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Zunino L. Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen-Shannon Distance. ENTROPY (BASEL, SWITZERLAND) 2024; 26:432. [PMID: 38785681 PMCID: PMC11119498 DOI: 10.3390/e26050432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/10/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
Taking into account the complexity of the human brain dynamics, the appropriate characterization of any brain state is a challenge not easily met. Actually, even the discrimination of simple behavioral tasks, such as resting with eyes closed or eyes open, represents an intricate problem and many efforts have been and are being made to overcome it. In this work, the aforementioned issue is carefully addressed by performing multiscale analyses of electroencephalogram records with the permutation Jensen-Shannon distance. The influence that linear and nonlinear temporal correlations have on the discrimination is unveiled. Results obtained lead to significant conclusions that help to achieve an improved distinction between these resting brain states.
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Affiliation(s)
- Luciano Zunino
- Centro de Investigaciones Ópticas (CONICET La Plata-CIC-UNLP), 1897 Gonnet, La Plata, Argentina;
- Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), 1900 La Plata, Argentina
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2
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Bódizs R, Schneider B, Ujma PP, Horváth CG, Dresler M, Rosenblum Y. Fundamentals of sleep regulation: Model and benchmark values for fractal and oscillatory neurodynamics. Prog Neurobiol 2024; 234:102589. [PMID: 38458483 DOI: 10.1016/j.pneurobio.2024.102589] [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: 08/19/2023] [Revised: 01/26/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024]
Abstract
Homeostatic, circadian and ultradian mechanisms play crucial roles in the regulation of sleep. Evidence suggests that ratios of low-to-high frequency power in the electroencephalogram (EEG) spectrum indicate the instantaneous level of sleep pressure, influenced by factors such as individual sleep-wake history, current sleep stage, age-related differences and brain topography characteristics. These effects are well captured and reflected in the spectral exponent, a composite measure of the constant low-to-high frequency ratio in the periodogram, which is scale-free and exhibits lower interindividual variability compared to slow wave activity, potentially serving as a suitable standardization and reference measure. Here we propose an index of sleep homeostasis based on the spectral exponent, reflecting the level of membrane hyperpolarization and/or network bistability in the central nervous system in humans. In addition, we advance the idea that the U-shaped overnight deceleration of oscillatory slow and fast sleep spindle frequencies marks the biological night, providing somnologists with an EEG-index of circadian sleep regulation. Evidence supporting this assertion comes from studies based on sleep replacement, forced desynchrony protocols and high-resolution analyses of sleep spindles. Finally, ultradian sleep regulatory mechanisms are indicated by the recurrent, abrupt shifts in dominant oscillatory frequencies, with spindle ranges signifying non-rapid eye movement and non-spindle oscillations - rapid eye movement phases of the sleep cycles. Reconsidering the indicators of fundamental sleep regulatory processes in the framework of the new Fractal and Oscillatory Adjustment Model (FOAM) offers an appealing opportunity to bridge the gap between the two-process model of sleep regulation and clinical somnology.
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Affiliation(s)
- Róbert Bódizs
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.
| | - Bence Schneider
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
| | - Péter P Ujma
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
| | - Csenge G Horváth
- Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary
| | - Martin Dresler
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
| | - Yevgenia Rosenblum
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands
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3
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Waters F, Ling I, Azimi S, Blom JD. Sleep-Related Hallucinations. Sleep Med Clin 2024; 19:143-157. [PMID: 38368061 DOI: 10.1016/j.jsmc.2023.10.008] [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: 02/19/2024]
Abstract
The diagnostic category of sleep-related hallucinations (SRH) replaces the previous category of Terrifying Hypnagogic Hallucinations in the 2001 edition of International Classification of Sleep Disorders-R. Hypnagogic and hypnopompic hallucinations (HHH) that occur in the absence of other symptoms or disorder and, within the limits of normal sleep, are most likely non-pathological. By contrast, complex nocturnal visual hallucinations (CNVH) may reflect a dimension of psychopathology reflecting different combinations of etiologic influences. The identification and conceptualization of CNVH is relatively new, and more research is needed to clarify whether CNVH share common mechanisms with HHH.
