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Nucci L, Miraglia F, Pappalettera C, Rossini PM, Vecchio F. Exploring the complexity of EEG patterns in Parkinson's disease. GeroScience 2024:10.1007/s11357-024-01277-y. [PMID: 38997574 DOI: 10.1007/s11357-024-01277-y] [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: 11/15/2023] [Accepted: 07/02/2024] [Indexed: 07/14/2024] Open
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20 Hz) and beta 2 (20-30 Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.
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Affiliation(s)
- Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy.
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
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2
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Ahire N, Awale RN, Wagh A. Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning. APPLIED NEUROPSYCHOLOGY. ADULT 2023:1-12. [PMID: 37647332 DOI: 10.1080/23279095.2023.2247702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
"Attention-Deficit Hyperactivity Disorder (ADHD)" is a neuro-developmental disorder in children under 12 years old. Learning deficits, anxiety, depression, sensory processing disorder, and oppositional defiant disorder are the most frequent comorbidities of ADHD. This research focuses on ADHD in children, considering its common occurrence and frequent coexistence with other mental disorders. The study utilizes the resting-state open-eye "Electroencephalogram" (EEG) signals of 61 children with ADHD and 60 healthy children. Morphological and "Power Spectral Density" (PSD) features associated with ADHD are analysed and "Principal Component Analysis" (PCA) is employed to reduce data dimensionality. Classification algorithms including AdaBoost, "K-Nearest Neighbour" (KNN) classifier, Naive Bayes, and random forest are utilized, with the Bernoulli Naive Bayes classifier achieving the highest accuracy of 96%. This study found some relevant characteristics for classification at the frontal (F), central (C), and parietal (P) electrode placement sites. Finally, this reveals distinct EEG patterns in children with ADHD and the study provides a potential supplementary method for ADHD diagnosis.
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Affiliation(s)
- Nitin Ahire
- Department of Electronics and Telecommunication, Xavier Institute of Engineering, Mumbai, India
| | - R N Awale
- Department of Electrical Engineering, VJTI, Mumbai, India
| | - Abhay Wagh
- Department of Technical Education, VJTI, Mumbai, India
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3
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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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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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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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Çetin FH, Barış Usta M, Aydın S, Güven AS. A Case Study on EEG Analysis: Embedding Entropy Estimations Indicate the Decreased Neuro-Cortical Complexity Levels Mediated by Methylphenidate Treatment in Children with ADHD. Clin EEG Neurosci 2022; 53:406-417. [PMID: 34923863 DOI: 10.1177/15500594211064008] [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] [Indexed: 11/17/2022]
Abstract
Objective: Complexity analysis is a method employed to understand the activity of the brain. The effect of methylphenidate (MPH) treatment on neuro-cortical complexity changes is still unknown. This study aimed to reveal how MPH treatment affects the brain complexity of children with attention deficit hyperactivity disorder (ADHD) using entropy-based quantitative EEG analysis. Three embedding entropy approaches were applied to short segments of both pre- and post- medication EEG series. EEG signals were recorded for 25 boys with combined type ADHD prior to the administration of MPH and at the end of the first month of the treatment. Results: In comparison to Approximate Entropy (ApEn) and Sample Entropy (SampEn), Permutation Entropy (PermEn) provided the most sensitive estimations in investigating the impact of MPH treatment. In detail, the considerable decrease in EEG complexity levels were observed at six cortical regions (F3, F4, P4, T3, T6, O2) with statistically significant level (p < .05). As well, PermEn provided the most meaningful associations at central lobes as follows: 1) The largeness of EEG complexity levels was moderately related to the severity of ADHD symptom detected at pre-treatment stage. 2) The percentage change in the severity of opposition as the symptom cluster was moderately reduced by the change in entropy. Conclusion: A significant decrease in entropy levels in the frontal region was detected in boys with combined type ADHD undergoing MPH treatment at resting-state mode. The changes in entropy correlated with pre-treatment general symptom severity of ADHD and conduct disorder symptom cluster severity.
