)
- Li-Juan Shi
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Can-Cheng Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Xia-Ting Zhang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing 100053, China
| | - Yi-Cong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing 100053, China
| | - Yu-Ping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing 100053, China.
| | - Ji-Cong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China; Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China.
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Tan W, Zhang H, Wang Z, Li H, Gao X, Zeng N. S 3T-Net: A novel electroencephalogram signals-oriented emotion recognition model. Comput Biol Med 2024; 179:108808. [PMID: 38996556 DOI: 10.1016/j.compbiomed.2024.108808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 06/01/2024] [Accepted: 06/24/2024] [Indexed: 07/14/2024]
Abstract
In this paper, a novel skipping spatial-spectral-temporal network (S3T-Net) is developed to handle intra-individual differences in electroencephalogram (EEG) signals for accurate, robust, and generalized emotion recognition. In particular, aiming at the 4D features extracted from the raw EEG signals, a multi-branch architecture is proposed to learn spatial-spectral cross-domain representations, which benefits enhancing the model generalization ability. Time dependency among different spatial-spectral features is further captured via a bi-directional long-short term memory module, which employs an attention mechanism to integrate context information. Moreover, a skip-change unit is designed to add another auxiliary pathway for updating model parameters, which alleviates the vanishing gradient problem in complex spatial-temporal network. Evaluation results show that the proposed S3T-Net outperforms other advanced models in terms of the emotion recognition accuracy, which yields an performance improvement of 0.23% , 0.13%, and 0.43% as compared to the sub-optimal model in three test scenes, respectively. In addition, the effectiveness and superiority of the key components of S3T-Net are demonstrated from various experiments. As a reliable and competent emotion recognition model, the proposed S3T-Net contributes to the development of intelligent sentiment analysis in human-computer interaction (HCI) realm.
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Affiliation(s)
- Weilong Tan
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Fujian 361024, China
| | - Hongyi Zhang
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Fujian 361024, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK
| | - Han Li
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361105, China
| | - Xingen Gao
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Fujian 361024, China
| | - Nianyin Zeng
- Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361105, China.
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Gorshkov O, Ombao H. Assessment of Fractal Synchronization during an Epileptic Seizure. ENTROPY (BASEL, SWITZERLAND) 2024; 26:666. [PMID: 39202136 PMCID: PMC11353581 DOI: 10.3390/e26080666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 07/31/2024] [Accepted: 08/01/2024] [Indexed: 09/03/2024]
Abstract
In this paper, we define fractal synchronization (FS) based on the idea of stochastic synchronization and propose a mathematical apparatus for estimating FS. One major advantage of our proposed approach is that fractal synchronization makes it possible to estimate the aggregate strength of the connection on multiple time scales between two projections of the attractor, which are time series with a fractal structure. We believe that one of the promising uses of FS is the assessment of the interdependence of encephalograms. To demonstrate this approach in evaluating the cross-dependence between channels in a network of electroencephalograms, we evaluated the FS of encephalograms during an epileptic seizure. Fractal synchronization demonstrates the presence of desynchronization during an epileptic seizure.
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Affiliation(s)
- Oleg Gorshkov
- Statistics Program, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia;
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Semba S, Yang H, Chen X, Wan H, Gu C. Estimation of Carleman operator from a univariate time series. CHAOS (WOODBURY, N.Y.) 2024; 34:083103. [PMID: 39088344 DOI: 10.1063/5.0209612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/10/2024] [Indexed: 08/03/2024]
Abstract
Reconstructing a nonlinear dynamical system from empirical time series is a fundamental task in data-driven analysis. One of the main challenges is the existence of hidden variables; we only have records for some variables, and those for hidden variables are unavailable. In this work, the techniques for Carleman linearization, phase-space embedding, and dynamic mode decomposition are integrated to rebuild an optimal dynamical system from time series for one specific variable. Using the Takens theorem, the embedding dimension is determined, which is adopted as the dynamical system's dimension. The Carleman linearization is then used to transform this finite nonlinear system into an infinite linear system, which is further truncated into a finite linear system using the dynamic mode decomposition technique. We illustrate the performance of this integrated technique using data generated by the well-known Lorenz model, the Duffing oscillator, and empirical records of electrocardiogram, electroencephalogram, and measles outbreaks. The results show that this solution accurately estimates the operators of the nonlinear dynamical systems. This work provides a new data-driven method to estimate the Carleman operator of nonlinear dynamical systems.
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Affiliation(s)
- Sherehe Semba
- Department of Systems Science, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
- Faculty of Science, Dar es Salaam University College of Education, University of Dar es Salaam, Dar es Salaam, Tanzania
| | - Huijie Yang
- Department of Systems Science, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xiaolu Chen
- Nanxun Innovation Institute, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
| | - Huiyun Wan
- Department of Systems Science, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Changgui Gu
- Department of Systems Science, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
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Javaid H, Nouman M, Cheaha D, Kumarnsit E, Chatpun S. Complexity measures reveal age-dependent changes in electroencephalogram during working memory task. Behav Brain Res 2024; 470:115070. [PMID: 38806100 DOI: 10.1016/j.bbr.2024.115070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 05/09/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
The alterations in electroencephalogram (EEG) signals are the complex outputs of functional factors, such as normal physiological aging, pathological process, which results in further cognitive decline. It is not clear that when brain aging initiates, but elderly people are vulnerable to be incipient of neurodegenerative diseases such as Alzheimer's disease. The EEG signals were recorded from 20 healthy middle age and 20 healthy elderly subjects while performing a working memory task. Higuchi's fractal dimension (HFD), Katz's fractal dimension (KFD), sample entropy and three Hjorth parameters were extracted to analyse the complexity of EEG signals. Four machine learning classifiers, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbour (KNN), and logistic model tree (LMT) were employed to distinguish the EEG signals of middle age and elderly age groups. HFD, KFD and Hjorth complexity were found significantly correlated with age. MLP achieved the highest overall accuracy of 93.75%. For posterior region, the maximum accuracy of 92.50% was achieved using MLP. Since fractal dimension associated with the complexity of EEG signals, HFD, KFD and Hjorth complexity demonstrated the decreased complexity from middle age to elderly groups. The complexity features appear to be more appropriate indicators of monitoring EEG signal complexity in healthy aging.
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Affiliation(s)
- Hamad Javaid
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand; Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, Exeter, Ex4 4QG, United Kingdom
| | - Muhammad Nouman
- Sirindhorn School of Prosthetics and Orthotics, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Dania Cheaha
- Biology program, Division of Biological Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand; Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand
| | - Ekkasit Kumarnsit
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand; Physiology Program, Division of Health and Applied Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
| | - Surapong Chatpun
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand; Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand; Institute of Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand.
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Liao XY, Gao YX, Qian TT, Zhou LH, Li LQ, Gong Y, Ye TF. Bibliometric analysis of electroencephalogram research in Parkinson's disease from 2004 to 2023. Front Neurosci 2024; 18:1433583. [PMID: 39099632 PMCID: PMC11294212 DOI: 10.3389/fnins.2024.1433583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 07/08/2024] [Indexed: 08/06/2024] Open
Abstract
Background Parkinson's disease (PD) is a prevalent neurodegenerative disorder affecting millions globally. It encompasses both motor and non-motor symptoms, with a notable impact on patients' quality of life. Electroencephalogram (EEG) is a non-invasive tool that is increasingly utilized to investigate neural mechanisms in PD, identify early diagnostic markers, and assess therapeutic responses. Methods The data were sourced from the Science Citation Index Expanded within the Web of Science Core Collection database, focusing on publications related to EEG research in PD from 2004 to 2023. A comprehensive bibliometric analysis was conducted using CiteSpace and VOSviewer software. The analysis began with an evaluation of the selected publications, identifying leading countries, institutions, authors, and journals, as well as co-cited references, to summarize the current state of EEG research in PD. Keywords are employed to identify research topics that are currently of interest in this field through the analysis of high-frequency keyword co-occurrence and cluster analysis. Finally, burst keywords were identified to uncover emerging trends and research frontiers in the field, highlighting shifts in interest and identifying future research directions. Results A total of 1,559 publications on EEG research in PD were identified. The United States, Germany, and England have made notable contributions to the field. The University of London is the leading institution in terms of publication output, with the University of California closely following. The most prolific authors are Brown P, Fuhr P, and Stam C In terms of total citations and per-article citations, Stam C has the highest number of citations, while Brown P has the highest H-index. In terms of the total number of publications, Clinical Neurophysiology is the leading journal, while Brain is the most highly cited. The most frequently cited articles pertain to software toolboxes for EEG analysis, neural oscillations, and PD pathophysiology. Through analyzing the keywords, four research hotspots were identified: research on the neural oscillations and connectivity, research on the innovations in EEG Analysis, impact of therapies on EEG, and research on cognitive and emotional assessments. Conclusion This bibliometric analysis demonstrates a growing global interest in EEG research in PD. The investigation of neural oscillations and connectivity remains a primary focus of research. The application of machine learning, deep learning, and task analysis techniques offers promising avenues for future research in EEG and PD, suggesting the potential for advancements in this field. This study offers valuable insights into the major research trends, influential contributors, and evolving themes in this field, providing a roadmap for future exploration.
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Affiliation(s)
- Xiao-Yu Liao
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Ya-Xin Gao
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Ting-Ting Qian
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Lu-Han Zhou
- The Fourth Rehabilitation Hospital of Shanghai, Shanghai, China
| | - Li-Qin Li
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Yan Gong
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Tian-Fen Ye
- Department of Rehabilitation Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
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Nucci L, Miraglia F, Pappalettera C, Rossini PM, Vecchio F. Exploring the complexity of EEG patterns in Parkinson's disease. GeroScience 2024:10.1007/s11357-024-01277-y. [PMID: 38997574 DOI: 10.1007/s11357-024-01277-y] [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: 11/15/2023] [Accepted: 07/02/2024] [Indexed: 07/14/2024] Open
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20 Hz) and beta 2 (20-30 Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.
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Affiliation(s)
- Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy.
