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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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2
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E DO, V MS, S LV, E SY. Fractal Structure of Brain Electrical Activity of Patients With Mental Disorders. Front Physiol 2022; 13:905318. [PMID: 35923231 PMCID: PMC9340582 DOI: 10.3389/fphys.2022.905318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/23/2022] [Indexed: 11/19/2022] Open
Abstract
This work was aimed at a comparative analysis of the degree of multifractality of electroencephalographic time series obtained from a group of healthy subjects and from patients with mental disorders. We analyzed long-term records of patients with paranoid schizophrenia and patients with depression. To evaluate the properties of multifractal scaling of various electroencephalographic time series, the method of maximum modulus of the wavelet transform and multifractal analysis of fluctuations without a trend were used. The stability of the width and position of the singularity spectrum for each of the test groups was revealed, and a relationship was established between the correlation and anticorrelation dynamics of successive values of the electroencephalographic time series and the type of mental disorders. It was shown that the main differences between the multifractal properties of brain activity in normal and pathological conditions lie in the different width of the multifractality spectrum and its location associated with the correlated or anticorrelated dynamics of the values of successive time series. It was found that the schizophrenia group is characterized by a greater degree of multifractality compared to the depression group. Thus, the degree of multifractality can be included in a set of tests for differential diagnosis and research of mental disorders.
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Affiliation(s)
- Dick O. E
- Laboratory of Physiology of Reception, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
- *Correspondence: Dick O. E,
| | - Murav’eva S. V
- Laboratory of Vision Physiology, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
| | - Lebedev V. S
- Laboratory of Vision Physiology, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
| | - Shelepin Yu. E
- Laboratory of Vision Physiology, Pavlov Institute of Physiology of Russian Academy of Science, St. Petersburg, Russia
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3
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Catrambone V, Barbieri R, Wendt H, Abry P, Valenza G. Functional brain-heart interplay extends to the multifractal domain. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200260. [PMID: 34689620 PMCID: PMC8543048 DOI: 10.1098/rsta.2020.0260] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/12/2021] [Indexed: 05/09/2023]
Abstract
The study of functional brain-heart interplay has provided meaningful insights in cardiology and neuroscience. Regarding biosignal processing, this interplay involves predominantly neural and heartbeat linear dynamics expressed via time and frequency domain-related features. However, the dynamics of central and autonomous nervous systems show nonlinear and multifractal behaviours, and the extent to which this behaviour influences brain-heart interactions is currently unknown. Here, we report a novel signal processing framework aimed at quantifying nonlinear functional brain-heart interplay in the non-Gaussian and multifractal domains that combines electroencephalography (EEG) and heart rate variability series. This framework relies on a maximal information coefficient analysis between nonlinear multiscale features derived from EEG spectra and from an inhomogeneous point-process model for heartbeat dynamics. Experimental results were gathered from 24 healthy volunteers during a resting state and a cold pressor test, revealing that synchronous changes between brain and heartbeat multifractal spectra occur at higher EEG frequency bands and through nonlinear/complex cardiovascular control. We conclude that significant bodily, sympathovagal changes such as those elicited by cold-pressure stimuli affect the functional brain-heart interplay beyond second-order statistics, thus extending it to multifractal dynamics. These results provide a platform to define novel nervous-system-targeted biomarkers. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
- Vincenzo Catrambone
- Research Center E.Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Herwig Wendt
- IRIT–ENSEEIHT, Université de Toulouse, CNRS, Toulouse, France
| | - Patrice Abry
- University of Lyon, ENS de Lyon, University Claude Bernard, CNRS, Laboratoire de Physique, Lyon, France
| | - Gaetano Valenza
- Research Center E.Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
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4
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Attaining the recesses of the cognitive space. Cogn Neurodyn 2021; 16:767-778. [PMID: 35847536 PMCID: PMC9279523 DOI: 10.1007/s11571-021-09755-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 10/31/2021] [Accepted: 11/08/2021] [Indexed: 11/26/2022] Open
Abstract
Existing neuropsychological tests of executive function often manifest a difficulty pinpointing cognitive deficits when these are intermittent and come in the form of omissions. We discuss the hypothesis that two partially interrelated reasons for this failure stem from relative inability of neuropsychological tests to explore the cognitive space and to explicitly take into account strategic and opportunistic resource allocation decisions, and to address the temporal aspects of both behaviour and task-related brain function in data analysis. Criteria for tasks suitable for neuropsychological assessment of executive function, as well as appropriate ways to analyse and interpret observed behavioural data are suggested. It is proposed that experimental tasks should be devised which emphasize typical rather than optimal performance, and that analyses should quantify path-dependent fluctuations in performance levels rather than averaged behaviour. Some implications for experimental neuropsychology are illustrated for the case of planning and problem-solving abilities and with particular reference to cognitive impairment in closed-head injury.