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Affiliation(s)
| | | | - Serap Aydın
- 37515Hacettepe University, Sıhhiye, Ankara, Turkey
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7
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Cortical auditory evoked potentials, brain signal variability and cognition as biomarkers to detect the presence of chronic tinnitus. Hear Res 2022; 420:108489. [DOI: 10.1016/j.heares.2022.108489] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/03/2022] [Accepted: 03/19/2022] [Indexed: 12/31/2022]
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8
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Arpaia P, Covino A, Cristaldi L, Frosolone M, Gargiulo L, Mancino F, Mantile F, Moccaldi N. A Systematic Review on Feature Extraction in Electroencephalography-Based Diagnostics and Therapy in Attention Deficit Hyperactivity Disorder. SENSORS 2022; 22:s22134934. [PMID: 35808424 PMCID: PMC9269717 DOI: 10.3390/s22134934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 02/01/2023]
Abstract
A systematic review on electroencephalographic (EEG)-based feature extraction strategies to diagnosis and therapy of attention deficit hyperactivity disorder (ADHD) in children is presented. The analysis is realized at an executive function level to improve the research of neurocorrelates of heterogeneous disorders such as ADHD. The Quality Assessment Tool for Quantitative Studies (QATQS) and field-weighted citation impact metric (Scopus) were used to assess the methodological rigor of the studies and their impact on the scientific community, respectively. One hundred and one articles, concerning the diagnostics and therapy of ADHD children aged from 8 to 14, were collected. Event-related potential components were mainly exploited for executive functions related to the cluster inhibition, whereas band power spectral density is the most considered EEG feature for executive functions related to the cluster working memory. This review identifies the most used (also by rigorous and relevant articles) EEG signal processing strategies for executive function assessment in ADHD.
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Affiliation(s)
- Pasquale Arpaia
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples “Federico II”, 80121 Naples, Italy; (M.F.); (L.G.); (F.M.); (N.M.)
- Interdepartmental Research Center on Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, 80121 Naples, Italy
- Correspondence:
| | - Attilio Covino
- Villa delle Ginestre, Rehabilitation Center, 80040 Naples, Italy; (A.C.); (F.M.)
| | - Loredana Cristaldi
- Department of Electronics, Information e Bioengineering, Milan Polytechnic, 20133 Milan, Italy;
| | - Mirco Frosolone
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples “Federico II”, 80121 Naples, Italy; (M.F.); (L.G.); (F.M.); (N.M.)
| | - Ludovica Gargiulo
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples “Federico II”, 80121 Naples, Italy; (M.F.); (L.G.); (F.M.); (N.M.)
| | - Francesca Mancino
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples “Federico II”, 80121 Naples, Italy; (M.F.); (L.G.); (F.M.); (N.M.)
| | - Federico Mantile
- Villa delle Ginestre, Rehabilitation Center, 80040 Naples, Italy; (A.C.); (F.M.)
| | - Nicola Moccaldi
- Department of Electrical Engineering and Information Technologies (DIETI), University of Naples “Federico II”, 80121 Naples, Italy; (M.F.); (L.G.); (F.M.); (N.M.)
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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: 8] [Impact Index Per Article: 4.0] [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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10
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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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11
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Zhang S, Spoletini LJ, Gold BP, Morgan VL, Rogers BP, Chang C. Interindividual Signatures of fMRI Temporal Fluctuations. Cereb Cortex 2021; 31:4450-4463. [PMID: 33903915 PMCID: PMC8408464 DOI: 10.1093/cercor/bhab099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/28/2021] [Accepted: 03/26/2021] [Indexed: 11/13/2022] Open
Abstract
The complexity and variability of human brain activity, such as quantified from Functional Magnetic Resonance Imaging (fMRI) time series, have been widely studied as potential markers of healthy and pathological states. However, the extent to which fMRI temporal features exhibit stable markers of inter-individual differences in brain function across healthy young adults is currently an open question. In this study, we draw upon two widely used time-series measures-a nonlinear complexity measure (sample entropy; SampEn) and a spectral measure of low-frequency content (fALFF)-to capture dynamic properties of resting-state fMRI in a large sample of young adults from the Human Connectome Project. We observe that these two measures are closely related, and that both generate reproducible patterns across brain regions over four different fMRI runs, with intra-class correlations of up to 0.8. Moreover, we find that both metrics can uniquely differentiate subjects with high identification rates (ca. 89%). Canonical correlation analysis revealed a significant relationship between multivariate brain temporal features and behavioral measures. Overall, these findings suggest that regional profiles of fMRI temporal characteristics may provide stable markers of individual differences, and motivate future studies to further probe relationships between fMRI time series metrics and behavior.
