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neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

EUROPEAN JOURNAL OF NEUROSCIENCE 2024年第5期59卷 822-841页

作者： Alnes, Sigurd L. Bachlin, Lea Z. M. Schindler, Kaspar Tzovara, Athina Univ Bern Inst Comp Sci Bern Switzerland Univ Hosp Bern Zent Expt Neurol Dept Neurol Inselspital Bern Switzerland Bern Univ Hosp Univ Bern Sleep Wake Epilepsy Ctr Dept NeurolNeuroTec Bern Switzerland

Auditory processing and the complexity of neural activity can both indicate residual consciousness levels and differentiate states of arousal. However, how measures of neural signal complexity manifest in neural activity following environmental stimulation and, more generally, how the electrophysiological characteristics of auditory responses change in states of reduced consciousness remain under-explored. Here, we tested the hypothesis that measures of neural complexity and the spectral slope would discriminate stages of sleep and wakefulness not only in baseline electroencephalography (EEG) activity but also in EEG signals following auditory stimulation. High-density EEG was recorded in 21 participants to determine the spatial relationship between these measures and between EEG recorded pre- and post-auditory stimulation. Results showed that the complexity and the spectral slope in the 2-20 Hz range discriminated between sleep stages and had a high correlation in sleep. In wakefulness, complexity was strongly correlated to the 20-40 Hz spectral slope. Auditory stimulation resulted in reduced complexity in sleep compared to the pre-stimulation EEG activity and modulated the spectral slope in wakefulness. These findings confirm our hypothesis that electrophysiological markers of arousal are sensitive to sleep/wake states in EEG activity during baseline and following auditory stimulation. Our results have direct applications to studies using auditory stimulation to probe neural functions in states of reduced consciousness. neural complexity and the spectral slope are sensitive indicators of sleep/wake state not only in the spontaneous EEG but also following auditory stimulation. complexity and the spectral slope in the 2-20 Hz range discriminated between sleep stages and correlated in sleep, while, in wakefulness, complexity was strongly correlated to the 20-40 Hz slope. Auditory stimulation led to reduced complexity in sleep compared to the pre-stimulation EEG and m

关键词： auditory stimulation EEG neural complexity sleep and wakefulness spectral slope

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Exploring the neurophysiological basis of misinformation: A behavioral and neural complexity analysis

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BEHAVIOURAL BRAIN RESEARCH 2025年 487卷 115592页

作者： Molina, R. Crespo, Y. Arbol, J. R. Arias-Orduna, A. V. Ibanez-Molina, A. J. Iglesias-Parro, S. Univ Jaen Dept Psychol Jaen Spain Natl Univ Educ Distance UNED Dept Social Psychol Madrid Spain

The proliferation of misinformation on social media platforms poses significant challenges to public health, political discourse, and social cohesion. This study investigates the efficacy of a World Health Organization (WHO) infodemic intervention in mitigating the spread of misinformation and explores the underlying neural mechanisms involved in information processing. A sample of 77 university students was randomly assigned to an experimental group, which was exposed to the WHO's infodemic intervention, or a control group, which received a campaign on healthy lifestyle habits. Participants viewed a series of manipulated and non-manipulated tweets before and after the intervention, rating their likelihood to share, verify, and perceive the truthfulness of the information. Electroencephalogram (EEG) data were collected throughout the experiment to assess neural complexity using Sample Entropy (SampEn) measures. Results revealed that the experimental group significantly reduced their intention to share information and perceived truthfulness of both manipulated and nonmanipulated items post-intervention. The control group showed no significant changes. EEG analysis demonstrated higher SampEn scores in the frontal and temporal regions for the experimental group post-intervention, indicating increased neural complexity and more homogeneous activation patterns. These findings suggest that the WHO intervention effectively enhanced participants' critical evaluation of information, reflected in both behavioral and neurophysiological changes. This study contributes to the growing body of research on misinformation interventions by providing evidence for the effectiveness of passive, less demanding campaigns in fostering critical thinking and information discernment. Moreover, it offers novel insights into the neural correlates of information processing following such interventions, highlighting the potential of combining behavioral and neurophysiological measures in misinfor

