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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; 25:104523. [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] [MESH Headings] [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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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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Li F, Zhang S, Jiang L, Duan K, Feng R, Zhang Y, Zhang G, Zhang Y, Li P, Yao D, Xie J, Xu W, Xu P. Recognition of autism spectrum disorder in children based on electroencephalogram network topology. Cogn Neurodyn 2024; 18:1033-1045. [PMID: 38826670 PMCID: PMC11143134 DOI: 10.1007/s11571-023-09962-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 02/24/2023] [Accepted: 03/17/2023] [Indexed: 06/04/2024] Open
Abstract
Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.
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Affiliation(s)
- Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
| | - Shu Zhang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Lin Jiang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Keyi Duan
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Rui Feng
- Rainbow Biotechnology Co., Ltd., Chengdu, 610041 China
| | - Yingli Zhang
- Rainbow Biotechnology Co., Ltd., Chengdu, 610041 China
| | - Gao Zhang
- The Preston Robert Tisch Brain Tumor Center, Department of Neurosurgery, Department of Pathology, Duke University School of Medicine, Durham, NC 27710 USA
| | - Yangsong Zhang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010 China
| | - Peiyang Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001 China
| | - Jiang Xie
- Chengdu Third People’s Hospital, Affiliated Hospital of Southwest JiaoTong University Medical School, Chengdu, 610031 China
| | - Wenming Xu
- Department of Obstetrics/Gynecology, Joint Laboratory of Reproductive Medicine (SCU-CUHK), Key Laboratory of Obstetric, Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, 610041 China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610042 China
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Wolfson SS, Kirk I, Waldie K, King C. EEG Complexity Analysis of Brain States, Tasks and ASD Risk. ADVANCES IN NEUROBIOLOGY 2024; 36:733-759. [PMID: 38468061 DOI: 10.1007/978-3-031-47606-8_37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Autism spectrum disorder is an increasingly prevalent and debilitating neurodevelopmental condition and an electroencephalogram (EEG) diagnostic challenge. Despite large amounts of electrophysiological research over many decades, an EEG biomarker for autism spectrum disorder (ASD) has not been found. We hypothesized that reductions in complex dynamical system behaviour in the human central nervous system as part of the macroscale neuronal function during cognitive processes might be detectable in whole EEG for higher-risk ASD adults. In three studies, we compared the medians of correlation dimension, largest Lyapunov exponent, Higuchi's fractal dimension, multiscale entropy, multifractal detrended fluctuation analysis and Kolmogorov complexity during resting, cognitive and social skill tasks in 20 EEG channels of 39 adults over a range of ASD risk. We found heterogeneous complexity distribution with clusters of hierarchical sequences pointing to potential cognitive processing differences, but no clear distinction based on ASD risk. We suggest that there is indication of statistically significant differences between complexity measures of brain states and tasks. Though replication of our studies is needed with a larger sample, we believe that our electrophysiological and analytic approach has potential as a biomarker for earlier ASD diagnosis.
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Affiliation(s)
- Stephen S Wolfson
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand.
| | - Ian Kirk
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
| | - Karen Waldie
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
| | - Chris King
- The University of Auckland School of Psychology, Auckland, Auckland, New Zealand
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Angulo-Ruiz BY, Ruiz-Martínez FJ, Rodríguez-Martínez EI, Ionescu A, Saldaña D, Gómez CM. Linear and Non-linear Analyses of EEG in a Group of ASD Children During Resting State Condition. Brain Topogr 2023; 36:736-749. [PMID: 37330940 PMCID: PMC10415465 DOI: 10.1007/s10548-023-00976-7] [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: 02/07/2023] [Accepted: 06/06/2023] [Indexed: 06/20/2023]
Abstract
This study analyses the spontaneous electroencephalogram (EEG) brain activity of 14 children diagnosed with Autism Spectrum Disorder (ASD) compared to 18 children with normal development, aged 5-11 years. (i) Power Spectral Density (PSD), (ii) variability across trials (coefficient of variation: CV), and (iii) complexity (multiscale entropy: MSE) of the brain signal analysis were computed on the resting state EEG. PSD (0.5-45 Hz) and CV were averaged over different frequency bands (low-delta, delta, theta, alpha, low-beta, high-beta and gamma). MSE were calculated with a coarse-grained procedure on 67 time scales and divided into fine, medium and coarse scales. In addition, significant neurophysiological variables were correlated with behavioral performance data (Kaufman Brief Intelligence Test (KBIT) and Autism Spectrum Quotient (AQ)). Results show increased PSD fast frequency bands (high-beta and gamma), higher variability (CV) and lower complexity (MSE) in children with ASD when compared to typically developed children. These results suggest a more variable, less complex and, probably, less adaptive neural networks with less capacity to generate optimal responses in ASD children.
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Affiliation(s)
- Brenda Y. Angulo-Ruiz
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/ Camilo José Cela S/N 41018, Seville, Spain
| | - Francisco J. Ruiz-Martínez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/ Camilo José Cela S/N 41018, Seville, Spain
| | - Elena I. Rodríguez-Martínez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/ Camilo José Cela S/N 41018, Seville, Spain
| | - Anca Ionescu
- Département de Psychologie, Université de Montréal, Montréal, Canada
| | - David Saldaña
- Laboratorio de Diversidad, Cognición y Lenguaje, Departamento de Psicología Evolutiva y de la Educación, University of Seville, C/ Camilo José Cela S/N 41018, Seville, Spain
| | - Carlos M. Gómez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/ Camilo José Cela S/N 41018, Seville, 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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Angulo-Ruiz BY, Muñoz V, Rodríguez-Martínez EI, Cabello-Navarro C, Gómez CM. Multiscale entropy of ADHD children during resting state condition. Cogn Neurodyn 2023; 17:869-891. [PMID: 37522046 PMCID: PMC10374506 DOI: 10.1007/s11571-022-09869-0] [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: 03/31/2022] [Revised: 07/18/2022] [Accepted: 08/05/2022] [Indexed: 11/28/2022] Open
Abstract
This present study aims to investigate neural mechanisms underlying ADHD compared to healthy children through the analysis of the complexity and the variability of the EEG brain signal using multiscale entropy (MSE), EEG signal standard deviation (SDs), as well as the mean, standard deviation (SDp) and coefficient of variation (CV) of absolute spectral power (PSD). For this purpose, a sample of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) between 6 and 17 years old were selected based on the number of trials and diagnostic agreement, 32 for the open-eyes (OE) experimental condition and 25 children for the close-eyes (CE) experimental condition. Healthy control subjects were age- and gender-matched with the ADHD group. The MSE and SDs of resting-state EEG activity were calculated on 34 time scales using a coarse-grained procedure. In addition, the PSD was averaged in delta, theta, alpha, and beta frequency bands, and its mean, SDp, and CV were calculated. The results show that the MSE changes with age during development, increases as the number of scales increases and has a higher amplitude in controls than in ADHD. The absolute PSD results show CV differences between subjects in low and beta frequency bands, with higher variability values in the ADHD group. All these results suggest an increased EEG variability and reduced complexity in ADHD compared to controls. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09869-0.
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Affiliation(s)
- Brenda Y. Angulo-Ruiz
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Vanesa Muñoz
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Elena I. Rodríguez-Martínez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Celia Cabello-Navarro
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Carlos M. Gómez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
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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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10
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Lopez KL, Monachino AD, Vincent KM, Peck FC, Gabard-Durnam LJ. Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities. Neuroimage 2023; 275:120116. [PMID: 37169118 DOI: 10.1016/j.neuroimage.2023.120116] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/27/2023] [Accepted: 04/13/2023] [Indexed: 05/13/2023] Open
Abstract
Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.
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Affiliation(s)
- K L Lopez
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - A D Monachino
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - K M Vincent
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - F C Peck
- University of California, Los Angeles, Los Angeles, CA, United States
| | - L J Gabard-Durnam
- Northeastern University, 360 Huntington Ave, Boston, MA, United States.
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Asuke N, Yamagami T, Mihana T, Röhm A, Horisaki R, Naruse M. Information-theoretical analysis of statistical measures for multiscale dynamics. CHAOS (WOODBURY, N.Y.) 2023; 33:043138. [PMID: 37097964 DOI: 10.1063/5.0141099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Abstract
Multiscale entropy (MSE) has been widely used to examine nonlinear systems involving multiple time scales, such as biological and economic systems. Conversely, Allan variance has been used to evaluate the stability of oscillators, such as clocks and lasers, ranging from short to long time scales. Although these two statistical measures were developed independently for different purposes in different fields, their interest lies in examining the multiscale temporal structures of physical phenomena under study. We demonstrate that from an information-theoretical perspective, they share some foundations and exhibit similar tendencies. We experimentally confirmed that similar properties of the MSE and Allan variance can be observed in low-frequency fluctuations (LFF) in chaotic lasers and physiological heartbeat data. Furthermore, we calculated the condition under which this consistency between the MSE and Allan variance exists, which is related to certain conditional probabilities. Heuristically, natural physical systems including the aforementioned LFF and heartbeat data mostly satisfy this condition, and hence, the MSE and Allan variance demonstrate similar properties. As a counterexample, we demonstrate an artificially constructed random sequence, for which the MSE and Allan variance exhibit different trends.
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Affiliation(s)
- Naoki Asuke
- Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Tomoki Yamagami
- Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Takatomo Mihana
- Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - André Röhm
- Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Ryoichi Horisaki
- Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Makoto Naruse
- Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
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12
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Mintz Hemed N, Melosh NA. An integrated perspective for the diagnosis and therapy of neurodevelopmental disorders - From an engineering point of view. Adv Drug Deliv Rev 2023; 194:114723. [PMID: 36746077 DOI: 10.1016/j.addr.2023.114723] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 12/14/2022] [Accepted: 01/29/2023] [Indexed: 02/05/2023]
Abstract
Neurodevelopmental disorders (NDDs) are complex conditions with largely unknown pathophysiology. While many NDD symptoms are familiar, the cause of these disorders remains unclear and may involve a combination of genetic, biological, psychosocial, and environmental risk factors. Current diagnosis relies heavily on behaviorally defined criteria, which may be biased by the clinical team's professional and cultural expectations, thus a push for new biological-based biomarkers for NDDs diagnosis is underway. Emerging new research technologies offer an unprecedented view into the electrical, chemical, and physiological activity in the brain and with further development in humans may provide clinically relevant diagnoses. These could also be extended to new treatment options, which can start to address the underlying physiological issues. When combined with current speech, language, occupational therapy, and pharmacological treatment these could greatly improve patient outcomes. The current review will discuss the latest technologies that are being used or may be used for NDDs diagnosis and treatment. The aim is to provide an inspiring and forward-looking view for future research in the field.