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
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Zandbagleh A, Miltiadous A, Sanei S, Azami H. Beta-to-Theta Entropy Ratio of EEG in Aging, Frontotemporal Dementia, and Alzheimer's Dementia. Am J Geriatr Psychiatry 2024:S1064-7481(24)00380-4. [PMID: 39004533 DOI: 10.1016/j.jagp.2024.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND Aging, frontotemporal dementia (FTD), and Alzheimer's dementia (AD) manifest electroencephalography (EEG) alterations, particularly in the beta-to-theta power ratio derived from linear power spectral density (PSD). Given the brain's nonlinear nature, the EEG nonlinear features could provide valuable physiological indicators of aging and cognitive impairment. Multiscale dispersion entropy (MDE) serves as a sensitive nonlinear metric for assessing the information content in EEGs across biologically relevant time scales. OBJECTIVE To compare the MDE-derived beta-to-theta entropy ratio with its PSD-based counterpart to detect differences between healthy young and elderly subjects and between different dementia subtypes. METHODS Scalp EEG recordings were obtained from two datasets: 1) Aging dataset: 133 healthy young and 65 healthy older adult individuals; and 2) Dementia dataset: 29 age-matched healthy controls (HC), 23 FTD, and 36 AD participants. The beta-to-theta ratios based on MDE vs. PSD were analyzed for both datasets. Finally, the relationships between cognitive performance and the beta-to-theta ratios were explored in HC, FTD, and AD. RESULTS In the Aging dataset, older adults had significantly higher beta-to-theta entropy ratios than young adults. In the Dementia dataset, this ratio outperformed the beta-to-theta PSD approach in distinguishing between HC, FTD, and AD. The AD participants had a significantly lower beta-to-theta entropy ratio than FTD, especially in the temporal region, unlike its corresponding PSD-based ratio. The beta-to-theta entropy ratio correlated significantly with cognitive performance. CONCLUSION Our study introduces the beta-to-theta entropy ratio using nonlinear MDE for EEG analysis, highlighting its potential as a sensitive biomarker for aging and cognitive impairment.
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Affiliation(s)
- Ahmad Zandbagleh
- School of Electrical Engineering (AZ), Iran University of Science and Technology, Tehran, Iran
| | - Andreas Miltiadous
- Department of Informatics and Telecommunications (AM), University of Ioannina, Arta, Greece
| | - Saeid Sanei
- Electrical and Electronic Engineering Department (SS), Imperial College London, London, UK
| | - Hamed Azami
- Centre for Addiction and Mental Health (HA), University of Toronto, Toronto, ON, Canada.
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Ghorbani F, Zhou X, Talebi N, Roessner V, Hommel B, Prochnow A, Beste C. Neural connectivity patterns explain why adolescents perceive the world as moving slow. Commun Biol 2024; 7:759. [PMID: 38909084 PMCID: PMC11193795 DOI: 10.1038/s42003-024-06439-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 06/11/2024] [Indexed: 06/24/2024] Open
Abstract
That younger individuals perceive the world as moving slower than adults is a familiar phenomenon. Yet, it remains an open question why that is. Using event segmentation theory, electroencephalogram (EEG) beamforming and nonlinear causal relationship estimation using artificial neural network methods, we studied neural activity while adolescent and adult participants segmented a movie. We show when participants were instructed to segment a movie into meaningful units, adolescents partitioned incoming information into fewer encapsulated segments or episodes of longer duration than adults. Importantly, directed communication between medial frontal and lower-level perceptual areas and between occipito-temporal regions in specific neural oscillation spectrums explained behavioral differences between groups. Overall, the study reveals that a different organization of directed communication between brain regions and inefficient transmission of information between brain regions are key to understand why younger people perceive the world as moving slow.
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Affiliation(s)
- Foroogh Ghorbani
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Schubertstrasse 42, 01307, Dresden, Germany
| | - Xianzhen Zhou
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Schubertstrasse 42, 01307, Dresden, Germany
| | - Nasibeh Talebi
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Schubertstrasse 42, 01307, Dresden, Germany
| | - Veit Roessner
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Schubertstrasse 42, 01307, Dresden, Germany
| | - Bernhard Hommel
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Schubertstrasse 42, 01307, Dresden, Germany
- School of Psychology, Shandong Normal University, Jinan, China
| | - Astrid Prochnow
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Schubertstrasse 42, 01307, Dresden, Germany
| | - Christian Beste
- Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Schubertstrasse 42, 01307, Dresden, Germany.
- School of Psychology, Shandong Normal University, Jinan, China.
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Porcaro C, Seppi D, Pellegrino G, Dainese F, Kassabian B, Pellegrino L, De Nardi G, Grego A, Corbetta M, Ferreri F. Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension. Front Neurosci 2024; 18:1401068. [PMID: 38911599 PMCID: PMC11192015 DOI: 10.3389/fnins.2024.1401068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 05/20/2024] [Indexed: 06/25/2024] Open
Abstract
Objectives An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods. Materials Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC). Methods EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups. Results The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ. Discussion FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes. Conclusion Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.
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Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Institute of Cognitive Sciences and Technologies (ISTC) – National Research Council (CNR), Rome, Italy
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Dario Seppi
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Giovanni Pellegrino
- Epilepsy Program, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Filippo Dainese
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Benedetta Kassabian
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Luciano Pellegrino
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Gianluigi De Nardi
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Alberto Grego
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Veneto Institute of Molecular Medicine (VIMM), Fondazione Biomedica, Padua, Italy
| | - Florinda Ferreri
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
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13
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Carvalho VR, Mendes EMAM, Fallah A, Sejnowski TJ, Comstock L, Lainscsek C. Decoding imagined speech with delay differential analysis. Front Hum Neurosci 2024; 18:1398065. [PMID: 38826617 PMCID: PMC11140152 DOI: 10.3389/fnhum.2024.1398065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 04/25/2024] [Indexed: 06/04/2024] Open
Abstract
Speech decoding from non-invasive EEG signals can achieve relatively high accuracy (70-80%) for strictly delimited classification tasks, but for more complex tasks non-invasive speech decoding typically yields a 20-50% classification accuracy. However, decoder generalization, or how well algorithms perform objectively across datasets, is complicated by the small size and heterogeneity of existing EEG datasets. Furthermore, the limited availability of open access code hampers a comparison between methods. This study explores the application of a novel non-linear method for signal processing, delay differential analysis (DDA), to speech decoding. We provide a systematic evaluation of its performance on two public imagined speech decoding datasets relative to all publicly available deep learning methods. The results support DDA as a compelling alternative or complementary approach to deep learning methods for speech decoding. DDA is a fast and efficient time-domain open-source method that fits data using only few strong features and does not require extensive preprocessing.
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Affiliation(s)
- Vinícius Rezende Carvalho
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Postgraduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | | | - Aria Fallah
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States
| | - Terrence J. Sejnowski
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, United States
- Institute for Neural Computation University of California, San Diego, La Jolla, CA, United States
- Department of Neurobiology, University of California, San Diego, La Jolla, CA, United States
| | - Lindy Comstock
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Claudia Lainscsek
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, United States
- Institute for Neural Computation University of California, San Diego, La Jolla, CA, United States
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14
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Mercier M, Pepi C, Carfi-Pavia G, De Benedictis A, Espagnet MCR, Pirani G, Vigevano F, Marras CE, Specchio N, De Palma L. The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach. Sci Rep 2024; 14:10887. [PMID: 38740844 PMCID: PMC11091060 DOI: 10.1038/s41598-024-60622-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 04/25/2024] [Indexed: 05/16/2024] Open
Abstract
Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.
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Affiliation(s)
- Mattia Mercier
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
- Department of Physiology, Behavioural Neuroscience PhD Program, Sapienza University, Rome, Italy
| | - Chiara Pepi
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Giusy Carfi-Pavia
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | | | - Greta Pirani
- Department of Mechanical and Aerospace Engineering - DIMA, Sapienza University of Rome, Rome, Italy
| | - Federico Vigevano
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
| | - Carlo Efisio Marras
- Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Nicola Specchio
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy.
| | - Luca De Palma
- Neurology, Epilepsy and Movement Disorders Unit, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, Piazza S. Onofrio 4, 00165, Rome, Italy
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15
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Caravati E, Barbeni F, Chiarion G, Raggi M, Mesin L. Closed-Loop Transcranial Electrical Neurostimulation for Sustained Attention Enhancement: A Pilot Study towards Personalized Intervention Strategies. Bioengineering (Basel) 2024; 11:467. [PMID: 38790334 PMCID: PMC11118513 DOI: 10.3390/bioengineering11050467] [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: 04/06/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Sustained attention is pivotal for tasks like studying and working for which focus and low distractions are necessary for peak productivity. This study explores the effectiveness of adaptive transcranial direct current stimulation (tDCS) in either the frontal or parietal region to enhance sustained attention. The research involved ten healthy university students performing the Continuous Performance Task-AX (AX-CPT) while receiving either frontal or parietal tDCS. The study comprised three phases. First, we acquired the electroencephalography (EEG) signal to identify the most suitable metrics related to attention states. Among different spectral and complexity metrics computed on 3 s epochs of EEG, the Fuzzy Entropy and Multiscale Sample Entropy Index of frontal channels were selected. Secondly, we assessed how tDCS at a fixed 1.0 mA current affects attentional performance. Finally, a real-time experiment involving continuous metric monitoring allowed personalized dynamic optimization of the current amplitude and stimulation site (frontal or parietal). The findings reveal statistically significant improvements in mean accuracy (94.04 vs. 90.82%) and reaction times (262.93 vs. 302.03 ms) with the adaptive tDCS compared to a non-stimulation condition. Average reaction times were statistically shorter during adaptive stimulation compared to a fixed current amplitude condition (262.93 vs. 283.56 ms), while mean accuracy stayed similar (94.04 vs. 93.36%, improvement not statistically significant). Despite the limited number of subjects, this work points out the promising potential of adaptive tDCS as a tailored treatment for enhancing sustained attention.
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Affiliation(s)
| | | | | | | | - Luca Mesin
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy; (E.C.); (F.B.); (G.C.); (M.R.)
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16
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Keshmiri S, Tomonaga S, Mizutani H, Doya K. Respiratory modulation of the heart rate: A potential biomarker of cardiorespiratory function in human. Comput Biol Med 2024; 173:108335. [PMID: 38564855 DOI: 10.1016/j.compbiomed.2024.108335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 03/07/2024] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans' physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans' digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (7 days, 4.63 ± 1.52) HR and RR of 89 freely behaving human subjects (Female: 39, age 57.28 ± 5.67, Male: 50, age 58.48 ± 6.32) and analyzed their dynamics using linear models and information theoretic measures. While HR's linear and nonlinear characteristics were expressed within the plane of the HR-RR directed flow of information (HR→RR - RR→HR), their dynamics were determined by its RR→HR axis. More importantly, RR→HR quantified the effect of alcohol consumption on individuals' cardiorespiratory function independent of their consumed amount of alcohol, thereby signifying the presence of this habit in their daily life activities. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals. These results can contribute to humans' phenotyping research by presenting RR→HR as a digital diagnosis/prognosis marker of humans' cardiorespiratory pathology.
