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Hernandez H, Baez S, Medel V, Moguilner S, Cuadros J, Santamaria-Garcia H, Tagliazucchi E, Valdes-Sosa PA, Lopera F, OchoaGómez JF, González-Hernández A, Bonilla-Santos J, Gonzalez-Montealegre RA, Aktürk T, Yıldırım E, Anghinah R, Legaz A, Fittipaldi S, Yener GG, Escudero J, Babiloni C, Lopez S, Whelan R, Lucas AAF, García AM, Huepe D, Caterina GD, Soto-Añari M, Birba A, Sainz-Ballesteros A, Coronel C, Herrera E, Abasolo D, Kilborn K, Rubido N, Clark R, Herzog R, Yerlikaya D, Güntekin B, Parra MA, Prado P, Ibanez A. Brain health in diverse settings: How age, demographics and cognition shape brain function. Neuroimage 2024; 295:120636. [PMID: 38777219 DOI: 10.1016/j.neuroimage.2024.120636] [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: 02/08/2024] [Revised: 04/17/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024] Open
Abstract
Diversity in brain health is influenced by individual differences in demographics and cognition. However, most studies on brain health and diseases have typically controlled for these factors rather than explored their potential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex, and education; n = 1298) and cognition (n = 725) as predictors of different metrics usually used in case-control studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity (fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and connectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from the source space resting-state EEG activity in a diverse sample from the global south and north populations. Brain-phenotype models were computed using EEG metrics reflecting local activity (power spectrum and aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Electrophysiological brain dynamics were modulated by individual differences despite the varied methods of data acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by methodological discrepancies. Variations in brain signals were mainly influenced by age and cognition, while education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated with reduced alpha power, whereas older age and less education were associated with reduced network integration and segregation. Findings suggest that basic individual differences impact core metrics of brain function that are used in standard case-control studies. Considering individual variability and diversity in global settings would contribute to a more tailored understanding of brain function.
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Affiliation(s)
- Hernan Hernandez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sandra Baez
- Universidad de los Andes, Bogota, Colombia; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland
| | - Vicente Medel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Harvard Medical School, Boston, MA, USA
| | - Jhosmary Cuadros
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile; Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Hernando Santamaria-Garcia
- Pontificia Universidad Javeriana (PhD Program in Neuroscience) Bogotá, San Ignacio, Colombia; Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio Bogotá, San Ignacio, Colombia
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; University of Buenos Aires, Argentina
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, University of Antioquia, Medellín, Colombia
| | | | | | | | | | - Tuba Aktürk
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Ebru Yıldırım
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Renato Anghinah
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil; Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center, University of Sao Paulo, Sao Paulo, Brazil
| | - Agustina Legaz
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Facultad de Psicología, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Görsev G Yener
- Faculty of Medicine, Izmir University of Economics, 35330, Izmir, Turkey; Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, Izmir, Turkey; Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Javier Escudero
- School of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Scotland, UK
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy; Hospital San Raffaele Cassino, Cassino, (FR), Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, Rome, Italy
| | - Robert Whelan
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Alberto A Fernández Lucas
- Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain
| | - Adolfo M García
- Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center, Universidad de San Andréss, Buenos Aires, Argentina; Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez
| | - Gaetano Di Caterina
- Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
| | | | - Agustina Birba
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile
| | | | - Carlos Coronel
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland; Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
| | - Eduar Herrera
- Departamento de Estudios Psicológicos, Universidad ICESI, Cali, Colombia
| | - Daniel Abasolo
- Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
| | - Kerry Kilborn
- School of Psychology, University of Glasgow, Glasgow, Scotland, UK
| | - Nicolás Rubido
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, AB24 3UE, UK
| | - Ruaridh Clark
- Centre for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, UK
| | - Ruben Herzog
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris 75013, France
| | - Deniz Yerlikaya
- Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey
| | - Bahar Güntekin
- Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey; Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey
| | - Mario A Parra
- Department of Psychological Sciences and Health, University of Strathclyde, United Kingdom and Associate Researcher of the Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Agustin Ibanez
- Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile; Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA; Cognitive Neuroscience Center, Universidad de San Andrés and Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina; Trinity College Dublin, The University of Dublin, Dublin, Ireland.
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2
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Lu Y, Yang W, Zhang X, Wu L, Li Y, Wang X, Huai Y. Unraveling the complexity of rapid eye movement microstates: insights from nonlinear EEG analysis. Sleep 2024; 47:zsae105. [PMID: 38695327 DOI: 10.1093/sleep/zsae105] [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: 01/29/2024] [Revised: 03/24/2024] [Indexed: 07/12/2024] Open
Abstract
Although rapid eye movement (REM) sleep is conventionally treated as a unified state, it comprises two distinct microstates: phasic and tonic REM. Recent research emphasizes the importance of understanding the interplay between these microstates, hypothesizing their role in transient shifts between sensory detachment and external awareness. Previous studies primarily employed linear metrics to probe cognitive states, such as oscillatory power, while in this study, we adopt Lempel-Ziv Complexity (LZC), to examine the nonlinear features of electroencephalographic (EEG) data from the REM microstates and to gain complementary insights into neural dynamics during REM sleep. Our findings demonstrate a noteworthy reduction in LZC during phasic REM compared to tonic REM states, signifying diminished EEG complexity in the former. Additionally, we noted a negative correlation between decreased LZC and delta band power, along with a positive correlation with alpha band power. This study highlights the potential of nonlinear EEG metrics, particularly LZC, in elucidating the distinct features of REM microstates. Overall, this research contributes to advancing our understanding of the complex dynamics within REM sleep and opens new avenues for exploring its implications in both clinical and nonclinical contexts.
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Affiliation(s)
- Yiqing Lu
- Department of Rehabilitation Medicine, Shenzhen Longhua District Central Hospital, Shenzhen, China
- Shenzhen Longhua District Rehabilitation Medical Equipment Development and Transformation Joint Key Laboratory, Shenzhen, China
| | - Weiwei Yang
- Department of Rehabilitation Medicine, Shenzhen Longhua District Central Hospital, Shenzhen, China
- Shenzhen Longhua District Rehabilitation Medical Equipment Development and Transformation Joint Key Laboratory, Shenzhen, China
| | - Xiaoyun Zhang
- Department of Rehabilitation Medicine, Shenzhen Longhua District Central Hospital, Shenzhen, China
- Shenzhen Longhua District Rehabilitation Medical Equipment Development and Transformation Joint Key Laboratory, Shenzhen, China
| | - Liang Wu
- Department of Rehabilitation Medicine, Shenzhen Longhua District Central Hospital, Shenzhen, China
- Shenzhen Longhua District Rehabilitation Medical Equipment Development and Transformation Joint Key Laboratory, Shenzhen, China
| | - Yongcheng Li
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China
| | - Xin Wang
- Department of Rehabilitation Medicine, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Yaping Huai
- Department of Rehabilitation Medicine, Shenzhen Longhua District Central Hospital, Shenzhen, China
- Shenzhen Longhua District Rehabilitation Medical Equipment Development and Transformation Joint Key Laboratory, Shenzhen, China
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Wolfson EJ, Fekete T, Loewenstein Y, Shriki O. Multi-scale entropy assessment of magnetoencephalography signals in schizophrenia. Sci Rep 2024; 14:14680. [PMID: 38918430 PMCID: PMC11199523 DOI: 10.1038/s41598-024-64704-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 06/12/2024] [Indexed: 06/27/2024] Open
Abstract
Schizophrenia is a severe disruption in cognition and emotion, affecting fundamental human functions. In this study, we applied Multi-Scale Entropy analysis to resting-state Magnetoencephalography data from 54 schizophrenia patients and 98 healthy controls. This method quantifies the temporal complexity of the signal across different time scales using the concept of sample entropy. Results show significantly higher sample entropy in schizophrenia patients, primarily in central, parietal, and occipital lobes, peaking at time scales equivalent to frequencies between 15 and 24 Hz. To disentangle the contributions of the amplitude and phase components, we applied the same analysis to a phase-shuffled surrogate signal. The analysis revealed that most differences originate from the amplitude component in the δ, α, and β power bands. While the phase component had a smaller magnitude, closer examination reveals clear spatial patterns and significant differences across specific brain regions. We assessed the potential of multi-scale entropy as a schizophrenia biomarker by comparing its classification performance to conventional spectral analysis and a cognitive task (the n-back paradigm). The discriminative power of multi-scale entropy and spectral features was similar, with a slight advantage for multi-scale entropy features. The results of the n-back test were slightly below those obtained from multi-scale entropy and spectral features.
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Affiliation(s)
- E J Wolfson
- Department of Cognitive and Brain Sciences, Ben Gurion University of the Negev, 1 Ben-Gurion Blvd., Beer-Sheva, Israel
| | - T Fekete
- Department of Cognitive and Brain Sciences, Ben Gurion University of the Negev, 1 Ben-Gurion Blvd., Beer-Sheva, Israel
| | - Y Loewenstein
- The Edmond & Lily Safra Center for Brain Sciences, Department of Cognitive and Brain Sciences,The Alexander Silberman Institute of Life Sciences and The Federmann Center for the Study of Rationality, The Hebrew University, Jerusalem, Israel
| | - O Shriki
- Department of Cognitive and Brain Sciences, Ben Gurion University of the Negev, 1 Ben-Gurion Blvd., Beer-Sheva, Israel.