关键词： neural complexity Analysis Misinformation Disinformation Social media Counter-disinformation Electroencephalography Sample entropy neural complexity

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Evolution of neural complexity in Division of Labor Tasks

Evolution of Neural Complexity in Division of Labor Tasks

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2021 Conference on Artificial Life, ALIFE 2021

作者： Sangati, Ekaterina Keshmiri, Soheil Sangati, Federico Embodied Cognitive Science Unit Okinawa Institute of Science and Technology Graduate University Japan

We evolve artificial agents to perform a simple tracking task in three conditions: one individual (Isolated Condition) and two joint action conditions with division of labor. The joint conditions differ by whether two agents switch complementary roles during the task (Generalist Condition) or always play the same role (Specialist Condition). At the end of evolutionary runs we calculate the agents’ neural complexity using Tononi-Sporns-Edelman (TSE) complexity measure which relates to Integrated Information Theory (IIT). We show that (1) division of labor with specialization leads to a level of neural complexity comparable to the complexity of performing the same task alone, and that (2) both are lower than neural complexity when performing the task jointly with role switching. We further consider viewing collaborating agents as a single extended system and calculate its joint neural complexity. We demonstrate that contrary to our predictions, the same pattern of results, i.e., Generalists’ complexity being higher than Specialists’, holds also in this conceptualization. © 2021 ALIFE. All Rights Reserved.

关键词： distributed cognition division of labor Integrated Information Theory neural complexity social brain hypothesis

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Complementary roles of neural synchrony and complexity for indexing consciousness and chances of surviving in acute coma

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NEUROIMAGE 2021年 245卷 118638-118638页

作者： Alnes, Sigurd L. De Lucia, Marzia Rossetti, Andrea O. Tzovara, Athina Univ Bern Inst Comp Sci Bern Switzerland Univ Hosp Bern Dept Neurol Zentrum Expt Neurol Inselspital Bern Switzerland Ctr Hosp Univ Vaudois CHUV Dept Clin Neurosci Lab Res Neuroimaging LREN Lausanne Switzerland Lausanne Univ Hosp Dept Clin Neurosci Neurol Serv Lausanne Switzerland Univ Lausanne Lausanne Switzerland Univ Bern Bern Univ Hosp Sleep Wake Epilepsy Ctr NeuroTec Dept NeurolInselspital Bern Switzerland Univ Calif Berkeley Helen Wills Neurosci Inst Berkeley CA 94720 USA

An open challenge in consciousness research is understanding how neural functions are altered by pathological loss of consciousness. To maintain consciousness, the brain needs synchronized communication of information across brain regions, and sufficient complexity in neural activity. Coordination of brain activity, typically indexed through measures of neural synchrony, has been shown to decrease when consciousness is lost and to reflect the clinical state of patients with disorders of consciousness. Moreover, when consciousness is lost, neural activity loses complexity, while the levels of neural noise, indexed by the slope of the electroencephalography (EEG) spectral exponent decrease. Although these properties have been well investigated in resting state activity, it remains unknown whether the sensory processing network, which has been shown to be preserved in coma, suffers from a loss of synchronization or information content. Here, we focused on acute coma and hypothesized that neural synchrony in response to auditory stimuli would reflect coma severity, while complexity, or neural noise, would reflect the presence or loss of consciousness. Results showed that neural synchrony of EEG signals was stronger for survivors than non-survivors and predictive of patients' outcome, but indistinguishable between survivors and healthy controls. Measures of neural complexity and neural noise were not informative of patients' outcome and had high or low values for patients compared to controls. Our results suggest different roles for neural synchrony and complexity in acute coma. Synchrony represents a precondition for consciousness, while complexity needs an equilibrium between high or low values to support conscious cognition.