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Affiliation(s)
- Shengchao Zhang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
| | - Liam J Spoletini
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
| | - Benjamin P Gold
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Baxter P Rogers
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37212, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37212, USA
- Vanderbilt University, Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37212, USA
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12
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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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13
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Identification of attention-deficit hyperactivity disorder based on the complexity and symmetricity of pupil diameter. Sci Rep 2021; 11:8439. [PMID: 33875772 PMCID: PMC8055872 DOI: 10.1038/s41598-021-88191-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 04/06/2021] [Indexed: 02/02/2023] Open
Abstract
Adult attention-deficit/hyperactivity disorder (ADHD) frequently leads to psychological/social dysfunction if unaddressed. Identifying a reliable biomarker would assist the diagnosis of adult ADHD and ensure that adults with ADHD receive treatment. Pupil diameter can reflect inherent neural activity and deficits of attention or arousal characteristic of ADHD. Furthermore, distinct profiles of the complexity and symmetricity of neural activity are associated with some psychiatric disorders. We hypothesized that analysing the relationship between the size, complexity of temporal patterns, and asymmetricity of pupil diameters will help characterize the nervous systems of adults with ADHD and that an identification method combining these features would ease the diagnosis of adult ADHD. To validate this hypothesis, we evaluated the resting state hippus in adult participants with or without ADHD by examining the pupil diameter and its temporal complexity using sample entropy and the asymmetricity of the left and right pupils using transfer entropy. We found that large pupil diameters and low temporal complexity and symmetry were associated with ADHD. Moreover, the combination of these factors by the classifier enhanced the accuracy of ADHD identification. These findings may contribute to the development of tools to diagnose adult ADHD.
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14
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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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15
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Chu YJ, Chang CF, Weng WC, Fan PC, Shieh JS, Lee WT. Electroencephalography complexity in infantile spasms and its association with treatment response. Clin Neurophysiol 2021; 132:480-486. [PMID: 33450568 DOI: 10.1016/j.clinph.2020.12.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/03/2020] [Accepted: 12/14/2020] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To investigate the potential of EEG multiscale entropy and complexity as biomarkers in infantile spasms. METHODS We collected EEG data retrospectively from 16 newly diagnosed patients, 16 age- and gender-matched healthy controls, and 15 drug-resistant patients. The multiscale entropy (MSE) and total EEG complexity before anti-epileptic drug (AED) treatment, before adrenocorticotropic hormone (ACTH) treatment, 14 days after ACTH therapy, and after 6 months of follow-up were calculated. RESULTS The total EEG complexity of 16 newly diagnosed infantile spasms patients was lower than the 16 healthy controls (median [IQR]: 351.5 [323.1-388.1] vs 461.6 [407.7-583.4]). The total EEG complexity before treatment was higher in the six patients with good response to AED than the 10 patients without response (median [IQR]: 410.0 [388.1-475.0] vs 344.5 [319.6-352.0]). The total EEG complexity before and after 14-days of ACTH therapy was not different between 13 ACTH therapy responders and nine non-responders. After 6-months follow-up, the total EEG complexity of ACTH therapy responders were higher than non-responders (median [IQR]: 598.5 [517.4-623.3] vs 448.6 [347.1-536.3]). CONCLUSIONS The total EEG complexity before AED and 6 months after ACTH are associated with spasm-freedom. SIGNIFICANCE The total EEG complexity is a potential biomarker to predict and monitor the treatment effect in infantile spasms.
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Affiliation(s)
- Yen-Ju Chu
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan; Department of Pediatric Neurology, National Taiwan University Children's Hospital, Taipei, Taiwan
| | - Chi-Feng Chang
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Wen-Chin Weng
- Department of Pediatric Neurology, National Taiwan University Children's Hospital, Taipei, Taiwan; Department of Pediatrics, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Pi-Chuan Fan
- Department of Pediatric Neurology, National Taiwan University Children's Hospital, Taipei, Taiwan; Department of Pediatrics, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jiann-Shing Shieh
- Department of Mechanical Engineering, Yuan Ze University, Taoyuan, Taiwan; Innovation Center for Biomedical and Healthcare Technology, Yuan Ze University, Taoyuan, Taiwan; Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Taiwan
| | - Wang-Tso Lee
- Department of Pediatric Neurology, National Taiwan University Children's Hospital, Taipei, Taiwan; Department of Pediatrics, National Taiwan University College of Medicine, Taipei, Taiwan; Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan.