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Affiliation(s)
- Soheil Keshmiri
- Optical Neuroimaging Unit, Okinawa Institute of Science and Technology, Okinawa, Japan.
| | - Sutashu Tomonaga
- Neural Computation Unit (NCU), Okinawa Institute of Science and Technology, Okinawa, Japan.
| | - Haruo Mizutani
- Suntory Global Innovation Center Limited (SGIC), Suntory, Kyoto, Japan.
| | - Kenji Doya
- Neural Computation Unit (NCU), Okinawa Institute of Science and Technology, Okinawa, Japan.
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17
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Penalba-Sánchez L, Silva G, Crook-Rumsey M, Sumich A, Rodrigues PM, Oliveira-Silva P, Cifre I. Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:2811. [PMID: 38732917 PMCID: PMC11086092 DOI: 10.3390/s24092811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/20/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024]
Abstract
Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.
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Affiliation(s)
- Lucía Penalba-Sánchez
- Facultat de Psicología, Ciències de l’Educació i de l’Esport (FPCEE), Blanquerna, Universitat Ramon Llull, 08022 Barcelona, Spain; (L.P.-S.)
- Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculty of Education and Psychology, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
- Department of Psychology, Nottingham Trent University (NTU), Nottingham NG1 4FQ, UK
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke-University Magdeburg (OVGU), 39120 Magdeburg, Germany
| | - Gabriel Silva
- Centro de Biotecnologia e Química Fina (CBQF)—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
| | - Mark Crook-Rumsey
- UK Dementia Research Institute (UK DRI), Centre for Care Research and Technology, Imperial College London, London W1T 7NF, UK
- UK Dementia Research Institute (UK DRI), Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RX, UK
| | - Alexander Sumich
- Department of Psychology, Nottingham Trent University (NTU), Nottingham NG1 4FQ, UK
| | - Pedro Miguel Rodrigues
- Centro de Biotecnologia e Química Fina (CBQF)—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
| | - Patrícia Oliveira-Silva
- Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculty of Education and Psychology, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
| | - Ignacio Cifre
- Facultat de Psicología, Ciències de l’Educació i de l’Esport (FPCEE), Blanquerna, Universitat Ramon Llull, 08022 Barcelona, Spain; (L.P.-S.)
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18
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Kang JH, Bae JH, Jeon YJ. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering (Basel) 2024; 11:418. [PMID: 38790286 PMCID: PMC11118246 DOI: 10.3390/bioengineering11050418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
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Affiliation(s)
- Jae-Hwan Kang
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jang-Han Bae
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Young-Ju Jeon
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
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Średniawa W, Borzymowska Z, Kondrakiewicz K, Jurgielewicz P, Mindur B, Hottowy P, Wójcik DK, Kublik E. Local contribution to the somatosensory evoked potentials in rat's thalamus. PLoS One 2024; 19:e0301713. [PMID: 38593141 PMCID: PMC11003638 DOI: 10.1371/journal.pone.0301713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/19/2024] [Indexed: 04/11/2024] Open
Abstract
Local Field Potential (LFP), despite its name, often reflects remote activity. Depending on the orientation and synchrony of their sources, both oscillations and more complex waves may passively spread in brain tissue over long distances and be falsely interpreted as local activity at such distant recording sites. Here we show that the whisker-evoked potentials in the thalamic nuclei are of local origin up to around 6 ms post stimulus, but the later (7-15 ms) wave is overshadowed by a negative component reaching from cortex. This component can be analytically removed and local thalamic LFP can be recovered reliably using Current Source Density analysis. We used model-based kernel CSD (kCSD) method which allowed us to study the contribution of local and distant currents to LFP from rat thalamic nuclei and barrel cortex recorded with multiple, non-linear and non-regular multichannel probes. Importantly, we verified that concurrent recordings from the cortex are not essential for reliable thalamic CSD estimation. The proposed framework can be used to analyze LFP from other brain areas and has consequences for general LFP interpretation and analysis.
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Affiliation(s)
- Władysław Średniawa
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
| | - Zuzanna Borzymowska
- Neurobiology of Emotions Laboratory, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
| | - Kacper Kondrakiewicz
- Neurobiology of Emotions Laboratory, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
| | - Paweł Jurgielewicz
- AGH University of Science and Technology in Kraków, Faculty of Physics and Applied Computer Science, Krakow, Poland
| | - Bartosz Mindur
- AGH University of Science and Technology in Kraków, Faculty of Physics and Applied Computer Science, Krakow, Poland
| | - Paweł Hottowy
- AGH University of Science and Technology in Kraków, Faculty of Physics and Applied Computer Science, Krakow, Poland
| | - Daniel K. Wójcik
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
- Jagiellonian University, Faculty of Management and Social Communication, Jagiellonian University, Krakow, Poland
| | - Ewa Kublik
- Neurobiology of Emotions Laboratory, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
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20
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van Nifterick AM, Scheijbeler EP, Gouw AA, de Haan W, Stam CJ. Local signal variability and functional connectivity: Sensitive measures of the excitation-inhibition ratio? Cogn Neurodyn 2024; 18:519-537. [PMID: 38699618 PMCID: PMC11061092 DOI: 10.1007/s11571-023-10003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/08/2023] [Accepted: 08/13/2023] [Indexed: 05/05/2024] Open
Abstract
A novel network version of permutation entropy, the inverted joint permutation entropy (JPEinv), holds potential as non-invasive biomarker of abnormal excitation-inhibition (E-I) ratio in Alzheimer's disease (AD). In this computational modelling study, we test the hypotheses that this metric, and related measures of signal variability and functional connectivity, are sensitive to altered E-I ratios. The E-I ratio in each neural mass of a whole-brain computational network model was systematically varied. We evaluated whether JPEinv, local signal variability (by permutation entropy) and functional connectivity (by weighted symbolic mutual information (wsMI)) were related to E-I ratio, on whole-brain and regional level. The hub disruption index can identify regions primarily affected in terms of functional connectivity strength (or: degree) by the altered E-I ratios. Analyses were performed for a range of coupling strengths, filter and time-delay settings. On whole-brain level, higher E-I ratios were associated with higher functional connectivity (by JPEinv and wsMI) and lower local signal variability. These relationships were nonlinear and depended on the coupling strength, filter and time-delay settings. On regional level, hub-like regions showed a selective decrease in functional degree (by JPEinv and wsMI) upon a lower E-I ratio, and non-hub-like regions showed a selective increase in degree upon a higher E-I ratio. These results suggest that abnormal functional connectivity and signal variability, as previously reported in patients across the AD continuum, can inform us about altered E-I ratios. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10003-x.
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Affiliation(s)
- Anne M. van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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Guo Z, Wang J, Jing T, Fu L. Investigating the interpretability of schizophrenia EEG mechanism through a 3DCNN-based hidden layer features aggregation framework. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108105. [PMID: 38447316 DOI: 10.1016/j.cmpb.2024.108105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/07/2024] [Accepted: 02/26/2024] [Indexed: 03/08/2024]
Abstract
BACKGROUND AND OBJECTIVE Electroencephalogram (EEG) signals record brain activity, with growing interest in quantifying neural activity through complexity analysis as a potential biological marker for schizophrenia. Presently, EEG complexity analysis primarily relies on manual feature extraction, which is subjective and yields varied findings in studies involving schizophrenia and healthy controls. METHODS This study aims to leverage deep learning methods for enhanced EEG complexity exploration, aiding early schizophrenia screening and diagnosis. Our proposed approach utilizes a three-dimensional Convolutional Neural Network (3DCNN) to extract enhanced data features for early schizophrenia identification and subsequent complexity analysis. Leveraging the spatiotemporal capabilities of 3DCNN, we extract advanced latent features and employ knowledge distillation to reintegrate these features into the original channels, creating feature-enhanced data. RESULTS We employ a 10-fold cross-validation strategy, achieving the average accuracies of 99.46% and 98.06% in subject-dependent experiments on Dataset 1(14SZ and 14HC) and Dataset 2 (45SZ and 39HC). The average accuracy for subject-independent is 96.04% and 92.67% on both datasets. Feature extraction and classification are conducted on both the re-aggregated data and the original data. Our results demonstrate that re-aggregated data exhibit superior classification performance and a more stable training process after feature extraction. In the complexity analysis of re-aggregated data, we observe lower entropy features in schizophrenic patients compared to healthy controls, with more pronounced differences in the temporal and frontal lobes. Analyzing Katz's Fractal Dimension (KFD) across three sub-bands of lobe channels reveals the lowest α band KFD value in schizophrenia patients. CONCLUSIONS This emphasizes the ability of our method to enhance the discrimination and interpretability in schizophrenia detection and analysis. Our approach enhances the potential for EEG-based schizophrenia diagnosis by leveraging deep learning, offering superior discrimination capabilities and richer interpretive insights.
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Affiliation(s)
- Zhifen Guo
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
| | - Jiao Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
| | - Tianyu Jing
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
| | - Longyue Fu
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
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Riehm CD, Bonnette S, Rush JL, Diekfuss JA, Koohestani M, Myer GD, Norte GE, Sherman DA. Corticomuscular cross-recurrence analysis reveals between-limb differences in motor control among individuals with ACL reconstruction. Exp Brain Res 2024; 242:355-365. [PMID: 38092900 PMCID: PMC10872341 DOI: 10.1007/s00221-023-06751-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/16/2023] [Indexed: 01/04/2024]
Abstract
Surgical reconstruction of the anterior cruciate ligament (ACL) and subsequent physical therapy can help athletes return to competition; however, re-injury rates remain disproportionately high due, in part, to lingering biomechanical and neurological factors that are not fully addressed during rehabilitation. Prior reports indicate that individuals exhibit altered electrical activity in both brain and muscle after ACL reconstruction (ACLR). In this investigation, we aimed to extend existing approaches by introducing a novel non-linear analysis of corticomuscular dynamics, which does not assume oscillatory coupling between brain and muscle: Corticomuscular cross-recurrence analysis (CM-cRQA). Our findings indicate that corticomuscular dynamics vary significantly between involved (injured) and uninvolved legs of participants with ACLR during voluntary isometric contractions between the brain and both the vastus medialis and lateralis. This finding points to a potential lingering neural deficit underlying re-injury for athletes after surgical reconstruction, namely the dynamical structure of neuromuscular (brain to quad muscle) coordination, which is significantly asymmetric, between limbs, in those who have ACLR.