关键词： Coma Cardiac arrest Outcome prognosis neural synchrony neural complexity Consciousness Electroencephalography (EEG) E I balance

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neural complexity as a potential translational biomarker for psychosis

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JOURNAL OF AFFECTIVE DISORDERS 2017年第0期216卷 89-99页

作者： Hager, Brandon Yang, Albert C. Brady, Roscoe Meda, Shashwath Clementz, Brett Pearlson, Godfrey D. Sweeney, John A. Tamminga, Carol Keshavan, Matcheri Massachusetts Mental Hlth Ctr Boston MA 02115 USA Harvard Med Sch Div Publ Psychiat Beth Israel Deaconess Med Ctr Dept Psychiat Boston MA USA Harvard Med Sch Div Interdisciplinary Med & Biotechnol Beth Israel Deaconess Med Ctr Boston MA USA Natl Yang Ming Univ Dept Psychiat Taipei Vet Gen Hosp Sch Med Taipei Taiwan Yale Univ Sch Med Dept Psychiat New Haven CT USA Yale Univ Sch Med Dept Neurosci New Haven CT USA Hartford Hosp Inst Living Hartford CT 06115 USA Univ Georgia Dept Psychol Bioimaging Res Ctr Athens GA 30602 USA Univ Georgia Dept Neurosci Bioimaging Res Ctr Athens GA 30602 USA Univ Cincinnati Sch Med Dept Psychiat & Behav Neurosci Cincinnati OH USA Univ Texas Southwestern Med Ctr Dallas Dept Psychiat Dallas TX USA

Background: The adaptability of the human brain to the constantly changing environment is reduced in patients with psychotic disorders, leading to impaired cognitive functions. Brain signal complexity, which may reflect adaptability, can be readily quantified via resting-state functional magnetic resonance imaging (fMRI) signals. We hypothesized that resting-state brain signal complexity is altered in psychotic disorders, and is correlated with cognitive impairment. Methods: We assessed 156 healthy controls (HC) and 330 probands, including 125 patients with psychotic bipolar disorder (BP), 107 patients with schizophrenia (SZ), 98 patients with schizoaffective disorder (SAD) and 230 of their unaffected first-degree relatives (76 BPR, 79 SADR, and 75 SZR) from four sites of the Bipolar Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium. Using multi-scale entropy analysis, we determined whether patients and/or relatives had pathologic differences in complexity of resting-state fMRI signals toward regularity (reduced entropy in all time scales), or toward uncorrelated randomness (increased entropy in fine time scales that decays as the time scale increases) and how these complexity differences might be associated with cognitive impairment. Results: Compared to HC subjects, proband groups showed either decreased complexity toward regularity or toward randomness. SZ probands showed decreased complexity toward regular signal in hypothalamus, and BP probands in left inferior occipital, right precentral and left superior parietal regions, whereas no brain region with decreased complexity toward regularity was found in SAD probands. All proband groups showed significantly increased brain signal randomness in dorsal and ventral prefrontal cortex (PFC), and unaffected relatives showed no complexity differences in PFC regions. SZ had the largest area of involvement in both dorsal and ventral PFC. BP and SAD probands shared increased brain signal randomness in v

关键词： Psychosis Schizophrenia, schizoaffective disorder psychotic bipolar disorder neural complexity Multiscale entropy

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neural complexity in patients with poststroke depression: A resting EEG study

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JOURNAL OF AFFECTIVE DISORDERS 2015年第0期188卷 310-318页

作者： Zhang, Ying Wang, Chunfang Sun, Changcheng Zhang, Xi Wang, Yongjun Qi, Hongzhi He, Feng Zhao, Xin Wan, Baikun Du, Jingang Ming, Dong Rehabil Med Res Ctr Tianjin Tianjin Union Med Ctr Dept Rehabil Med Tianjin Peoples R China Tianjin Univ Dept Biomed Engn Lab Neural Engn & Rehabil Tianjin 300072 Peoples R China