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Affiliation(s)
- Nofar Mintz Hemed
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA.
| | - Nicholas A Melosh
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
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13
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Aguerre NV, Gómez-Ariza CJ, Ibáñez-Molina AJ, Bajo MT. Electrophysiological correlates of dispositional mindfulness: A quantitative and complexity EEG study. Br J Psychol 2023. [PMID: 36748402 DOI: 10.1111/bjop.12636] [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: 07/01/2022] [Revised: 01/12/2023] [Accepted: 01/16/2023] [Indexed: 02/08/2023]
Abstract
While growing evidence supports that dispositional mindfulness relates to psychological health and cognitive enhancement, to date there have been only a few attempts to characterize its neural underpinnings. In the present study, we aimed at exploring the electrophysiological (EEG) signature of dispositional mindfulness using quantitative and complexity measures of EEG during resting state and while performing a learning task. Hundred twenty participants were assessed with the Five Facet Mindfulness Questionnaire and underwent 5 min eyes-closed resting state and 5 min at task EEG recording. We hypothesized that high mindfulness individuals would show patterns of brain activity related to (a) lower involvement of the default mode network (DMN) at rest (reduced frontal gamma power) and (b) a state of 'task readiness' reflected in a more similar pattern from rest to task (reduced overall q-EEG power at rest but not at task), as compared to their low mindfulness counterparts. Dispositional mindfulness was significantly linked to reduced frontal gamma power at rest and lower overall power during rest but not at task. In addition, we found a trend towards higher entropy during task performance in mindful individuals, which has recently been reported during mindfulness meditation. Altogether, our results add to those from expert meditators to show that high (dispositional) mindfulness seems to have a specific electrophysiological pattern characteristic of less involvement of the DMN and mind-wandering processes.
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Affiliation(s)
- Nuria Victoria Aguerre
- Department of Experimental Psychology - Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
| | | | | | - María Teresa Bajo
- Department of Experimental Psychology - Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada, Spain
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14
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Li C, Zhang T, Li J. Identifying autism spectrum disorder in resting-state fNIRS signals based on multiscale entropy and a two-branch deep learning network. J Neurosci Methods 2023; 383:109732. [PMID: 36349567 DOI: 10.1016/j.jneumeth.2022.109732] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/10/2022] [Accepted: 10/23/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND The demand for early and precise identification of autism spectrum disorder (ASD) presented a challenge to the prediction of ASD with a non-invasive neuroimaging method. NEW METHOD A deep learning model was proposed to identify children with ASD using the resting-state functional near-infrared spectroscopy (fNIRS) signals. In this model, the input was the pattern of brain complexity represented by multiscale entropy of fNIRS time-series signals, with the purpose to solve the problem of deep learning analysis when the raw signals were limited by length and the number of subjects. The model consisted of a two-branch deep learning network, where one branch was a convolution neural network and the other was a long short-term memory neural network based on an attention mechanism. RESULTS Our model could achieve an identification accuracy of 94%. Further analysis used the SHapley Additive exPlanations (SHAP) method to balance the accuracy and the number of optical channels, thus reducing the complexity of fNIRS experiment. COMPARISON WITH PREVIOUSLY USED METHOD(S): in identification accuracy, our model was about 14% higher than previously used deep learning models with the same input and 4% higher than the same model but directly using fNIRS signals as input. We could obtain a discriminative accuracy of 90% with nearly half of the measurement channels by the SHAP method. CONCLUSIONS Using the pattern of brain complexity as input was effective in the deep learning model when the fNIRS signals were insufficient. With the SHAP method, it was possible to reduce the number of optical channels, while maintaining high accuracy in ASD identification.
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Affiliation(s)
- Chengxin Li
- South China Academy of Advanced Optoelectronics, South China Normal University, China
| | - Tingzhen Zhang
- South China Academy of Advanced Optoelectronics, South China Normal University, China
| | - Jun Li
- South China Academy of Advanced Optoelectronics, South China Normal University, China.
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15
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Pelc K, Gajewska A, Napiórkowski N, Dan J, Verhoeven C, Dan B. Multiscale entropy as a metric of brain maturation in a large cohort of typically developing children born preterm using longitudinal high-density EEG in the first two years of life. Physiol Meas 2022; 43. [PMID: 36374000 DOI: 10.1088/1361-6579/aca26c] [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: 08/23/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022]
Abstract
Objective.We aimed to analyze whether complexity of brain electrical activity (EEG) measured by multiscale entropy (MSE) increases with brain maturation during the first two years of life. We also aimed to investigate whether this complexity shows regional differences across the brain, and whether changes in complexity are influenced by extrauterine life experience duration.Approach.We measured MSE of EEG signals recorded longitudinally using a high-density setup (64 or 128 electrodes) in 84 typically developing infants born preterm (<32 weeks' gestation) from term age to two years. We analyzed the complexity index and maximum value of MSE over increasing age, across brain regions, and in function of extrauterine life duration, and used correlation matrices as a metric of functional connectivity of the cerebral cortex.Main results.We found an increase of strong inter-channel correlation of MSE (R > 0.8) with increasing age. Regional analysis showed significantly increased MSE between 3 and 24 months of corrected age in the posterior and middle regions with respect to the anterior region. We found a weak relationship (adjusted R2= 0.135) between MSE and extrauterine life duration.Significance.These findings suggest that brain functional connectivity increases with maturation during the first two years of life. EEG complexity shows regional differences with earlier maturation of the visual cortex and brain regions involved in joint attention than of regions involved in cognitive analysis, abstract thought, and social behavior regulation. Finally, our MSE analysis suggested only a weak influence of early extrauterine life experiences (prior to term age) on EEG complexity.
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Affiliation(s)
- Karine Pelc
- Université libre de Bruxelles (ULB), Facuty of Motor Sciences, Brussels, Belgium.,Inkendaal Rehabilitation Hospital, Vlezenbeek, Belgium
| | | | | | - Jonathan Dan
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium.,Byteflies, Berchem, Belgium
| | - Caroline Verhoeven
- Université libre de Bruxelles (ULB), Facuty of Medicine, Department of Mathematics Education, Brussels, Belgium
| | - Bernard Dan
- Inkendaal Rehabilitation Hospital, Vlezenbeek, Belgium.,Université libre de Bruxelles (ULB), Faculty of Psychology and Education Sciences, Brussels, Belgium
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16
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Wan L, Zhang CT, Zhu G, Chen J, Shi XY, Wang J, Zou LP, Zhang B, Shi WB, Yeh CH, Yang G. Integration of multiscale entropy and BASED scale of electroencephalography after adrenocorticotropic hormone therapy predict relapse of infantile spasms. World J Pediatr 2022; 18:761-770. [PMID: 35906344 DOI: 10.1007/s12519-022-00583-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 06/12/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Even though adrenocorticotropic hormone (ACTH) demonstrated powerful efficacy in the initially successful treatment of infantile spasms (IS), nearly half of patients have experienced a relapse. We sought to investigate whether features of electroencephalogram (EEG) predict relapse in those IS patients without structural brain abnormalities. METHODS We retrospectively reviewed data from children with IS who achieved initial response after ACTH treatment, along with EEG recorded within the last two days of treatment. The recurrence of epileptic spasms following treatment was tracked for 12 months. Subjects were categorized as either non-relapse or relapse groups. General clinical and EEG recordings were collected, burden of amplitudes and epileptiform discharges (BASED) score and multiscale entropy (MSE) were carefully explored for cross-group comparisons. RESULTS Forty-one patients were enrolled in the study, of which 26 (63.4%) experienced a relapse. The BASED score was significantly higher in the relapse group. MSE in the non-relapse group was significantly lower than the relapse group in the γ band but higher in the lower frequency range (δ, θ, α). Sensitivity and specificity were 85.71% and 92.31%, respectively, when combining MSE in the δ/γ frequency of the occipital region, plus BASED score were used to distinguish relapse from non-relapse groups. CONCLUSIONS BASED score and MSE of EEG after ACTH treatment could be used to predict relapse for IS patients without brain structural abnormalities. Patients with BASED score ≥ 3, MSE increased in higher frequency, and decreased in lower frequency had a high risk of relapse.
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Affiliation(s)
- Lin Wan
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China.,Medical School of Chinese People's Liberation Army, Beijing, China
| | - Chu-Ting Zhang
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Gang Zhu
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China.,Medical School of Chinese People's Liberation Army, Beijing, China
| | - Jian Chen
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Xiu-Yu Shi
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China.,Medical School of Chinese People's Liberation Army, Beijing, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Jing Wang
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Li-Ping Zou
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China.,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China.,Medical School of Chinese People's Liberation Army, Beijing, China.,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Bo Zhang
- Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Wen-Bin Shi
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Chien-Hung Yeh
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China.
| | - Guang Yang
- Senior Department of Pediatrics, Chinese PLA General Hospital, Beijing, 100000, China. .,Department of Pediatrics, the First Medical Centre, Chinese PLA General Hospital, Beijing, China. .,Medical School of Chinese People's Liberation Army, Beijing, China. .,The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
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17
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Puglia MH, Slobin JS, Williams CL. The automated preprocessing pipe-line for the estimation of scale-wise entropy from EEG data (APPLESEED): Development and validation for use in pediatric populations. Dev Cogn Neurosci 2022; 58:101163. [PMID: 36270100 PMCID: PMC9586850 DOI: 10.1016/j.dcn.2022.101163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 10/12/2022] [Accepted: 10/12/2022] [Indexed: 01/13/2023] Open
Abstract
It is increasingly understood that moment-to-moment brain signal variability - traditionally modeled out of analyses as mere "noise" - serves a valuable functional role related to development, cognitive processing, and psychopathology. Multiscale entropy (MSE) - a measure of signal irregularity across temporal scales - is an increasingly popular analytic technique in human neuroscience calculated from time series such as electroencephalography (EEG) signals. MSE provides insight into the time-structure and (non)linearity of fluctuations in neural activity and network dynamics, capturing the brain's moment-to-moment complexity as it operates on multiple time scales. MSE is emerging as a powerful predictor of developmental processes and outcomes. However, differences in data preprocessing and MSE computation make it challenging to compare results across studies. Here, we (1) provide an introduction to MSE for developmental researchers, (2) demonstrate the effect of preprocessing procedures on scale-wise entropy estimates, and (3) establish a standardized EEG preprocessing and entropy estimation pipeline that adapts a critical modification to the original MSE algorithm, and generates reliable scale-wise entropy estimates capable of differentiating developmental stages and cognitive states. This novel pipeline - the Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED) is fully automated, customizable, and freely available for download from https://github.com/mhpuglia/APPLESEED.