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Affiliation(s)
- Christopher D Riehm
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, GA, USA.
- Emory Sports Medicine Center, Atlanta, GA, USA.
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA.
| | - Scott Bonnette
- Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Justin L Rush
- Division of Physical Therapy, School of Rehabilitation Sciences, Ohio University, Athens, OH, USA
| | - Jed A Diekfuss
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, GA, USA
- Emory Sports Medicine Center, Atlanta, GA, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
| | - Moein Koohestani
- Neuroplasticity, & Sarcopenia (CNS) Lab, Institute of Exercise Physiology and Rehabilitation Science, University of Central Florida, Orlando, FL, USA
| | - Gregory D Myer
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, GA, USA
- Emory Sports Medicine Center, Atlanta, GA, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
- The Micheli Center for Sports Injury Prevention, Waltham, MA, USA
- Youth Physical Development Centre, Cardiff Metropolitan University, Wales, UK
| | - Grant E Norte
- Neuroplasticity, & Sarcopenia (CNS) Lab, Institute of Exercise Physiology and Rehabilitation Science, University of Central Florida, Orlando, FL, USA
| | - David A Sherman
- Live4 Physical Therapy and Wellness, Acton, MA, USA
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA
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23
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Naghdabadi Z, Jahed M. Heterogeneous correlate and potential diagnostic biomarker of tinnitus based on nonlinear dynamics of resting-state EEG recordings. PLoS One 2024; 19:e0290563. [PMID: 38166014 PMCID: PMC10760901 DOI: 10.1371/journal.pone.0290563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 08/09/2023] [Indexed: 01/04/2024] Open
Abstract
Tinnitus is a heterogeneous condition of hearing a rattling sound when there is no auditory stimulus. This rattling sound is associated with abnormal synchronous oscillations in auditory and non-auditory cortical areas. Since tinnitus is a highly heterogeneous condition with no objective detection criteria, it is necessary to search for indicators that can be compared between and within participants for diagnostic purposes. This study introduces heterogeneous though comparable indicators of tinnitus through investigation of spontaneous fluctuations in resting-state brain dynamics. The proposed approach uses nonlinear measures of chaos theory, to detect tinnitus and cross correlation patterns to reflect many of the previously reported neural correlates of tinnitus. These indicators may serve as effective measures of tinnitus risk even at early ages before any symptom is reported. The approach quantifies differences in oscillatory brain dynamics of tinnitus and normal subjects. It demonstrates that the left temporal areas of subjects with tinnitus exhibit larger lyapunov exponent indicating irregularity of brain dynamics in these regions. More complex dynamics is further recognized in tinnitus cases through entropy. We use this evidence to distinguish tinnitus patients from normal participants. Besides, we illustrate that certain anticorrelation patterns appear in these nonlinear measures across temporal and frontal areas in the brain perhaps corresponding to increased/decreased connectivity in certain brain networks and a shift in the balance of excitation and inhibition in tinnitus. Additionally, the main correlations are lost in tinnitus participants compared to control group suggesting involvement of distinct neural mechanisms in generation and persistence of tinnitus.
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Affiliation(s)
- Zahra Naghdabadi
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Mehran Jahed
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
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24
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Chen X, Niu Y, Zhao Y, Qin X. An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection. Int J Neural Syst 2024; 34:2450003. [PMID: 37964570 DOI: 10.1142/s0129065724500035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
To avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of privacy breaches. To address these issues, a framework called Group Federated Learning (Group-FL) for large-scale driver drowsiness detection is proposed, which can efficiently utilize diverse client data while protecting privacy. First, by arranging the clients into different levels of groups and gradually aggregating their model parameters from low-level groups to high-level groups, communication and time costs are reduced. In addition, to solve the problem of notable variations in EEG signals among different clients, a global-personalized deep neural network is designed. The global model extracts shared features from various clients, while the personalized model extracts fine-grained features from each client and outputs classification results. Finally, to address special issues such as scale/category imbalance and data pollution, three checking modules are designed for adjusting grouping, evaluating client data, and effectively applying personalized models. Through extensive experimentation, the effectiveness of each component within the framework was validated, and a mean accuracy, F1-score, and Area Under Curve (AUC) of 81.0%, 82.0%, and 87.9% was achieved, respectively, on a publicly available dataset comprising 11 subjects.
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Affiliation(s)
- Xinyuan Chen
- School of Information Science and Engineering, Shandong Normal University, Jinan 250014, P. R. China
| | - Yi Niu
- School of Information Science and Engineering, Shandong Normal University, Jinan 250014, P. R. China
| | - Yanna Zhao
- School of Information Science and Engineering, Shandong Normal University, Jinan 250014, P. R. China
| | - Xue Qin
- School of Information Science and Engineering, Shandong Normal University, Jinan 250014, P. R. China
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25
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Riehm CD, Zuleger T, Diekfuss JA, Arellano E, Myer GD. The Evolution of Neuroimaging Technologies to Evaluate Neural Activity Related to Knee Pain and Injury Risk. Curr Rev Musculoskelet Med 2024; 17:14-22. [PMID: 38109007 PMCID: PMC10766917 DOI: 10.1007/s12178-023-09877-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/26/2023] [Indexed: 12/19/2023]
Abstract
PURPOSE OF REVIEW In this review, we present recent findings and advancements in the use of neuroimaging to evaluate neural activity relative to ACL injury risk and patellofemoral pain. In particular, we describe prior work using fMRI and EEG that demonstrate the value of these techniques as well as the necessity of continued development in this area. Our goal is to support future work by providing guidance for the successful application of neuroimaging techniques that most effectively expose pain and injury mechanisms. RECENT FINDINGS Recent studies that utilized both fMRI and EEG indicate that athletes who are at risk for future ACL injury exhibit divergent brain activity both during active lower extremity movement and at rest. Such activity patterns are likely due to alterations to cognitive, visual, and attentional processes that manifest as coordination deficits during naturalistic movement that may result in higher risk of injury. Similarly, in individuals with PFP altered brain activity in a number of key regions is related to subjective pain judgements as well as measures of fear of movement. Although these findings may begin to allow objective pain assessment and identification, continued refinement is needed. One key limitation across both ACL and PFP related work is the restriction of movement during fMRI and EEG data collection, which drastically limits ecological validity. Given the lack of sufficient research using EEG and fMRI within a naturalistic setting, our recommendation is that researchers target the use of mobile, source localized EEG as a primary methodology for exposing neural mechanisms of ACL injury risk and PFP. Our contention is that this method provides an optimal balance of spatial and temporal resolution with ecological validity via naturalistic movement.
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Affiliation(s)
- Christopher D Riehm
- Emory Sports Performance And Research Center (SPARC), 4450 Falcon Pkwy, Flowery Branch, GA, 30542, USA.
- Emory Sports Medicine Center, Atlanta, GA, USA.
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA.
| | - Taylor Zuleger
- Emory Sports Performance And Research Center (SPARC), 4450 Falcon Pkwy, Flowery Branch, GA, 30542, USA
- Emory Sports Medicine Center, Atlanta, GA, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
- Neuroscience Graduate Program, University of Cincinnati School of Medicine, Cincinnati, OH, USA
| | - Jed A Diekfuss
- Emory Sports Performance And Research Center (SPARC), 4450 Falcon Pkwy, Flowery Branch, GA, 30542, USA
- Emory Sports Medicine Center, Atlanta, GA, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
| | - Emilio Arellano
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
| | - Gregory D Myer
- Emory Sports Performance And Research Center (SPARC), 4450 Falcon Pkwy, Flowery Branch, GA, 30542, USA
- Emory Sports Medicine Center, Atlanta, GA, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
- Youth Physical Development Centre, Cardiff Metropolitan University, Wales, UK
- The Micheli Center for Sports Injury Prevention, Waltham, MA, USA
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26
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Multimodal evidence suggests the linearity of brain dynamics at the macroscale. Nat Biomed Eng 2024; 8:7-8. [PMID: 38129657 DOI: 10.1038/s41551-023-01127-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
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27
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Nozari E, Bertolero MA, Stiso J, Caciagli L, Cornblath EJ, He X, Mahadevan AS, Pappas GJ, Bassett DS. Macroscopic resting-state brain dynamics are best described by linear models. Nat Biomed Eng 2024; 8:68-84. [PMID: 38082179 PMCID: PMC11357987 DOI: 10.1038/s41551-023-01117-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 09/26/2023] [Indexed: 12/22/2023]
Abstract
It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear behaviours. Here we challenge this assumption by leveraging mathematical models derived from measurements of local field potentials via intracranial electroencephalography and of whole-brain blood-oxygen-level-dependent brain activity via functional magnetic resonance imaging. We used state-of-the-art linear and nonlinear families of models to describe spontaneous resting-state activity of 700 participants in the Human Connectome Project and 122 participants in the Restoring Active Memory project. We found that linear autoregressive models provide the best fit across both data types and three performance metrics: predictive power, computational complexity and the extent of the residual dynamics unexplained by the model. To explain this observation, we show that microscopic nonlinear dynamics can be counteracted or masked by four factors associated with macroscopic dynamics: averaging over space and over time, which are inherent to aggregated macroscopic brain activity, and observation noise and limited data samples, which stem from technological limitations. We therefore argue that easier-to-interpret linear models can faithfully describe macroscopic brain dynamics during resting-state conditions.
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Affiliation(s)
- Erfan Nozari
- Department of Mechanical Engineering, University of California, Riverside, CA, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA
- Department of Bioengineering, University of California, Riverside, CA, USA
| | - Maxwell A Bertolero
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Eli J Cornblath
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
| | - Xiaosong He
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Arun S Mahadevan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - George J Pappas
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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28
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Rostaghi M, Rostaghi S, Humeau-Heurtier A, Rajji TK, Azami H. NLDyn - An open source MATLAB toolbox for the univariate and multivariate nonlinear dynamical analysis of physiological data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107941. [PMID: 38006684 DOI: 10.1016/j.cmpb.2023.107941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 11/27/2023]
Abstract
BACKGROUND AND OBJECTIVE We present NLDyn, an open-source MATLAB toolbox tailored for in-depth analysis of nonlinear dynamics in biomedical signals. Our objective is to offer a user-friendly yet comprehensive platform for researchers to explore the intricacies of time series data. METHODS NLDyn integrates approximately 80 distinct methods, encompassing both univariate and multivariate nonlinear dynamics, setting it apart from existing solutions. This toolbox combines state-of-the-art nonlinear dynamical techniques with advanced multivariate entropy methods, providing users with powerful analytical capabilities. NLDyn enables analyses with or without a sliding window, and users can easily access and customize default parameters. RESULTS NLDyn generates results that are both exportable and visually informative, facilitating seamless integration into research and presentations. Its ongoing development ensures it remains at the forefront of nonlinear dynamics analysis. CONCLUSIONS NLDyn is a valuable resource for researchers in the biomedical field, offering an intuitive interface and a wide array of nonlinear analysis tools. Its integration of advanced techniques empowers users to gain deeper insights from their data. As we continually refine and expand NLDyn's capabilities, we envision it becoming an indispensable tool for the exploration of complex dynamics in biomedical signals.