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16
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Proteau-Lemieux M, Knoth IS, Agbogba K, Côté V, Barlahan Biag HM, Thurman AJ, Martin CO, Bélanger AM, Rosenfelt C, Tassone F, Abbeduto LJ, Jacquemont S, Hagerman R, Bolduc F, Hessl D, Schneider A, Lippé S. EEG Signal Complexity Is Reduced During Resting-State in Fragile X Syndrome. Front Psychiatry 2021; 12:716707. [PMID: 34858220 PMCID: PMC8632368 DOI: 10.3389/fpsyt.2021.716707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 10/06/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Fragile X syndrome (FXS) is a genetic disorder caused by a mutation of the fragile X mental retardation 1 gene (FMR1). FXS is associated with neurophysiological abnormalities, including cortical hyperexcitability. Alterations in electroencephalogram (EEG) resting-state power spectral density (PSD) are well-defined in FXS and were found to be linked to neurodevelopmental delays. Whether non-linear dynamics of the brain signal are also altered remains to be studied. Methods: In this study, resting-state EEG power, including alpha peak frequency (APF) and theta/beta ratio (TBR), as well as signal complexity using multi-scale entropy (MSE) were compared between 26 FXS participants (ages 5-28 years), and 7 neurotypical (NT) controls with a similar age distribution. Subsequently a replication study was carried out, comparing our cohort to 19 FXS participants independently recorded at a different site. Results: PSD results confirmed the increased gamma, decreased alpha power and APF in FXS participants compared to NT controls. No alterations in TBR were found. Importantly, results revealed reduced signal complexity in FXS participants, specifically in higher scales, suggesting that altered signal complexity is sensitive to brain alterations in this population. The replication study mostly confirmed these results and suggested critical points of stagnation in the neurodevelopmental curve of FXS. Conclusion: Signal complexity is a powerful feature that can be added to the electrophysiological biomarkers of brain maturation in FXS.
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Affiliation(s)
- Mélodie Proteau-Lemieux
- Department of Psychology, University of Montreal, Montreal, QC, Canada.,Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
| | - Inga Sophia Knoth
- Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
| | - Kristian Agbogba
- Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
| | - Valérie Côté
- Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
| | - Hazel Maridith Barlahan Biag
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States
| | - Angela John Thurman
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States
| | | | - Anne-Marie Bélanger
- Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
| | - Cory Rosenfelt
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - Flora Tassone
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States.,Department of Biochemistry and Molecular Medicine, University of California Davis School of Medicine, Sacramento, CA, United States
| | - Leonard J Abbeduto
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States.,Department of Psychiatry and Behavioral Sciences, University of California Davis School of Medicine, Sacramento, CA, United States
| | - Sébastien Jacquemont
- Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada.,Department of Pediatrics, University of Montreal, Montreal, QC, Canada
| | - Randi Hagerman
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States
| | - François Bolduc
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - David Hessl
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States.,Department of Psychiatry and Behavioral Sciences, University of California Davis School of Medicine, Sacramento, CA, United States
| | - Andrea Schneider
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States.,California North State University, College of Psychology, Rancho Cordova, CA, United States
| | - Sarah Lippé
- Department of Psychology, University of Montreal, Montreal, QC, Canada.,Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
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17
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Ahmadi A, Kashefi M, Shahrokhi H, Nazari MA. Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102227] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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18
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Moghaddari M, Lighvan MZ, Danishvar S. Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105738. [PMID: 32927404 DOI: 10.1016/j.cmpb.2020.105738] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 08/31/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Attention-Deficit/Hyperactivity Disorder (ADHD) is a chronic behavioral disorder in children. Children with ADHD face many difficulties in maintaining their concentration and controlling their behaviors. Early diagnosis of this disorder is one of the most important challenges in its control and treatment. No definitive expert method has been found to detect this disorder early. Our goal in this study is to develop an assistive tool for physicians to recognize ADHD children from healthy children using electroencephalography (EEG) based on a continuous mental task. METHODS We used EEG signals recorded from 31 ADHD children and 30 healthy children. In this study, we developed a deep learning model using a convolutional neural network that have had significant performance in image processing fields. For this purpose, we first preprocessed EEG signals to eliminate noise and artifacts. Then we segmented preprocessed samples into more samples. We extracted the theta, alpha, beta, and gamma frequency bands from each segmented sample and formed a color RGB image with three channels. Eventually, we imported the resulting images into a 13-layer convolutional neural network for feature extraction and classification. RESULTS The proposed model was evaluated by 5-fold cross validation for train, evaluation, and test data and achieved an average accuracy of 99.06%, 97.81%, 97.47% for segmented samples. The average accuracy for subject-based test samples was 98.48%. Also, the performance of the model was evaluated using the confusion matrix with precision, recall, and f1-score metrics. The results of these metrics also confirmed the outstanding performance of the model. CONCLUSIONS The accuracy, precision, recall, and f1-score of our model were better than all previous works for diagnosing ADHD in children. Based on these prominent and reliable results, this technique can be used as an assistive tool for the physicians in the early diagnosis of ADHD in children.