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Affiliation(s)
- Meghan H. Puglia
- Correspondence to: University of Virginia Department of Neurology, PO Box 800834, Charlottesville, VA 22908, USA.
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18
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Lau ZJ, Pham T, Chen SHA, Makowski D. Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations. Eur J Neurosci 2022; 56:5047-5069. [PMID: 35985344 PMCID: PMC9826422 DOI: 10.1111/ejn.15800] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 01/11/2023]
Abstract
There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
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Affiliation(s)
- Zen J. Lau
- School of Social SciencesNanyang Technological UniversitySingapore
| | - Tam Pham
- School of Social SciencesNanyang Technological UniversitySingapore
| | - S. H. Annabel Chen
- School of Social SciencesNanyang Technological UniversitySingapore,Centre for Research and Development in LearningNanyang Technological UniversitySingapore,Lee Kong Chian School of MedicineNanyang Technological UniversitySingapore,National Institute of EducationNanyang Technological UniversitySingapore
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19
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Sheela P, Puthankattil SD. MVME-RCMFDE framework for discerning hyper-responsivity in Autism Spectrum Disorders. Comput Biol Med 2022; 149:105958. [PMID: 36007291 DOI: 10.1016/j.compbiomed.2022.105958] [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: 02/02/2022] [Revised: 07/26/2022] [Accepted: 08/06/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND Autism Spectrum Disorder (ASD), characterized by impaired sensory processing, has a wide range of clinical heterogeneity, which handicaps effective therapeutic interventions. Therefore, it is imperative to develop potential mechanisms for delineating clinically meaningful subgroups, so as to provide individualised medical treatment. In this study, an attempt is being made to differentiate the hyper-responsive subgroup from ASD by analysing the complexity pattern of Visual Evoked Potentials (VEPs), recorded from a group of 30 ASD participants, in the presence of vertical achromatic sinewave gratings at varying contrast conditions of low (5%), medium (50%) and high (90%). METHOD This study proposes a new diagnostic framework incorporating a novel signal decomposition method termed as Modified Variational Mode Extraction (MVME) and a multiscale entropy approach. MVME segments the signal into five constituent modes with less spectral overlap in lower frequencies. Refined Composite Multiscale Fluctuation-based Dispersion entropy (RCMFDE) is extracted from these constituent modes, thereby facilitating the identification of hyper-responsive subgroup in ASD. RESULTS When tested on both simulated and real VEPs, MVME displays appreciable performance in terms of root mean square error and minimal spectral overlap in the lower frequencies, in comparison with the other state-of-the-art techniques. Relative Complexity analysis with RCMFDE exhibits a rising trend in 43%-50% of ASD in modes 1, 2, 3 and 4. CONCLUSION The proposed MVME-RCMFDE approach is efficient in discriminating the hyper-responsive subgroup in ASD in multiple modes namely mode 1, 2, 3 and 4, which correspond to delta, theta, alpha and beta frequency bands of brain signals.
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Affiliation(s)
- Priyalakshmi Sheela
- Department of Electrical Engineering, National Institute of Technology, Calicut, 673601, Kerala, India
| | - Subha D Puthankattil
- Department of Electrical Engineering, National Institute of Technology, Calicut, 673601, Kerala, India.
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20
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Han J, Jiang G, Ouyang G, Li X. A Multimodal Approach for Identifying Autism Spectrum Disorders in Children. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2003-2011. [PMID: 35853070 DOI: 10.1109/tnsre.2022.3192431] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Identification of autism spectrum disorder (ASD) in children is challenging due to the complexity and heterogeneity of ASD. Currently, most existing methods mainly rely on a single modality with limited information and often cannot achieve satisfactory performance. To address this issue, this paper investigates from internal neurophysiological and external behavior perspectives simultaneously and proposes a new multimodal diagnosis framework for identifying ASD in children with fusion of electroencephalogram (EEG) and eye-tracking (ET) data. Specifically, we designed a two-step multimodal feature learning and fusion model based on a typical deep learning algorithm, stacked denoising autoencoder (SDAE). In the first step, two SDAE models are designed for feature learning for EEG and ET modality, respectively. Then, a third SDAE model in the second step is designed to perform multimodal fusion with learned EEG and ET features in a concatenated way. Our designed multimodal identification model can automatically capture correlations and complementarity from behavior modality and neurophysiological modality in a latent feature space, and generate informative feature representations with better discriminability and generalization for enhanced identification performance. We collected a multimodal dataset containing 40 ASD children and 50 typically developing (TD) children to evaluate our proposed method. Experimental results showed that our proposed method achieved superior performance compared with two unimodal methods and a simple feature-level fusion method, which has promising potential to provide an objective and accurate diagnosis to assist clinicians.
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21
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Forbes O, Schwenn PE, Wu PPY, Santos-Fernandez E, Xie HB, Lagopoulos J, McLoughlin LT, Sacks DD, Mengersen K, Hermens DF. EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents. Biol Psychol 2022; 173:108403. [DOI: 10.1016/j.biopsycho.2022.108403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 06/27/2022] [Accepted: 07/26/2022] [Indexed: 11/25/2022]
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22
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Sho'ouri N. Hard Boundary-Based Neurofeedback Training Procedure: A Modified Fixed Thresholding Method for More Accurate Guidance of Subjects Within Target Areas During Neurofeedback Training. Clin EEG Neurosci 2022; 54:228-237. [PMID: 35686319 DOI: 10.1177/15500594221100159] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In nearly all studies within the domain of neurofeedback, a threshold has been defined for each training feature in a way that subjects' status can be evaluated during training according to the given value. In this study, a hard boundary-based neurofeedback training (HBNFT) method based on the determination of decision boundary using support vector machine (SVM) classifier was proposed in which subjects' status were clarified considering a decision boundary and they could also be encouraged once entering a target area. In this method, a scoring index (SI) was similarly defined whose value was determined in accordance with subject performance during training. The results revealed that employing a classifier and determining a decision boundary instead of using a threshold could prove more successful in accurately guiding them towards a target area and also meet no needs to choose a basis for determining a threshold. Moreover, it was likely that the proposed method could be more efficient in controlling features and preventing extreme changes compared to those using variable thresholds.
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Affiliation(s)
- Nasrin Sho'ouri
- Faculty of Technology and Engineering, 201585Central Tehran Branch, Islamic Azad University, Tehran, Iran
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23
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Li X, Yang C, An Z, Wang X, Su R, Kang J. Localization and diagnosis of abnormal channels in children with ASD based on WMSSE and ASI. J Neurosci Methods 2022; 375:109595. [DOI: 10.1016/j.jneumeth.2022.109595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 03/14/2022] [Accepted: 04/08/2022] [Indexed: 11/30/2022]
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24
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Abdulhay E, Alafeef M, Hadoush H, Venkataraman V, Arunkumar N. EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool -for Autism diagnosis- compared to multi-scale entropy approach. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5031-5054. [PMID: 35430852 DOI: 10.3934/mbe.2022235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Autism spectrum disorder (ASD) is usually characterised by altered social skills, repetitive behaviours, and difficulties in verbal/nonverbal communication. It has been reported that electroencephalograms (EEGs) in ASD are characterised by atypical complexity. The most commonly applied method in studies of ASD EEG complexity is multiscale entropy (MSE), where the sample entropy is evaluated across several scales. However, the accuracy of MSE-based classifications between ASD and neurotypical EEG activities is poor owing to several shortcomings in scale extraction and length, the overlap between amplitude and frequency information, and sensitivity to frequency. The present study proposes a novel, nonlinear, non-stationary, adaptive, data-driven, and accurate method for the classification of ASD and neurotypical groups based on EEG complexity and entropy without the shortcomings of MSE. APPROACH The proposed method is as follows: (a) each ASD and neurotypical EEG (122 subjects × 64 channels) is decomposed using empirical mode decomposition (EMD) to obtain the intrinsic components (intrinsic mode functions). (b) The extracted components are normalised through the direct quadrature procedure. (c) The Hilbert transforms of the components are computed. (d) The analytic counterparts of components (and normalised components) are found. (e) The instantaneous frequency function of each analytic normalised component is calculated. (f) The instantaneous amplitude function of each analytic component is calculated. (g) The Shannon entropy values of the instantaneous frequency and amplitude vectors are computed. (h) The entropy values are classified using a neural network (NN). (i) The achieved accuracy is compared to that obtained with MSE-based classification. (j) The consistency of the results of entropy 3D mapping with clinical data is assessed. MAIN RESULTS The results demonstrate that the proposed method outperforms MSE (accuracy: 66.4%), with an accuracy of 93.5%. Moreover, the entropy 3D mapping results are more consistent with the available clinical data regarding brain topography in ASD. SIGNIFICANCE This study presents a more robust alternative to MSE, which can be used for accurate classification of ASD/neurotypical as well as for the examination of EEG entropy across brain zones in ASD.