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5
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Stylianou O, Racz FS, Kim K, Kaposzta Z, Czoch A, Yabluchanskiy A, Eke A, Mukli P. Multifractal Functional Connectivity Analysis of Electroencephalogram Reveals Reorganization of Brain Networks in a Visual Pattern Recognition Paradigm. Front Hum Neurosci 2021; 15:740225. [PMID: 34733145 PMCID: PMC8558231 DOI: 10.3389/fnhum.2021.740225] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/23/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain consists of anatomically distant neuronal assemblies that are interconnected via a myriad of synapses. This anatomical network provides the neurophysiological wiring framework for functional connectivity (FC), which is essential for higher-order brain functions. While several studies have explored the scale-specific FC, the scale-free (i.e., multifractal) aspect of brain connectivity remains largely neglected. Here we examined the brain reorganization during a visual pattern recognition paradigm, using bivariate focus-based multifractal (BFMF) analysis. For this study, 58 young, healthy volunteers were recruited. Before the task, 3-3 min of resting EEG was recorded in eyes-closed (EC) and eyes-open (EO) states, respectively. The subsequent part of the measurement protocol consisted of 30 visual pattern recognition trials of 3 difficulty levels graded as Easy, Medium, and Hard. Multifractal FC was estimated with BFMF analysis of preprocessed EEG signals yielding two generalized Hurst exponent-based multifractal connectivity endpoint parameters, H(2) and ΔH 15; with the former indicating the long-term cross-correlation between two brain regions, while the latter captures the degree of multifractality of their functional coupling. Accordingly, H(2) and ΔH 15 networks were constructed for every participant and state, and they were characterized by their weighted local and global node degrees. Then, we investigated the between- and within-state variability of multifractal FC, as well as the relationship between global node degree and task performance captured in average success rate and reaction time. Multifractal FC increased when visual pattern recognition was administered with no differences regarding difficulty level. The observed regional heterogeneity was greater for ΔH 15 networks compared to H(2) networks. These results show that reorganization of scale-free coupled dynamics takes place during visual pattern recognition independent of difficulty level. Additionally, the observed regional variability illustrates that multifractal FC is region-specific both during rest and task. Our findings indicate that investigating multifractal FC under various conditions - such as mental workload in healthy and potentially in diseased populations - is a promising direction for future research.
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Affiliation(s)
- Orestis Stylianou
- Department of Physiology, Semmelweis University, Budapest, Hungary,Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
| | | | - Keumbi Kim
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Zalan Kaposzta
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Akos Czoch
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Biochemistry and Molecular Biology, Oklahoma Center for Geroscience and Healthy Brain Aging, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States,The Peggy and Charles Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States,Department of Health Promotion Sciences, College of Public Health, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary,Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States,Andras Eke,
| | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary,Vascular Cognitive Impairment and Neurodegeneration Program, Department of Biochemistry and Molecular Biology, Oklahoma Center for Geroscience and Healthy Brain Aging, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States,*Correspondence: Peter Mukli,
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6
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Lavanga M, De Ridder J, Kotulska K, Moavero R, Curatolo P, Weschke B, Riney K, Feucht M, Krsek P, Nabbout R, Jansen AC, Wojdan K, Domanska-Pakieła D, Kaczorowska-Frontczak M, Hertzberg C, Ferrier CH, Samueli S, Jahodova A, Aronica E, Kwiatkowski DJ, Jansen FE, Jóźwiak S, Lagae L, Van Huffel S, Caicedo A. Results of quantitative EEG analysis are associated with autism spectrum disorder and development abnormalities in infants with tuberous sclerosis complex. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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7
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Lavanga M, Bollen B, Caicedo A, Dereymaeker A, Jansen K, Ortibus E, Van Huffel S, Naulaers G. The effect of early procedural pain in preterm infants on the maturation of electroencephalogram and heart rate variability. Pain 2021; 162:1556-1566. [PMID: 33110029 PMCID: PMC8054544 DOI: 10.1097/j.pain.0000000000002125] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 01/18/2023]
Abstract
ABSTRACT Preterm infants show a higher incidence of cognitive, social, and behavioral problems, even in the absence of major medical complications during their stay in the neonatal intensive care unit (NICU). Several authors suggest that early-life experience of stress and procedural pain could impact cerebral development and maturation resulting in an altered development of cognition, behavior, or motor patterns in later life. However, it remains very difficult to assess this impact of procedural pain on physiological development. This study describes the maturation of electroencephalogram (EEG) signals and heart rate variability in a prospective cohort of 92 preterm infants (<34 weeks gestational age) during their NICU stay. We took into account the number of noxious, ie, skin-breaking, procedures they were subjected in the first 5 days of life, which corresponded to a median age of 31 weeks and 4 days. Using physiological signal modelling, this study shows that a high exposure to early procedural pain, measured as skin-breaking procedures, increased the level of discontinuity in both EEG and heart rate variability in preterm infants. These findings have also been confirmed in a subset of the most vulnerable preterm infants with a gestational age lower than 29 weeks. We conclude that a high level of early pain exposure in the NICU increases the level of functional dysmaturity, which can ultimately impact preterm infants' future developmental outcome.