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Gu C, Chou T, Widge AS, Dougherty DD. EEG complexity in emotion conflict task in individuals with psychiatric disorders. Behav Brain Res 2024; 467:114997. [PMID: 38621461 DOI: 10.1016/j.bbr.2024.114997] [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: 08/15/2023] [Revised: 03/01/2024] [Accepted: 04/09/2024] [Indexed: 04/17/2024]
Abstract
Analyzing EEG complexity may help to elucidate complex brain dynamics in individuals with psychiatric disorders and provide insight into neural connectivity and its relationship with deficits such as emotion-related impulsivity. EEG complexity was calculated through multiscale entropy and compared between a heterogeneous psychiatric patient group and a healthy control group during the emotion conflict resolution task. Twenty-eight healthy adults and ten psychiatric patients were recruited and compared on the multiscale entropy of EEG acquired in the task. Our results revealed a lower multiscale entropy in the psychiatric patient group compared to the healthy group during the task. This decrease in multiscale entropy suggests reduced long-range interaction between the left frontal region and other brain regions during the emotion conflict resolution task among psychiatric patients. Notably, a positive correlation was observed between multiscale entropy and impulsivity measures in the psychiatric patient group, where the higher the EEG complexity during the emotion regulation task, the higher the level of self-reported impulsivity in the psychiatric patients. Such impulsivity was evident in both healthy individuals and psychiatric patients, with healthy individuals showing shorter reaction times on incongruent conditions compared to congruent conditions and psychiatric patients displaying similar reaction times in both conditions, This study highlights the significance of investigating EEG complexity and its potential applications in the transdiagnostic exploration of impulsivity in psychiatric disorders.
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Affiliation(s)
- Chao Gu
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, USA.
| | - Tina Chou
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, USA
| | - Alik S Widge
- Department of Psychiatry, University of Minnesota, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, USA
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Zheng H, Xiong X, Zhang X. Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer's Disease and Frontotemporal Dementia. Brain Sci 2024; 14:565. [PMID: 38928565 PMCID: PMC11202180 DOI: 10.3390/brainsci14060565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 05/27/2024] [Accepted: 05/28/2024] [Indexed: 06/28/2024] Open
Abstract
This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research.
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Affiliation(s)
- Huang Zheng
- School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China
| | - Xingliang Xiong
- Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China;
| | - Xuejun Zhang
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
- Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning 530004, China
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Hwang HH, Choi KM, Im CH, Yang C, Kim S, Lee SH. Comparative analysis of resting-state EEG-based multiscale entropy between schizophrenia and bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 134:111048. [PMID: 38825306 DOI: 10.1016/j.pnpbp.2024.111048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 05/14/2024] [Accepted: 05/30/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND Studies that use nonlinear methods to identify abnormal brain dynamics in patients with psychiatric disorders are limited. This study investigated brain dynamics based on EEG using multiscale entropy (MSE) analysis in patients with schizophrenia (SZ) and bipolar disorder (BD). METHODS The eyes-closed resting-state EEG data were collected from 51 patients with SZ, 51 patients with BD, and 51 healthy controls (HCs). Patients with BD were further categorized into type I (n = 23) and type II (n = 16), and then compared with patients with SZ. A sample entropy-based MSE was evaluated from the bilateral frontal, central, and parieto-occipital regions using 30-s artifact-free EEG data for each individual. Correlation analyses of MSE values and psychiatric symptoms were performed. RESULTS For patients with SZ, higher MSE values were observed at higher-scale factors (i.e., 41-70) across all regions compared with both HCs and patients with BD. Furthermore, there were positive correlations between the MSE values in the left frontal and parieto-occipital regions and PANSS scores. For patients with BD, higher MSE values were observed at middle-scale factors (i.e., 13-40) in the bilateral frontal and central regions compared with HCs. Patients with BD type I exhibited higher MSE values at higher-scale factors across all regions compared with those with BD type II. In BD type I, positive correlations were found between MSE values in all left regions and YMRS scores. CONCLUSIONS Patients with psychiatric disorders exhibited group-dependent MSE characteristics. These results suggest that MSE features may be useful biomarkers that reflect pathophysiological characteristics.
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Affiliation(s)
- Hyeon-Ho Hwang
- Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea; Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea
| | - Kang-Min Choi
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea; Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea
| | - Chang-Hwan Im
- Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea; Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Chaeyeon Yang
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea
| | - Sungkean Kim
- Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea.
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea; Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Juhwa-ro 170, Ilsanseo-Gu, Goyang 10370, Republic of Korea.
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Miraglia F, Pappalettera C, Barbati SA, Podda MV, Grassi C, Rossini PM, Vecchio F. Brain complexity in stroke recovery after bihemispheric transcranial direct current stimulation in mice. Brain Commun 2024; 6:fcae137. [PMID: 38741663 PMCID: PMC11089417 DOI: 10.1093/braincomms/fcae137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/22/2023] [Accepted: 05/07/2024] [Indexed: 05/16/2024] Open
Abstract
Stroke is one of the leading causes of disability worldwide. There are many different rehabilitation approaches aimed at improving clinical outcomes for stroke survivors. One of the latest therapeutic techniques is the non-invasive brain stimulation. Among non-invasive brain stimulation, transcranial direct current stimulation has shown promising results in enhancing motor and cognitive recovery both in animal models of stroke and stroke survivors. In this framework, one of the most innovative methods is the bihemispheric transcranial direct current stimulation that simultaneously increases excitability in one hemisphere and decreases excitability in the contralateral one. As bihemispheric transcranial direct current stimulation can create a more balanced modulation of brain activity, this approach may be particularly useful in counteracting imbalanced brain activity, such as in stroke. Given these premises, the aim of the current study has been to explore the recovery after stroke in mice that underwent a bihemispheric transcranial direct current stimulation treatment, by recording their electric brain activity with local field potential and by measuring behavioural outcomes of Grip Strength test. An innovative parameter that explores the complexity of signals, namely the Entropy, recently adopted to describe brain activity in physiopathological states, was evaluated to analyse local field potential data. Results showed that stroke mice had higher values of Entropy compared to healthy mice, indicating an increase in brain complexity and signal disorder due to the stroke. Additionally, the bihemispheric transcranial direct current stimulation reduced Entropy in both healthy and stroke mice compared to sham stimulated mice, with a greater effect in stroke mice. Moreover, correlation analysis showed a negative correlation between Entropy and Grip Strength values, indicating that higher Entropy values resulted in lower Grip Strength engagement. Concluding, the current evidence suggests that the Entropy index of brain complexity characterizes stroke pathology and recovery. Together with this, bihemispheric transcranial direct current stimulation can modulate brain rhythms in animal models of stroke, providing potentially new avenues for rehabilitation in humans.
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Affiliation(s)
- Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, 22060, Como, Italy
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, 22060, Como, Italy
| | - Saviana Antonella Barbati
- Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Maria Vittoria Podda
- Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Claudio Grassi
- Department of Neuroscience, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, 00163, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, 22060, Como, Italy
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8
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Golestani AM. Editorial for "Associations of Brain Entropy Estimated by Resting State fMRI With Physiological Indices, Body Mass Index, and Cognition". J Magn Reson Imaging 2024; 59:1708-1709. [PMID: 37667467 DOI: 10.1002/jmri.28997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/06/2023] Open
Affiliation(s)
- Ali M Golestani
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
- Department of Oncology, University of Calgary, Calgary, Alberta, Canada
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9
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Penalba-Sánchez L, Silva G, Crook-Rumsey M, Sumich A, Rodrigues PM, Oliveira-Silva P, Cifre I. Classification of Sleep Quality and Aging as a Function of Brain Complexity: A Multiband Non-Linear EEG Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:2811. [PMID: 38732917 PMCID: PMC11086092 DOI: 10.3390/s24092811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 04/20/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024]
Abstract
Understanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.
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Affiliation(s)
- Lucía Penalba-Sánchez
- Facultat de Psicología, Ciències de l’Educació i de l’Esport (FPCEE), Blanquerna, Universitat Ramon Llull, 08022 Barcelona, Spain; (L.P.-S.)
- Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculty of Education and Psychology, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
- Department of Psychology, Nottingham Trent University (NTU), Nottingham NG1 4FQ, UK
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke-University Magdeburg (OVGU), 39120 Magdeburg, Germany
| | - Gabriel Silva
- Centro de Biotecnologia e Química Fina (CBQF)—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
| | - Mark Crook-Rumsey
- UK Dementia Research Institute (UK DRI), Centre for Care Research and Technology, Imperial College London, London W1T 7NF, UK
- UK Dementia Research Institute (UK DRI), Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London SE5 9RX, UK
| | - Alexander Sumich
- Department of Psychology, Nottingham Trent University (NTU), Nottingham NG1 4FQ, UK
| | - Pedro Miguel Rodrigues
- Centro de Biotecnologia e Química Fina (CBQF)—Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
| | - Patrícia Oliveira-Silva
- Human Neurobehavioral Laboratory (HNL), Research Centre for Human Development (CEDH), Faculty of Education and Psychology, Universidade Católica Portuguesa, 4169-005 Porto, Portugal
| | - Ignacio Cifre
- Facultat de Psicología, Ciències de l’Educació i de l’Esport (FPCEE), Blanquerna, Universitat Ramon Llull, 08022 Barcelona, Spain; (L.P.-S.)
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10
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Schmidig FJ, Ruch S, Henke K. Episodic long-term memory formation during slow-wave sleep. eLife 2024; 12:RP89601. [PMID: 38661727 PMCID: PMC11045222 DOI: 10.7554/elife.89601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024] Open
Abstract
We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words' linguistic processing raised neural complexity. The words' semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.