Background: Poststroke depression (PSD) is one of the most common emotional disorders affecting post-stroke patients. However, he neurophysiological mechanism remains elusive. This study was aimed to study the relationship between complexity of neural electrical activity and PSD. Methods: Resting state eye-closed electroencephalogram (EEG) signals of 16 electrodes were recorded in 21 ischemic poststroke depression (PSD) patients, 22 ischemic poststroke non-depression (PSND) patients and 15 healthy controls (CONT). Lempel-Ziv complexity (LZC) was used to evaluate changes in EEG complexity in PSD patients. Statistical analysis was performed to explore difference among different groups and electrodes. Correlation between the severity of depression (HDRS) and EEG complexity was determined with pearson correlation coefficients. Receiver operating characteristic (ROC) and binary logistic regression analysis were conducted to estimate the discriminating ability of LZC for PSD in specificity, sensitivity and accuracy. Results: PSD patients showed lower neural complexity compared with PSND and CONT subjects in the whole brain regions. There was no significant difference among different brain regions, and no interactions between group and electrodes. None of the 1.2C significantly correlated with overall depression severity or differentiated symptom severity of 7 items in PSD patients, but in stroke patients, significant correlation was found between HDRS and LZC in the whole brain regions, especially in frontal and temporal. LZC parameters used for PSD recognition possessed more than 85% in specificity, sensitivity and accuracy, suggesting the feasibility of LZC to serve as screening indicators for PSD. Increased slow wave rhythms were found in PSD patients and clearly correlation was confirmed between neuronal complexity and spectral power of the four EEG rhythms. Limitations: Lesion location of stroke patients in the study distributed in different brain regions, and most o

关键词： Poststroke depression (PSD) neural complexity EEG Lempel-Ziv complexity (LZC)

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Evaluating individual sensitivity to propofol through EEG complexity and information integration: from neural dynamics to precision anesthesia

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JOURNAL OF neural ENGINEERING 2025年第3期22卷

作者： Jin, Xing Liang, Zhenhu Li, Fu Li, Xiaoli Xidian Univ Sch Artificial Intelligence Xian 710126 Peoples R China Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Peoples R China Guangdong Artificial Intelligence & Digital Econ L Guangzhou 510335 Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China

Objective. Understanding the neural mechanisms underlying consciousness during anesthesia is critical for advancing anesthesiology and neuroscience. However, given the high variability in individual sensitivity to anesthetic agents, accurately elucidating the relationship between individual characteristics and drug responses is also crucial for ensuring clinical anesthesia safety. Approach. This study utilized high-density EEG data from 20 participants under various propofol-induced sedation states. We stratified participants into low- and high-sensitivity cohorts based on their behavioral responsiveness to standardized auditory stimuli during sedation. Then the metrics such as permutation entropy (PE), phase-lag entropy (PLE), and permutation cross mutual information (PCMI) were analyzed to evaluate neural complexity, the diversity of connectivity, and information integration. Machine learning models, including support vector machines (SVM), were applied to classify individual sensitivity to propofol, with SHapley Additive exPlanations (SHAP) analysis providing feature interpretability. Main results. Subjects were divided into high-performance (low-sensitivity) group and low-performance (high-sensitivity) group based on the accuracy of their responses to auditory stimuli. In the moderate sedation, the high-performance group exhibited elevated PE, increased PLE in alpha band and the decreased PLE in beta band, and decreased PCMI in alpha band. In the resting-state, we extracted 18 metrics that were significantly different between the two groups. Using these resting-state metrics as features, the SVM model achieved an accuracy of 87.5% +/- 0.06% in classifying individuals into high- or low-sensitivity groups. SHAP analysis results indicated that the features, including the PLE value of temporal in alpha band (alpha-PLET) and the PCMI value of frontal-parietal in beta band (beta-PCMIFP), were identified as robust predictors of propofol sensitivity, with high weights a

关键词： propofol general anesthesia neural complexity diversity of connectivity information integration machine learning SHAP

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From temporal to spatial topography: hierarchy of neural dynamics in higher- and lower-order networks shapes their complexity

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CEREBRAL CORTEX 2022年第24期32卷 5637-5653页

作者： Golesorkhi, Mehrshad Gomez-Pilar, Javier Catal, Yasir Tumati, Shankar Yagoub, Mustapha C. E. Stamatakis, Emanuel A. Northoff, Georg Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1Z 7K4 Canada Royal Ottawa Mental Hlth Ctr Inst Mental Hlth Mind Brain Imaging & Neuroeth Res Unit Ottawa ON K1Z 7K4 Canada Univ Ottawa Ottawa ON K1Z 7K4 Canada Univ Valladolid Biomed Engn Paseo Belen 15 Valladolid 47011 Spain Ctr Invest Biomed Red Bioingn Biomat & Nanomed CI Madrid 28029 Spain Univ Cambridge Sch Clin Med Div Anaesthesia Cambridge CB1 0SP England Hangzhou Normal Univ Ctr Cognit & Brain Disorder Hangzhou 311121 Peoples R China Zhejiang Univ Sch Med Mental Hlth Ctr Hangzhou 310053 Zhejiang Peoples R China