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Affiliation(s)
- Mario Lavanga
- Department of Electrical Engineering (ESAT), Division STADIUS, KU Leuven, Leuven, Belgium
| | - Bieke Bollen
- Department of Development and Regeneration, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Alexander Caicedo
- Department of Applied Mathematics and Computer Science, School of Engineering, Science and Technology, Universidad Del Rosario, Bogota', Colombia
| | - Anneleen Dereymaeker
- Department of Development and Regeneration, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Katrien Jansen
- Department of Development and Regeneration, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Els Ortibus
- Department of Development and Regeneration, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), Division STADIUS, KU Leuven, Leuven, Belgium
| | - Gunnar Naulaers
- Department of Development and Regeneration, Faculty of Medicine, KU Leuven, Leuven, Belgium
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8
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La Rocca D, Wendt H, van Wassenhove V, Ciuciu P, Abry P. Revisiting Functional Connectivity for Infraslow Scale-Free Brain Dynamics Using Complex Wavelets. Front Physiol 2021; 11:578537. [PMID: 33488390 PMCID: PMC7818786 DOI: 10.3389/fphys.2020.578537] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/25/2020] [Indexed: 01/18/2023] Open
Abstract
The analysis of human brain functional networks is achieved by computing functional connectivity indices reflecting phase coupling and interactions between remote brain regions. In magneto- and electroencephalography, the most frequently used functional connectivity indices are constructed based on Fourier-based cross-spectral estimation applied to specific fast and band-limited oscillatory regimes. Recently, infraslow arrhythmic fluctuations (below the 1 Hz) were recognized as playing a leading role in spontaneous brain activity. The present work aims to propose to assess functional connectivity from fractal dynamics, thus extending the assessment of functional connectivity to the infraslow arrhythmic or scale-free temporal dynamics of M/EEG-quantified brain activity. Instead of being based on Fourier analysis, new Imaginary Coherence and weighted Phase Lag indices are constructed from complex-wavelet representations. Their performances are first assessed on synthetic data by means of Monte-Carlo simulations, and they are then compared favorably against the classical Fourier-based indices. These new assessments of functional connectivity indices are also applied to MEG data collected on 36 individuals both at rest and during the learning of a visual motion discrimination task. They demonstrate a higher statistical sensitivity, compared to their Fourier counterparts, in capturing significant and relevant functional interactions in the infraslow regime and modulations from rest to task. Notably, the consistent overall increase in functional connectivity assessed from fractal dynamics from rest to task correlated with a change in temporal dynamics as well as with improved performance in task completion, which suggests that the complex-wavelet weighted Phase Lag index is the sole index is able to capture brain plasticity in the infraslow scale-free regime.