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Affiliation(s)
| | - Simon Ruch
- Institute of Psychology, University of BernBernSwitzerland
- Faculty of Psychology, UniDistance SuisseBrigSwitzerland
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11
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Kang JH, Bae JH, Jeon YJ. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering (Basel) 2024; 11:418. [PMID: 38790286 PMCID: PMC11118246 DOI: 10.3390/bioengineering11050418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
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Affiliation(s)
- Jae-Hwan Kang
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jang-Han Bae
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Young-Ju Jeon
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
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12
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Tang S, Li Z. EEG complexity measures for detecting mind wandering during video-based learning. Sci Rep 2024; 14:8209. [PMID: 38589498 PMCID: PMC11001605 DOI: 10.1038/s41598-024-58889-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 04/04/2024] [Indexed: 04/10/2024] Open
Abstract
This study explores the efficacy of various EEG complexity measures in detecting mind wandering during video-based learning. Employing a modified probe-caught method, we recorded EEG data from participants engaged in viewing educational videos and subsequently focused on the discrimination between mind wandering (MW) and non-MW states. We systematically investigated various EEG complexity metrics, including metrics that reflect a system's regularity like multiscale permutation entropy (MPE), and metrics that reflect a system's dimensionality like detrended fluctuation analysis (DFA). We also compare these features to traditional band power (BP) features. Data augmentation methods and feature selection were applied to optimize detection accuracy. Results show BP features excelled (mean area under the receiver operating characteristic curve (AUC) 0.646) in datasets without eye-movement artifacts, while MPE showed similar performance (mean AUC 0.639) without requiring removal of eye-movement artifacts. Combining all kinds of features improved decoding performance to 0.66 mean AUC. Our findings demonstrate the potential of these complexity metrics in EEG analysis for mind wandering detection, highlighting their practical implications in educational contexts.
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Affiliation(s)
- Shaohua Tang
- School of Systems Science, Beijing Normal University, Beijing, China
- International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, China
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China
| | - Zheng Li
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China.
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13
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Yoshino A, Maekawa T, Kato M, Chan HL, Otsuru N, Yamawaki S. Changes in Resting-State Brain Activity After Cognitive Behavioral Therapy for Chronic Pain: A Magnetoencephalography Study. THE JOURNAL OF PAIN 2024:104523. [PMID: 38582288 DOI: 10.1016/j.jpain.2024.104523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 03/18/2024] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
Cognitive behavioral therapy (CBT) is believed to be an effective treatment for chronic pain due to its association with cognitive and emotional factors. Nevertheless, there is a paucity of magnetoencephalography (MEG) investigations elucidating its underlying mechanisms. This study investigated the neurophysiological effects of CBT employing MEG and analytical techniques. We administered resting-state MEG scans to 30 patients with chronic pain and 31 age-matched healthy controls. Patients engaged in a 12-session group CBT program. We conducted pretreatment (T1) and post-treatment (T2) MEG and clinical assessments. MEG data were examined within predefined regions of interest, guided by the authors' and others' prior magnetic resonance imaging studies. Initially, we selected regions displaying significant changes in power spectral density and multiscale entropy between patients at T1 and healthy controls. Then, we examined the changes within these regions after conducting CBT. Furthermore, we applied support vector machine analysis to MEG data to assess the potential for classifying treatment effects. We observed normalization of power in the gamma2 band (61-90 Hz) within the right inferior frontal gyrus (IFG) and multiscale entropy within the right dorsolateral prefrontal cortex (DLPFC) of patients with chronic pain after CBT. Notably, changes in pain intensity before and after CBT positively correlated with the alterations of multiscale entropy. Importantly, responders predicted by the support vector machine classifier had significantly higher treatment improvement rates than nonresponders. These findings underscore the pivotal role of the right IFG and DLPFC in ameliorating pain intensity through CBT. Further accumulation of evidence is essential for future applications. PERSPECTIVE: We conducted MEG scans on 30 patients with chronic pain before and after a CBT program, comparing results with 31 healthy individuals. There were CBT-related changes in the right IFG and DLPFC. These results highlight the importance of specific brain regions in pain reduction through CBT.
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Affiliation(s)
- Atsuo Yoshino
- Health Service Center, Hiroshima University, Minami-Ku, Hiroshima, Japan; Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Minami-Ku, Hiroshima, Japan
| | - Toru Maekawa
- Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Minami-Ku, Hiroshima, Japan
| | - Miyuki Kato
- Department of Psychiatry and Neurosciences, Graduate School of Biomedical and Health Sciences, Hiroshima University, Minami-Ku, Hiroshima, Japan
| | - Hui-Ling Chan
- Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Minami-Ku, Hiroshima, Japan; Department of Computer Science and Information Engineering, Institute of Medical Informatics, National Cheng Kung University, Tainan City, Taiwan
| | - Naofumi Otsuru
- Department of Physical Therapy, Niigata University of Health and Welfare, Kita-Ku, Niigata, Japan
| | - Shigeto Yamawaki
- Center for Brain, Mind and KANSEI Sciences Research, Hiroshima University, Minami-Ku, Hiroshima, Japan
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14
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Cataldo A, Criscuolo S, De Benedetto E, Masciullo A, Pesola M, Schiavoni R. A Novel Metric for Alzheimer's Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy. Bioengineering (Basel) 2024; 11:324. [PMID: 38671746 PMCID: PMC11048692 DOI: 10.3390/bioengineering11040324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques and cognitive questionnaires, present limitations such as invasiveness, high costs, and subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, and high temporal resolution. In this regard, this work introduces a novel metric for AD detection by using multiscale fuzzy entropy (MFE) to assess brain complexity, offering clinicians an objective, cost-effective diagnostic tool to aid early intervention and patient care. To this purpose, brain entropy patterns in different frequency bands for 35 healthy subjects (HS) and 35 AD patients were investigated. Then, based on the resulting MFE values, a specific detection algorithm, able to assess brain complexity abnormalities that are typical of AD, was developed and further validated on 24 EEG test recordings. This MFE-based method achieved an accuracy of 83% in differentiating between HS and AD, with a diagnostic odds ratio of 25, and a Matthews correlation coefficient of 0.67, indicating its viability for AD diagnosis. Furthermore, the algorithm showed potential for identifying anomalies in brain complexity when tested on a subject with mild cognitive impairment (MCI), warranting further investigation in future research.
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Affiliation(s)
- Andrea Cataldo
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
| | - Sabatina Criscuolo
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Egidio De Benedetto
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Antonio Masciullo
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
| | - Marisa Pesola
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Raissa Schiavoni
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
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15
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Höhn C, Hahn MA, Lendner JD, Hoedlmoser K. Spectral Slope and Lempel-Ziv Complexity as Robust Markers of Brain States during Sleep and Wakefulness. eNeuro 2024; 11:ENEURO.0259-23.2024. [PMID: 38471778 PMCID: PMC10978822 DOI: 10.1523/eneuro.0259-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 01/22/2024] [Accepted: 02/09/2024] [Indexed: 03/14/2024] Open
Abstract
Nonoscillatory measures of brain activity such as the spectral slope and Lempel-Ziv complexity are affected by many neurological disorders and modulated by sleep. A multitude of frequency ranges, particularly a broadband (encompassing the full spectrum) and a narrowband approach, have been used especially for estimating the spectral slope. However, the effects of choosing different frequency ranges have not yet been explored in detail. Here, we evaluated the impact of sleep stage and task engagement (resting, attention, and memory) on slope and complexity in a narrowband (30-45 Hz) and broadband (1-45 Hz) frequency range in 28 healthy male human subjects (21.54 ± 1.90 years) using a within-subject design over 2 weeks with three recording nights and days per subject. We strived to determine how different brain states and frequency ranges affect slope and complexity and how the two measures perform in comparison. In the broadband range, the slope steepened, and complexity decreased continuously from wakefulness to N3 sleep. REM sleep, however, was best discriminated by the narrowband slope. Importantly, slope and complexity also differed between tasks during wakefulness. While narrowband complexity decreased with task engagement, the slope flattened in both frequency ranges. Interestingly, only the narrowband slope was positively correlated with task performance. Our results show that slope and complexity are sensitive indices of brain state variations during wakefulness and sleep. However, the spectral slope yields more information and could be used for a greater variety of research questions than Lempel-Ziv complexity, especially when a narrowband frequency range is used.
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Affiliation(s)
- Christopher Höhn
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, 5020 Salzburg, Austria
| | - Michael A Hahn
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany
| | - Janna D Lendner
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany
- Department of Anesthesiology and Intensive Care Medicine, University Medical Center Tübingen, 72076 Tübingen, Germany
| | - Kerstin Hoedlmoser
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, 5020 Salzburg, Austria
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16
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Stan TL, Ronaghi A, Barrientos SA, Halje P, Censoni L, Garro-Martínez E, Nasretdinov A, Malinina E, Hjorth S, Svensson P, Waters S, Sahlholm K, Petersson P. Neurophysiological treatment effects of mesdopetam, pimavanserin and clozapine in a rodent model of Parkinson's disease psychosis. Neurotherapeutics 2024; 21:e00334. [PMID: 38368170 PMCID: PMC10937958 DOI: 10.1016/j.neurot.2024.e00334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 02/19/2024] Open
Abstract
Psychosis in Parkinson's disease is a common phenomenon associated with poor outcomes. To clarify the pathophysiology of this condition and the mechanisms of antipsychotic treatments, we have here characterized the neurophysiological brain states induced by clozapine, pimavanserin, and the novel prospective antipsychotic mesdopetam in a rodent model of Parkinson's disease psychosis, based on chronic dopaminergic denervation by 6-OHDA lesions, levodopa priming, and the acute administration of an NMDA antagonist. Parallel recordings of local field potentials from eleven cortical and sub-cortical regions revealed shared neurophysiological treatment effects for the three compounds, despite their different pharmacological profiles, involving reversal of features associated with the psychotomimetic state, such as a reduction of aberrant high-frequency oscillations in prefrontal structures together with a decrease of abnormal synchronization between different brain regions. Other drug-induced neurophysiological features were more specific to each treatment, affecting network oscillation frequencies and entropy, pointing to discrete differences in mechanisms of action. These findings indicate that neurophysiological characterization of brain states is particularly informative when evaluating therapeutic mechanisms in conditions involving symptoms that are difficult to assess in rodents such as psychosis, and that mesdopetam should be further explored as a potential novel antipsychotic treatment option for Parkinson psychosis.