The brain shows a topographical hierarchy along the lines of lower- and higher-order networks. The exact temporal dynamics characterization of this lower-higher-order topography at rest and its impact on task states remains unclear, though. Using 2 functional magnetic resonance imaging data sets, we investigate lower- and higher-order networks in terms of the signal compressibility, operationalized by Lempel-Ziv complexity (LZC). As we assume that this degree of complexity is related to the slow-fast frequency balance, we also compute the median frequency (MF), an estimation of frequency distribution. We demonstrate (i) topographical differences at rest between higher- and lower-order networks, showing lower LZC and MF in the former;(ii) task-related and task-specific changes in LZC and MF in both lower- and higher-order networks;(iii) hierarchical relationship between LZC and MF, as MF at rest correlates with LZC rest-task change along the lines of lower- and higher-order networks;and (iv) causal and nonlinear relation between LZC at rest and LZC during task, with MF at rest acting as mediator. Together, results show that the topographical hierarchy of lower- and higher-order networks converges with their temporal hierarchy, with these neural dynamics at rest shaping their range of complexity during task states in a nonlinear way.

关键词： core-periphery organization lower-higher-order network topography neural complexity slow-fast frequency balance spatiotemporal neuroscience

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Entropy-Based EEG Measures for Revealing Altered neural Dynamics in Alzheimer's Disease: A Preliminary Study 2

Entropy-Based EEG Measures for Revealing Altered Neural Dyna...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and neural Engineering, MetroXRAINE 2023

作者： Cataldo, Andrea Criscuolo, Sabatina De Benedetto, Egidio Masciullo, Antonio Pesola, Marisa Schiavoni, Raissa University of Salento Department of Engineering for Innovation Lecce Italy Naples Italy

ISBN: (纸本)9798350300802

Alzheimer's disease (AD) is a prevalent age-related neurodegenerative condition, whose timely diagnosis is nowadays still complicated. In this context, investigating brain complexity can provide insight into the underlying mechanisms of neural dynamics in AD patients. In this regard, this study explores the differences in neural complexity between AD patients and healthy controls by using entropy measures. Specifically, multi-scale sample entropy (MSE) and multiscale approximate entropy (MAE) was computed on multi-channel electroencephalografic (EEG) signals at different time scales, highlighting the advantages and disadvantages of these two techniques, and outlining future improvements. Results show the potential of entropy measures as a promising strategy for early diagnosis of AD and for a better understanding of EEG dynamics alteration in AD, allowing for the development of more targeted therapies. © 2023 IEEE.

关键词： Alzheimer's disease EEG signal entropy measures multiscale fuzzy entropy neural complexity

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The Environment and Body-Brain complexity

The Environment and Body-Brain Complexity

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2nd Genetic and Evolutionary Computation Conference (GECCO)

作者： Spanellis, Christina Stewart, Brooke Nitschke, Geoff Univ Cape Town Dept Comp Sci Cape Town South Africa

ISBN: (纸本)9781450383509

An open question for both natural and artificial evolutionary systems is how, and under what environmental and evolutionary conditions complexity evolves. This study investigates the impact of increasingly complex task environments on the evolution of robot complexity. Specifically, the impact of evolving body-brain couplings on locomotive task performance, where robot evolution was directed by either body-brain exploration (novelty search) or objective-based (fitness function) evolutionary search. Results indicated that novelty search enabled the evolution of increased robot body-brain complexity and efficacy given specific environment conditions. The key contribution is thus the demonstration that body-brain exploration is suitable for evolving robot complexity that enables high fitness robots in specific environments.

关键词： Evolutionary Robotics Body-Brain Coevolution neural complexity Morphological complexity Novelty Search

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