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Affiliation(s)
- Daria La Rocca
- CEA, NeuroSpin, University of Paris-Saclay, Paris, France.,Inria Saclay Île-de-France, Parietal, University of Paris-Saclay, Paris, France
| | - Herwig Wendt
- IRIT, CNRS, University of Toulouse, Toulouse, France
| | - Virginie van Wassenhove
- CEA, NeuroSpin, University of Paris-Saclay, Paris, France.,INSERM U992, Collège de France, University of Paris-Saclay, Paris, France
| | - Philippe Ciuciu
- CEA, NeuroSpin, University of Paris-Saclay, Paris, France.,Inria Saclay Île-de-France, Parietal, University of Paris-Saclay, Paris, France
| | - Patrice Abry
- Univ. Lyon, ENS de Lyon, Univ. Claude Bernard, CNRS, Laboratoire de Physique, Lyon, France
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9
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Lavanga M, Heremans E, Moeyersons J, Bollen B, Jansen K, Ortibus E, Naulaers G, Van Huffel S, Caicedo A. Maturation of the Autonomic Nervous System in Premature Infants: Estimating Development Based on Heart-Rate Variability Analysis. Front Physiol 2021; 11:581250. [PMID: 33584326 PMCID: PMC7873975 DOI: 10.3389/fphys.2020.581250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 12/02/2020] [Indexed: 11/13/2022] Open
Abstract
This study aims at investigating the development of premature infants' autonomic nervous system (ANS) based on a quantitative analysis of the heart-rate variability (HRV) with a variety of novel features. Additionally, the role of heart-rate drops, known as bradycardias, has been studied in relation to both clinical and novel sympathovagal indices. ECG data were measured for at least 3 h in 25 preterm infants (gestational age ≤32 weeks) for a total number of 74 recordings. The post-menstrual age (PMA) of each patient was estimated from the RR interval time-series by means of multivariate linear-mixed effects regression. The tachograms were segmented based on bradycardias in periods after, between and during bradycardias. For each of those epochs, a set of temporal, spectral and fractal indices were included in the regression model. The best performing model has R 2 = 0.75 and mean absolute error MAE = 1.56 weeks. Three main novelties can be reported. First, the obtained maturation models based on HRV have comparable performance to other development models. Second, the selected features for age estimation show a predominance of power and fractal features in the very-low- and low-frequency bands in explaining the infants' sympathovagal development from 27 PMA weeks until 40 PMA weeks. Third, bradycardias might disrupt the relationship between common temporal indices of the tachogram and the age of the infant and the interpretation of sympathovagal indices. This approach might provide a novel overview of post-natal autonomic maturation and an alternative development index to other electrophysiological data analysis.
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Affiliation(s)
- Mario Lavanga
- Division STADIUS, Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Leuven, Belgium
| | - Elisabeth Heremans
- Division STADIUS, Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Leuven, Belgium
| | - Jonathan Moeyersons
- Division STADIUS, Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Leuven, Belgium
| | - Bieke Bollen
- Department of Development and Regeneration, Faculty of Medicine, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Katrien Jansen
- Department of Development and Regeneration, Faculty of Medicine, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Els Ortibus
- Department of Development and Regeneration, Faculty of Medicine, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Gunnar Naulaers
- Department of Development and Regeneration, Faculty of Medicine, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- Division STADIUS, Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Leuven, Belgium
| | - Alexander Caicedo
- Applied Mathematics and Computer Science, School of Engineering, Science and Technology, Universidad del Rosario, Bogotá, Colombia
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10
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Lavanga M, Smets L, Bollen B, Jansen K, Ortibus E, Huffel SV, Naulaers G, Caicedo A. A perinatal stress calculator for the neonatal intensive care unit: an unobtrusive approach. Physiol Meas 2020; 41:075012. [PMID: 32521528 DOI: 10.1088/1361-6579/ab9b66] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
OBJECTIVE Early experience of pain and stress in the neonatal intensive care unit is known to have an effect on the neurodevelopment of the infant. However, an automated method to quantify the procedural pain or perinatal stress in premature patients does not exist. APPROACH In the current study, EEG and ECG data were collected for more than 3 hours from 136 patients in order to quantify stress exposure. Specifically, features extracted from the EEG and heart-rate variability in both quiet and non-quiet sleep segments were used to develop a subspace linear-discriminant analysis stress classifier. MAIN RESULTS The main novelty of the study lies in the absence of intrusive methods or pain elicitation protocols to develop the stress classifier. Three main findings can be reported. First, we developed different stress classifiers for the different age groups and stress intensities, obtaining an area under the curve in the range [0.78-0.93] for non-quiet sleep and [0.77-0.96] for quiet sleep. Second, a dysmature EEG was found in patients under stress. Third, an enhanced cortical connectivity and increased brain-heart communication was correlated with a higher stress load, while the autonomic activity did not seem to be associated to stress exposure. SIGNIFICANCE The results shed a light on the pain and stress processing in preterm neonates, suggesting that software tools to investigate dysmature EEG might be helpful to assess stress load in premature patients. These results could be the foundation to assess the impact of stress on infants' development and to tune preventive care.