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Affiliation(s)
- Flavie Waters
- Clinical Research Centre, Graylands Hospital, North Metropolitan Health Service Mental Health, Brockway Road, John XXIII Avenue, Mount Claremont, Perth, Western Australia 6009, Australia; School of Psychological Science, The University of Western Australia, Crawley, Western Australia, Australia.
| | - Ivan Ling
- West Australian Sleep Disorders Research Institute, Perth, Australia; Department of Pulmonary Physiology & Sleep Medicine, Sir Charles Gairdner Hospital, 5th Floor, G-block, Nedlands, Western Australia 6009, Australia
| | - Somayyeh Azimi
- Clinical Research Centre, Graylands Hospital, North Metropolitan Health Service Mental Health, Brockway Road, John XXIII Avenue, Mount Claremont, Perth, Western Australia 6009, Australia; School of Human Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Jan Dirk Blom
- Parnassia Psychiatric Institute, Kiwistraat 43, The Hague 2552 DH, the Netherlands; Faculty of Social and Behavioural Sciences, Leiden University, Leiden, the Netherlands; Department of Psychiatry, University Medical Center Groningen, Groningen, the Netherlands
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4
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Davis JJJ, Schübeler F, Kozma R. Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities. SENSORS (BASEL, SWITZERLAND) 2024; 24:1605. [PMID: 38475141 DOI: 10.3390/s24051605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/21/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
It is of great interest to develop advanced sensory technologies allowing non-invasive monitoring of neural correlates of cognitive processing in people performing everyday tasks. A lot of progress has been reported in recent years in this research area using scalp EEG arrays, but the high level of noise in the electrode signals poses a lot of challenges. This study presents results of detailed statistical analysis of experimental data on the cycle of creation of knowledge and meaning in human brains under multiple cognitive modalities. We measure brain dynamics using a HydroCel Geodesic Sensor Net, 128-electrode dense-array electroencephalography (EEG). We compute a pragmatic information (PI) index derived from analytic amplitude and phase, by Hilbert transforming the EEG signals of 20 participants in six modalities, which combine various audiovisual stimuli, leading to different mental states, including relaxed and cognitively engaged conditions. We derive several relevant measures to classify different brain states based on the PI indices. We demonstrate significant differences between engaged brain states that require sensory information processing to create meaning and knowledge for intentional action, and relaxed-meditative brain states with less demand on psychophysiological resources. We also point out that different kinds of meanings may lead to different brain dynamics and behavioral responses.
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Affiliation(s)
- Joshua J J Davis
- Dodd-Walls Centre for Photonics and Quantum Technologies, Department of Physics & Ian Kirk's Lab., Centre for Brain Research, The University of Auckland, Auckland 1142, New Zealand
| | | | - Robert Kozma
- Department of Mathematics, University of Memphis, Memphis, TN 38152, USA
- School of Informatics, Obuda University, H-1034 Budapest, Hungary
- Kozmos Research Laboratories, Boston, MA 02215, USA
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5
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Buccellato A, Çatal Y, Bisiacchi P, Zang D, Zilio F, Wang Z, Qi Z, Zheng R, Xu Z, Wu X, Del Felice A, Mao Y, Northoff G. Probing Intrinsic Neural Timescales in EEG with an Information-Theory Inspired Approach: Permutation Entropy Time Delay Estimation (PE-TD). ENTROPY (BASEL, SWITZERLAND) 2023; 25:1086. [PMID: 37510033 PMCID: PMC10378026 DOI: 10.3390/e25071086] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023]
Abstract
Time delays are a signature of many physical systems, including the brain, and considerably shape their dynamics; moreover, they play a key role in consciousness, as postulated by the temporo-spatial theory of consciousness (TTC). However, they are often not known a priori and need to be estimated from time series. In this study, we propose the use of permutation entropy (PE) to estimate time delays from neural time series as a more robust alternative to the widely used autocorrelation window (ACW). In the first part, we demonstrate the validity of this approach on synthetic neural data, and we show its resistance to regimes of nonstationarity in time series. Mirroring yet another example of comparable behavior between different nonlinear systems, permutation entropy-time delay estimation (PE-TD) is also able to measure intrinsic neural timescales (INTs) (temporal windows of neural activity at rest) from hd-EEG human data; additionally, this replication extends to the abnormal prolongation of INT values in disorders of consciousness (DoCs). Surprisingly, the correlation between ACW-0 and PE-TD decreases in a state-dependent manner when consciousness is lost, hinting at potential different regimes of nonstationarity and nonlinearity in conscious/unconscious states, consistent with many current theoretical frameworks on consciousness. In summary, we demonstrate the validity of PE-TD as a tool to extract relevant time scales from neural data; furthermore, given the divergence between ACW and PE-TD specific to DoC subjects, we hint at its potential use for the characterization of conscious states.