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Affiliation(s)
- Majid Moghaddari
- Department of Electronic and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Mina Zolfy Lighvan
- Department of Electronic and Computer Engineering, University of Tabriz, Tabriz, Iran.
| | - Sebelan Danishvar
- Department of Electronic and Computer Engineering, University of Tabriz, Tabriz, Iran; Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, UK
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19
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Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion. ENTROPY 2020; 22:e22070748. [PMID: 33286519 PMCID: PMC7517291 DOI: 10.3390/e22070748] [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: 06/01/2020] [Revised: 07/01/2020] [Accepted: 07/02/2020] [Indexed: 01/10/2023]
Abstract
Atrial fibrillation (AF) is nowadays the most common cardiac arrhythmia, being associated with an increase in cardiovascular mortality and morbidity. When AF lasts for more than seven days, it is classified as persistent AF and external interventions are required for its termination. A well-established alternative for that purpose is electrical cardioversion (ECV). While ECV is able to initially restore sinus rhythm (SR) in more than 90% of patients, rates of AF recurrence as high as 20-30% have been found after only a few weeks of follow-up. Hence, new methods for evaluating the proarrhythmic condition of a patient before the intervention can serve as efficient predictors about the high risk of early failure of ECV, thus facilitating optimal management of AF patients. Among the wide variety of predictors that have been proposed to date, those based on estimating organization of the fibrillatory (f-) waves from the surface electrocardiogram (ECG) have reported very promising results. However, the existing methods are based on traditional entropy measures, which only assess a single time scale and often are unable to fully characterize the dynamics generated by highly complex systems, such as the heart during AF. The present work then explores whether a multi-scale entropy (MSE) analysis of the f-waves may provide early prediction of AF recurrence after ECV. In addition to the common MSE, two improved versions have also been analyzed, composite MSE (CMSE) and refined MSE (RMSE). When analyzing 70 patients under ECV, of which 31 maintained SR and 39 relapsed to AF after a four week follow-up, the three methods provided similar performance. However, RMSE reported a slightly better discriminant ability of 86%, thus improving the other multi-scale-based outcomes by 3-9% and other previously proposed predictors of ECV by 15-30%. This outcome suggests that investigation of dynamics at large time scales yields novel insights about the underlying complex processes generating f-waves, which could provide individual proarrhythmic condition estimation, thus improving preoperative predictions of ECV early failure.
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20
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Attention Deficit Hyperactivity Disorder Diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study. Phys Eng Sci Med 2020; 43:577-592. [DOI: 10.1007/s13246-020-00858-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 03/10/2020] [Indexed: 12/16/2022]
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21
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Furutani N, Nariya Y, Takahashi T, Ito H, Yoshimura Y, Hiraishi H, Hasegawa C, Ikeda T, Kikuchi M. Neural Decoding of Multi-Modal Imagery Behavior Focusing on Temporal Complexity. Front Psychiatry 2020; 11:746. [PMID: 32848924 PMCID: PMC7406828 DOI: 10.3389/fpsyt.2020.00746] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 07/16/2020] [Indexed: 12/14/2022] Open
Abstract
Mental imagery behaviors of various modalities include visual, auditory, and motor behaviors. Their alterations are pathologically involved in various psychiatric disorders. Results of earlier studies suggest that imagery behaviors are correlated with the modulated activities of the respective modality-specific regions and the additional activities of supramodal imagery-related regions. Additionally, despite the availability of complexity analysis in the neuroimaging field, it has not been used for neural decoding approaches. Therefore, we sought to characterize neural oscillation related to multimodal imagery through complexity-based neural decoding. For this study, we modified existing complexity measures to characterize the time evolution of temporal complexity. We took magnetoencephalography (MEG) data of eight healthy subjects as they performed multimodal imagery and non-imagery tasks. The MEG data were decomposed into amplitude and phase of sub-band frequencies by Hilbert-Huang transform. Subsequently, we calculated the complexity values of each reconstructed time series, along with raw data and band power for comparison, and applied these results as inputs to decode visual perception (VP), visual imagery (VI), motor execution (ME), and motor imagery (MI) functions. Consequently, intra-subject decoding with the complexity yielded a characteristic sensitivity map for each task with high decoding accuracy. The map is inverted in the occipital regions between VP and VI and in the central regions between ME and MI. Additionally, replacement of the labels into two classes as imagery and non-imagery also yielded better classification performance and characteristic sensitivity with the complexity. It is particularly interesting that some subjects showed characteristic sensitivities not only in modality-specific regions, but also in supramodal regions. These analyses indicate that two-class and four-class classifications each provided better performance when using complexity than when using raw data or band power as input. When inter-subject decoding was used with the same model, characteristic sensitivity maps were also obtained, although their decoding performance was lower. Results of this study underscore the availability of complexity measures in neural decoding approaches and suggest the possibility of a modality-independent imagery-related mechanism. The use of time evolution of temporal complexity in neural decoding might extend our knowledge of the neural bases of hierarchical functions in the human brain.