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Affiliation(s)
- M Lavanga
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, box 2446, 3001, Leuven, Belgium. Authors contributed equally to this work
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11
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Catrambone V, Wendt H, Barbieri R, Abry P, Valenza G. Quantifying Functional Links between Brain and Heartbeat Dynamics in the Multifractal Domain: a Preliminary Analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:561-564. [PMID: 33018051 DOI: 10.1109/embc44109.2020.9175859] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Quantification of brain-heart interplay (BHI) has mainly been performed in the time and frequency domains. However, such functional interactions are likely to involve nonlinear dynamics associated with the two systems. To this extent, in this preliminary study we investigate the functional coupling between multifractal properties of Electroencephalography (EEG) and Heart Rate Variability (HRV) series using a channel- and time scale-wise maximal information coefficient analysis. Experimental results were gathered from 24 healthy volunteers undergoing a resting state and a cold-pressure test, and suggest that significant changes between the two experimental conditions might be associated with nonlinear quantifiers of the multifractal spectrum. Particularly, major brain-heart functional coupling was associated with the secondorder cumulant of the multifractal spectrum. We conclude that a functional nonlinear relationship between brain- and heartbeat-related multifractal sprectra exist, with higher values associated with the resting state.
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12
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Lanata A, Sebastiani L, Di Gruttola F, Di Modica S, Scilingo EP, Greco A. Nonlinear Analysis of Eye-Tracking Information for Motor Imagery Assessments. Front Neurosci 2020; 13:1431. [PMID: 32009892 PMCID: PMC6974582 DOI: 10.3389/fnins.2019.01431] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 12/18/2019] [Indexed: 01/10/2023] Open
Abstract
This study investigates the assessment of motor imagery (MI) ability in humans. Commonly, MI ability is measured through two methodologies: a self-administered questionnaire (MIQ-3) and the mental chronometry (MC), which measures the temporal discrepancy between the actual and the imagined motor tasks. However, both measures rely on subjects' self-assessment and do not use physiological measures. In this study, we propose a novel set of features extracted from the nonlinear dynamics of the eye gaze signal to discriminate between good and bad imagers. To this aim, we designed an experiment where twenty volunteers, categorized as good or bad imagers according to MC, performed three tasks: a motor task (MT), a visual Imagery task (VI), and a kinaesthetic Imagery task (KI). Throughout the experiment, the subjects' eye gaze was continuously monitored using an eye-tracking system. Eye gaze time series were analyzed through recurrence quantification analysis of the reconstructed phase space and compared between the two groups. Statistical results have shown how nonlinear eye behavior can express an inner dynamics of imagery mental process and may be used as a more objective and physiological-based measure of MI ability.
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Affiliation(s)
- Antonio Lanata
- Department of Information Engineering & Research Centre E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Laura Sebastiani
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Francesco Di Gruttola
- Department of Information Engineering & Research Centre E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Stefano Di Modica
- Department of Information Engineering & Research Centre E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Enzo Pasquale Scilingo
- Department of Information Engineering & Research Centre E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Alberto Greco
- Department of Information Engineering & Research Centre E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
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13
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Catrambone V, Valenza G, Scilingo EP, Vanello N, Wendt H, Barbieri R, Abry P. Wavelet p-Leader Non-Gaussian Multiscale Expansions for EEG series: an Exploratory Study on Cold-Pressor Test. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:7096-7099. [PMID: 31947472 DOI: 10.1109/embc.2019.8856396] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Brain dynamics recorded through electroencephalography (EEG) have been proven to be the output of a nonstationary and nonlinear system. Thus, multifractality of EEG series has been exploited as a useful tool for a neurophysiological characterization in health and disease. However, the role of EEG multifractality under peripheral stress is unknown. In this study, we propose to make use of a novel tool, the recently defined non-Gaussian multiscale analysis, to investigate brain dynamics in the range of 4-8Hz following a cold-pressor test versus a resting state. The method builds on the wavelet p-leader multifractal spectrum to quantify different types of departure from Gaussian and linear properties, and is compared here to standard linear descriptive indices. Results suggest that the proposed non-Gaussian multiscale indices were able to detect expected changes over the somatosensory and premotor cortices, over regions different from those detected by linear analyses. They further indicate that preferred responses for the contralateral somatosensory cortex occur at scales 2.5s and 5s. These findings contribute to the characterization of the so-called central autonomic network, linking dynamical changes at a peripheral and a central nervous system levels.