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Affiliation(s)
- Andrea Buccellato
- Padova Neuroscience Center, University of Padova, Via Orus 2/B, 35129 Padova, Italy
- Department of General Psychology, University of Padova, Via Venezia, 8, 35131 Padova, Italy
| | - Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa, Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa, ON K1Z 7K4, Canada
| | - Patrizia Bisiacchi
- Padova Neuroscience Center, University of Padova, Via Orus 2/B, 35129 Padova, Italy
- Department of General Psychology, University of Padova, Via Venezia, 8, 35131 Padova, Italy
| | - Di Zang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200032, China
- National Center for Neurological Disorders, Shanghai 200040, China
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
| | - Federico Zilio
- Department of Philosophy, Sociology, Education and Applied Psychology, University of Padova, Piazza Capitaniato, 3, 35139 Padova, Italy
| | - Zhe Wang
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200032, China
- National Center for Neurological Disorders, Shanghai 200040, China
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
| | - Zengxin Qi
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200032, China
- National Center for Neurological Disorders, Shanghai 200040, China
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
| | - Ruizhe Zheng
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200032, China
- National Center for Neurological Disorders, Shanghai 200040, China
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
| | - Zeyu Xu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200032, China
- National Center for Neurological Disorders, Shanghai 200040, China
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
| | - Xuehai Wu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200032, China
- National Center for Neurological Disorders, Shanghai 200040, China
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
| | - Alessandra Del Felice
- Padova Neuroscience Center, University of Padova, Via Orus 2/B, 35129 Padova, Italy
- Department of Neuroscience, Section of Neurology, University of Padova, Via Belzoni, 160, 35121 Padova, Italy
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai 200040, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai 200032, China
- National Center for Neurological Disorders, Shanghai 200040, China
- Neurosurgical Institute, Fudan University, Shanghai 200040, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai 200040, China
| | - Georg Northoff
- The Royal's Institute of Mental Health Research & University of Ottawa, Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa, ON K1Z 7K4, Canada
- Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310013, China
- Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 310013, China
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Su AT, Xavier G, Kuan JW. The measurement of mental fatigue following an overnight on-call duty among doctors using electroencephalogram. PLoS One 2023; 18:e0287999. [PMID: 37406016 DOI: 10.1371/journal.pone.0287999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 06/16/2023] [Indexed: 07/07/2023] Open
Abstract
This study aimed to measure the spectral power differences in the brain rhythms among a group of hospital doctors before and after an overnight on-call duty. Thirty-two healthy doctors who performed regular on-call duty in a tertiary hospital in Sarawak, Malaysia were voluntarily recruited into this study. All participants were interviewed to collect relevant background information, followed by a self-administered questionnaire using Chalder Fatigue Scale and electroencephalogram test before and after an overnight on-call duty. The average overnight sleep duration during the on-call period was 2.2 hours (p<0.001, significantly shorter than usual sleep duration) among the participants. The mean (SD) Chalder Fatigue Scale score of the participants were 10.8 (5.3) before on-call and 18.4 (6.6) after on-call (p-value < 0.001). The theta rhythm showed significant increase in spectral power globally after an overnight on-call duty, especially when measured at eye closure. In contrast, the alpha and beta rhythms showed reduction in spectral power, significantly at temporal region, at eye closure, following an overnight on-call duty. These effects are more statistically significant when we derived the respective relative theta, alpha, and beta values. The finding of this study could be useful for development of electroencephalogram screening tool to detect mental fatigue.
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Affiliation(s)
- Anselm Ting Su
- Department of Community Medicine and Public Health, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia
| | - Gregory Xavier
- Kinta District Health Office, Ministry of Health Malaysia, Malaysia
| | - Jew Win Kuan
- Department of Medicine, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia
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Hu M, Zhang H, Ang KK. Brain Criticality EEG analysis for tracking neurodevelopment from Childhood to Adolescence. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082967 DOI: 10.1109/embc40787.2023.10340775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The brain criticality hypothesis suggests that neural networks and multiple aspects of brain activity self-organize into a critical state, and criticality marks the transition between ordered and disordered states. This hypothesis is appealing from computer science perspective because neural networks at criticality exhibit optimal processing and computing properties while having implications in clinical applications to neurological disorders. In this paper, we introduced brain criticality analysis to track neurodevelopment from childhood to adolescence using the electroencephalogram (EEG) data of 662 subjects aged 5 to 16 years from the Child Mind Institute. We computed brain criticality from long-range temporal correlation (LRTC) using detrended fluctuation analysis (DFA). We also compared the brain criticality analysis with standard EEG power analysis. The results showed a statistically significant increase in brain criticality from childhood to adolescence in the alpha band. A decreasing trend was observed in theta band from EEG power analysis, but a much higher variance was observed compared to the brain criticality analysis. However, the significant results were only observed in some EEG channels, and not observed if the analysis were performed separately with eyes-open and eyes-close condition. Nonetheless, the results suggest that brain criticality may serve as a biomarker of brain development and maturation, but further research is needed to improve brain criticality algorithms and EEG analysis methods.Clinical Relevance- The brain criticality analysis may be used to characterize and predict neurodevelopment in early childhood.