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Affiliation(s)
- Tiberiu Loredan Stan
- The Group for Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Abdolaziz Ronaghi
- The Group for Integrative Neurophysiology, Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
| | - Sebastian A Barrientos
- The Group for Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Pär Halje
- The Group for Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Luciano Censoni
- The Group for Integrative Neurophysiology, Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
| | - Emilio Garro-Martínez
- The Group for Integrative Neurophysiology, Department of Medical and Translational Biology, Umeå University, Umeå, Sweden; Department of Medical and Translational Biology, Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Azat Nasretdinov
- The Group for Integrative Neurophysiology, Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
| | - Evgenya Malinina
- The Group for Integrative Neurophysiology, Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
| | - Stephan Hjorth
- Integrative Research Laboratories Sweden AB, Göteborg, Sweden
| | - Peder Svensson
- Integrative Research Laboratories Sweden AB, Göteborg, Sweden
| | - Susanna Waters
- Integrative Research Laboratories Sweden AB, Göteborg, Sweden
| | - Kristoffer Sahlholm
- Department of Medical and Translational Biology, Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden; Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Sweden
| | - Per Petersson
- The Group for Integrative Neurophysiology, Department of Medical and Translational Biology, Umeå University, Umeå, Sweden; The Group for Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Science, Lund University, Lund, Sweden.
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17
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Kleeva D, Soghoyan G, Biktimirov A, Piliugin N, Matvienko Y, Sintsov M, Lebedev M. Modulations in high-density EEG during the suppression of phantom-limb pain with neurostimulation in upper limb amputees. Cereb Cortex 2024; 34:bhad504. [PMID: 38220575 DOI: 10.1093/cercor/bhad504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/12/2023] [Accepted: 11/13/2023] [Indexed: 01/16/2024] Open
Abstract
Phantom limb pain (PLP) is a distressing and persistent sensation that occurs after the amputation of a limb. While medication-based treatments have limitations and adverse effects, neurostimulation is a promising alternative approach whose mechanism of action needs research, including electroencephalographic (EEG) recordings for the assessment of cortical manifestation of PLP relieving effects. Here we collected and analyzed high-density EEG data in 3 patients (P01, P02, and P03). Peripheral nerve stimulation suppressed PLP in P01 but was ineffective in P02. In contrast, transcutaneous electrical nerve stimulation was effective in P02. In P03, spinal cord stimulation was used to suppress PLP. Changes in EEG oscillatory components were analyzed using spectral analysis and Petrosian fractal dimension. With these methods, changes in EEG spatio-spectral components were found in the theta, alpha, and beta bands in all patients, with these effects being specific to each individual. The changes in the EEG patterns were found for both the periods when PLP level was stationary and the periods when PLP was gradually changing after neurostimulation was turned on or off. Overall, our findings align with the proposed roles of brain rhythms in thalamocortical dysrhythmia or disruption of cortical excitation and inhibition which has been linked to neuropathic pain. The individual differences in the observed effects could be related to the specifics of each patient's treatment and the unique spectral characteristics in each of them. These findings pave the way to the closed-loop systems for PLP management where neurostimulation parameters are adjusted based on EEG-derived markers.
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Affiliation(s)
- Daria Kleeva
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Bolshoy Boulevard, 30, p. 1, Moscow 121205, Russia
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University
| | - Gurgen Soghoyan
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Bolshoy Boulevard, 30, p. 1, Moscow 121205, Russia
| | - Artur Biktimirov
- Laboratory of Experimental and Translational Medicine, School of Biomedicine, Far Eastern Federal University
| | - Nikita Piliugin
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Bolshoy Boulevard, 30, p. 1, Moscow 121205, Russia
| | | | | | - Mikhail Lebedev
- MSU Institute for Artificial Intelligence, Lomonosov Moscow State University
- Faculty of Mechanics and Mathematics, Lomonosov Moscow State University
- Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences
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18
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Ruffini G, Lopez-Sola E, Vohryzek J, Sanchez-Todo R. Neural Geometrodynamics, Complexity, and Plasticity: A Psychedelics Perspective. ENTROPY (BASEL, SWITZERLAND) 2024; 26:90. [PMID: 38275498 PMCID: PMC11154528 DOI: 10.3390/e26010090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/14/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Abstract
We explore the intersection of neural dynamics and the effects of psychedelics in light of distinct timescales in a framework integrating concepts from dynamics, complexity, and plasticity. We call this framework neural geometrodynamics for its parallels with general relativity's description of the interplay of spacetime and matter. The geometry of trajectories within the dynamical landscape of "fast time" dynamics are shaped by the structure of a differential equation and its connectivity parameters, which themselves evolve over "slow time" driven by state-dependent and state-independent plasticity mechanisms. Finally, the adjustment of plasticity processes (metaplasticity) takes place in an "ultraslow" time scale. Psychedelics flatten the neural landscape, leading to heightened entropy and complexity of neural dynamics, as observed in neuroimaging and modeling studies linking increases in complexity with a disruption of functional integration. We highlight the relationship between criticality, the complexity of fast neural dynamics, and synaptic plasticity. Pathological, rigid, or "canalized" neural dynamics result in an ultrastable confined repertoire, allowing slower plastic changes to consolidate them further. However, under the influence of psychedelics, the destabilizing emergence of complex dynamics leads to a more fluid and adaptable neural state in a process that is amplified by the plasticity-enhancing effects of psychedelics. This shift manifests as an acute systemic increase of disorder and a possibly longer-lasting increase in complexity affecting both short-term dynamics and long-term plastic processes. Our framework offers a holistic perspective on the acute effects of these substances and their potential long-term impacts on neural structure and function.
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Affiliation(s)
- Giulio Ruffini
- Brain Modeling Department, Neuroelectrics, 08035 Barcelona, Spain; (E.L.-S.); (R.S.-T.)
| | - Edmundo Lopez-Sola
- Brain Modeling Department, Neuroelectrics, 08035 Barcelona, Spain; (E.L.-S.); (R.S.-T.)
- Computational Neuroscience Group, Universitat Pompeu Fabra, 08018 Barcelona, Spain;
| | - Jakub Vohryzek
- Computational Neuroscience Group, Universitat Pompeu Fabra, 08018 Barcelona, Spain;
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford OX3 9BX, UK
| | - Roser Sanchez-Todo
- Brain Modeling Department, Neuroelectrics, 08035 Barcelona, Spain; (E.L.-S.); (R.S.-T.)
- Computational Neuroscience Group, Universitat Pompeu Fabra, 08018 Barcelona, Spain;
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19
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Laufer I, Mizrahi D, Zuckerman I. Enhancing EEG-based attachment style prediction: unveiling the impact of feature domains. Front Psychol 2024; 15:1326791. [PMID: 38318079 PMCID: PMC10838989 DOI: 10.3389/fpsyg.2024.1326791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/04/2024] [Indexed: 02/07/2024] Open
Abstract
Introduction Attachment styles are crucial in human relationships and have been explored through neurophysiological responses and EEG data analysis. This study investigates the potential of EEG data in predicting and differentiating secure and insecure attachment styles, contributing to the understanding of the neural basis of interpersonal dynamics. Methods We engaged 27 participants in our study, employing an XGBoost classifier to analyze EEG data across various feature domains, including time-domain, complexity-based, and frequency-based attributes. Results The study found significant differences in the precision of attachment style prediction: a high precision rate of 96.18% for predicting insecure attachment, and a lower precision of 55.34% for secure attachment. Balanced accuracy metrics indicated an overall model accuracy of approximately 84.14%, taking into account dataset imbalances. Discussion These results highlight the challenges in using EEG patterns for attachment style prediction due to the complex nature of attachment insecurities. Individuals with heightened perceived insecurity predominantly aligned with the insecure attachment category, suggesting a link to their increased emotional reactivity and sensitivity to social cues. The study underscores the importance of time-domain features in prediction accuracy, followed by complexity-based features, while noting the lesser impact of frequency-based features. Our findings advance the understanding of the neural correlates of attachment and pave the way for future research, including expanding demographic diversity and integrating multimodal data to refine predictive models.