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Affiliation(s)
- Naoki Furutani
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Yuta Nariya
- Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Haruka Ito
- General course, Sundai-Kofu High School, Kofu, Japan
| | - Yuko Yoshimura
- Institute of Human and Social Sciences, Kanazawa University, Kanazawa, Japan
| | - Hirotoshi Hiraishi
- Department of Biofunctional Imaging, Preeminent Medical Photonics Education & Research Center, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Chiaki Hasegawa
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
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22
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Contrasting Age Effects on Complexity of Tracking Force and Force Fluctuations During Monorhythmic Contraction. J Aging Phys Act 2020; 28:114-121. [PMID: 31629359 DOI: 10.1123/japa.2019-0034] [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: 01/21/2019] [Revised: 05/20/2019] [Accepted: 05/30/2019] [Indexed: 11/18/2022]
Abstract
This study contrasted the stochastic force component between young and older adults, who performed pursuit tracking/compensatory tracking by exerting in-phase/antiphase forces to match a sinusoidal target. Tracking force was decomposed into the force component containing the target frequency and the nontarget force fluctuations (stochastic component). Older adults with inferior task performance had higher complexity (entropy across time; p = .005) in total force. For older adults, task errors were negatively correlated with force fluctuation complexity (pursuit tracking: r = -.527 to -.551; compensatory tracking: r = -.626 to -.750). Notwithstanding an age-related increase in total force complexity (p = .004), older adults exhibited lower complexity of the stochastic force component than young adults did (low frequency: p = .017; high frequency: p = .035). Those older adults with a higher complexity of stochastic force had better task performance due to the underlying use of a richer gradation strategy to compensate for impaired oscillatory control.
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Zhou M, Jiang W, Zhong D, Zheng J. Resting-state brain entropy in right temporal lobe epilepsy and its relationship with alertness. Brain Behav 2019; 9:e01446. [PMID: 31605452 PMCID: PMC6851803 DOI: 10.1002/brb3.1446] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 09/14/2019] [Accepted: 09/18/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND To date, no functional MRI (fMRI) studies have focused on brain entropy in right temporal lobe epilepsy (rTLE) patients. Here, we characterized brain entropy (BEN) alterations in patients with rTLE using resting-state functional MRI(rs-fMRI) and explored the relationship between BEN and alertness. METHOD Thirty-one rTLE patients and 33 controls underwent MRI scanning to investigate differences in BEN and resting-state functional connectivity (rs-FC) in regions of interest (ROIs) between patients and controls. Correlation analyses were performed to examine relationships between the BEN of each ROI and alertness reaction times (RTs) in rTLE patients. RESULTS Compared with controls, the BEN of rTLE patients was significantly increased in the right middle temporal gyrus, inferior temporal gyrus, and other regions of the left hemisphere and significantly decreased in the right middle frontal gyrus and left supplementary motor area (p < .05). The rs-FCs between the ROIs (at p < .01, with the left superior parietal lobule and right precentral gyrus defined as ROI1 and ROI2, respectively) and the whole brain showed an increasing trend in rTLE patients. In addition, the BEN of ROI2 was associated with the intrinsic alertness and phasic alertness RTs of patients with rTLE. CONCLUSIONS Our findings suggest that BEN is altered in patients with rTLE and that decreased BEN in the right precentral gyrus is positively related to intrinsic and phasic alertness; the abnormal FC in the brain regions with altered entropy suggests a reconstruction of brain functional connectivity. These findings suggest that BEN mapping may provide a useful tool for probing brain mechanisms related to TLE.
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Affiliation(s)
- Muhua Zhou
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Wenyu Jiang
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dan Zhong
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Boroujeni YK, Rastegari AA, Khodadadi H. Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal. IET Syst Biol 2019; 13:260-266. [PMID: 31538960 PMCID: PMC8687398 DOI: 10.1049/iet-syb.2018.5130] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Revised: 04/21/2019] [Accepted: 06/28/2019] [Indexed: 09/01/2023] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%-8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non-linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non-linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non-linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD.