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Tozzi A, Peters JF, Çankaya MN. The informational entropy endowed in cortical oscillations. Cogn Neurodyn 2018; 12:501-507. [PMID: 30250628 DOI: 10.1007/s11571-018-9491-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 05/31/2018] [Accepted: 06/14/2018] [Indexed: 12/20/2022] Open
Abstract
A two-dimensional shadow may encompass more information than its corresponding three-dimensional object. Indeed, if we rotate the object, we achieve a pool of observed shadows from different angulations, gradients, shapes and variable length contours that make it possible for us to increase our available information. Starting from this simple observation, we show how informational entropies might turn out to be useful in the evaluation of scale-free dynamics in the brain. Indeed, brain activity exhibits a scale-free distribution that leads to the variations in the power law exponent typical of different functional neurophysiological states. Here we show that modifications in scaling slope are associated with variations in Rényi entropy, a generalization of Shannon informational entropy. From a three-dimensional object's perspective, by changing its orientation (standing for the cortical scale-free exponent), we detect different two-dimensional shadows from different perception angles (standing for Rényi entropy in different brain areas). We show how, starting from known values of Rényi entropy (easily detectable in brain fMRIs or EEG traces), it is feasible to calculate the scaling slope in a given moment and in a given brain area. Because changes in scale-free cortical dynamics modify brain activity, this issue points towards novel approaches to mind reading and description of the forces required for transcranial stimulation.
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Affiliation(s)
- Arturo Tozzi
- 1Computational Intelligence Laboratory, University of Manitoba, Winnipeg, MB R3T 5V6 Canada
| | - James F Peters
- 2Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor's Circle, Winnipeg, MB R3T 5V6 Canada
- 3Department of Mathematics, Faculty of Arts and Sciences, Adıyaman University, 02040 Adıyaman, Turkey
| | - Mehmet Niyazi Çankaya
- 4Applied Sciences School, Department of International Trading, Department of Statistics, Faculty of Arts and Science, Usak University, Usak, Turkey
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15
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Borges AFT, Giraud AL, Mansvelder HD, Linkenkaer-Hansen K. Scale-Free Amplitude Modulation of Neuronal Oscillations Tracks Comprehension of Accelerated Speech. J Neurosci 2018; 38:710-722. [PMID: 29217685 PMCID: PMC6596185 DOI: 10.1523/jneurosci.1515-17.2017] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 10/24/2017] [Accepted: 11/20/2017] [Indexed: 01/17/2023] Open
Abstract
Speech comprehension is preserved up to a threefold acceleration, but deteriorates rapidly at higher speeds. Current models posit that perceptual resilience to accelerated speech is limited by the brain's ability to parse speech into syllabic units using δ/θ oscillations. Here, we investigated whether the involvement of neuronal oscillations in processing accelerated speech also relates to their scale-free amplitude modulation as indexed by the strength of long-range temporal correlations (LRTC). We recorded MEG while 24 human subjects (12 females) listened to radio news uttered at different comprehensible rates, at a mostly unintelligible rate and at this same speed interleaved with silence gaps. δ, θ, and low-γ oscillations followed the nonlinear variation of comprehension, with LRTC rising only at the highest speed. In contrast, increasing the rate was associated with a monotonic increase in LRTC in high-γ activity. When intelligibility was restored with the insertion of silence gaps, LRTC in the δ, θ, and low-γ oscillations resumed the low levels observed for intelligible speech. Remarkably, the lower the individual subject scaling exponents of δ/θ oscillations, the greater the comprehension of the fastest speech rate. Moreover, the strength of LRTC of the speech envelope decreased at the maximal rate, suggesting an inverse relationship with the LRTC of brain dynamics when comprehension halts. Our findings show that scale-free amplitude modulation of cortical oscillations and speech signals are tightly coupled to speech uptake capacity.SIGNIFICANCE STATEMENT One may read this statement in 20-30 s, but reading it in less than five leaves us clueless. Our minds limit how much information we grasp in an instant. Understanding the neural constraints on our capacity for sensory uptake is a fundamental question in neuroscience. Here, MEG was used to investigate neuronal activity while subjects listened to radio news played faster and faster until becoming unintelligible. We found that speech comprehension is related to the scale-free dynamics of δ and θ bands, whereas this property in high-γ fluctuations mirrors speech rate. We propose that successful speech processing imposes constraints on the self-organization of synchronous cell assemblies and their scale-free dynamics adjusts to the temporal properties of spoken language.