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Affiliation(s)
- Enas Abdulhay
- Biomedical Engineering department, Jordan University of Science and Technology, 22110 Irbid, Jordan
| | - Maha Alafeef
- Biomedical Engineering department, Jordan University of Science and Technology, 22110 Irbid, Jordan
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Hikmat Hadoush
- Rehabilitation Sciences department, Jordan University of Science and Technology, 22110 Irbid, Jordan
| | - V Venkataraman
- Department of Mathematics, School of Arts, Science and Humanities, SASTRA Deemed University, Thanjavur, 613401, India
| | - N Arunkumar
- Biomedical Engineering department, Rathinam Technical Campus, Coimbatore, India
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Gu C, Liu ZX, Woltering S. Electroencephalography complexity in resting and task states in adults with attention-deficit/hyperactivity disorder. Brain Commun 2022; 4:fcac054. [PMID: 35368615 PMCID: PMC8971899 DOI: 10.1093/braincomms/fcac054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 12/19/2021] [Accepted: 03/04/2022] [Indexed: 11/15/2022] Open
Abstract
Analysing EEG complexity could provide insight into neural connectivity underlying attention-deficit/hyperactivity disorder symptoms. EEG complexity was calculated through multiscale entropy and compared between adults with attention-deficit/hyperactivity disorder and their peers during resting and go/nogo task states. Multiscale entropy change from the resting state to the task state was also examined as an index of the brain’s ability to change from a resting to an active state. Thirty unmedicated adults with attention-deficit/hyperactivity disorder were compared with 30 match-paired healthy peers on the multiscale entropy in the resting and task states as well as their multiscale entropy change. Results showed differences in multiscale entropy between individuals with attention-deficit/hyperactivity disorder and their peers during the resting state as well as the task state. The multiscale entropy measured from the comparison group was larger than that from the attention-deficit/hyperactivity disorder group in the resting state, whereas the reverse pattern was found during the task state. Our most robust finding showed that the multiscale entropy change from individuals with attention-deficit/hyperactivity disorder was smaller than that from their peers, specifically at frontal sites. Interestingly, individuals without attention-deficit/hyperactivity disorder performed better with decreasing multiscale entropy changes, demonstrating higher accuracy, faster reaction time and less variability in their reaction times. These data suggest that multiscale entropy could not only provide insight into neural connectivity differences between adults with attention-deficit/hyperactivity disorder and their peers but also into their behavioural performance.
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Affiliation(s)
- Chao Gu
- Department of Neuroscience, Texas A&M University, USA
- Department of Psychiatry, Massachusetts General Hospital, USA
| | - Zhong-Xu Liu
- Department of Behavioral Sciences, University of Michigan-Dearborn, USA
| | - Steven Woltering
- Department of Educational Psychology, Texas A&M University, USA
- Department of Applied Psychology and Human Development, University of Toronto, Canada
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Migliorelli C, Medina-Rivera I, Bachiller A, Tost A, Alonso JF, López-Sala A, Armstrong J, O'Callahan MDM, Pineda M, Mañanas MA, Romero S, García-Cazorla Á. Cognitive stimulation has potential for brain activation in individuals with Rett syndrome. JOURNAL OF INTELLECTUAL DISABILITY RESEARCH : JIDR 2022; 66:213-224. [PMID: 34796573 DOI: 10.1111/jir.12902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 10/15/2021] [Accepted: 10/19/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Knowledge regarding neuropsychological training in Rett syndrome (RS) is scarce. The aim of this study was to assess the outcome and the duration of the effect of cognitive stimulation on topographic electroencephalography (EEG) data in RS. METHODS Twenty female children diagnosed with RS were included in the analysis. Girls with RS conducted a cognitive task using an eye-tracker designed to evaluate access and choice skills. EEG data were acquired during the experimental procedure including two 10-min baseline stages before and after the task. Topographical changes of several EEG spectral markers including absolute and relative powers, Brain Symmetry Index and entropy were assessed. RESULTS Topographic significance probability maps suggested statistical decreases on delta activity and increases on beta rhythm associated with the cognitive task. Entropy increased during and after the task, likely related to more complex brain activity. A significant positive interaction was obtained between Brain Symmetry Index and age showing that the improvement of interhemispheric symmetry was higher in younger girls (5-10 years). CONCLUSIONS According to our findings, significant alterations of brain rhythms were observed during and after cognitive stimulation, suggesting that cognitive stimulation may have effects on brain activity beyond the stimulation period. Finally, our promising results also showed an increase brain symmetry that was especially relevant for the younger group. This could suggest an interaction of the eye-tracking cognitive task; however, further studies in this field are needed to assess the relation between brain asymmetries and age.
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Affiliation(s)
- C Migliorelli
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - I Medina-Rivera
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Neurology Department, Neurometabolic Unit and Synaptic Metabolism Lab, Institut Pediàtric de Recerca, Hospital Sant Joan de Déu, metabERN and CIBERER-ISCIII, Barcelona, Spain
| | - A Bachiller
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - A Tost
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - J F Alonso
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - A López-Sala
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Neurology Department, Neurometabolic Unit and Synaptic Metabolism Lab, Institut Pediàtric de Recerca, Hospital Sant Joan de Déu, metabERN and CIBERER-ISCIII, Barcelona, Spain
| | - J Armstrong
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Molecular Genetics Medicine Section, Hospital Sant Joan de Déu, Barcelona, Spain
| | - M D M O'Callahan
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Neurology Department, Neurometabolic Unit and Synaptic Metabolism Lab, Institut Pediàtric de Recerca, Hospital Sant Joan de Déu, metabERN and CIBERER-ISCIII, Barcelona, Spain
| | - M Pineda
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - M A Mañanas
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - S Romero
- Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Á García-Cazorla
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- Neurology Department, Neurometabolic Unit and Synaptic Metabolism Lab, Institut Pediàtric de Recerca, Hospital Sant Joan de Déu, metabERN and CIBERER-ISCIII, Barcelona, Spain
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McCready FP, Gordillo-Sampedro S, Pradeepan K, Martinez-Trujillo J, Ellis J. Multielectrode Arrays for Functional Phenotyping of Neurons from Induced Pluripotent Stem Cell Models of Neurodevelopmental Disorders. BIOLOGY 2022; 11:biology11020316. [PMID: 35205182 PMCID: PMC8868577 DOI: 10.3390/biology11020316] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 12/11/2022]
Abstract
Simple Summary Multielectrode array technology allows researchers to record the spontaneous firing activity of cultured neurons over a period of multiple weeks or months. These data can be valuable for understanding how the functional relationships between neurons evolve as they begin to form connections and wire into a functional network. This technology has been adopted by researchers using stem cells to produce human neurons in culture to study neurodevelopmental disorders. However, the dizzying complexity and scale of the data generated have posed some challenges with the analysis and interpretation of experimental results. Here, we first provide historical context as to why multielectrode array platforms were originally developed, and use this perspective to explore some of the challenges currently facing the field. We then highlight new analysis methods, provide some guidance for improving the analysis of multielectrode array data, and discuss standardizing how these findings are communicated in scientific publications. Abstract In vitro multielectrode array (MEA) systems are increasingly used as higher-throughput platforms for functional phenotyping studies of neurons in induced pluripotent stem cell (iPSC) disease models. While MEA systems generate large amounts of spatiotemporal activity data from networks of iPSC-derived neurons, the downstream analysis and interpretation of such high-dimensional data often pose a significant challenge to researchers. In this review, we examine how MEA technology is currently deployed in iPSC modeling studies of neurodevelopmental disorders. We first highlight the strengths of in vitro MEA technology by reviewing the history of its development and the original scientific questions MEAs were intended to answer. Methods of generating patient iPSC-derived neurons and astrocytes for MEA co-cultures are summarized. We then discuss challenges associated with MEA data analysis in a disease modeling context, and present novel computational methods used to better interpret network phenotyping data. We end by suggesting best practices for presenting MEA data in research publications, and propose that the creation of a public MEA data repository to enable collaborative data sharing would be of great benefit to the iPSC disease modeling community.
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Affiliation(s)
- Fraser P. McCready
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; (F.P.M.); (S.G.-S.)
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Sara Gordillo-Sampedro
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; (F.P.M.); (S.G.-S.)
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Kartik Pradeepan
- Department of Physiology and Pharmacology, Department of Psychiatry, Robarts Research and Brain and Mind Institutes, Schulich School of Medicine and Dentistry, Western University, London, ON N6A 5B7, Canada; (K.P.); (J.M.-T.)
| | - Julio Martinez-Trujillo
- Department of Physiology and Pharmacology, Department of Psychiatry, Robarts Research and Brain and Mind Institutes, Schulich School of Medicine and Dentistry, Western University, London, ON N6A 5B7, Canada; (K.P.); (J.M.-T.)
| | - James Ellis
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; (F.P.M.); (S.G.-S.)
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
- Correspondence:
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Reduced System Complexity of Heart Rate Dynamics in Patients with Hyperthyroidism: A Multiscale Entropy Analysis. ENTROPY 2022; 24:e24020258. [PMID: 35205552 PMCID: PMC8871399 DOI: 10.3390/e24020258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/04/2022] [Accepted: 02/07/2022] [Indexed: 02/04/2023]
Abstract
Studying heart rate dynamics would help understand the effects caused by a hyperkinetic heart in patients with hyperthyroidism. By using a multiscale entropy (MSE) analysis of heart rate dynamics derived from one-channel electrocardiogram recording, we aimed to compare the system complexity of heart rate dynamics between hyperthyroid patients and control subjects. A decreased MSE complexity index (CI) computed from MSE analysis reflects reduced system complexity. Compared with the control subjects (n = 37), the hyperthyroid patients (n = 37) revealed a significant decrease (p < 0.001) in MSE CI (hyperthyroid patients 10.21 ± 0.37 versus control subjects 14.08 ± 0.21), sample entropy for each scale factor (from 1 to 9), and high frequency power (HF) as well as a significant increase (p < 0.001) in low frequency power (LF) in normalized units (LF%) and ratio of LF to HF (LF/HF). In conclusion, besides cardiac autonomic dysfunction, the system complexity of heart rate dynamics is reduced in hyperthyroidism. This finding implies that the adaptability of the heart rate regulating system is impaired in hyperthyroid patients. Additionally, it might explain the exercise intolerance experienced by hyperthyroid patients. In addition, hyperthyroid patients and control subjects could be distinguished by the MSE CI computed from MSE analysis of heart rate dynamics.