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Affiliation(s)
- Mostafa Rostaghi
- Modal Analysis Research Laboratory, Faculty of Mechanical Engineering, Semnan University, Semnan, Iran
| | - Sadegh Rostaghi
- Department of Mechanical Engineering, Naghshejahan Higher Education Institute, Isfahan, Iran
| | | | - Tarek K Rajji
- Centre for Addiction and Mental Health, University of Toronto, Toronto Dementia Research Alliance, Toronto, ON, Canada
| | - Hamed Azami
- Centre for Addiction and Mental Health, University of Toronto, Toronto Dementia Research Alliance, Toronto, ON, Canada.
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29
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Olejarczyk E, Cukic M, Porcaro C, Zappasodi F, Tecchio F. Clinical Sensitivity of Fractal Neurodynamics. ADVANCES IN NEUROBIOLOGY 2024; 36:285-312. [PMID: 38468039 DOI: 10.1007/978-3-031-47606-8_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with "self-emerging." This awareness drives us to identify the measures that best quantify the "complexity" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.
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Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland.
| | - Milena Cukic
- Department of Biomimetic Membranes and Textiles, EMPA Material Science and Technology, St. Gallen, Switzerland
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Filippo Zappasodi
- Department of Neuroscienze, Imaging and Clinical Sciences, Gabriele D'annunzio University, Chieti, Italy
| | - Franca Tecchio
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
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30
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Tian F, Zhu L, Shi Q, Wang R, Zhang L, Dong Q, Qian K, Zhao Q, Hu B. The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:1305-1318. [PMID: 37402182 DOI: 10.1109/tbcas.2023.3292237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Abstract
For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have emerged. However, previous research has virtually neglected practical application scenarios, as most studies have focused on analyzing and modeling EEG data. Furthermore, EEG data is typically obtained from specialized devices that are large, complex to operate, and poorly ubiquitous. To address these challenges, a wearable three-lead EEG sensor with flexible electrodes was developed to obtain prefrontal-lobe EEG data. Experimental measurements show that the EEG sensor achieves promising performance (background noise of no more than 0.91 μVpp, Signal-to-Noise Ratio (SNR) of 26--48 dB, and electrode-skin contact impedance of less than 1 K Ω). In addition, EEG data from 70 depressed patients and 108 healthy controls were collected using the EEG sensor, and the linear and nonlinear features were extracted. The features were then weighted and selected using the Ant Lion Optimization (ALO) algorithm to improve classification performance. The experimental results show that the k-NN classifier achieves a classification accuracy of 90.70%, specificity of 96.53%, and sensitivity of 81.79%, indicating the promising potential of the three-lead EEG sensor combined with the ALO algorithm and the k-NN classifier for EEG-assisted depression diagnosis.
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31
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Ahammer H, Reiss MA, Hackhofer M, Andronache I, Radulovic M, Labra-Spröhnle F, Jelinek HF. ComsystanJ: A collection of Fiji/ImageJ2 plugins for nonlinear and complexity analysis in 1D, 2D and 3D. PLoS One 2023; 18:e0292217. [PMID: 37796873 PMCID: PMC10553304 DOI: 10.1371/journal.pone.0292217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/18/2023] [Indexed: 10/07/2023] Open
Abstract
Complex systems such as the global climate, biological organisms, civilisation, technical or social networks exhibit diverse behaviours at various temporal and spatial scales, often characterized by nonlinearity, feedback loops, and emergence. These systems can be characterized by physical quantities such as entropy, information, chaoticity or fractality rather than classical quantities such as time, velocity, energy or temperature. The drawback of these complexity quantities is that their definitions are not always mathematically exact and computational algorithms provide estimates rather than exact values. Typically, evaluations can be cumbersome, necessitating specialized tools. We are therefore introducing ComsystanJ, a novel and user-friendly software suite, providing a comprehensive set of plugins for complex systems analysis, without the need for prior programming knowledge. It is platform independent, end-user friendly and extensible. ComsystanJ combines already known algorithms and newer methods for generalizable analysis of 1D signals, 2D images and 3D volume data including the generation of data sets such as signals and images for testing purposes. It is based on the framework of the open-source image processing software Fiji and ImageJ2. ComsystanJ plugins are macro recordable and are maintained as open-source software. ComsystanJ includes effective surrogate analysis in all dimensions to validate the features calculated by the different algorithms. Future enhancements of the project will include the implementation of parallel computing for image stacks and volumes and the integration of artificial intelligence methods to improve feature recognition and parameter calculation.
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Affiliation(s)
- Helmut Ahammer
- GSRC, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Martin A. Reiss
- Community Coordinated Modeling Center, Greenbelt, Maryland, United States of America
| | - Moritz Hackhofer
- GSRC, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Ion Andronache
- Research Center for Integrated Analysis and Territorial Management, Faculty of Geography, University of Bucharest, Bucharest, Romania
| | - Marko Radulovic
- Experimental Oncology, Institute for Oncology and Radiology of Serbia, Belgrade, Serbia
| | - Fabián Labra-Spröhnle
- School of Biological Sciences - Te Kura Mātauranga Koiora, Victoria University of Wellington - Te Herenga Waka & Paediatrics Research Unit, Te Whatu Ora | Health New Zealand – Nelson Marlborough, Nelson, New Zealand
| | - Herbert Franz Jelinek
- Department of Biomedical Engineering and Health Engineering Innovation Center, Khalifa University, Abu Dhabi, United Arab Emirates
- Biotechnology Center, Khalifa University, Abu Dhabi, United Arab Emirates
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32
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Roland PE. How far neuroscience is from understanding brains. Front Syst Neurosci 2023; 17:1147896. [PMID: 37867627 PMCID: PMC10585277 DOI: 10.3389/fnsys.2023.1147896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/31/2023] [Indexed: 10/24/2023] Open
Abstract
The cellular biology of brains is relatively well-understood, but neuroscientists have not yet generated a theory explaining how brains work. Explanations of how neurons collectively operate to produce what brains can do are tentative and incomplete. Without prior assumptions about the brain mechanisms, I attempt here to identify major obstacles to progress in neuroscientific understanding of brains and central nervous systems. Most of the obstacles to our understanding are conceptual. Neuroscience lacks concepts and models rooted in experimental results explaining how neurons interact at all scales. The cerebral cortex is thought to control awake activities, which contrasts with recent experimental results. There is ambiguity distinguishing task-related brain activities from spontaneous activities and organized intrinsic activities. Brains are regarded as driven by external and internal stimuli in contrast to their considerable autonomy. Experimental results are explained by sensory inputs, behavior, and psychological concepts. Time and space are regarded as mutually independent variables for spiking, post-synaptic events, and other measured variables, in contrast to experimental results. Dynamical systems theory and models describing evolution of variables with time as the independent variable are insufficient to account for central nervous system activities. Spatial dynamics may be a practical solution. The general hypothesis that measurements of changes in fundamental brain variables, action potentials, transmitter releases, post-synaptic transmembrane currents, etc., propagating in central nervous systems reveal how they work, carries no additional assumptions. Combinations of current techniques could reveal many aspects of spatial dynamics of spiking, post-synaptic processing, and plasticity in insects and rodents to start with. But problems defining baseline and reference conditions hinder interpretations of the results. Furthermore, the facts that pooling and averaging of data destroy their underlying dynamics imply that single-trial designs and statistics are necessary.
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Affiliation(s)
- Per E. Roland
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
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33
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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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Assadzadeh S, Annen J, Sanz L, Barra A, Bonin E, Thibaut A, Boly M, Laureys S, Gosseries O, Robinson PA. Method for quantifying arousal and consciousness in healthy states and severe brain injury via EEG-based measures of corticothalamic physiology. J Neurosci Methods 2023; 398:109958. [PMID: 37661056 DOI: 10.1016/j.jneumeth.2023.109958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/09/2023] [Accepted: 08/27/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND Characterization of normal arousal states has been achieved by fitting predictions of corticothalamic neural field theory (NFT) to electroencephalographic (EEG) spectra to yield relevant physiological parameters. NEW METHOD A prior fitting method is extended to distinguish conscious and unconscious states in healthy and brain injured subjects by identifying additional parameters and clusters in parameter space. RESULTS Fits of NFT predictions to EEG spectra are used to estimate neurophysiological parameters in healthy and brain injured subjects. Spectra are used from healthy subjects in wake and sleep and from patients with unresponsive wakefulness syndrome, in a minimally conscious state (MCS), and emerged from MCS. Subjects cluster into three groups in parameter space: conscious healthy (wake and REM), sleep, and brain injured. These are distinguished by the difference X-Y between corticocortical (X) and corticothalamic (Y) feedbacks, and by mean neural response rates α and β to incoming spikes. X-Y tracks consciousness in healthy individuals, with smaller values in wake/REM than sleep, but cannot distinguish between brain injuries. Parameters α and β differentiate deep sleep from wake/REM and brain injury. COMPARISON WITH EXISTING METHODS Other methods typically rely on laborious clinical assessment, manual EEG scoring, or evaluation of measures like Φ from integrated information theory, for which no efficient method exists. In contrast, the present method can be automated on a personal computer. CONCLUSION The method provides a means to quantify consciousness and arousal in healthy and brain injured subjects, but does not distinguish subtypes of brain injury.
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Affiliation(s)
- S Assadzadeh
- School of Physics, The University of Sydney, NSW 2006, Australia; Center for Integrative Brain Function, The University of Sydney, NSW 2006, Australia
| | - J Annen
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - L Sanz
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - A Barra
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - E Bonin
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - A Thibaut
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - M Boly
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA; Department of Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - S Laureys
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, U Laval, Canada; International Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
| | - O Gosseries
- Coma Science Group, GIGA-Consciousness, University of Liège, Belgium; Centre du Cerveau, University Hospital of Liège, Belgium
| | - P A Robinson
- School of Physics, The University of Sydney, NSW 2006, Australia; Center for Integrative Brain Function, The University of Sydney, NSW 2006, Australia.