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Affiliation(s)
| | - Dor Mizrahi
- Department of Industrial Engineering and Management, Ariel University, Ariel, Israel
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20
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Huang PH, Hsiao TC. Use of Intrinsic Entropy to Assess the Instantaneous Complexity of Thoracoabdominal Movement Patterns to Indicate the Effect of the Iso-Volume Maneuver Trial on the Performance of the Step Test. ENTROPY (BASEL, SWITZERLAND) 2023; 26:27. [PMID: 38248153 PMCID: PMC10814788 DOI: 10.3390/e26010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 01/23/2024]
Abstract
The recent surge in interest surrounds the analysis of physiological signals with a non-linear dynamic approach. The measurement of entropy serves as a renowned method for indicating the complexity of a signal. However, there is a dearth of research concerning the non-linear dynamic analysis of respiratory signals. Therefore, this study employs a novel method known as intrinsic entropy (IE) to assess the short-term dynamic changes in thoracoabdominal movement patterns, as measured by respiratory inductance plethysmography (RIP), during various states such as resting, step test, recovery, and iso-volume maneuver (IVM) trials. The findings reveal a decrease in IE of thoracic wall movement (TWM) and an increase in IE of abdominal wall movement (AWM) following the IVM trial. This suggests that AWM may dominate the breathing exercise after the IVM trial. Moreover, due to the high temporal resolution of IE, it proves to be a suitable measure for assessing the complexity of thoracoabdominal movement patterns under non-stationary states such as the step test and recovery. The results also demonstrate that the instantaneous complexity of TWM and AWM can effectively capture instantaneous changes during non-stationary states, which may prove valuable in understanding the respiratory mechanism for healthcare purposes in daily life.
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Affiliation(s)
- Po-Hsun Huang
- Institute of Computer Science and Engineering, College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
| | - Tzu-Chien Hsiao
- Institute of Computer Science and Engineering, College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
- Department of Computer Science, College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
- Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
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21
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Liang Z, Lan Z, Wang Y, Bai Y, He J, Wang J, Li X. The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia. J Neural Eng 2023; 20:066030. [PMID: 38055962 DOI: 10.1088/1741-2552/ad12dc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 12/06/2023] [Indexed: 12/08/2023]
Abstract
Objective.General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed.Approach.We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel-Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces.Main results.Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p< 0.001), PFC increased (p< 0.001) and PCMI decreased (p< 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p< 0.001) and nodal efficiency (NE) (p< 0.001) decreased in these two spaces. Global network indicators normalized average path length (Lave/Lr) (p< 0.01) and modularity (Q) (p< 0.05) only decreased in sensor space, while the normalized average clustering coefficient (Cave/Cr) and small-world index (σ) did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicatorsLave/LrandQwere not significantly different from the preoperative resting state in sensor space.Significance.GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, People's Republic of China
| | - Zhilei Lan
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, People's Republic of China
| | - Yong Wang
- Zhuhai UM Science & Technology Research Institute, Zhuhai 519031, People's Republic of China
| | - Yang Bai
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi, People's Republic of China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang 330006, Jiangxi, People's Republic of China
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Juan Wang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, People's Republic of China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, People's Republic of China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, People's Republic of China
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22
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Gobert F, Corneyllie A, Bastuji H, Berthomier C, Thevenet M, Abernot J, Raverot V, Dailler F, Guérin C, Gronfier C, Luauté J, Perrin F. Twenty-four-hour rhythmicities in disorders of consciousness are associated with a favourable outcome. Commun Biol 2023; 6:1213. [PMID: 38030756 PMCID: PMC10687012 DOI: 10.1038/s42003-023-05588-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 11/15/2023] [Indexed: 12/01/2023] Open
Abstract
Fluctuations of consciousness and their rhythmicities have been rarely studied in patients with a disorder of consciousness after acute brain injuries. 24-h assessment of brain (EEG), behaviour (eye-opening), and circadian (clock-controlled hormones secretion from urine) functions was performed in acute brain-injured patients. The distribution, long-term predictability, and rhythmicity (circadian/ultradian) of various EEG features were compared with the initial clinical status, the functional outcome, and the circadian rhythmicities of behaviour and clock-controlled hormones. Here we show that more physiological and favourable patterns of fluctuations are associated with a higher 24 h predictability and sharp up-and-down shape of EEG switches, reminiscent of the Flip-Flop model of sleep. Multimodal rhythmic analysis shows that patients with simultaneous circadian rhythmicity for brain, behaviour, and hormones had a favourable outcome. Finally, both re-emerging EEG fluctuations and homogeneous 24-h cycles for EEG, eye-opening, and hormones appeared as surrogates for preserved functionality in brainstem and basal forebrain, which are key prognostic factors for later improvement. While the recovery of consciousness has previously been related to a high short-term complexity, we suggest in this exploratory study the importance of the high predictability of the 24 h long-term generation of brain rhythms and highlight the importance of circadian body-brain rhythms in awakening.
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Affiliation(s)
- Florent Gobert
- Neuro-Intensive care unit, Hospices Civils de Lyon, Neurological hospital Pierre-Wertheimer, 59 Boulevard Pinel, Bron, France.
- Trajectoires Team, Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), Bâtiment Inserm 16 avenue Doyen Lépine, Bron, France.
- CAP Team (Cognition Auditive et Psychoacoustique), Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 95 boulevard Pinel, Bron, France.
| | - Alexandra Corneyllie
- CAP Team (Cognition Auditive et Psychoacoustique), Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 95 boulevard Pinel, Bron, France
| | - Hélène Bastuji
- Sleep medicine centre, Hospices Civils de Lyon, Bron, F-69677, France
- Neuropain Team, Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 59 Boulevard Pinel, Bron, France
| | | | - Marc Thevenet
- CAP Team (Cognition Auditive et Psychoacoustique), Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 95 boulevard Pinel, Bron, France
| | - Jonas Abernot
- CAP Team (Cognition Auditive et Psychoacoustique), Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 95 boulevard Pinel, Bron, France
| | - Véronique Raverot
- Hormone Laboratory, Hospices Civils de Lyon, Neurological hospital Pierre-Wertheimer, 59 Boulevard Pinel, Bron, France
| | - Frédéric Dailler
- Neuro-Intensive care unit, Hospices Civils de Lyon, Neurological hospital Pierre-Wertheimer, 59 Boulevard Pinel, Bron, France
| | - Claude Guérin
- Intensive care unit, Hospices Civils de Lyon, Croix-Rousse hospital, 103 Grande-Rue de la Croix-Rousse, Lyon, France
- Intensive care unit, Hospices Civils de Lyon, Édouard Herriot hospital, 5 Place d'Arsonval, 69003, Lyon, France
| | - Claude Gronfier
- Waking team (Integrative Physiology of the Brain Arousal Systems), Lyon Neuroscience Research Centre, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Jacques Luauté
- Trajectoires Team, Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), Bâtiment Inserm 16 avenue Doyen Lépine, Bron, France
- Neuro-rehabilitation unit, Hospices Civils de Lyon, Neurological hospital Pierre-Wertheimer, 59 Boulevard Pinel, Bron, France
| | - Fabien Perrin
- CAP Team (Cognition Auditive et Psychoacoustique), Lyon Neuroscience Research Centre (Université Claude Bernard Lyon 1, INSERM U1028, CNRS UMR5292), 95 boulevard Pinel, Bron, France
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23
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Xing X, Dong WF, Xiao R, Song M, Jiang C. Analysis of the Chaotic Component of Photoplethysmography and Its Association with Hemodynamic Parameters. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1582. [PMID: 38136462 PMCID: PMC10742563 DOI: 10.3390/e25121582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023]
Abstract
Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios.
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Affiliation(s)
- Xiaoman Xing
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Sciences and Technology of China, Suzhou 215163, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Wen-Fei Dong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Renjie Xiao
- Medical Health Information Center, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Mingxuan Song
- Suzhou GK Medtech Science and Technology Development (Group) Co., Ltd., Suzhou 215163, China
| | - Chenyu Jiang
- Jinan Guoke Medical Technology Development Co., Ltd., Jinan 250100, China
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24
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Li Q, Seraji M, Calhoun VD, Iraji A. Complexity Measures of Psychotic Brain Activity in the Fmri Signal. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566647. [PMID: 38014214 PMCID: PMC10680663 DOI: 10.1101/2023.11.10.566647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
When viewing the brain as a sophisticated, nonlinear dynamic system, employing complexity measures offers a valuable way to measure the intricate and dynamic aspects of spontaneous psychotic brain activity. These measures can help us identify irregularities and patterns in complex systems. In our study, we utilized fuzzy recurrence plots and sample entropy to evaluate the dynamic characteristics of psychiatric disorders. This assessment focused on understanding the temporal and spatial neural activity patterns, and more specifically, we applied complexity measures to investigate the functional connectivity within the psychotic brain. This involves understanding how different brain regions synchronize their activity, and complexity measures can reveal the patterns of these connections. It provides a means to understand how different brain regions interact and communicate under resting-state abnormal conditions. This study offers evidence demonstrating that fuzzy recurrence plots can serve as descriptors for functional connectivity and discusses their relevance to sample entropy in the context of the psychotic brain. In summary, complexity measures offer valuable insights that enrich our comprehension of atypical brain activity and the complexities present in the psychotic brain 1 .
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25
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Orlando G, Lampart M. Expecting the Unexpected: Entropy and Multifractal Systems in Finance. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1527. [PMID: 37998219 PMCID: PMC10670846 DOI: 10.3390/e25111527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 10/31/2023] [Accepted: 11/01/2023] [Indexed: 11/25/2023]
Abstract
Entropy serves as a measure of chaos in systems by representing the average rate of information loss about a phase point's position on the attractor. When dealing with a multifractal system, a single exponent cannot fully describe its dynamics, necessitating a continuous spectrum of exponents, known as the singularity spectrum. From an investor's point of view, a rise in entropy is a signal of abnormal and possibly negative returns. This means he has to expect the unexpected and prepare for it. To explore this, we analyse the New York Stock Exchange (NYSE) U.S. Index as well as its constituents. Through this examination, we assess their multifractal characteristics and identify market conditions (bearish/bullish markets) using entropy, an effective method for recognizing fluctuating fractal markets. Our findings challenge conventional beliefs by demonstrating that price declines lead to increased entropy, contrary to some studies in the literature that suggest that reduced entropy in market crises implies more determinism. Instead, we propose that bear markets are likely to exhibit higher entropy, indicating a greater chance of unexpected extreme events. Moreover, our study reveals a power-law behaviour and indicates the absence of variance.
