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

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

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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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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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PRUNING ARTIFICIAL neural NETWORKS USING neural complexity MEASURES

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INTERNATIONAL JOURNAL OF neural SYSTEMS 2008年第5期18卷 389-403页

作者： Jorgensen, Thomas D. Haynes, Barry P. Norlund, Charlotte C. F. Univ Portsmouth Dept Elect & Comp Engn Portsmouth Hants England Univ Southampton Sch Elect & Comp Sci Southampton Hants England

This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.

关键词： Pruning neural networks neuroevolution robot controllers neural complexity

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A neural complexity measure applied to MEG data in Alzheimer's disease

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CLINICAL NEUROPHYSIOLOGY 2003年第6期114卷 1034-1040页

作者： van Walsum, AMV Pijnenburg, YAL Berendse, HW van Dijk, BW Knol, DL Scheltens, P Stam, CJ VU Univ Med Ctr Dept Clin Neurophysiol NL-1007 MB Amsterdam Netherlands VU Univ Med Ctr Dept Neurol Alzheimer Ctr NL-1007 MB Amsterdam Netherlands VU Univ Med Ctr MEG Ctr Amsterdam Netherlands VU Univ Med Ctr Dept Clin Epidemiol & Biostat NL-1007 MB Amsterdam Netherlands

Objective: A measure of neural complexity (C-N) (Proc. Natl Acad. Sci. USA 91 (1994) 5033) was applied to magnetoencephalography (MEG) data to test the hypothesis that C-N decreases when information processing in the brain is impaired, as is the case in patients with Alzheimer's Disease (AD). Methods: One hundred and fifty-one channel MEGs were recorded in 20 AD patients and 20 healthy age-matched controls in a resting condition with eyes open (EO) and eyes closed (EC). Artifact-free epochs of 117 channels were selected for analysis. C-N and D-2 were computed in different frequency bands, and correlated with the MMSE. Results: The Group X Frequency band interaction was significant for both C-N and D-2. C-N was higher in AD, as compared with controls, in the 2-4 and 4-8 Hz bands, and D-2 was higher in AD patients in the 14-20 and 20-30 Hz bands. The C-N was higher in the EC condition compared to the EO condition, whereas the D-2 was higher in the EO condition. Conclusions: The hypothesis of Tononi et al. (Proc. Natl Acad. Sci. USA 91 (1994) 5033) that the neural complexity decreases in AD patients has to be rejected. However, both neural complexity and the correlation dimension did show differences between controls and AD patients which depended on frequency band. (C) 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Science Ireland Ltd. All rights reserved.

关键词： Alzheimer's disease cognition correlation dimension frequency bands magnetoencephalography neural complexity

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Changes in neural complexity during the perception of 3D images using random dot stereograms

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INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY 2003年第1期48卷 35-42页

作者： Burgess, AP Rehman, J Williams, JD Univ London Imperial Coll Sci Technol & Med Fac Med Div Neurosci & Psychol Med Dept Cognit Neurosci & Behav London W6 8RP England Coventry Univ Sch Hlth & Social Sci Coventry CV1 5FB W Midlands England

One developing theme in consciousness research is that consciousness is not the product of any specific component of the brain, rather it is an emergent property of the changing patterns of connectivity between different specialised functional components. For example, the dynamic core hypothesis proposes that conscious experience requires high levels of neural complexity, where complexity is defined in terms of functional connectivity. To test this hypothesis, electroencephalography was recorded while participants were shown random dot-stereograms. Consistent with the dynamic core hypothesis, neural complexity increased as the participants changed from simply viewing the stereogram to consciously perceiving the hidden 3D image. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： neural complexity stereogram hypothesis

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Differences in eeg between gifted and average students: neural complexity and functional cluster analysis

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INTERNATIONAL JOURNAL OF NEUROSCIENCE 2007年第8期117卷 1167-1184页

作者： Jin, Seung-Hyun Kim, Soo Yong Park, Kyung Hee Lee, Kil-Jae NIH NINDS Human Motor Control Sect Bethesda MD 20892 USA Korea Adv Inst Sci & Technol Dept Phys Taejon 305701 South Korea Jeonju Dukil Middle Sch Jeonju South Korea Korea Natl Univ Educ Dept Sci Educ Chungbuk South Korea

The main aim of the present study was to assess the differences in EEG between gifted and average students. Another aim of the present study was to investigate which brain areas are related to a Rey-Osterrieth complex figure (ROCF) memorizing using a functional cluster (FC) analysis and how the complexity of cortical activities changes in both gifted and average students. The EEG was recorded from 16 electrodes in both 18 right-handed healthy gifted and age-matched average students before and during ROCF memorizing. FC was estimated to characterize the joint interactions among many brain regions and neural complexity. The study assessed the visuo-spatial memory abilities through examining EEG profiles using the measure of FC, and planning and executive function using recall score. The gifted students made a significantly high score compared to the average students during ROCF memorizing. Both groups showed very different FC patterns. ROCF memorizing is related to the visual mental process, thus simultaneous neuronal activities appears on the right central, temporal, occipital, and bilateral prefrontal regions. One of the notable characteristics of gifted students' FC map is the dominance of the right hemisphere compared with that of average students, and it is accordance with the characteristics of gifted brain.

关键词： functional cluster analysis gifted students neural complexity Rey-Osterrieth complex figure test

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Evaluating Movement skills from Extended neural complexity

Evaluating Movement skills from Extended Neural Complexity

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Kwon, Woo Young Suh, Il Hong You, Bum-Jae Oh, Sang-Rok Hanyang Univ Dept Elect & Comp Engn Seoul 133791 South Korea Korea Inst Sci & Technol Seoul South Korea

ISBN: (纸本)9781467317146

For a robot to learn complex movement skills, programming by demonstration and/or learning by trial and error is necessary. Measuring the complexity of such movement skills is important to decide the appropriate learning model, and the required size of dataset and additional prior knowledge. To deal with measuring the complexity of movement skills for robots, we propose an information-theoretic complexity measure. By modeling proprioceptive as well as exteroceptive sensory data as a multivariate Gaussian distribution, movement skills can be modeled as a probabilistic model. Next, complexity of the movement skills is measured by using neural complexity. In addition to the original neural complexity measure, endogeneous changes in time of the movement skills are modeled by sampling in time and modeling as individual random variables. To evaluate our proposed complexity measure, several experiments are performed on real robotic movement skills.

关键词： complexity measure neural complexity Time-sliced neural complexity Movement skills

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neural complexity and structural connectivity

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Physical Review E 2009年第5期79卷 051914-051914页

作者： L. Barnett C. L. Buckley S. Bullock []Department of Informatics Centre for Computational Neuroscience and Robotics School of Science and Technology University of Sussex Brighton BN1 9QH United Kingdom

Tononi et al. [Proc. Natl. Acad. Sci. U.S.A. 91, 5033 (1994)] proposed a measure of neural complexity based on mutual information between complementary subsystems of a given neural network, which has attracted much interest in the neuroscience community and beyond. We develop an approximation of the measure for a popular Gaussian model which, applied to a continuous-time process, elucidates the relationship between the complexity of a neural system and its structural connectivity. Moreover, the approximation is accurate for weakly coupled systems and computationally cheap, scaling polynomially with system size in contrast to the full complexity measure, which scales exponentially. We also discuss connectivity normalization and resolve some issues stemming from an ambiguity in the original Gaussian model.

关键词： .Gaussian PHYSICAL REVIEW approximation Normalization neural network Proc Natl subsystems cheap continuous-time neural systems system size neural complexity structural connectivity complexity measure

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