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Gimenez-Aparisi G, Guijarro-Estelles E, Chornet-Lurbe A, Ballesta-Martinez S, Pardo-Hernandez M, Ye-Lin Y. Early detection of Parkinson's disease: Systematic analysis of the influence of the eyes on quantitative biomarkers in resting state electroencephalography. Heliyon 2023; 9:e20625. [PMID: 37829809 PMCID: PMC10565694 DOI: 10.1016/j.heliyon.2023.e20625] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/24/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
While resting state electroencephalography (EEG) provides relevant information on pathological changes in Parkinson's disease, most studies focus on the eyes-closed EEG biomarkers. Recent evidence has shown that both eyes-open EEG and reactivity to eyes-opening can also differentiate Parkinson's disease from healthy aging, but no consensus has been reached on a discriminatory capability benchmark. The aim of this study was to determine the resting-state EEG biomarkers suitable for real-time application that can differentiate Parkinson's patients from healthy subjects under both eyes closed and open. For this, we analysed and compared the quantitative EEG analyses of 13 early-stage cognitively normal Parkinson's patients with an age and sex-matched healthy group. We found that Parkinson's disease exhibited abnormal excessive theta activity in eyes-closed, which was reflected by a significantly higher relative theta power, a higher time percentage with a frequency peak in the theta band and a reduced alpha/theta ratio, while Parkinson's patients showed a significantly steeper non-oscillatory spectral slope activity than that of healthy subjects. We also found considerably less alpha and beta reactivity to eyes-opening in Parkinson's disease plus a significant moderate correlation between these EEG-biomarkers and the MDS-UPDRS score, used to assesses the clinical symptoms of Parkinson's Disease. Both EEG recordings with the eyes open and reactivity to eyes-opening provided additional information to the eyes-closed condition. We thus strongly recommend that both eyes open and closed be used in clinical practice recording protocols to promote EEG as a complementary non-invasive screening method for the early detection of Parkinson's disease, which would allow clinicians to design patient-oriented treatment and improve the patient's quality of life.
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Affiliation(s)
- G. Gimenez-Aparisi
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain
| | - E. Guijarro-Estelles
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain
| | - A. Chornet-Lurbe
- Servicio de Neurofisiología Clínica, Hospital Lluís Alcanyís, departamento de salud Xàtiva-Ontinyent, 46800, Xàtiva, València, Spain
| | - S. Ballesta-Martinez
- Servicio de Neurofisiología Clínica, Hospital Lluís Alcanyís, departamento de salud Xàtiva-Ontinyent, 46800, Xàtiva, València, Spain
| | - M. Pardo-Hernandez
- Servicio de Neurofisiología Clínica, Hospital Lluís Alcanyís, departamento de salud Xàtiva-Ontinyent, 46800, Xàtiva, València, Spain
| | - Y. Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain
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Averna A, Coelli S, Ferrara R, Cerutti S, Priori A, Bianchi AM. Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease. J Neural Eng 2023; 20:051001. [PMID: 37746822 DOI: 10.1088/1741-2552/acf8fa] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 09/12/2023] [Indexed: 09/26/2023]
Abstract
Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of 'scale-free' behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer's disease, while a local increase of brain signals complexity characterizes Parkinson's disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer's and Parkinson's diseases.
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Affiliation(s)
- Alberto Averna
- Department of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Stefania Coelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Rosanna Ferrara
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Sergio Cerutti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
| | - Alberto Priori
- CRC 'Aldo Ravelli' per le Neurotecnologie e le Terapie Neurologiche Sperimentali, Dipartimento di Scienze della Salute, Università degli Studi di Milano, via Antonio di Rudinì 8, 20122 Milano, Italy
| | - Anna Maria Bianchi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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Razzaq FA, Calzada-Reyes A, Tang Q, Guo Y, Rabinowitz AG, Bosch-Bayard J, Galan-Garcia L, Virues-Alba T, Suarez-Murias C, Miranda I, Riaz U, Bernardo Lagomasino V, Bryce C, Anderson SG, Galler JR, Bringas-Vega ML, Valdes-Sosa PA. Spectral quantitative and semi-quantitative EEG provide complementary information on the life-long effects of early childhood malnutrition on cognitive decline. Front Neurosci 2023; 17:1149102. [PMID: 37781256 PMCID: PMC10540225 DOI: 10.3389/fnins.2023.1149102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 07/18/2023] [Indexed: 10/03/2023] Open
Abstract
Objective This study compares the complementary information from semi-quantitative EEG (sqEEG) and spectral quantitative EEG (spectral-qEEG) to detect the life-long effects of early childhood malnutrition on the brain. Methods Resting-state EEGs (N = 202) from the Barbados Nutrition Study (BNS) were used to examine the effects of protein-energy malnutrition (PEM) on childhood and middle adulthood outcomes. sqEEG analysis was performed on Grand Total EEG (GTE) protocol, and a single latent variable, the semi-quantitative Neurophysiological State (sqNPS) was extracted. A univariate linear mixed-effects (LME) model tested the dependence of sqNPS and nutritional group. sqEEG was compared with scores on the Montreal Cognitive Assessment (MoCA). Stable sparse classifiers (SSC) also measured the predictive power of sqEEG, spectral-qEEG, and a combination of both. Multivariate LME was applied to assess each EEG modality separately and combined under longitudinal settings. Results The univariate LME showed highly significant differences between previously malnourished and control groups (p < 0.001); age (p = 0.01) was also significant, with no interaction between group and age detected. Childhood sqNPS (p = 0.02) and adulthood sqNPS (p = 0.003) predicted MoCA scores in adulthood. The SSC demonstrated that spectral-qEEG combined with sqEEG had the highest predictive power (mean AUC 0.92 ± 0.005). Finally, multivariate LME showed that the combined spectral-qEEG+sqEEG models had the highest log-likelihood (-479.7). Conclusion This research has extended our prior work with spectral-qEEG and the long-term impact of early childhood malnutrition on the brain. Our findings showed that sqNPS was significantly linked to accelerated cognitive aging at 45-51 years of age. While sqNPS and spectral-qEEG produced comparable results, our study indicated that combining sqNPS and spectral-qEEG yielded better performance than either method alone, suggesting that a multimodal approach could be advantageous for future investigations. Significance Based on our findings, a semi-quantitative approach utilizing GTE could be a valuable diagnostic tool for detecting the lasting impacts of childhood malnutrition. Notably, sqEEG has not been previously explored or reported as a biomarker for assessing the longitudinal effects of malnutrition. Furthermore, our observations suggest that sqEEG offers unique features and information not captured by spectral quantitative EEG analysis and could lead to its improvement.
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Affiliation(s)
- Fuleah A. Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | - Yanbo Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | | | | | | | | | | | - Ileana Miranda
- National Center for Animal and Plant Health, CENSA, San José de las Lajas, Mayabeque, Cuba
| | - Usama Riaz
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Cyralene Bryce
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
| | - Simon G. Anderson
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
| | - Janina R. Galler
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
- Division of Pediatric Gastroenterology and Nutrition, MassGeneral Hospital for Children, Boston, MA, United States
| | - Maria L. Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, La Habana, Cuba
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Di Sapia R, Rizzi M, Moro F, Lisi I, Caccamo A, Ravizza T, Vezzani A, Zanier ER. ECoG spiking activity and signal dimension are early predictive measures of epileptogenesis in a translational mouse model of traumatic brain injury. Neurobiol Dis 2023; 185:106251. [PMID: 37536383 DOI: 10.1016/j.nbd.2023.106251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 08/05/2023] Open
Abstract
The latency between traumatic brain injury (TBI) and the onset of epilepsy (PTE) represents an opportunity for counteracting epileptogenesis. Antiepileptogenesis trials are hampered by the lack of sensitive biomarkers that allow to enrich patient's population at-risk for PTE. We aimed to assess whether specific ECoG signals predict PTE in a clinically relevant mouse model with ∼60% epilepsy incidence. TBI was provoked in adult CD1 male mice by controlled cortical impact on the left parieto-temporal cortex, then mice were implanted with two perilesional cortical screw electrodes and two similar electrodes in the hemisphere contralateral to the lesion site. Acute seizures and spikes/sharp waves were ECoG-recorded during 1 week post-TBI. These early ECoG events were analyzed according to PTE incidence as assessed by measuring spontaneous recurrent seizures (SRS) at 5 months post-TBI. We found that incidence, number and duration of acute seizures during 3 days post-TBI were similar in PTE mice and mice not developing epilepsy (No SRS mice). Control mice with cortical electrodes (naïve, n = 5) or with electrodes and craniotomy (sham, n = 5) exhibited acute seizures but did not develop epilepsy. The daily number of spikes/sharp waves at the perilesional electrodes was increased similarly in PTE (n = 15) and No SRS (n = 8) mice vs controls (p < 0.05, n = 10) from day 2 post-injury. Differently, the daily number of spikes/sharp waves at both contralateral electrodes showed a progressive increase in PTE mice vs No SRS and control mice. In particular, spikes number was higher in PTE vs No SRS mice (p < 0.05) at 6 and 7 days post-TBI, and this measure predicted epilepsy development with high accuracy (AUC = 0.77, p = 0.03; CI 0.5830-0.9670). The cut-off value was validated in an independent cohort of TBI mice (n = 12). The daily spike number at the contralateral electrodes showed a circadian distribution in PTE mice which was not observed in No SRS mice. Analysis of non-linear dynamics at each electrode site showed changes in dimensionality during 4 days post-TBI. This measure yielded the best discrimination between PTE and No SRS mice (p < 0.01) at the cortical electrodes contralateral to injury. Data show that epileptiform activity contralateral to the lesion site has the the highest predictive value for PTE in this model reinforcing the hypothesis that the hemisphere contralateral to the lesion core may drive epileptogenic networks after TBI.