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Affiliation(s)
- Giuseppe Orlando
- Department of Mathematics, University of Bari, Via Edoardo Orabona 4, 70125 Bari, Italy
- Department of Mathematics, University of Jaen, Campus Las Lagunillas s/n, 23071 Jaén, Spain
- Department of Economics, HSE University, 3A Kantemirovskaya Ulitsa, St. Petersburg 190121, Russia
| | - Marek Lampart
- IT4Innovations, VSB—Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic;
- Department of Applied Mathematics, VSB—Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic
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26
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Sklenarova B, Chladek J, Macek M, Brazdil M, Chrastina J, Jurkova T, Burilova P, Plesinger F, Zatloukalova E, Dolezalova I. Entropy in scalp EEG can be used as a preimplantation marker for VNS efficacy. Sci Rep 2023; 13:18849. [PMID: 37914788 PMCID: PMC10620210 DOI: 10.1038/s41598-023-46113-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/27/2023] [Indexed: 11/03/2023] Open
Abstract
Vagus nerve stimulation (VNS) is a therapeutic option in drug-resistant epilepsy. VNS leads to ≥ 50% seizure reduction in 50 to 60% of patients, termed "responders". The remaining 40 to 50% of patients, "non-responders", exhibit seizure reduction < 50%. Our work aims to differentiate between these two patient groups in preimplantation EEG analysis by employing several Entropy methods. We identified 59 drug-resistant epilepsy patients treated with VNS. We established their response to VNS in terms of responders and non-responders. A preimplantation EEG with eyes open/closed, photic stimulation, and hyperventilation was found for each patient. The EEG was segmented into eight time intervals within four standard frequency bands. In all, 32 EEG segments were obtained. Seven Entropy methods were calculated for all segments. Subsequently, VNS responders and non-responders were compared using individual Entropy methods. VNS responders and non-responders differed significantly in all Entropy methods except Approximate Entropy. Spectral Entropy revealed the highest number of EEG segments differentiating between responders and non-responders. The most useful frequency band distinguishing responders and non-responders was the alpha frequency, and the most helpful time interval was hyperventilation and rest 4 (the end of EEG recording).
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Affiliation(s)
- B Sklenarova
- Brno Epilepsy Center, First Department of Neurology, Member of ERN-Epicar, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Pekařská 53, 602 00, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - J Chladek
- Brno Epilepsy Center, First Department of Neurology, Member of ERN-Epicar, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Pekařská 53, 602 00, Brno, Czech Republic
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic
- Behavioral and Social Neuroscience Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - M Macek
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic
| | - M Brazdil
- Brno Epilepsy Center, First Department of Neurology, Member of ERN-Epicar, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Pekařská 53, 602 00, Brno, Czech Republic
- Behavioral and Social Neuroscience Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - J Chrastina
- Brno Epilepsy Center, Department of Neurosurgery, St. Anne's University Hospital and Masaryk University, Brno, Czech Republic
| | - T Jurkova
- Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - P Burilova
- Department of Health Sciences, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - F Plesinger
- Institute of Scientific Instruments, Czech Academy of Sciences, Brno, Czech Republic
| | - E Zatloukalova
- Brno Epilepsy Center, First Department of Neurology, Member of ERN-Epicar, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Pekařská 53, 602 00, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic
| | - I Dolezalova
- Brno Epilepsy Center, First Department of Neurology, Member of ERN-Epicar, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Pekařská 53, 602 00, Brno, Czech Republic.
- International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic.
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27
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Maschke C, O'Byrne J, Colombo MA, Boly M, Gosseries O, Laureys S, Rosanova M, Jerbi K, Blain-Moraes S. Criticality of resting-state EEG predicts perturbational complexity and level of consciousness during anesthesia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.26.564247. [PMID: 37994368 PMCID: PMC10664178 DOI: 10.1101/2023.10.26.564247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigated dynamical properties of the resting-state electroencephalogram of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. We then studied the relation of these dynamic properties with the perturbational complexity index (PCI), which has shown remarkably high sensitivity in detecting consciousness independent of behavior. All participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams)., enabling an experimental dissociation between unresponsiveness and unconsciousness. We estimated (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related measures, and found that states of unconsciousness were characterized by a distancing from both the edge of activity propagation and the edge of chaos. We were then able to predict individual subjects' PCI (i.e., PCImax) with a mean absolute error below 7%. Our results establish a firm link between the PCI and criticality and provide further evidence for the role of criticality in the emergence of consciousness.
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Affiliation(s)
- Charlotte Maschke
- Montreal General Hospital, McGill University Health Centre, Montreal, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
- Cognitive & Computational Neuroscience Lab, Psychology Department, University of Montreal, Québec, Canada
| | - Jordan O'Byrne
- Cognitive & Computational Neuroscience Lab, Psychology Department, University of Montreal, Québec, Canada
- MILA (Québec Artificial Intelligence Institute), Montréal, Québec, Canada
| | | | - Melanie Boly
- Department of Neurology and Department of Psychiatry, University of Wisconsin, Madison, USA
| | - Olivia Gosseries
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- Centre du cerveau, CHU of Liège, Liège, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
- CERVO Brain Research Centre, Laval University, Canada
- Consciousness Science Institute, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Karim Jerbi
- Cognitive & Computational Neuroscience Lab, Psychology Department, University of Montreal, Québec, Canada
- MILA (Québec Artificial Intelligence Institute), Montréal, Québec, Canada
- Centre UNIQUE (Union Neurosciences & Intelligence Artificielle), Montréal, Québec, Canada
| | - Stefanie Blain-Moraes
- Montreal General Hospital, McGill University Health Centre, Montreal, Canada
- School of Physical and Occupational Therapy, McGill University, Montreal, Canada
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28
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Hernández RM, Ponce-Meza JC, Saavedra-López MÁ, Campos Ugaz WA, Chanduvi RM, Monteza WC. Brain Complexity and Psychiatric Disorders. IRANIAN JOURNAL OF PSYCHIATRY 2023; 18:493-502. [PMID: 37881422 PMCID: PMC10593988 DOI: 10.18502/ijps.v18i4.13637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/14/2023] [Accepted: 03/16/2023] [Indexed: 10/27/2023]
Abstract
Objective: In recent years, researchers and neuroscientists have begun to use a variety of nonlinear techniques for analyzing neurophysiologic signals derived from fMRI, MEG, and EEG in order to describe the complex dynamical aspects of neural mechanisms. In this work, we first attempted to describe different algorithms to estimate neural complexity in a simple manner understandable for psychiatrists, psychologists, and neuroscientists. Then, we reviewed the findings of the brain complexity analysis in psychiatric disorders and their clinical implications. Method : A non-systematic comprehensive literature search was conducted for original studies on the complexity analysis of neurophysiological signals such as electroencephalogram, magnetoencephalogram, and blood-oxygen-level-dependent obtained from functional magnetic resonance imaging or functional near infrared spectroscopy. The search encompassed online scientific databases such as PubMed and Google Scholar. Results: Complexity measures mainly include entropy-based methods, the correlation dimension, fractal dimension, Lempel-Ziv complexity, and the Lyapunov exponent. There are important differences in the physical notions between these measures. Our literature review shows that dementia, autism, and adult ADHD exhibit less complexity in their neurophysiologic signals than healthy controls. However, children with ADHD, drug-naïve young schizophrenic patients with positive symptoms, and patients with mood disorders (i.e., depression and bipolar disorder) exhibit higher complexity in their neurophysiologic signals compared to healthy controls. In addition, contradictory findings still exist in some psychiatric disorders such as schizophrenia regarding brain complexity, which can be due to technical issues, large heterogeneity in psychiatric disorders, and interference of typical factors. Conclusion: In summary, complexity analysis may present a new dimension to understanding psychiatric disorders. While complexity analysis is still far from having practical applications in routine clinical settings, complexity science can play an important role in comprehending the system dynamics of psychiatric disorders.
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29
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Murphy N, Tamman AJF, Lijffijt M, Amarneh D, Iqbal S, Swann A, Averill LA, O'Brien B, Mathew SJ. Neural complexity EEG biomarkers of rapid and post-rapid ketamine effects in late-life treatment-resistant depression: a randomized control trial. Neuropsychopharmacology 2023; 48:1586-1593. [PMID: 37076582 PMCID: PMC10516885 DOI: 10.1038/s41386-023-01586-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 04/21/2023]
Abstract
Ketamine is an effective intervention for treatment-resistant depression (TRD), including late-in-life (LL-TRD). The proposed mechanism of antidepressant effects of ketamine is a glutamatergic surge, which can be measured by electroencephalogram (EEG) gamma oscillations. Yet, non-linear EEG biomarkers of ketamine effects such as neural complexity are needed to capture broader systemic effects, represent the level of organization of synaptic communication, and elucidate mechanisms of action for treatment responders. In a secondary analysis of a randomized control trial, we investigated two EEG neural complexity markers (Lempel-Ziv complexity [LZC] and multiscale entropy [MSE]) of rapid (baseline to 240 min) and post-rapid ketamine (24 h and 7 days) effects after one 40-min infusion of IV ketamine or midazolam (active control) in 33 military veterans with LL-TRD. We also studied the relationship between complexity and Montgomery-Åsberg Depression Rating Scale score change at 7 days post-infusion. We found that LZC and MSE both increased 30 min post-infusion, with effects not localized to a single timescale for MSE. Post-rapid effects of reduced complexity with ketamine were observed for MSE. No relationship was observed between complexity and reduction in depressive symptoms. Our findings support the hypothesis that a single sub-anesthetic ketamine infusion has time-varying effects on system-wide contributions to the evoked glutamatergic surge in LL-TRD. Further, changes to complexity were observable outside the time-window previously shown for effects on gamma oscillations. These preliminary results have clinical implications in providing a functional marker of ketamine that is non-linear, amplitude-independent, and represents larger dynamic properties, providing strong advantages over linear measures in highlighting ketamine's effects.