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Affiliation(s)
- Ana Filipa Teixeira Borges
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, Netherlands
- Amsterdam Neuroscience, Amsterdam, Netherlands, and
| | - Anne-Lise Giraud
- Department of Neuroscience, University of Geneva, Biotech Campus, Geneva 1211, Switzerland
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, Netherlands
- Amsterdam Neuroscience, Amsterdam, Netherlands, and
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, Netherlands,
- Amsterdam Neuroscience, Amsterdam, Netherlands, and
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Raiesdana S. Quantifying the dynamic of OSA brain using multifractal formalism: A novel measure for sleep fragmentation. Technol Health Care 2017; 25:265-284. [PMID: 27886023 DOI: 10.3233/thc-161278] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
It is thought that the critical brain dynamics in sleep is modulated during frequent periods of wakefulness. This paper utilizes the capacity of EEG based scaling analysis to quantify sleep fragmentation in patients with obstructive sleep apnea. The scale-free (fractal) behavior refers to a state where no characteristic scale dominates the dynamics of the underlying process which is evident as long range correlations in a time series. Here, Multiscaling (multifractal) spectrum is utilized to quantify the disturbed dynamic of an OSA brain with fragmented sleep. The whole night multichannel sleep EEG recordings of 18 subjects were employed to compute and quantify variable power-law long-range correlations and singularity spectra. Based on this characteristic, a new marker for sleep fragmentation named ``scaling based sleep fragmentation'' was introduced. This measure takes into account the sleep run length and stage transition quality within a fuzzy inference system to improve decisions made on sleep fragmentation. The proposed index was implemented, validated with sleepiness parameters and compared to some common indexes including sleep fragmentation index, arousal index, sleep diversity index, and sleep efficiency index. Correlations were almost significant suggesting that the sleep characterizing measure, based on singularity spectra range, could properly detect fragmentations and quantify their rate. This method can be an alternative for quantifying the sleep fragmentation in clinical practice after being approved experimentally. Control of sleep fragmentation and, subsequently, suppression of excessive daytime sleepiness will be a promising outlook of this kind of researches.
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Lavanga M, De Wel O, Caicedo A, Heremans E, Jansen K, Dereymaeker A, Naulaers G, Van Huffel S. Automatic quiet sleep detection based on multifractality in preterm neonates: Effects of maturation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:2010-2013. [PMID: 29060290 DOI: 10.1109/embc.2017.8037246] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify quiet sleep epochs, but the classifier performances seem to be highly affected by the infant's age. In particular, from the developed classifiers, the lowest area under the curve (AUC) has been obtained for EEG recordings at very young age (≤ 31 weeks post-menstrual age), and the maximum at full-term age (≥ 37 weeks post-menstrual age). The improvement in classification performances can be due to a change in the multifractality properties of neonatal EEG during the maturation of the infant, which makes the EEG sleep stages more distinguishable.
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18
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Beyond the anatomy-based representation of brain function: Comment on "Topodynamics of metastable brains" by Arturo Tozzi et al. Phys Life Rev 2017; 21:42-45. [PMID: 28460822 DOI: 10.1016/j.plrev.2017.04.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 04/25/2017] [Indexed: 11/21/2022]
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19
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Papo D. Commentary: The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs. Front Hum Neurosci 2016; 10:423. [PMID: 27624312 PMCID: PMC5004455 DOI: 10.3389/fnhum.2016.00423] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 08/09/2016] [Indexed: 12/12/2022] Open
Affiliation(s)
- David Papo
- GISC and Laboratory of Biological Networks, Center for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain
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20
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Z-Flores E, Trujillo L, Sotelo A, Legrand P, Coria LN. Regularity and Matching Pursuit feature extraction for the detection of epileptic seizures. J Neurosci Methods 2016; 266:107-25. [DOI: 10.1016/j.jneumeth.2016.03.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Revised: 03/30/2016] [Accepted: 03/31/2016] [Indexed: 11/25/2022]
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21
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Papo D. Functional significance of complex fluctuations in brain activity: from resting state to cognitive neuroscience. Front Syst Neurosci 2014; 8:112. [PMID: 24966818 PMCID: PMC4052734 DOI: 10.3389/fnsys.2014.00112] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 05/26/2014] [Indexed: 11/15/2022] Open
Abstract
Behavioral studies have shown that human cognition is characterized by properties such as temporal scale invariance, heavy-tailed non-Gaussian distributions, and long-range correlations at long time scales, suggesting models of how (non observable) components of cognition interact. On the other hand, results from functional neuroimaging studies show that complex scaling and intermittency may be generic spatio-temporal properties of the brain at rest. Somehow surprisingly, though, hardly ever have the neural correlates of cognition been studied at time scales comparable to those at which cognition shows scaling properties. Here, we analyze the meanings of scaling properties and the significance of their task-related modulations for cognitive neuroscience. It is proposed that cognitive processes can be framed in terms of complex generic properties of brain activity at rest and, ultimately, of functional equations, limiting distributions, symmetries, and possibly universality classes characterizing them.