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Affiliation(s)
- Rossella Di Sapia
- Department of Acute Brain and Cardiovascular Injury, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Massimo Rizzi
- Department of Acute Brain and Cardiovascular Injury, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Federico Moro
- Department of Acute Brain and Cardiovascular Injury, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Ilaria Lisi
- Department of Acute Brain and Cardiovascular Injury, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Alessia Caccamo
- Department of Acute Brain and Cardiovascular Injury, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Teresa Ravizza
- Department of Acute Brain and Cardiovascular Injury, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Annamaria Vezzani
- Department of Acute Brain and Cardiovascular Injury, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.
| | - Elisa R Zanier
- Department of Acute Brain and Cardiovascular Injury, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.
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Han L, Drover DR, Chen MC, Saxena AR, Eagleman SL, Nekhendzy V, Pritchard A, Capasso R. EEG response of dexmedetomidine during drug induced sleep endoscopy. Front Neurosci 2023; 17:1144141. [PMID: 37521700 PMCID: PMC10375416 DOI: 10.3389/fnins.2023.1144141] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 06/27/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction Dexmedetomidine is one of the anesthetics of choice for drug induced sleep endoscopy (DISE), with advantages including limited respiratory depression, analgesia, and decreased incidence of emergence delirium. However, challenges with determining sedation levels and prolonged recovery have limited its usage. An improved understanding of the effect of dexmedetomidine on the level of sedation and the corresponding electroencephalographic (EEG) changes could help overcome these barriers. Methods Fifty-one patients received dexmedetomidine sedation with Richmond Agitation-Sedation Scale (RASS) score assessment and continuous EEG monitoring via SedLine for DISE. We constructed a pharmacokinetic model to determine continuous dexmedetomidine blood concentration. From the SedLine, we extracted the patient state index (PSI), and from the EEG we calculated the spectral edge frequency 95% (SEF95) and the correlation dimension (CD), a type of fractal dimension used to assess the complexity of a system. These metrics were subsequently compared against one another and with the dexmedetomidine concentration. Results Our pharmacokinetic model yielded a two-compartment model with volumes of 51.8 L and 106.2 L, with clearances of 69.5 and 168.9 L/h, respectively, and a time to effect of 9 min, similar to prior studies. Based on this model, decreasing RASS score, SEF95, CD, and PSI were all significantly associated with increasing dexmedetomidine concentration (p < 0.001, p = 0.006, p < 0.001 respectively). The CD, SEF95, and PSI better captured the effects of increasing dexmedetomidine concentration as compared to the RASS score. Simulating dexmedetomidine concentration based on titration to target levels derived from CD and PSI confirmed commonly used dexmedetomidine infusion dosages. Conclusion Dexmedetomidine use for DISE confirmed previous pharmacokinetic models seen with dexmedetomidine. Complex EEG metrics such as PSI and CD, as compared to RASS score and SEF95, better captured changes in brain state from dexmedetomidine and have potential to improve the monitoring of dexmedetomidine sedation.
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Affiliation(s)
- Lichy Han
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, United States
| | - David R. Drover
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, United States
| | - Marianne C. Chen
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, United States
| | - Amit R. Saxena
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, United States
| | - Sarah L. Eagleman
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, United States
| | - Vladimir Nekhendzy
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, United States
| | - Angelica Pritchard
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, United States
| | - Robson Capasso
- Department of Otolaryngology, Head and Neck Surgery, Stanford University, Stanford, CA, United States
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Feng YZ, Chen JT, Hu ZY, Liu GX, Zhou YS, Zhang P, Su AX, Yang S, Zhang YM, Wei RM, Chen GH. Effects of Sleep Reactivity on Sleep Macro-Structure, Orderliness, and Cortisol After Stress: A Preliminary Study in Healthy Young Adults. Nat Sci Sleep 2023; 15:533-546. [PMID: 37434994 PMCID: PMC10332417 DOI: 10.2147/nss.s415464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 06/29/2023] [Indexed: 07/13/2023] Open
Abstract
Purpose To investigate changes and links of stress and high sleep reactivity (H-SR) on the macro-structure and orderliness of sleep and cortisol levels in good sleepers (GS). Patients and Methods Sixty-two GS (18-40 years old) were recruited, with 32 in the stress group and 30 in the control group. Each group was further divided into H-SR and low SR subgroups based on the Ford Insomnia Response to Stress Test. All participants completed two nights of polysomnography in a sleep laboratory. Before conducting polysomnography on the second night, the stress group completed the Trier Social Stress Test and saliva was collected. Results The duration of NREM sleep stages 1, 2 (N1, N2) and rapid eye movement sleep (REM) decreased, and the values of approximate entropy, sample entropy, fuzzy entropy, and multiscale entropy increased under stress and SR effects. Stress increased rapid eye movement density, and H-SR increased cortisol reactivity. Conclusion Stress can damage the sleep and increase cortisol release in GS, especially those with H-SR. N1, N2 and REM sleep are more easily affected, while NREM sleep stage 3 sleep is relatively stable.
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Affiliation(s)
- Yi-Zhou Feng
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Jun-Tao Chen
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
- Department of Neurology, Shangyu People’s Hospital of Shaoxing, Shaoxing, Zhejiang, 312000, People’s Republic of China
| | - Zhen-Yu Hu
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Gao-Xia Liu
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Yu-Shun Zhou
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Ping Zhang
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Ai-Xi Su
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Shuai Yang
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Yue-Ming Zhang
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Ru-Meng Wei
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Gui-Hai Chen
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
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Aderinwale A, Tolossa GB, Kim AY, Jang EH, Lee YI, Jeon HJ, Kim H, Yu HY, Jeong J. Two-channel EEG based diagnosis of panic disorder and major depressive disorder using machine learning and non-linear dynamical methods. Psychiatry Res Neuroimaging 2023; 332:111641. [PMID: 37054495 DOI: 10.1016/j.pscychresns.2023.111641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/27/2023] [Accepted: 04/02/2023] [Indexed: 04/15/2023]
Abstract
The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder.
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Affiliation(s)
- Adedoyin Aderinwale
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, South Korea
| | - Gemechu Bekele Tolossa
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; Department of Neuroscience, Washington University School of Medicine, St Louis, MO, 63110, USA
| | - Ah Young Kim
- Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, South Korea
| | - Eun Hye Jang
- Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, South Korea
| | - Yong-Il Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Hong Jin Jeon
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Hyewon Kim
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Han Young Yu
- Electronics and Telecommunications Research Institute (ETRI), Daejeon, 34129, South Korea.
| | - Jaeseung Jeong
- Department of Brain and Cognitive Sciences, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Daejeon 34141, South Korea.
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42
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Davoudi S, Schwartz T, Labbe A, Trainor L, Lippé S. Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants. Cereb Cortex 2023; 33:8734-8747. [PMID: 37143183 PMCID: PMC10321121 DOI: 10.1093/cercor/bhad154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/06/2023] Open
Abstract
Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.
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Affiliation(s)
- Saeideh Davoudi
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Neuroscience, Université de Montréal, Montréal H3T 1J4, Canada
| | - Tyler Schwartz
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Aurélie Labbe
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Laurel Trainor
- Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton L8S 4K1, Canada
| | - Sarah Lippé
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Psychology, Université de Montréal, Montréal H2V 2S9, Canada
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43
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Gunawardena R, Sarrigiannis PG, Blackburn DJ, He F. Kernel-based Nonlinear Manifold Learning for EEG-based Functional Connectivity Analysis and Channel Selection with Application to Alzheimer's Disease. Neuroscience 2023:S0306-4522(23)00253-1. [PMID: 37301505 DOI: 10.1016/j.neuroscience.2023.05.033] [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: 12/23/2022] [Revised: 05/15/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
Dynamical, causal, and cross-frequency coupling analysis using the electroencephalogram (EEG) has gained significant attention for diagnosing and characterizing neurological disorders. Selecting important EEG channels is crucial for reducing computational complexity in implementing these methods and improving classification accuracy. In neuroscience, measures of (dis)similarity between EEG channels are often used as functional connectivity (FC) features, and important channels are selected via feature selection. Developing a generic measure of (dis)similarity is important for FC analysis and channel selection. In this study, learning of (dis)similarity information within the EEG is achieved using kernel-based nonlinear manifold learning. The focus is on FC changes and, thereby, EEG channel selection. Isomap and Gaussian Process Latent Variable Model (Isomap-GPLVM) are employed for this purpose. The resulting kernel (dis)similarity matrix is used as a novel measure of linear and nonlinear FC between EEG channels. The analysis of EEG from healthy controls (HC) and patients with mild to moderate Alzheimer's disease (AD) are presented as a case study. Classification results are compared with other commonly used FC measures. Our analysis shows significant differences in FC between bipolar channels of the occipital region and other regions (i.e. parietal, centro-parietal, and fronto-central) between AD and HC groups. Furthermore, our results indicate that FC changes between channels along the fronto-parietal region and the rest of the EEG are important in diagnosing AD. Our results and its relation to functional networks are consistent with those obtained from previous studies using fMRI, resting-state fMRI and EEG.
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Affiliation(s)
- Rajintha Gunawardena
- Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, CV1 5FB, UK
| | | | - Daniel J Blackburn
- Department of Neuroscience, The University of Sheffield, Sheffield, S10 2HQ, UK
| | - Fei He
- Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, CV1 5FB, UK.
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Walter N, Meinersen-Schmidt N, Kulla P, Loew T, Kruse J, Hinterberger T. Sensory-Processing Sensitivity Is Associated with Increased Neural Entropy. ENTROPY (BASEL, SWITZERLAND) 2023; 25:890. [PMID: 37372234 DOI: 10.3390/e25060890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND This study aimed at answering the following research questions: (1) Does the self-reported level of sensory-processing sensitivity (SPS) correlate with complexity, or criticality features of the electroencephalogram (EEG)? (2) Are there significant EEG differences comparing individuals with high and low levels of SPS? METHODS One hundred fifteen participants were measured with 64-channel EEG during a task-free resting state. The data were analyzed using criticality theory tools (detrended fluctuation analysis, neuronal avalanche analysis) and complexity measures (sample entropy, Higuchi's fractal dimension). Correlations with the 'Highly Sensitive Person Scale' (HSPS-G) scores were determined. Then, the cohort's lowest and the highest 30% were contrasted as opposites. EEG features were compared between the two groups by applying a Wilcoxon signed-rank test. RESULTS During resting with eyes open, HSPS-G scores correlated significantly positively with the sample entropy and Higuchi's fractal dimension (Spearman's ρ = 0.22, p < 0.05). The highly sensitive group revealed higher sample entropy values (1.83 ± 0.10 vs. 1.77 ± 0.13, p = 0.031). The increased sample entropy in the highly sensitive group was most pronounced in the central, temporal, and parietal regions. CONCLUSION For the first time, neurophysiological complexity features associated with SPS during a task-free resting state were demonstrated. Evidence is provided that neural processes differ between low- and highly-sensitive persons, whereby the latter displayed increased neural entropy. The findings support the central theoretical assumption of enhanced information processing and could be important for developing biomarkers for clinical diagnostics.