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Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031778. [PMID: 35162800 PMCID: PMC8835158 DOI: 10.3390/ijerph19031778] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/18/2022] [Accepted: 02/02/2022] [Indexed: 12/27/2022]
Abstract
Synchronization of the dynamic processes in structural networks connect the brain across a wide range of temporal and spatial scales, creating a dynamic and complex functional network. Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data. Few studies have focused on potential brain spatiotemporal dynamics in the early stages of depression to use as an early screening feature for depression. Thus, this study aimed to explore large-scale brain network dynamics of individuals both with and without subclinical depression, from the perspective of temporal and spatial dimensions and to input them as features into a machine learning framework for the automatic diagnosis of early-stage depression. To achieve this, spatio–temporal dynamics of rest-state EEG signals in female college students (n = 40) with and without (n = 38) subclinical depression were analyzed using EEG microstate and omega complexity analysis. Then, based on differential features of EEGs between the two groups, a support vector machine was utilized to compare performances of spatio–temporal features and single features in the classification of early depression. Microstate results showed that the occurrence rate of microstate class B was significantly higher in the group with subclinical depression when compared with the group without. Moreover, the duration and contribution of microstate class C in the subclinical group were both significantly lower than in the group without subclinical depression. Omega complexity results showed that the global omega complexity of β-2 and γ band was significantly lower for the subclinical depression group compared with the other group (p < 0.05). In addition, the anterior and posterior regional omega complexities were lower for the subclinical depression group compared to the comparison group in α-1, β-2 and γ bands. It was found that AUC of 81% for the differential indicators of EEG microstates and omega complexity was deemed better than a single index for predicting subclinical depression. Thus, since temporal and spatial complexity of EEG signals were manifestly altered in female college students with subclinical depression, it is possible that this characteristic could be adopted as an early auxiliary diagnostic indicator of depression.
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Alotaibi N, Bakheet D, Konn D, Vollmer B, Maharatna K. Cognitive Outcome Prediction in Infants With Neonatal Hypoxic-Ischemic Encephalopathy Based on Functional Connectivity and Complexity of the Electroencephalography Signal. Front Hum Neurosci 2022; 15:795006. [PMID: 35153702 PMCID: PMC8830486 DOI: 10.3389/fnhum.2021.795006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/10/2021] [Indexed: 12/03/2022] Open
Abstract
Impaired neurodevelopmental outcome, in particular cognitive impairment, after neonatal hypoxic-ischemic encephalopathy is a major concern for parents, clinicians, and society. This study aims to investigate the potential benefits of using advanced quantitative electroencephalography analysis (qEEG) for early prediction of cognitive outcomes, assessed here at 2 years of age. EEG data were recorded within the first week after birth from a cohort of twenty infants with neonatal hypoxic-ischemic encephalopathy (HIE). A proposed regression framework was based on two different sets of features, namely graph-theoretical features derived from the weighted phase-lag index (WPLI) and entropies metrics represented by sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn). Both sets of features were calculated within the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain. Correlation analysis showed a significant association in the delta band between the proposed features, graph attributes (radius, transitivity, global efficiency, and characteristic path length) and entropy features (Pen and SpEn) from the neonatal EEG data and the cognitive development at age two years. These features were used to train and test the tree ensemble (boosted and bagged) regression models. The highest prediction performance was reached to 14.27 root mean square error (RMSE), 12.07 mean absolute error (MAE), and 0.45 R-squared using the entropy features with a boosted tree regression model. Thus, the results demonstrate that the proposed qEEG features show the state of brain function at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate identifying those who might benefit from early targeted intervention.
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Affiliation(s)
- Noura Alotaibi
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
- Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia
| | - Dalal Bakheet
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
- Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia
| | - Daniel Konn
- Clinical Neurophysiology, University Hospital Southampton, Southampton, United Kingdom
| | - Brigitte Vollmer
- Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Paediatric Neurology, Southampton Children’s Hospital, Southampton, United Kingdom
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
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31
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Walter J. Consciousness as a multidimensional phenomenon: implications for the assessment of disorders of consciousness. Neurosci Conscious 2021; 2021:niab047. [PMID: 34992792 PMCID: PMC8716840 DOI: 10.1093/nc/niab047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 10/19/2021] [Accepted: 12/10/2021] [Indexed: 01/10/2023] Open
Abstract
Disorders of consciousness (DoCs) pose a significant clinical and ethical challenge because they allow for complex forms of conscious experience in patients where intentional behaviour and communication are highly limited or non-existent. There is a pressing need for brain-based assessments that can precisely and accurately characterize the conscious state of individual DoC patients. There has been an ongoing research effort to develop neural measures of consciousness. However, these measures are challenging to validate not only due to our lack of ground truth about consciousness in many DoC patients but also because there is an open ontological question about consciousness. There is a growing, well-supported view that consciousness is a multidimensional phenomenon that cannot be fully described in terms of the theoretical construct of hierarchical, easily ordered conscious levels. The multidimensional view of consciousness challenges the utility of levels-based neural measures in the context of DoC assessment. To examine how these measures may map onto consciousness as a multidimensional phenomenon, this article will investigate a range of studies where they have been applied in states other than DoC and where more is known about conscious experience. This comparative evidence suggests that measures of conscious level are more sensitive to some dimensions of consciousness than others and cannot be assumed to provide a straightforward hierarchical characterization of conscious states. Elevated levels of brain complexity, for example, are associated with conscious states characterized by a high degree of sensory richness and minimal attentional constraints, but are suboptimal for goal-directed behaviour and external responsiveness. Overall, this comparative analysis indicates that there are currently limitations to the use of these measures as tools to evaluate consciousness as a multidimensional phenomenon and that the relationship between these neural signatures and phenomenology requires closer scrutiny.
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Affiliation(s)
- Jasmine Walter
- Cognition and Philosophy Lab, 21 Chancellor’s Walk, Monash University, Melbourne, VIC 3800, Australia
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32
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Kang J, Zhang Z, Wan L, Casanova MF, Sokhadze EM, Li X. Effects of 1Hz repetitive transcranial magnetic stimulation on autism with intellectual disability: A pilot study. Comput Biol Med 2021; 141:105167. [PMID: 34959111 DOI: 10.1016/j.compbiomed.2021.105167] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 12/27/2022]
Abstract
OBJECTIVE To explore whether 1 Hz repetitive transcranial magnetic stimulation (rTMS) has positive effects on brain activity and behavior of autistic children with intellectual disability. METHODS 32 autistic children with intellectual disability (26 boys and 6 girls) were recruited to participate in this feasibility study. The autistic children were divided randomly and equally into an experimental group and a control group. 16 children (three girls and 13 boys; mean ± SD age: 7.8 ± 2.1 years) who received rTMS treatment twice a week were served as the experimental group, while 16 children (three girls and 13 boys; mean ± SD age: 7.2 ± 1.6 years) with sham stimulation were considered as the control group. Recurrence quantification analysis (RQA) was employed to quantify the nonlinear features of electroencephalogram (EEG) signals recorded during the resting state. Three RQA measures, including recursive rate (RR), deterministic (DET) and mean diagonal length (L) were extracted from the EEG signals to characterize the deterministic features of cortical activity. RESULTS Significant differences in RR and DET were observed between the experimental group and the control group. We also found discernible discrepancies in the Autism Behavior Checklist (ABC) score pre- and post-rTMS for the experimental group. CONCLUSIONS 1 Hz repetitive transcranial magnetic stimulation (rTMS) could positively influence brain activity and behavior of autistic children with intellectual disability.
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Affiliation(s)
- Jiannan Kang
- College of Electronic & Information Engineering, Hebei University, Baoding, China
| | - Zhiming Zhang
- College of Electronic & Information Engineering, Hebei University, Baoding, China
| | - Lingyan Wan
- College of Electronic & Information Engineering, Hebei University, Baoding, China
| | - Manuel F Casanova
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville Campus, Greenville Health System, Greenville, SC, USA
| | - Estate M Sokhadze
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville Campus, Greenville Health System, Greenville, SC, USA
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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McPartland JC, Lerner MD, Bhat A, Clarkson T, Jack A, Koohsari S, Matuskey D, McQuaid GA, Su WC, Trevisan DA. Looking Back at the Next 40 Years of ASD Neuroscience Research. J Autism Dev Disord 2021; 51:4333-4353. [PMID: 34043128 PMCID: PMC8542594 DOI: 10.1007/s10803-021-05095-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/14/2021] [Indexed: 12/18/2022]
Abstract
During the last 40 years, neuroscience has become one of the most central and most productive approaches to investigating autism. In this commentary, we assemble a group of established investigators and trainees to review key advances and anticipated developments in neuroscience research across five modalities most commonly employed in autism research: magnetic resonance imaging, functional near infrared spectroscopy, positron emission tomography, electroencephalography, and transcranial magnetic stimulation. Broadly, neuroscience research has provided important insights into brain systems involved in autism but not yet mechanistic understanding. Methodological advancements are expected to proffer deeper understanding of neural circuitry associated with function and dysfunction during the next 40 years.
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Affiliation(s)
| | - Matthew D Lerner
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - Anjana Bhat
- Department of Physical Therapy, University of Delaware, Newark, DE, USA
| | - Tessa Clarkson
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Allison Jack
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Sheida Koohsari
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - David Matuskey
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Goldie A McQuaid
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Wan-Chun Su
- Department of Physical Therapy, University of Delaware, Newark, DE, USA
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34
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Guerrero D, Rivera P, Febres G, Gershenson C. Towards a Measure for Characterizing the Informational Content of Audio Signals and the Relation between Complexity and Auditory Encoding. ENTROPY 2021; 23:e23121613. [PMID: 34945919 PMCID: PMC8700659 DOI: 10.3390/e23121613] [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: 10/07/2021] [Revised: 11/13/2021] [Accepted: 11/25/2021] [Indexed: 11/24/2022]
Abstract
The accurate description of a complex process should take into account not only the interacting elements involved but also the scale of the description. Therefore, there can not be a single measure for describing the associated complexity of a process nor a single metric applicable in all scenarios. This article introduces a framework based on multiscale entropy to characterize the complexity associated with the most identifiable characteristic of songs: the melody. We are particularly interested in measuring the complexity of popular songs and identifying levels of complexity that statistically explain the listeners’ preferences. We analyze the relationship between complexity and popularity using a database of popular songs and their relative position in a preferences ranking. There is a tendency toward a positive association between complexity and acceptance (success) of a song that is, however, not significant after adjusting for multiple testing.
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Affiliation(s)
- Daniel Guerrero
- Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
- Correspondence:
| | - Pedro Rivera
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico; (P.R.); (C.G.)
| | - Gerardo Febres
- Departamento de Procesos y Sistemas, Universidad Simón Bolívar, Sartenejas, Baruta, Miranda 1080, Venezuela;
| | - Carlos Gershenson
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico; (P.R.); (C.G.)