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Affiliation(s)
- Nicholas Murphy
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- The Menninger Clinic, Houston, TX, USA
| | - Amanda J F Tamman
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA.
| | - Marijn Lijffijt
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Dania Amarneh
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
| | - Sidra Iqbal
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Alan Swann
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Lynnette A Averill
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Brittany O'Brien
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- The Menninger Clinic, Houston, TX, USA
| | - Sanjay J Mathew
- Baylor College of Medicine, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA
- The Menninger Clinic, Houston, TX, USA
- Michael E. DeBakey VA Medical Center, Houston, TX, USA
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Ruiz de Miras J, Ibáñez-Molina AJ, Soriano MF, Iglesias-Parro S. Fractal dimension analysis of resting state functional networks in schizophrenia from EEG signals. Front Hum Neurosci 2023; 17:1236832. [PMID: 37799187 PMCID: PMC10547874 DOI: 10.3389/fnhum.2023.1236832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 09/06/2023] [Indexed: 10/07/2023] Open
Abstract
Fractal dimension (FD) has been revealed as a very useful tool in analyzing the changes in brain dynamics present in many neurological disorders. The fractal dimension index (FDI) is a measure of the spatiotemporal complexity of brain activations extracted from EEG signals induced by transcranial magnetic stimulation. In this study, we assess whether the FDI methodology can be also useful for analyzing resting state EEG signals, by characterizing the brain dynamic changes in different functional networks affected by schizophrenia, a mental disorder associated with dysfunction in the information flow dynamics in the spontaneous brain networks. We analyzed 31 resting-state EEG records of 150 s belonging to 20 healthy subjects (HC group) and 11 schizophrenia patients (SCZ group). Brain activations at each time sample were established by a thresholding process applied on the 15,002 sources modeled from the EEG signal. FDI was then computed individually in each resting-state functional network, averaging all the FDI values obtained using a sliding window of 1 s in the epoch. Compared to the HC group, significant lower values of FDI were obtained in the SCZ group for the auditory network (p < 0.05), the dorsal attention network (p < 0.05), and the salience network (p < 0.05). We found strong negative correlations (p < 0.01) between psychopathological scores and FDI in all resting-state networks analyzed, except the visual network. A receiver operating characteristic curve analysis also revealed that the FDI of the salience network performed very well as a potential feature for classifiers of schizophrenia, obtaining an area under curve value of 0.83. These results suggest that FDI is a promising method for assessing the complexity of the brain dynamics in different regions of interest, and from long resting-state EEG signals. Regarding the specific changes associated with schizophrenia in the dynamics of the spontaneous brain networks, FDI distinguished between patients and healthy subjects, and correlated to clinical variables.
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Affiliation(s)
- Juan Ruiz de Miras
- Software Engineering Department, Research Center for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
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Liuzzi P, Hakiki B, Draghi F, Romoli AM, Burali R, Scarpino M, Cecchi F, Grippo A, Mannini A. EEG fractal dimensions predict high-level behavioral responses in minimally conscious patients. J Neural Eng 2023; 20:046038. [PMID: 37494926 DOI: 10.1088/1741-2552/aceaac] [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: 06/14/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023]
Abstract
Objective.Brain-injured patients may enter a state of minimal or inconsistent awareness termed minimally conscious state (MCS). Such patient may (MCS+) or may not (MCS-) exhibit high-level behavioral responses, and the two groups retain two inherently different rehabilitative paths and expected outcomes. We hypothesized that brain complexity may be treated as a proxy of high-level cognition and thus could be used as a neural correlate of consciousness.Approach.In this prospective observational study, 68 MCS patients (MCS-: 30; women: 31) were included (median [IQR] age 69 [20]; time post-onset 83 [28]). At admission to intensive rehabilitation, 30 min resting-state closed-eyes recordings were performed together with consciousness diagnosis following international guidelines. The width of the multifractal singularity spectrum (MSS) was computed for each channel time series and entered nested cross-validated interpretable machine learning models targeting the differential diagnosis of MCS±.Main results.Frontal MSS widths (p< 0.05), as well as the ones deriving from the left centro-temporal network (C3:p= 0.018, T3:p= 0.017; T5:p= 0.003) were found to be significantly higher in the MCS+ cohort. The best performing solution was found to be the K-nearest neighbor model with an aggregated test accuracy of 75.5% (median [IQR] AuROC for 100 executions 0.88 [0.02]). Coherently, the electrodes with highest Shapley values were found to be Fz and Cz, with four out the first five ranked features belonging to the fronto-central network.Significance.MCS+ is a frequent condition associated with a notably better prognosis than the MCS-. High fractality in the left centro-temporal network results coherent with neurological networks involved in the language function, proper of MCS+ patients. Using EEG-based interpretable algorithm to complement differential diagnosis of consciousness may improve rehabilitation pathways and communications with caregivers.
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Affiliation(s)
- Piergiuseppe Liuzzi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
- The Biorobotics Institute, Scuola Superiore Sant'Anna Istituto di BioRobotica, Viale Rinaldo Piaggio 34, Pontedera, PI, Italy
| | - Bahia Hakiki
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Francesca Draghi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Anna Maria Romoli
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Rachele Burali
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Maenia Scarpino
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Francesca Cecchi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, Florence, 50143 FI, Italy
| | - Antonello Grippo
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
| | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, Firenze, FI, Italy
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Bosl WJ, Bosquet Enlow M, Lock EF, Nelson CA. A biomarker discovery framework for childhood anxiety. Front Psychiatry 2023; 14:1158569. [PMID: 37533889 PMCID: PMC10393248 DOI: 10.3389/fpsyt.2023.1158569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 07/04/2023] [Indexed: 08/04/2023] Open
Abstract
Introduction Anxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety. Methods We used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 years of age. We first examined the validity of this method by showing that calendar age is highly correlated with latent EEG complexity factors (r = 0.77). We then computed latent factors separately for distinguishing children with anxiety disorders from healthy controls using a 5-fold cross validation scheme and similarly for distinguishing children with externalizing disorders from healthy controls. Results We found that latent factors derived from EEG recordings at age 7 years were required to distinguish children with an anxiety disorder from healthy controls; recordings from infancy, 3 years, or 5 years alone were insufficient. However, recordings from two (5, 7 years) or three (3, 5, 7 years) recordings gave much better results than 7 year recordings alone. Externalizing disorders could be detected using 3- and 5 years EEG data, also giving better results with two or three recordings than any single snapshot. Further, sex assigned at birth was an important covariate that improved accuracy for both disorder groups, and birthweight as a covariate modestly improved accuracy for externalizing disorders. Recordings from infant EEG did not contribute to the classification accuracy for either anxiety or externalizing disorders. Conclusion This study suggests that latent factors extracted from EEG recordings in childhood are promising candidate biomarkers for anxiety and for externalizing disorders if chosen at appropriate ages.
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Affiliation(s)
- William J. Bosl
- Center for AI & Medicine, University of San Francisco, San Francisco, CA, United States
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Michelle Bosquet Enlow
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Eric F. Lock
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Charles A. Nelson
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, United States
- Harvard Graduate School of Education, Cambridge, MA, United States
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Feng YZ, Chen JT, Hu ZY, Liu GX, Zhou YS, Zhang P, Su AX, Yang S, Zhang YM, Wei RM, Chen GH. Effects of Sleep Reactivity on Sleep Macro-Structure, Orderliness, and Cortisol After Stress: A Preliminary Study in Healthy Young Adults. Nat Sci Sleep 2023; 15:533-546. [PMID: 37434994 PMCID: PMC10332417 DOI: 10.2147/nss.s415464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 06/29/2023] [Indexed: 07/13/2023] Open
Abstract
Purpose To investigate changes and links of stress and high sleep reactivity (H-SR) on the macro-structure and orderliness of sleep and cortisol levels in good sleepers (GS). Patients and Methods Sixty-two GS (18-40 years old) were recruited, with 32 in the stress group and 30 in the control group. Each group was further divided into H-SR and low SR subgroups based on the Ford Insomnia Response to Stress Test. All participants completed two nights of polysomnography in a sleep laboratory. Before conducting polysomnography on the second night, the stress group completed the Trier Social Stress Test and saliva was collected. Results The duration of NREM sleep stages 1, 2 (N1, N2) and rapid eye movement sleep (REM) decreased, and the values of approximate entropy, sample entropy, fuzzy entropy, and multiscale entropy increased under stress and SR effects. Stress increased rapid eye movement density, and H-SR increased cortisol reactivity. Conclusion Stress can damage the sleep and increase cortisol release in GS, especially those with H-SR. N1, N2 and REM sleep are more easily affected, while NREM sleep stage 3 sleep is relatively stable.