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Affiliation(s)
- David Papo
- Computational Systems Biology Group, Center for Biomedical Technology, Universidad Politécnica de Madrid Madrid, Spain
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22
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Potentialities of the wavelet and multifractal techniques to evaluate changes in the functional state of the human brain. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2011.11.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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23
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Bhattacharya J. Increase of universality in human brain during mental imagery from visual perception. PLoS One 2009; 4:e4121. [PMID: 19122817 PMCID: PMC2607012 DOI: 10.1371/journal.pone.0004121] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2008] [Accepted: 12/03/2008] [Indexed: 12/04/2022] Open
Abstract
Background Different complex systems behave in a similar way near their critical points of phase transitions which leads to an emergence of a universal scaling behaviour. Universality indirectly implies a long-range correlation between constituent subsystems. As the distributed correlated processing is a hallmark of higher complex cognition, I investigated a measure of universality in human brain during perception and mental imagery of complex real-life visual object like visual art. Methodology/Principal Findings A new method was presented to estimate the strength of hidden universal structure in a multivariate data set. In this study, I investigated this method in the electrical activities (electroencephalogram signals) of human brain during complex cognition. Two broad groups - artists and non-artists - were studied during the encoding (perception) and retrieval (mental imagery) phases of actual paintings. Universal structure was found to be stronger in visual imagery than in visual perception, and this difference was stronger in artists than in non-artists. Further, this effect was found to be largest in the theta band oscillations and over the prefrontal regions bilaterally. Conclusions/Significance Phase transition like dynamics was observed in the electrical activities of human brain during complex cognitive processing, and closeness to phase transition was higher in mental imagery than in real perception. Further, the effect of long-term training on the universal scaling was also demonstrated.
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Affiliation(s)
- Joydeep Bhattacharya
- Department of Psychology, Goldsmiths College, University of London, London, United Kingdom
- Commission for Scientific Visualization, Austrian Academy of Sciences, Vienna, Austria
- * E-mail:
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24
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Goldman LJ, Jarabo RM, Gómez RG. Airway pressure alters wavelet fractal dynamics and short-range dependence of respiratory variability. Respir Physiol Neurobiol 2008; 161:29-40. [DOI: 10.1016/j.resp.2007.11.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2006] [Revised: 11/24/2007] [Accepted: 11/26/2007] [Indexed: 11/25/2022]
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25
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Buiatti M, Papo D, Baudonnière PM, van Vreeswijk C. Feedback modulates the temporal scale-free dynamics of brain electrical activity in a hypothesis testing task. Neuroscience 2007; 146:1400-12. [PMID: 17418496 DOI: 10.1016/j.neuroscience.2007.02.048] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2006] [Revised: 01/23/2007] [Accepted: 02/15/2007] [Indexed: 10/23/2022]
Abstract
We used the electroencephalogram (EEG) to investigate whether positive and negative performance feedbacks exert different long-lasting modulations of electrical activity in a reasoning task. Nine college students serially tested hypotheses concerning a hidden rule by judging its presence or absence in triplets of digits, and revised them on the basis of an exogenous performance feedback. The scaling properties of the transition period between feedback and triplet presentation were investigated with detrended fluctuation analysis (DFA). DFA showed temporal scale-free dynamics of EEG activity in both feedback conditions for time scales larger than 150 ms. Furthermore, DFA revealed that negative feedback elicits significantly higher scaling exponents than positive feedback. This effect covers a wide network comprising parieto-occipital and left frontal regions. We thus showed that specific task demands can modify the temporal scale-free dynamics of the ongoing brain activity. Putative neural correlates of these long-lasting feedback-specific modulations are proposed.
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Affiliation(s)
- M Buiatti
- Laboratoire de Neurophysique et Physiologie, Université Paris Descartes, CNRS UMR 8119, and Cognitive Neuroimaging Unit, INSERM U562, Service Hospitalier Frederic Joliot, CEA/DRM/DSV, Orsay, France.
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