- Instituto de Investigaciones en Matemáticas Aplicadas y Sistemas, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
- Lakeside Labs GmbH, Lakeside Park B04, 9020 Klagenfurt am Wörthersee, Austria
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35
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Papaioannou A, Kalantzi E, Papageorgiou CC, Korombili K, Bokou A, Pehlivanidis A, Papageorgiou CC, Papaioannou G. Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment. Brain Sci 2021; 11:1531. [PMID: 34827530 PMCID: PMC8615740 DOI: 10.3390/brainsci11111531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 11/09/2021] [Accepted: 11/09/2021] [Indexed: 11/17/2022] Open
Abstract
We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle's valid and invalid syllogisms, and how these differences correlate with brain regions and behavioral data for each subject. We recorded EEGs from 14 scalp electrodes (channels) in 21 adults with ADHD, 21 with ASD, and 21 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), Aristotle's two types of syllogism mentioned above. A set of 39 questions were given to participants related to valid-invalid syllogisms as well as a separate set of questionnaires, in order to collect a number of demographic and behavioral data, with the aim of detecting shared information with values of a feature extracted from EEG, the multiscale entropy (MSE), in the 14 channels ('brain regions'). MSE, a nonlinear information-theoretic measure of complexity, was computed to extract a feature that quantifies the complexity of the EEG. Behavior-Partial Least Squares Correlation, PLSC, is the method to detect the correlation between two sets of data, brain, and behavioral measures. -PLSC, a variant of PLSC, was applied to build a functional connectivity of the brain regions involved in the reasoning tasks. Graph-theoretic measures were used to quantify the complexity of the functional networks. Based on the results of the analysis described in this work, a mixed 14 × 2 × 3 ANOVA showed significant main effects of group factor and brain region* syllogism factor, as well as a significant brain region* group interaction. There are significant differences between the means of MSE (complexity) values at the 14 channels of the members of the 'pathological' groups of participants, i.e., between ASD and ADHD, while the difference in means of MSE between both ASD and ADHD and that of the control group is not significant. In conclusion, the valid-invalid type of syllogism generates significantly different complexity values, MSE, between ASD and ADHD. The complexity of activated brain regions of ASD participants increased significantly when switching from a valid to an invalid syllogism, indicating the need for more resources to 'face' the task escalating difficulty in ASD subjects. This increase is not so evident in both ADHD and control. Statistically significant differences were found also in the behavioral response of ASD and ADHD, compared with those of control subjects, based on the principal brain and behavior saliences extracted by PLSC. Specifically, two behavioral measures, the emotional state and the degree of confidence of participants in answering questions in Aristotle's valid-invalid syllogisms, and one demographic variable, age, statistically and significantly discriminate the three groups' ASD. The seed-PLC generated functional connectivity networks for ASD, ADHD, and control, were 'projected' on the regions of the Default Mode Network (DMN), the 'reference' connectivity, of which the structural changes were found significant in distinguishing the three groups. The contribution of this work lies in the examination of the relationship between brain activity and behavioral responses of healthy and 'pathological' participants in the case of cognitive reasoning of the type of Aristotle's valid and invalid syllogisms, using PLSC, a machine learning approach combined with MSE, a nonlinear method of extracting a feature based on EEGs that captures a broad spectrum of EEGs linear and nonlinear characteristics. The results seem promising in adopting this type of reasoning, in the future, after further enhancements and experimental tests, as a supplementary instrument towards examining the differences in brain activity and behavioral responses of ASD and ADHD patients. The application of the combination of these two methods, after further elaboration and testing as new and complementary to the existing ones, may be considered as a tool of analysis in helping detecting more effectively such types of disorders.
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Affiliation(s)
- Anastasia Papaioannou
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
- Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS” (UMHRI), University Mental Health, Papagou, 15601 Athens, Greece
| | - Eva Kalantzi
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
| | | | - Kalliopi Korombili
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
| | - Anastasia Bokou
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
| | - Artemios Pehlivanidis
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
| | - Charalabos C. Papageorgiou
- 1st Department of Psychiatry, Eginition Hospital, Medical School, National University of Athens, 11528 Athens, Greece; (E.K.); (K.K.); (A.B.); (A.P.); (C.C.P.)
- Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS” (UMHRI), University Mental Health, Papagou, 15601 Athens, Greece
| | - George Papaioannou
- Center for Research of Nonlinear Systems (CRANS), Department of Mathematics, University of Patras, 26500 Patra, Greece;
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Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:1834123. [PMID: 34745491 PMCID: PMC8566056 DOI: 10.1155/2021/1834123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/20/2021] [Accepted: 09/28/2021] [Indexed: 11/17/2022]
Abstract
The bottleneck associated with the validation of the parameters of the entropy model has limited the application of this model to modern functional imaging technologies such as the resting-state functional magnetic resonance imaging (rfMRI). In this study, an optimization algorithm that could choose the parameters of the multiscale entropy (MSE) model was developed, while the optimized effectiveness for localizing the epileptogenic hemisphere was validated through the classification rate with a supervised machine learning method. The rfMRI data of 20 mesial temporal lobe epilepsy patients with positive indicators (the indicators of epileptogenic hemisphere in clinic) in the hippocampal formation on either left or right hemisphere (equally divided into two groups) on the structural MRI were collected and preprocessed. Then, three parameters in the MSE model were statistically optimized by both receiver operating characteristic (ROC) curve and the area under the ROC curve value in the sensitivity analysis, and the intergroup significance of optimized entropy values was utilized to confirm the biomarked brain areas sensitive to the epileptogenic hemisphere. Finally, the optimized entropy values of these biomarked brain areas were regarded as the feature vectors input for a support vector machine to classify the epileptogenic hemisphere, and the classification effectiveness was cross-validated. Nine biomarked brain areas were confirmed by the optimized entropy values, including medial superior frontal gyrus and superior parietal gyrus (p < .01). The mean classification accuracy was greater than 90%. It can be concluded that combination of the optimized MSE model with the machine learning model can accurately confirm the epileptogenic hemisphere by rfMRI. With the powerful information interaction capabilities of 5G communication, the epilepsy side-fixing algorithm that requires computing power can be integrated into a cloud platform. The demand side only needs to upload patient data to the service platform to realize the preoperative assessment of epilepsy.
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Papaioannou AG, Kalantzi E, Papageorgiou CC, Korombili K, Βokou A, Pehlivanidis A, Papageorgiou CC, Papaioannou G. Complexity analysis of the brain activity in Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) due to cognitive loads/demands induced by Aristotle's type of syllogism/reasoning. A Power Spectral Density and multiscale entropy (MSE) analysis. Heliyon 2021; 7:e07984. [PMID: 34611558 PMCID: PMC8477216 DOI: 10.1016/j.heliyon.2021.e07984] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/13/2021] [Accepted: 09/08/2021] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders), ADHD (attention-deficit/hyperactivity disorder), compared with healthy subjects during the performance of an innovative cognitive task: Aristotle's valid and invalid syllogisms. We follow the Neuroanatomical differences type of criterion in assessing the results of our study in supporting or not the dual-process theory of Kahneman, 2011) (Systems I & II of thinking). METHOD We recorded EEGs from 14 scalp electrodes in 30 adults with ADHD, 30 with ASD and 24 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), the Aristotle's four types of syllogism mentioned above. The multiscale entropy (MSE), a nonlinear information-theoretic measure or tool was computed to extract features that quantify the complexity of the EEG. RESULTS The dynamics of the curves of the grand average of MSE values of the ADHD and ASD participants was significantly in higher levels for the majority of time scales, than the healthy subjects over a number of brain regions (electrodes locations), during the performance of both valid and invalid types of syllogism. This result is seemingly not in accordance of the broadly accepted 'theory' of complexity loss in 'pathological' subjects, but actually this is not the case as explained in the text. ADHD subjects are engaged in System II of thinking, for both Valid and Invalid syllogism, ASD and Control in System I for valid and invalid syllogism, respectively. A surprising and 'provocative' result of this paper, as shown in the next sections, is that the Complexity-variability of ASD and ADHD subjects, when they face Aristotle's types of syllogisms, is higher than that of the control subjects. An explanation is suggested as described in the text. Also, in the case of invalid type of Aristotelian syllogisms, the linguistic and visuo-spatial systems are both engaged ONLY in the temporal and occipital regions of the brain, respectively, of ADHD subjects. In the case of valid type, both above systems are engaged in the temporal and occipital regions of the brain, respectively, of both ASD and ADHD subjects, while in the control subjects only the visuo-spatial type is engaged (Goel et al., 2000; Knauff, 2007). CONCLUSION Based on the results of the analysis described in this work, the differences in the EEG complexity between the three groups of participants lead to the conclusion that cortical information processing is changed in ASD and ADHD adults, therefore their level of cortical activation may be insufficient to meet the peculiar cognitive demand of Aristotle's reasoning. SIGNIFICANCE The present paper suggest that MSE, is a powerful and efficient nonlinear measure in detecting neural dysfunctions in adults with ASD and ADHD characteristics, when they are called on to perform in a very demanding as well as innovative set of cognitive tasks, that can be considered as a new diagnostic 'benchmark' in helping detecting more effectively such type of disorders. A linear measure alone, as the typical PSD, is not capable in making such a distinction. The work contributes in shedding light on the neural mechanisms of syllogism/reasoning of Aristotelian type, as well as toward understanding how humans reason logically and why 'pathological' subjects deviate from the norms of formal logic.