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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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Derchi CC, Mikulan E, Mazza A, Casarotto S, Comanducci A, Fecchio M, Navarro J, Devalle G, Massimini M, Sinigaglia C. Distinguishing intentional from nonintentional actions through eeg and kinematic markers. Sci Rep 2023; 13:8496. [PMID: 37231006 PMCID: PMC10213007 DOI: 10.1038/s41598-023-34604-y] [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: 01/10/2023] [Accepted: 05/04/2023] [Indexed: 05/27/2023] Open
Abstract
How can an intentional movement be distinguished from the same movement done nonintentionally? How can this distinction be drawn without asking the subject, or in patients who are unable to communicate? Here we address these questions, by focusing on blinking. This is one of the most frequent spontaneous actions in our daily life, but it can also be done intentionally. Furthermore, blinking is often spared in patients with severe brain injuries, and for some, it is the only way to report complex meanings. Using kinematic and EEG-based measures, we found that intentional and spontaneous blinking are preceded by different brain activities, even when they are indistinguishable. Unlike spontaneous ones, intentional blinks are characterized by a slow negative EEG drift, resembling the classic readiness potential. We investigated the theoretical implication of this finding in stochastic decision models as well as the practical significance of using brain-based signals to improve the discrimination between intentional and nonintentional actions. As proof of principle, we considered three brain-injured patients with rare neurological syndromes characterized by motor and communicative impairments. Although further research is needed, our results indicate that brain-based signals can offer a feasible way to infer intentionality even in absence of overt communication.
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Affiliation(s)
- C C Derchi
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - E Mikulan
- Department of Health Sciences, Università Degli Studi di Milano, Via di Rudinì 8, 20146, Milan, Italy
| | - A Mazza
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - S Casarotto
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
- Department of Biomedical and Clinical Sciences, Università Degli Studi Di Milano, Via G. B. Grassi 75, 20157, Milan, Italy
| | - A Comanducci
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - M Fecchio
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - J Navarro
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - G Devalle
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - M Massimini
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy.
- Department of Biomedical and Clinical Sciences, Università Degli Studi Di Milano, Via G. B. Grassi 75, 20157, Milan, Italy.
| | - C Sinigaglia
- Department of Philosophy, Università Degli Studi Di Milano, Via Festa del Perdono 7, 20122, Milan, Italy.
- Cognition in Action (CIA) Unit, PHILAB, 20122, Milan, Italy.
- Department of Philosophy, Stanford University, Stanford, CA, USA.
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Davis JJJ, Kozma R, Schübeler F. Analysis of Meditation vs. Sensory Engaged Brain States Using Shannon Entropy and Pearson's First Skewness Coefficient Extracted from EEG Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:1293. [PMID: 36772332 PMCID: PMC9920060 DOI: 10.3390/s23031293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
It has been proposed that meditative states show different brain dynamics than other more engaged states. It is known that when people sit with closed eyes instead of open eyes, they have different brain dynamics, which may be associated with a combination of deprived sensory input and more relaxed inner psychophysiological and cognitive states. Here, we study such states based on a previously established experimental methodology, with the aid of an electro-encephalography (EEG) array with 128 electrodes. We derived the Shannon Entropy (H) and Pearson's 1st Skewness Coefficient (PSk) from the power spectrum for the modalities of meditation and video watching, including 20 participants, 11 meditators and 9 non-meditators. The discriminating performance of the indices H and PSk was evaluated using Student's t-test. The results demonstrate a statistically significant difference between the mean H and PSk values during meditation and video watch modes. We show that the H index is useful to discriminate between Meditator and Non-Meditator participants during meditation over both the prefrontal and occipital areas, while the PSk index is useful to discriminate Meditators from Non-Meditators based on the prefrontal areas for both meditation and video modes. Moreover, we observe episodes of anti-correlation between the prefrontal and occipital areas during meditation, while there is no evidence for such anticorrelation periods during video watching. We outline directions of future studies incorporating further statistical indices for the characterization of brain states.
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Affiliation(s)
- Joshua J. J. Davis
- Department of Physics, Dodd-Walls Centre for Photonics and Quantum Technologies, University of Auckland, Auckland 1142, New Zealand
| | - Robert Kozma
- Department of Mathematics, University of Memphis, Memphis, TN 38152, USA
- Kozmos Research Laboratories, Boston, MA 02215, USA
- School of Informatics, Obuda University, H-1034 Budapest, Hungary
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