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Affiliation(s)
- Yasaman Kiani Boroujeni
- Department of Molecular and Cell Biochemistry, Falavarjan Branch, Islamic Azad University, Isfahan, Iran
| | - Ali Asghar Rastegari
- Department of Molecular and Cell Biochemistry, Falavarjan Branch, Islamic Azad University, Isfahan, Iran
| | - Hamed Khodadadi
- Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran.
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25
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Chen H, Song Y, Li X. A deep learning framework for identifying children with ADHD using an EEG-based brain network. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.04.058] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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26
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Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04294-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Yin Y, Sun K, He S. Multiscale permutation Rényi entropy and its application for EEG signals. PLoS One 2018; 13:e0202558. [PMID: 30180194 PMCID: PMC6122795 DOI: 10.1371/journal.pone.0202558] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Accepted: 08/06/2018] [Indexed: 11/18/2022] Open
Abstract
There is considerable interest in analyzing the complexity of electroencephalography (EEG) signals. However, some traditional complexity measure algorithms only quantify the complexities of signals, but cannot discriminate different signals very well. To analyze the complexity of epileptic EEG signals better, a new multiscale permutation Rényi entropy (MPEr) algorithm is proposed. In this algorithm, the coarse-grained procedure is introduced by using weighting-averaging method, and the weighted factors are determined by analyzing nonlinear signals. We apply the new algorithm to analyze epileptic EEG signals. The experimental results show that MPEr algorithm has good performance for discriminating different EEG signals. Compared with permutation Rényi entropy (PEr) and multiscale permutation entropy (MPE), MPEr distinguishes different EEG signals successfully. The proposed MPEr algorithm is effective and has good applications prospects in EEG signals analysis.
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Affiliation(s)
- Yinghuang Yin
- School of Physics and Electronics, Central South University, Changsha, P.R.China
| | - Kehui Sun
- School of Physics and Electronics, Central South University, Changsha, P.R.China
- * E-mail:
| | - Shaobo He
- School of Computer Science and Technology, Hunan University of Arts and Science, Changde, P.R.China
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Humeau-Heurtier A, Omoto ACM, Silva LEV. Bi-dimensional multiscale entropy: Relation with discrete Fourier transform and biomedical application. Comput Biol Med 2018; 100:36-40. [PMID: 29975852 DOI: 10.1016/j.compbiomed.2018.06.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 06/21/2018] [Accepted: 06/22/2018] [Indexed: 02/03/2023]
Abstract
The multiscale entropy (MSE1D) measure is now widely used to quantify the complexity of time series. The development of complexity measures for images is also a long-standing goal. Recently, the bi-dimensional version of MSE1D has been proposed (MSE2D) to analyze images. The interpretation of MSE2D curves and the applications to real data are still emergent. Because the coarse-graining step in the MSE2D computation changes the frequency content of the image, we hypothesized a possible dependence between MSE2D and the discrete Fourier transform (DFT). To analyze this dependence, synthetic as well as biomedical images are analyzed. Our results reveal that i) the profile of MSE2D is sensitive to both the amplitude and phase of the DFT; ii) MSE2D could find applications in the biomedical field. This work brings valuable information for MSE2D interpretation and opens possibilities to study images from an entropy point of view through spatial scales.
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Affiliation(s)
- Anne Humeau-Heurtier
- Univ Angers, LARIS - Laboratoire Angevin de Recherche en Ingénierie des Systèmes, 62 avenue Notre-Dame du Lac, 49000, Angers, France.
| | - Ana Carolina Mieko Omoto
- Department of Physiology, School of Medicine of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, SP, Brazil
| | - Luiz E V Silva
- Department of Physiology, School of Medicine of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, SP, Brazil; Department of Computer Science, Institute of Mathematics and Computer Science, University of Sao Paulo, Sao Carlos, SP, Brazil
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The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders. ENTROPY 2017; 19:e19080428. [PMID: 33535366 DOI: 10.3390/e19080428] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 08/11/2017] [Accepted: 08/16/2017] [Indexed: 01/25/2023]
Abstract
Electroencephalography (EEG) is frequently used in functional neurological assessment of children with neurological and neuropsychiatric disorders. Multiscale entropy (MSE) can reveal complexity in both short and long time scales and is more feasible in the analysis of EEG. Entropy-based estimation of EEG complexity is a powerful tool in investigating the underlying disturbances of neural networks of the brain. Most neurological and neuropsychiatric disorders in childhood affect the early stage of brain development. The analysis of EEG complexity may show the influences of different neurological and neuropsychiatric disorders on different regions of the brain during development. This article aims to give a brief summary of current concepts of MSE analysis in pediatric neurological and neuropsychiatric disorders. Studies utilizing MSE or its modifications for investigating neurological and neuropsychiatric disorders in children were reviewed. Abnormal EEG complexity was shown in a variety of childhood neurological and neuropsychiatric diseases, including autism, attention deficit/hyperactivity disorder, Tourette syndrome, and epilepsy in infancy and childhood. MSE has been shown to be a powerful method for analyzing the non-linear anomaly of EEG in childhood neurological diseases. Further studies are needed to show its clinical implications on diagnosis, treatment, and outcome prediction.