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Affiliation(s)
- Anastasia G. Papaioannou
- 1 Department of Psychiatry, National University of Athens, Medical School, Eginition Hospital, Athens, Greece
- University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, (UMHRI), Athens, Greece
| | - Eva Kalantzi
- 1 Department of Psychiatry, National University of Athens, Medical School, Eginition Hospital, Athens, Greece
| | | | - Kalliopi Korombili
- 1 Department of Psychiatry, National University of Athens, Medical School, Eginition Hospital, Athens, Greece
| | - Anastasia Βokou
- 1 Department of Psychiatry, National University of Athens, Medical School, Eginition Hospital, Athens, Greece
| | - Artemios Pehlivanidis
- 1 Department of Psychiatry, National University of Athens, Medical School, Eginition Hospital, Athens, Greece
| | - Charalabos C. Papageorgiou
- 1 Department of Psychiatry, National University of Athens, Medical School, Eginition Hospital, Athens, Greece
- University Mental Health, Neurosciences and Precision Medicine Research Institute “COSTAS STEFANIS”, (UMHRI), Athens, Greece
| | - George Papaioannou
- Center for Research of Nonlinear Systems (CRANS), Department of Mathematics, University of Patras, Patra, Greece
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Abstract
Various walking speeds may induce different responses on the plantar pressure patterns. Current methods used to analyze plantar pressure patterns are linear and ignore nonlinear features. The purpose of this study was to analyze the complexity of plantar pressure images after walking at various speeds using nonlinear bidimensional multiscale entropy (MSE2D). Twelve participants (age: 27.1 ± 5.8 years; height: 170.3 ± 10.0 cm; and weight: 63.5 ± 13.5 kg) were recruited for walking at three speeds (slow at 1.8 mph, moderate at 3.6 mph, and fast at 5.4 mph) for 20 minutes. A plantar pressure measurement system was used to measure plantar pressure patterns. Complexity index (CI), a summation of MSE2D from all time scales, was used to quantify the changes of complexity of plantar pressure images. The analysis of variance with repeated measures and Fisher’s least significant difference correction were used to examine the results of this study. The results showed that CI of plantar pressure images of 1.8 mph (1.780) was significantly lower compared with 3.6 (1.790) and 5.4 mph (1.792). The results also showed that CI significantly increased from the 1st min (1.780) to the 10th min (1.791) and 20th min (1.791) with slow walking (1.8 mph). Our results indicate that slow walking at 1.8 mph may not be good for postural control compared with moderate walking (3.6 mph) and fast walking (5.4 mph). This study demonstrates that bidimensional multiscale entropy is able to quantify complexity changes of plantar pressure images after different walking speeds.
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Zhang T, Huang W, Wu X, Sun W, Lin F, Sun H, Li J. Altered complexity in resting-state fNIRS signal in autism: a multiscale entropy approach. Physiol Meas 2021; 42. [PMID: 34315139 DOI: 10.1088/1361-6579/ac184d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 07/27/2021] [Indexed: 11/12/2022]
Abstract
Objective.Feature extraction and recognition in brain signal processing is of great significance for understanding the neurological mechanism of autism spectrum disorder (ASD). Resting-state (RS) functional near-infrared spectroscopy measurement provides a way to investigate the possible alteration in ASD-related complexity of resting-state (RS) functional near-infrared spectroscopy (fNIRS) signals and to explore the relationship between brain functional connectivity and complexity.Approach.Using the multiscale entropy (MSE) of fNIRS signals recorded from the bilateral temporal lobes (TLs) on 25 children with ASD and 22 typical development (TD) children, the pattern of brain complexity was assessed for both the ASD and TD groups.Main results.The quantitative analysis of MSE revealed the increased complexity in RS-fNIRS in children with ASD, particularly in the left temporal lobe. The complexity in the RS signal and resting state functional connectivity (RSFC) were also observed to exhibit negative correlation in the medium magnitude.Significance.These results indicated that the MSE might serve as a novel measure for RS-fNIRS signals in characterizing and understanding ASD.
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Affiliation(s)
- Tingzhen Zhang
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Wen Huang
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Xiaoyin Wu
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Weiting Sun
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Fang Lin
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Huiwen Sun
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Jun Li
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China.,Key Lab for Behavioral Economic Science & Technology, South China Normal University, Guangzhou 510006, People's Republic of China
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Sho’ouri N. Predicting the success rate of healthy participants in beta neurofeedback: Determining the factors affecting the success rate of individuals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102753] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Accurate assessment of low-function autistic children based on EEG feature fusion. J Clin Neurosci 2021; 90:351-358. [PMID: 34275574 DOI: 10.1016/j.jocn.2021.06.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 05/10/2021] [Accepted: 06/14/2021] [Indexed: 11/22/2022]
Abstract
Autism spectrum disorder (ASD) is a very serious neurodevelopmental disorder and diagnosis mainly depends on the clinical scale, which has a certain degree of subjectivity. It is necessary to make accurate evaluation by objective indicators. In this study, we enrolled 96 children aged from 3 to 6 years: 48 low-function autistic children (38 males and 10 females; mean±SD age: 4.9±1.1 years) and 48 typically developing (TD) children (38 males and 10 females; mean±SD age: 4.9 ± 1.2 years) to participate in our experiment. We investigated to fuse multi-features (entropy, relative power, coherence and bicoherence) to distinguish low-function autistic children and TD children accurately. Minimum redundancy maximum correlation algorithm was used to choose the features and support vector machine was used for classification. Ten-fold cross validation was used to test the accuracy of the model. Better classification result was obtained. We tried to provide a reliable basis for clinical evaluation and diagnosis for ASD.
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Measuring the effects of sleep on epileptogenicity with multifrequency entropy. Clin Neurophysiol 2021; 132:2012-2018. [PMID: 34284235 DOI: 10.1016/j.clinph.2021.06.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 06/03/2021] [Accepted: 06/06/2021] [Indexed: 01/23/2023]
Abstract
OBJECTIVE We demonstrate that multifrequency entropy gives insight into the relationship between epileptogenicity and sleep, and forms the basis for an improved measure of medical assessment of sleep impairment in epilepsy patients. METHODS Multifrequency entropy was computed from electroencephalography measurements taken from 31 children with Benign Epilepsy with Centrotemporal Spikes and 31 non-epileptic controls while awake and during sleep. Values were compared in the epileptic zone and away from the epileptic zone in various sleep stages. RESULTS We find that (I) in lower frequencies, multifrequency entropy decreases during non-rapid eye movement sleep stages when compared with wakefulness in a general population of pediatric patients, (II) patients with Benign Epilepsy with Centrotemporal Spikes had lower multifrequency entropy across stages of sleep and wakefulness, and (III) the epileptic regions of the brain exhibit lower multifrequency entropy patterns than the rest of the brain in epilepsy patients. CONCLUSIONS Our results show that multifrequency entropy decreases during sleep, particularly sleep stage 2, confirming, in a pediatric population, an association between sleep, lower multifrequency entropy, and increased likelihood of seizure. SIGNIFICANCE We observed a correlation between lowered multifrequency entropy and increased epileptogenicity that lays preliminary groundwork for the detection of a digital biomarker for epileptogenicity.
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Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony. ENTROPY 2021; 23:e23050617. [PMID: 34065692 PMCID: PMC8155885 DOI: 10.3390/e23050617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/11/2021] [Accepted: 05/11/2021] [Indexed: 12/02/2022]
Abstract
The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar. This research aims to introduce a processing system that can systematically identify the relationship between implicit learning events and their Encephalogram (EEG) signal characteristics. This study converted the EEG signal from participants while performing cognitive task experiments into Multiscale Entropy (MSE) data. Using MSE data from different frequency bands and channels as features, the system explored a wide range of classifiers and observed their performance to see how they classified the features related to participants’ performance. The Artificial Bee Colony (ABC) method was used for feature selection to improve the process to make the system more efficient. The results showed that the system could correctly identify the differences between participants’ performance using MSE data and the ABC method with 95% confidence.
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Himes BJ, Blotter JD, Kay DB, Steffensen SC, Feland JB, Bills KB, Mcpherson DL, Manwaring K, Lafont-Tanner D, Layton B. The Effect of Beat Frequency Vibration on Sleep Latency and Neural Complexity: A Pilot Study. IEEE Trans Neural Syst Rehabil Eng 2021; 29:872-883. [PMID: 33950842 DOI: 10.1109/tnsre.2021.3076983] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Insomnia affects millions of people worldwide, and non-pharmacological treatment options are limited. A bed excited with multiple vibration sources was used to explore beat frequency vibration (BFV) as a non-pharmacological treatment for insomnia. A repeated measures design pilot study of 14 participants with mild-moderate insomnia symptom severity (self-reported on the Insomnia Severity Index) was conducted to determine the effects of BFV, and traditional standing wave vibration (SWV) on sleep latency and sleep electrocortical activity. Participants were monitored using high-density electroencephalography (HD-EEG). Sleep latency was compared between treatment conditions. A trend of decreasing sleep latency due to BFV was found for unequivocal sleep latency (p ≤ 0.068). Neural complexity during wake, N1, and N2 stages were compared using Multi-Scale Sample Entropy (MSE), which demonstrated significantly lower MSE between wake and N2 stages (p ≤ 0.002). During N2 sleep, BFV showed lower MSE than the control session in the left frontoparietal region. As a measure of information integration, reduced entropy may indicate that BFV decreases conscious awareness during deeper stages of sleep. SWV caused reduced alpha activity and increased delta activity during wake. BFV caused increased delta activity during N2 sleep. These preliminary results suggest that BFV may help decrease sleep latency, reduce conscious awareness, and increase sleep drive expression during deeper stages of sleep. SWV may be beneficial for decreasing expression of arousal and increasing expression of sleep drive during wake, implying that beat frequency vibration may be beneficial to sleep.
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Kim B, Im HI. Chronic nicotine impairs sparse motor learning via striatal fast-spiking parvalbumin interneurons. Addict Biol 2021; 26:e12956. [PMID: 32767546 PMCID: PMC8243919 DOI: 10.1111/adb.12956] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 07/09/2020] [Accepted: 07/21/2020] [Indexed: 01/06/2023]
Abstract
Nicotine can diversely affect neural activity and motor learning in animals. However, the impact of chronic nicotine on striatal activity in vivo and motor learning at long-term sparse timescale remains unknown. Here, we demonstrate that chronic nicotine persistently suppresses the activity of striatal fast-spiking parvalbumin interneurons, which mediate nicotine-induced deficit in sparse motor learning. Six weeks of longitudinal in vivo single-unit recording revealed that mice show reduced activity of fast-spiking interneurons in the dorsal striatum during chronic nicotine exposure and withdrawal. The reduced firing of fast-spiking interneurons was accompanied by spike broadening, diminished striatal delta oscillation power, and reduced sample entropy in local field potential. In addition, chronic nicotine withdrawal impaired motor learning with a weekly sparse training regimen but did not affect general locomotion and anxiety-like behavior. Lastly, the excitatory DREADD hM3Dq-mediated activation of striatal fast-spiking parvalbumin interneurons reversed the chronic nicotine withdrawal-induced deficit in sparse motor learning. Taken together, we identified that chronic nicotine withdrawal impairs sparse motor learning via disruption of activity in striatal fast-spiking parvalbumin interneurons. These findings suggest that sparse motor learning paradigm can reveal the subtle effect of nicotine withdrawal on motor function and that striatal fast-spiking parvalbumin interneurons are a neural substrate of nicotine's effect on motor learning.