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Affiliation(s)
- Yi-Zhou Feng
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Jun-Tao Chen
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
- Department of Neurology, Shangyu People’s Hospital of Shaoxing, Shaoxing, Zhejiang, 312000, People’s Republic of China
| | - Zhen-Yu Hu
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Gao-Xia Liu
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Yu-Shun Zhou
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Ping Zhang
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Ai-Xi Su
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Shuai Yang
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Yue-Ming Zhang
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Ru-Meng Wei
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
| | - Gui-Hai Chen
- Department of Neurology (Sleep Disorders), Chaohu Hospital of Anhui Medical University, Hefei, Anhui, 238000, People’s Republic of China
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Ruiz de Miras J, Derchi CC, Atzori T, Mazza A, Arcuri P, Salvatore A, Navarro J, Saibene FL, Meloni M, Comanducci A. Spatio-Temporal Fractal Dimension Analysis from Resting State EEG Signals in Parkinson's Disease. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1017. [PMID: 37509964 PMCID: PMC10377880 DOI: 10.3390/e25071017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/01/2023] [Accepted: 07/02/2023] [Indexed: 07/30/2023]
Abstract
Complexity analysis of electroencephalogram (EEG) signals has emerged as a valuable tool for characterizing Parkinson's disease (PD). Fractal dimension (FD) is a widely employed method for measuring the complexity of shapes with many applications in neurodegenerative disorders. Nevertheless, very little is known on the fractal characteristics of EEG in PD measured by FD. In this study we performed a spatio-temporal analysis of EEG in PD using FD in four dimensions (4DFD). We analyzed 42 resting-state EEG recordings comprising two groups: 27 PD patients without dementia and 15 healthy control subjects (HC). From the original resting-state EEG we derived the cortical activations defined by a source reconstruction at each time sample, generating point clouds in three dimensions. Then, a sliding window of one second (the fourth dimension) was used to compute the value of 4DFD by means of the box-counting algorithm. Our results showed a significantly higher value of 4DFD in the PD group (p < 0.001). Moreover, as a diagnostic classifier of PD, 4DFD obtained an area under curve value of 0.97 for a receiver operating characteristic curve analysis. These results suggest that 4DFD could be a promising method for characterizing the specific changes in the brain dynamics associated with PD.
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Affiliation(s)
- Juan Ruiz de Miras
- Software Engineering Department, University of Granada, 18071 Granada, Spain
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
| | | | | | - Alice Mazza
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
| | - Pietro Arcuri
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
| | | | - Jorge Navarro
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
| | | | - Mario Meloni
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
| | - Angela Comanducci
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy
- Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
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Abramov DM, Tsallis C, Lima HS. Neural complexity through a nonextensive statistical-mechanical approach of human electroencephalograms. Sci Rep 2023; 13:10318. [PMID: 37365196 DOI: 10.1038/s41598-023-37219-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/18/2023] [Indexed: 06/28/2023] Open
Abstract
The brain is a complex system whose understanding enables potentially deeper approaches to mental phenomena. Dynamics of wide classes of complex systems have been satisfactorily described within q-statistics, a current generalization of Boltzmann-Gibbs (BG) statistics. Here, we study human electroencephalograms of typical human adults (EEG), very specifically their inter-occurrence times across an arbitrarily chosen threshold of the signal (observed, for instance, at the midparietal location in scalp). The distributions of these inter-occurrence times differ from those usually emerging within BG statistical mechanics. They are instead well approached within the q-statistical theory, based on non-additive entropies characterized by the index q. The present method points towards a suitable tool for quantitatively accessing brain complexity, thus potentially opening useful studies of the properties of both typical and altered brain physiology.
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Affiliation(s)
- Dimitri Marques Abramov
- Laboratório de Neurobiologia e Neurofisiologia Clínica, Instituto Nacional da Saude da Criança, da Mulher e do Adolescente Fernandes Figueira, Fundacao Oswaldo Cruz, Avenida Rui Barbosa 716, Flamengo, Rio de Janeiro, 22250-020, Brazil.
| | - Constantino Tsallis
- Centro Brasileiro de Pesquisas Fisicas and National Institute of Science and Technology for Complex Systems, Rua Xavier Sigaud 150, Rio de Janeiro, 22290-180, Brazil
- Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA
- Complexity Science Hub Vienna, Josefstädter Strasse 39, 1080, Vienna, Austria
| | - Henrique Santos Lima
- Centro Brasileiro de Pesquisas Fisicas and National Institute of Science and Technology for Complex Systems, Rua Xavier Sigaud 150, Rio de Janeiro, 22290-180, Brazil
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Davoudi S, Schwartz T, Labbe A, Trainor L, Lippé S. Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants. Cereb Cortex 2023; 33:8734-8747. [PMID: 37143183 PMCID: PMC10321121 DOI: 10.1093/cercor/bhad154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/06/2023] Open
Abstract
Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.
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Affiliation(s)
- Saeideh Davoudi
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Neuroscience, Université de Montréal, Montréal H3T 1J4, Canada
| | - Tyler Schwartz
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Aurélie Labbe
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Laurel Trainor
- Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton L8S 4K1, Canada
| | - Sarah Lippé
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Psychology, Université de Montréal, Montréal H2V 2S9, Canada
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Kosonogov V, Shelepenkov D, Rudenkiy N. EEG and peripheral markers of viewer ratings: a study of short films. Front Neurosci 2023; 17:1148205. [PMID: 37378009 PMCID: PMC10291053 DOI: 10.3389/fnins.2023.1148205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 05/17/2023] [Indexed: 06/29/2023] Open
Abstract
Introduction Cinema is an important part of modern culture, influencing millions of viewers. Research suggested many models for the prediction of film success, one of them being the use of neuroscientific tools. The aim of our study was to find physiological markers of viewer perception and correlate them to short film ratings given by our subjects. Short films are used as a test case for directors and screenwriters and can be created to raise funding for future projects; however, they have not been studied properly with physiological methods. Methods We recorded electroencephalography (18 sensors), facial electromyography (corrugator supercilii and zygomaticus major), photoplethysmography, and skin conductance in 21 participants while watching and evaluating 8 short films (4 dramas and 4 comedies). Also, we used machine learning (CatBoost, SVR) to predict the exact rating of each film (from 1 to 10), based on all physiological indicators. In addition, we classified each film as low or high rated by our subjects (with Logistic Regression, KNN, decision tree, CatBoost, and SVC). Results The results showed that ratings did not differ between genres. Corrugator supercilii activity ("frowning" muscle) was larger when watching dramas; whereas zygomaticus major ("smiling" muscle) activity was larger during the watching of comedies. Of all somatic and vegetative markers, only zygomaticus major activity, PNN50, SD1/SD2 (heart rate variability parameters) positively correlated to the film ratings. The EEG engagement indices, beta/(alpha+theta) and beta/alpha correlated positively with the film ratings in the majority of sensors. Arousal (betaF3 + betaF4)/(alphaF3 + alphaF4), and valence (alphaF4/betaF4) - (alphaF3/betaF3) indices also correlated positively to film ratings. When we attempted to predict exact ratings, MAPE was 0.55. As for the binary classification, logistic regression yielded the best values (area under the ROC curve = 0.62) than other methods (0.51-0.60). Discussion Overall, we revealed EEG and peripheral markers, which reflect viewer ratings and can predict them to a certain extent. In general, high film ratings can reflect a fusion of high arousal and different valence, positive valence being more important. These findings broaden our knowledge about the physiological basis of viewer perception and can be potentially used at the stage of film production.
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Buchman AS, Wang T, Oveisgharan S, Zammit AR, Yu L, Li P, Hu K, Hausdorff JM, Lim ASP, Bennett DA. Correlates of Person-Specific Rates of Change in Sensor-Derived Physical Activity Metrics of Daily Living in the Rush Memory and Aging Project. SENSORS (BASEL, SWITZERLAND) 2023; 23:4152. [PMID: 37112493 PMCID: PMC10142139 DOI: 10.3390/s23084152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/11/2023] [Accepted: 04/18/2023] [Indexed: 06/19/2023]
Abstract
This study characterized person-specific rates of change of total daily physical activity (TDPA) and identified correlates of this change. TDPA metrics were extracted from multiday wrist-sensor recordings from 1083 older adults (average age 81 years; 76% female). Thirty-two covariates were collected at baseline. A series of linear mixed-effect models were used to identify covariates independently associated with the level and annual rate of change of TDPA. Though, person-specific rates of change varied during a mean follow-up of 5 years, 1079 of 1083 showed declining TDPA. The average decline was 16%/year, with a 4% increased rate of decline for every 10 years of age older at baseline. Following variable selection using multivariate modeling with forward and then backward elimination, age, sex, education, and 3 of 27 non-demographic covariates including motor abilities, a fractal metric, and IADL disability remained significantly associated with declining TDPA accounting for 21% of its variance (9% non-demographic and 12% demographics covariates). These results show that declining TDPA occurs in many very old adults. Few covariates remained correlated with this decline and the majority of its variance remained unexplained. Further work is needed to elucidate the biology underlying TDPA and to identify other factors that account for its decline.
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Affiliation(s)
- Aron S. Buchman
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Tianhao Wang
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Shahram Oveisgharan
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Andrea R. Zammit
- Rush Alzheimer’s Disease Center, Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Lei Yu
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Peng Li
- Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Kun Hu
- Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey M. Hausdorff
- Rush Alzheimer’s Disease Center, Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6492416, Israel
- Sagol School of Neuroscience, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Andrew S. P. Lim
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON M4N 3M5, Canada
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
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