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Affiliation(s)
- Nike Walter
- Department of Psychosomatic Medicine, University Hospital Regensburg, 93059 Regensburg, Germany
| | - Nicole Meinersen-Schmidt
- Department for Clinical Psychology and Trauma Therapy, University of the Bundeswehr Munich, 85579 Neubiberg, Germany
| | - Patricia Kulla
- Department for Clinical Psychology and Trauma Therapy, University of the Bundeswehr Munich, 85579 Neubiberg, Germany
| | - Thomas Loew
- Department of Psychosomatic Medicine, University Hospital Regensburg, 93059 Regensburg, Germany
| | - Joachim Kruse
- Department for Clinical Psychology and Trauma Therapy, University of the Bundeswehr Munich, 85579 Neubiberg, Germany
| | - Thilo Hinterberger
- Department of Psychosomatic Medicine, University Hospital Regensburg, 93059 Regensburg, Germany
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Hu Y, Wang Y, Zhang R, Hu Y, Fang M, Li Z, Shi L, Zhang Y, Zhang Z, Gao J, Zhang L. Assessing stroke rehabilitation degree based on quantitative EEG index and nonlinear parameters. Cogn Neurodyn 2023; 17:661-669. [PMID: 37265653 PMCID: PMC10229519 DOI: 10.1007/s11571-022-09849-4] [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: 01/05/2022] [Revised: 06/03/2022] [Accepted: 07/06/2022] [Indexed: 11/03/2022] Open
Abstract
The assessment of motor function is critical to the rehabilitation of stroke patients. However, commonly used evaluation methods are based on behavior scoring, which lacks neurological indicators that directly reflect the motor function of the brain. The objective of this study was to investigate whether resting-state EEG indicators could improve stroke rehabilitation evaluation. We recruited 68 participants and recorded their resting-state EEG data. According to Brunnstrom stage, the participants were divided into three groups: severe, moderate, and mild. Ten quantitative electroencephalographic (QEEG) and five non-linear parameters of resting-state EEG were calculated for further analysis. Statistical tests were performed, and the genetic algorithm-support vector machine was used to select the best feature combination for classification. We found the QEEG parameters show significant differences in Delta, Alpha1, Alpha2, DAR, and DTABR (P < 0.05) among the three groups. Regarding nonlinear parameters, ApEn, SampEn, Lz, and C0 showed significant differences (P < 0.05). The optimal feature classification combination accuracy rate reached 85.3%. Our research shows that resting-state EEG indicators could be used for stroke rehabilitation evaluation.
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Affiliation(s)
- Yuxia Hu
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Yufei Wang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
| | - Rui Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Yubo Hu
- Shenqiu County People’s Hospital, Henan Province, China
| | - Mingzhu Fang
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhe Li
- The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Li Shi
- Department of Automation, Tsinghua University, Beijing, China
| | - Yankun Zhang
- Zhengzhou Boone Technology Company, Zhengzhou, China
| | - Zhong Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Jinfeng Gao
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
| | - Lipeng Zhang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
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46
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Catrambone V, Valenza G. Complex Brain-Heart Mapping in Mental and Physical Stress. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:495-504. [PMID: 37817820 PMCID: PMC10561752 DOI: 10.1109/jtehm.2023.3280974] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/29/2023] [Accepted: 05/25/2023] [Indexed: 10/12/2023]
Abstract
OBJECTIVE The central and autonomic nervous systems are deemed complex dynamic systems, wherein each system as a whole shows features that the individual system sub-components do not. They also continuously interact to maintain body homeostasis and appropriate react to endogenous and exogenous stimuli. Such interactions are comprehensively referred to functional brain-heart interplay (BHI). Nevertheless, it remains uncertain whether this interaction also exhibits complex characteristics, that is, whether the dynamics of the entire nervous system inherently demonstrate complex behavior, or if such complexity is solely a trait of the central and autonomic systems. Here, we performed complexity mapping of the BHI dynamics under mental and physical stress conditions. METHODS AND PROCEDURES Electroencephalographic and heart rate variability series were obtained from 56 healthy individuals performing mental arithmetic or cold-pressure tasks, and physiological series were properly combined to derive directional BHI series, whose complexity was quantified through fuzzy entropy. RESULTS The experimental results showed that BHI complexity is mainly modulated in the efferent functional direction from the brain to the heart, and mainly targets vagal oscillations during mental stress and sympathovagal oscillations during physical stress. CONCLUSION We conclude that the complexity of BHI mapping may provide insightful information on the dynamics of both central and autonomic activity, as well as on their continuous interaction. CLINICAL IMPACT This research enhances our comprehension of the reciprocal interactions between central and autonomic systems, potentially paving the way for more accurate diagnoses and targeted treatments of cardiovascular, neurological, and psychiatric disorders.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, and Department of Information EngineeringSchool of EngineeringUniversity of Pisa56126PisaItaly
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, and Department of Information EngineeringSchool of EngineeringUniversity of Pisa56126PisaItaly
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47
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Kargarnovin S, Hernandez C, Farahani FV, Karwowski W. Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review. Brain Sci 2023; 13:813. [PMID: 37239285 PMCID: PMC10216576 DOI: 10.3390/brainsci13050813] [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: 04/13/2023] [Revised: 05/09/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023] Open
Abstract
(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.
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Affiliation(s)
- Shaida Kargarnovin
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (C.H.); (F.V.F.); (W.K.)
| | - Christopher Hernandez
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (C.H.); (F.V.F.); (W.K.)
| | - Farzad V. Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (C.H.); (F.V.F.); (W.K.)
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA; (C.H.); (F.V.F.); (W.K.)
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48
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Xu Z, Karwowski W, Çakıt E, Reineman-Jones L, Murata A, Aljuaid A, Sapkota N, Hancock P. Nonlinear dynamics of EEG responses to unmanned vehicle visual detection with different levels of task difficulty. APPLIED ERGONOMICS 2023; 111:104045. [PMID: 37178489 DOI: 10.1016/j.apergo.2023.104045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 05/05/2023] [Accepted: 05/08/2023] [Indexed: 05/15/2023]
Abstract
The main objective of this study was to examine the presence of chaos in the EEG recordings of brain activity under simulated unmanned ground vehicle visual detection scenarios with different levels of task difficulty. One hundred and fifty people participated in the experiment and completed four visual detection task scenarios: (1) change detection, (2) a threat detection task, (3) a dual-task with different change detection task rates, and (4) a dual-task with different threat detection task rates. We used the largest Lyapunov exponent and correlation dimension of the EEG data and performed 0-1 tests on the EEG data. The results revealed a change in the level of nonlinearity in the EEG data corresponding to different levels of cognitive task difficulty. The differences in EEG nonlinearity measures among the studied levels of task difficulty, as well as between a single task scenario and a dual-task scenario, have also been assessed. The results increase our understanding of the nature of unmanned systems' operational requirements.
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Affiliation(s)
- Ziqing Xu
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, 32816-2993, USA
| | - Waldemar Karwowski
- Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, 32816-2993, USA
| | - Erman Çakıt
- Department of Industrial Engineering, Gazi University, 06570, Ankara, Turkey.
| | - Lauren Reineman-Jones
- Autonomous Mobility Simulation and Training Lab, University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Atsuo Murata
- Department of Intelligent Mechanical Systems, Graduate School of Natural Science and Technology, Okayama University, Okayama, 700-8530, Japan
| | - Awad Aljuaid
- Department of Industrial Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia
| | - Nabin Sapkota
- Department of Engineering Technology, Northwestern State University of Louisiana, Natchitoches, 71497, USA
| | - Peter Hancock
- Department of Psychology, University of Central Florida, Orlando, FL, 32816-2993, USA
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49
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Bernardi D, Shannahoff-Khalsa D, Sale J, Wright JA, Fadiga L, Papo D. The time scales of irreversibility in spontaneous brain activity are altered in obsessive compulsive disorder. Front Psychiatry 2023; 14:1158404. [PMID: 37234212 PMCID: PMC10208430 DOI: 10.3389/fpsyt.2023.1158404] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/30/2023] [Indexed: 05/27/2023] Open
Abstract
We study how obsessive-compulsive disorder (OCD) affects the complexity and time-reversal symmetry-breaking (irreversibility) of the brain resting-state activity as measured by magnetoencephalography (MEG). Comparing MEG recordings from OCD patients and age/sex matched control subjects, we find that irreversibility is more concentrated at faster time scales and more uniformly distributed across different channels of the same hemisphere in OCD patients than in control subjects. Furthermore, the interhemispheric asymmetry between homologous areas of OCD patients and controls is also markedly different. Some of these differences were reduced by 1-year of Kundalini Yoga meditation treatment. Taken together, these results suggest that OCD alters the dynamic attractor of the brain's resting state and hint at a possible novel neurophysiological characterization of this psychiatric disorder and how this therapy can possibly modulate brain function.
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Affiliation(s)
- Davide Bernardi
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
| | - David Shannahoff-Khalsa
- BioCircuits Institute, University of California, San Diego, La Jolla, CA, United States
- Center for Integrative Medicine, University of California, San Diego, La Jolla, CA, United States
- The Khalsa Foundation for Medical Science, Del Mar, CA, United States
| | - Jeff Sale
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, United States
| | - Jon A. Wright
- BioCircuits Institute, University of California, San Diego, La Jolla, CA, United States
| | - Luciano Fadiga
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
| | - David Papo
- Center for Translational Neurophysiology of Speech and Communication, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Section of Physiology, University of Ferrara, Ferrara, Italy
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50
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Lopez KL, Monachino AD, Vincent KM, Peck FC, Gabard-Durnam LJ. Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities. Neuroimage 2023; 275:120116. [PMID: 37169118 DOI: 10.1016/j.neuroimage.2023.120116] [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: 02/07/2023] [Revised: 03/27/2023] [Accepted: 04/13/2023] [Indexed: 05/13/2023] Open
Abstract
Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.
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Affiliation(s)
- K L Lopez
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - A D Monachino
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - K M Vincent
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - F C Peck
- University of California, Los Angeles, Los Angeles, CA, United States
| | - L J Gabard-Durnam
- Northeastern University, 360 Huntington Ave, Boston, MA, United States.
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