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Gu Y, Miao S, Han J, Zeng K, Ouyang G, Yang J, Li X. Complexity analysis of fNIRS signals in ADHD children during working memory task. Sci Rep 2017; 7:829. [PMID: 28400568 PMCID: PMC5429780 DOI: 10.1038/s41598-017-00965-4] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 03/21/2017] [Indexed: 12/26/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. Neuroimaging studies have revealed abnormalities of neural activities in some brain regions, including the frontal cortex, striatum, cerebellum, and occipital cortex. Recently, some investigators have demonstrated that nonlinear complexity analysis of neural activity may provide a new index to indicate ADHD. In the present study, we used the permutation entropy (PE) to measure the complexity of functional near-infrared spectroscopy (fNIRS) signals in children with and without ADHD during a working memory task, it was aimed to investigate the relationship between the PE values and the cortical activations, and the different PE values between the children with and without ADHD. We found that PE values exhibited significantly negative correlation with the cortical activations (r = -0.515, p = 0.003), and the PE values of right dorsolateral prefrontal cortex in ADHD children were significantly larger than those in normal controls (p = 0.027). In addition, the PE values of right dorsolateral prefrontal cortex were positively correlated to the ADHD index (r = 0.448, p = 0.012). These results suggest that complexity analysis of fNIRS signals could be a promising tool in diagnosing children with ADHD.
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Affiliation(s)
- Yue Gu
- Institute of Electrical Engineering, Yanshan University, No. 438, Hebei Street, HaiGang District, Qinhuangdao, 066004, China
| | - Shuo Miao
- Children's Hospital Attached to The Capital Institute of Paediatrics, No. 2, Yabao Street, ChaoYang District, Beijing, 100020, China
| | - Junxia Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing, 100875, China
| | - Ke Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing, 100875, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing, 100875, China
| | - Jian Yang
- Children's Hospital Attached to The Capital Institute of Paediatrics, No. 2, Yabao Street, ChaoYang District, Beijing, 100020, China.
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekou Wai Street, Haidian District, Beijing, 100875, China.
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Yi GS, Wang J, Deng B, Wei XL. Complexity of resting-state EEG activity in the patients with early-stage Parkinson's disease. Cogn Neurodyn 2016; 11:147-160. [PMID: 28348646 DOI: 10.1007/s11571-016-9415-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 09/21/2016] [Accepted: 10/14/2016] [Indexed: 01/21/2023] Open
Abstract
To investigate the abnormal brain activities in the early stage of Parkinson's disease (PD), the electroencephalogram (EEG) signals were recorded with 20 channels from non-dementia PD patients (18 patients, 8 females) and age matched healthy controls (18 subjects, 8 females) during the resting state. Two methods based on the ordinal patterns of the recorded series, i.e., permutation entropy (PE) and order index (OI), were introduced to characterize the complexity of the cortical activities for two groups. It was observed that the resting-state EEG of PD patients showed lower PE and higher OI than healthy controls, which indicated that the early-stage PD caused the reduced complexity of EEG. We further applied two methods to determine the complexity of EEG rhythms in five sub-bands. The results showed that the gamma, beta and alpha rhythms of PD patients were characterized by lower PE and higher OI, i.e., reduced complexity, than healthy subjects. No significant differences were observed in theta or delta rhythms between two groups. The findings suggested that PE and OI were promising methods to detect the abnormal changes in the dynamics of EEG signals associated with early-stage PD. Further, such changes in EEG complexity may be the early markers of the cortical or subcortical dysfunction caused by PD.
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Affiliation(s)
- Guo-Sheng Yi
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 30072 China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 30072 China
| | - Bin Deng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 30072 China
| | - Xi-Le Wei
- School of Electrical Engineering and Automation, Tianjin University, Tianjin, 30072 China
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