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Affiliation(s)
- Baeksun Kim
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia (DTC), Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology (UST), Seoul, Republic of Korea
| | - Heh-In Im
- Convergence Research Center for Diagnosis, Treatment and Care System of Dementia (DTC), Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
- Division of Bio-Medical Science and Technology, KIST School, Korea University of Science and Technology (UST), Seoul, Republic of Korea
- Center for Neuroscience, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea
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Shafer RL, Lewis MH, Newell KM, Bodfish JW. Atypical neural processing during the execution of complex sensorimotor behavior in autism. Behav Brain Res 2021; 409:113337. [PMID: 33933522 PMCID: PMC8188828 DOI: 10.1016/j.bbr.2021.113337] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 04/02/2021] [Accepted: 04/27/2021] [Indexed: 11/17/2022]
Abstract
Stereotyped behavior is rhythmic, repetitive movement that is essentially invariant in form. Stereotypy is common in several clinical disorders, such as autism spectrum disorders (ASD), where it is considered maladaptive. However, it also occurs early in typical development (TD) where it is hypothesized to serve as the foundation on which complex, adaptive motor behavior develops. This transition from stereotyped to complex movement in TD is thought to be supported by sensorimotor integration. Stereotypy in clinical disorders may persist due to deficits in sensorimotor integration. The present study assessed whether differences in sensorimotor processing may limit the expression of complex motor behavior in individuals with ASD and contribute to the clinical stereotypy observed in this population. Adult participants with ASD and TD performed a computer-based stimulus-tracking task in the presence and absence of visual feedback. Electroencephalography was recorded during the task. Groups were compared on motor performance (root mean square error), motor complexity (sample entropy), and neural complexity (multiscale sample entropy of the electroencephalography signal) in the presence and absence of visual feedback. No group differences were found for motor performance or motor complexity. The ASD group demonstrated greater neural complexity and greater differences between feedback conditions than TD individuals, specifically in signals relevant to sensorimotor processing. Motor performance and motor complexity correlated with clinical stereotypy in the ASD group. These findings support the hypothesis that individuals with ASD have differences in sensorimotor processing when executing complex motor behavior and that stereotypy is associated with low motor complexity.
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Affiliation(s)
- Robin L Shafer
- Vanderbilt Brain Institute, Vanderbilt University, 6133 Medical Research Building III, 465 21(st) Avenue South, Nashville, TN, 37232, USA.
| | - Mark H Lewis
- Department of Psychiatry, University of Florida College of Medicine, PO Box 100256, L4-100 McKnight Brain Institute, 1149 Newell Drive, Gainesville, FL, 3261, USA.
| | - Karl M Newell
- Department of Kinesiology, University of Georgia, G3 Aderhold Hall, 110 Carlton Street, Athens, GA, 30602, USA.
| | - James W Bodfish
- Vanderbilt Brain Institute, Vanderbilt University, 6133 Medical Research Building III, 465 21(st) Avenue South, Nashville, TN, 37232, USA; Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, 8310 Medical Center East, 1215 21(st) Avenue South, Nashville, TN, 37232, USA.
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Linear and nonlinear analysis of intrinsic mode function after facial stimuli presentation in children with autism spectrum disorder. Comput Biol Med 2021; 133:104376. [PMID: 33866255 DOI: 10.1016/j.compbiomed.2021.104376] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 03/31/2021] [Accepted: 04/01/2021] [Indexed: 11/23/2022]
Abstract
In this work, a method for classifying Autism Spectrum Disorders (ASD) from typically developing (TD) children is presented using the linear and nonlinear Event-Related Potential (ERP) analysis of the Electro-encephalogram (EEG) signals. The signals were acquired during the presentation of three types of face expression stimuli -happy, fearful and neutral faces. EEGs are first decomposed using the Multivariate Empirical Mode Decomposition (MEMD) method to extract its Intrinsic Mode Functions (IMFs), which provide information about the underlying activities of ERP components. The nonlinear sample entropy (SampEn) features, as well as the standard linear measurements utilizing maximum (Max.), minimum (Min), and standard deviation (Std.), are then extracted from each set of IMFs. These features are then evaluated by the statistical analysis tests and used to construct the input vectors for the Discriminant analysis (DA), Support vector machine (SVM), and k-Nearest Neighbors (kNN) classifiers. Experimental results show that the proposed features can differentiate the ASD and TD children using the happy stimulus dataset with high classification performance for all classifiers that reached 100% accuracy. This result suggests a general deficit in recognizing the positive expression in ASD children. Additionally, we found that the SampEn measurements computed from the alpha and theta bands and the linear features extracted from the delta band can be considered biomarkers for disturbances in Emotional Facial Expression (EFE) processing in ASD children.
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Al-Jawahiri R, Jones M, Milne E. Spontaneous neural activity relates to psychiatric traits in 16p11.2 CNV carriers: An analysis of EEG spectral power and multiscale entropy. J Psychiatr Res 2021; 136:610-618. [PMID: 33158556 DOI: 10.1016/j.jpsychires.2020.10.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 10/22/2020] [Accepted: 10/26/2020] [Indexed: 11/28/2022]
Abstract
Copy number variations (CNV) at the 16p11.2 chromosomal region are rare high-risk CNVs associated with various clinical features and psychiatric disorders including intellectual disability, developmental delays, and autism spectrum disorder. No study to date has investigated whether spontaneous neural activity is altered for 16p11.2 CNV carriers and whether this relates to psychiatric traits. The aim of this study is to examine the impact of 16p11.2 deletions (del) and duplications (dup) on spontaneous neural activity and its relationship to psychiatric problems. EEG was previously collected as part of the Simons Searchlight initiative. Using spectral power (delta, theta, alpha, and beta frequency bands), complexity index (CI), and multiscale entropy analysis techniques, we analyzed frontal resting-state EEG data collected from 22 16p11.2 del carriers, 14 dup carriers, and 13 controls. We then examined associations between neural activity and psychiatric traits, measured with the Child Behavior Checklist. Results indicated that EEG entropy was higher for del and dup compared to controls, respectively, at all timescales. CI was also higher for del and dup compared to controls. Theta power of 16p11.2 dup carriers was higher than controls. A strong association was found between entropy at higher timescales and anxiety problems. In addition, a strong correlation was found between theta power and pervasive developmental problems. Atypical spontaneous neural activity is implicated in 16p11.2 CNVs. With higher entropy or theta power, psychiatric traits increase in severity. Our findings provide evidence of the link between genotype, neural activity, and phenotypes in 16p11.2 CNVs.
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Affiliation(s)
- Reem Al-Jawahiri
- Department of Psychology, University of Sheffield, United Kingdom.
| | - Myles Jones
- Department of Psychology, University of Sheffield, United Kingdom
| | - Elizabeth Milne
- Department of Psychology, University of Sheffield, United Kingdom
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van Noordt S, Willoughby T. Cortical maturation from childhood to adolescence is reflected in resting state EEG signal complexity. Dev Cogn Neurosci 2021; 48:100945. [PMID: 33831821 PMCID: PMC8027532 DOI: 10.1016/j.dcn.2021.100945] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 02/09/2021] [Accepted: 03/21/2021] [Indexed: 11/18/2022] Open
Abstract
Endogenous cortical fluctuations captured by electroencephalograms (EEGs) reflect activity in large-scale brain networks that exhibit dynamic patterns over multiple time scales. Developmental changes in the coordination and integration of brain function leads to greater complexity in population level neural dynamics. In this study we examined multiscale entropy, a measure of signal complexity, in resting-state EEGs in a large (N = 405) cross-sectional sample of children and adolescents (9–16 years). Our findings showed consistent age-dependent increases in EEG complexity that are distributed across multiple temporal scales and spatial regions. Developmental changes were most robust as the age gap between groups increased, particularly between late childhood and adolescence, and were most prominent over fronto-central scalp regions. These results suggest that the transition from late childhood to adolescence is characterized by age-dependent changes in the underlying complexity of endogenous brain networks.
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Affiliation(s)
- Stefon van Noordt
- Azrieli Centre for Autism Research, Montreal Neurological Institute and Hospital, McGill University, Montréal, Canada; Department of Psychology, Brock University, St. Catharines, Ontario, Canada.
| | - Teena Willoughby
- Department of Psychology, Brock University, St. Catharines, Ontario, Canada
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Goldsworthy MR, Hordacre B, Rothwell JC, Ridding MC. Effects of rTMS on the brain: is there value in variability? Cortex 2021; 139:43-59. [PMID: 33827037 DOI: 10.1016/j.cortex.2021.02.024] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 02/16/2021] [Accepted: 02/26/2021] [Indexed: 01/02/2023]
Abstract
The ability of repetitive transcranial magnetic stimulation (rTMS) to non-invasively induce neuroplasticity in the human cortex has opened exciting possibilities for its application in both basic and clinical research. Changes in the amplitude of motor evoked potentials (MEPs) elicited by single-pulse transcranial magnetic stimulation has so far provided a convenient model for exploring the neurophysiology of rTMS effects on the brain, influencing the ways in which these stimulation protocols have been applied therapeutically. However, a growing number of studies have reported large inter-individual variability in the mean MEP response to rTMS, raising legitimate questions about the usefulness of this model for guiding therapy. Although the increasing application of different neuroimaging approaches has made it possible to probe rTMS-induced neuroplasticity outside the motor cortex to measure changes in neural activity that impact other aspects of human behaviour, the high variability of rTMS effects on these measurements remains an important issue for the field to address. In this review, we seek to move away from the conventional facilitation/inhibition dichotomy that permeates much of the rTMS literature, presenting a non-standard approach for measuring rTMS-induced neuroplasticity. We consider the evidence that rTMS is able to modulate an individual's moment-to-moment variability of neural activity, and whether this could have implications for guiding the therapeutic application of rTMS.
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Affiliation(s)
- Mitchell R Goldsworthy
- Lifespan Human Neurophysiology Group, Adelaide Medical School, University of Adelaide, Adelaide, Australia; Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, Australia; Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, Australia.
| | - Brenton Hordacre
- Innovation, IMPlementation and Clinical Translation (IIMPACT) in Health, University of South Australia, Adelaide, Australia
| | - John C Rothwell
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Michael C Ridding
- Innovation, IMPlementation and Clinical Translation (IIMPACT) in Health, University of South Australia, Adelaide, Australia
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