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Liang Z, Xie Y, Chen S, Liu J, Lv H, Muhoza BG, Xing F, Mao Y, Wei X, Xing N, Yang J, Wang Z, Yuan J. Predicting postoperative pain in children: an observational study using the pain threshold Index. Front Pediatr 2024; 12:1398182. [PMID: 39091987 PMCID: PMC11291306 DOI: 10.3389/fped.2024.1398182] [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: 03/09/2024] [Accepted: 07/03/2024] [Indexed: 08/04/2024] Open
Abstract
Objective While the pain threshold index (PTI) holds potential as a tool for monitoring analgesia-pain equilibrium, its precision in forecasting postoperative pain in children remains unconfirmed. This study's primary aim was to assess the PTI's predictive precision for postoperative pain. Methods Children (aged 2-16 years) undergoing general surgery under general anesthesia were included. Within 5 min prior to the patient's emergence from surgery, data including PTI, wavelet index (WLI), heart rates (HR) and mean arterial pressure (MAP) were collected. Subsequently, a 15-min pain assessment was conducted following the patient's awakening. The accuracy of these indicators in discerning between mild and moderate to severe postoperative pain was evaluated through receiver operating characteristic (ROC) analysis. Results The analysis encompassed data from 90 children. ROC analysis showed that PTI was slightly better than HR, MAP and WLI in predicting postoperative pain, but its predictive value was limited. The area under the curve (AUC) was 0.659 [0.537∼0.780] and the optimal threshold was 65[64-67]. Sensitivity and specificity were determined at 0.90 and 0.50, respectively. In a multivariable logistic regression model, a higher predictive accuracy was found for a multivariable predictor combining PTI values with gender, BMI, HR and MAP (AUC, 0.768; 95%CI, 0.669-0.866). Upon further scrutinizing the age groups, PTI's AUC was 0.796 for children aged 9-16, 0.656 for those aged 4-8, and 0.601 for younger individuals. Conclusions PTI, when used alone, lacks acceptable accuracy in predicting postoperative pain in children aged 2 to 16 years. However, when combined with other factors, it shows improved predictive accuracy. Notably, PTI appears to be more accurate in older children.
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Affiliation(s)
- Zenghui Liang
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yanle Xie
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Cognition and Emotion, Henan Province International Joint Laboratory of Pain, Zhengzhou, Henan, China
| | - Shuhan Chen
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Cognition and Emotion, Henan Province International Joint Laboratory of Pain, Zhengzhou, Henan, China
| | - Jing Liu
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Huimin Lv
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Cognition and Emotion, Henan Province International Joint Laboratory of Pain, Zhengzhou, Henan, China
| | - Bertrand-Geoffrey Muhoza
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Fei Xing
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Cognition and Emotion, Henan Province International Joint Laboratory of Pain, Zhengzhou, Henan, China
| | - Yuanyuan Mao
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Cognition and Emotion, Henan Province International Joint Laboratory of Pain, Zhengzhou, Henan, China
| | - Xin Wei
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Cognition and Emotion, Henan Province International Joint Laboratory of Pain, Zhengzhou, Henan, China
| | - Na Xing
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Cognition and Emotion, Henan Province International Joint Laboratory of Pain, Zhengzhou, Henan, China
| | - Jianjun Yang
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Cognition and Emotion, Henan Province International Joint Laboratory of Pain, Zhengzhou, Henan, China
| | - Zhongyu Wang
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Cognition and Emotion, Henan Province International Joint Laboratory of Pain, Zhengzhou, Henan, China
| | - Jingjing Yuan
- Department of Anesthesiology, Pain and Perioperative Medicine, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Cognition and Emotion, Henan Province International Joint Laboratory of Pain, Zhengzhou, Henan, China
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Socanski D, Ogrim G, Duric N. Children with ADHD and EEG abnormalities at baseline assessment, risk of epileptic seizures and maintenance on methylphenidate three years later. Ann Gen Psychiatry 2024; 23:22. [PMID: 38907242 PMCID: PMC11193234 DOI: 10.1186/s12991-024-00510-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/14/2024] [Indexed: 06/23/2024] Open
Abstract
PURPOSE This study aimed to assess the incidence of EEG abnormalities (EEG-ab) in children diagnosed with ADHD, investigate the risk of epileptic seizures (SZ) and maintenance on methylphenidate (MPH) over a three-year period. METHODS A total of 517 ADHD children aged 6-14 years were included. Baseline assessments included the identification of EEG-ab, ADHD inattentive subtype (ADHD-I), comorbid epilepsy, the use of antiepileptic drugs (AEDs) and the use of MPH. At the 3-year follow-up, assessments included the presence of EEG-ab, maintenance on MPH, AED usage, SZ risk in cases with EEG-epileptiform abnormalities (EEG-epi-ab), compared with control ADHD cases without EEG-epi-ab matched for age and gender. RESULTS EEG-ab were identified in 273 (52.8%) cases. No statistically significant differences were observed between the EEG-ab and EEG-non-ab groups in terms of age, gender, ADHD-I type or initial use of MPH. EEG non-epileptiform abnormalities (EEG-non-epi-ab) were found in 234 out of 478 (49%) cases without EEG-epi-ab. Notably, EEG-non-epi-ab occurred more frequently in the group of 39 cases with EEG-epi-ab (30/39 (76.9%) vs. 9/39, (21.3%), a subset selected for 3-year follow-up. At 3-year-follow-up no statistically significant difference was found in maintenance on MPH in ADHD cases with and without EEG-epi-ab. Nobody of ADHD cases without comorbid epilepsy or with comorbid epilepsy with achieved SZ freedom developed new SZ. Only 3 children with drug resistant epilepsy experienced SZs, without increase in SZ frequency. The disappearance rate of EEG-epi-ab was higher than that EEG-non-epi-ab (71.8% vs. 33.3%). CONCLUSIONS Children with and without EEG-ab exhibited similar patterns of MPH use (initial use, positive response, and maintenance on MPH). The presence of comorbid epilepsy and EEG-ab, with or without EEG-epi-ab, was not associated with an increased risk of SZ despite the use of MPH.
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Affiliation(s)
- Dobrinko Socanski
- Department of Child and Adolescent Psychiatry, Østfold Hospital Trust, Fredrikstad, Norway.
- Department of Child and Adolescent Psychiatry, Stavanger University Hospital, Stavanger, Norway.
| | - Geir Ogrim
- Neuropsychiatric Team, Åsebråten Clinic, Østfold Hospital Trust, Fredrikstad, Norway
- Gillberg Neuropsychiatry Centre, Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden
| | - Nezla Duric
- Department of Child and Adolescent Psychiatry, Fonna Health Trust, Haugesund, Norway
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Fatić S, Stanojević N, Jeličić L, Bilibajkić R, Marisavljević M, Maksimović S, Gavrilović A, Subotić M. Beta Spectral Power during Passive Listening in Preschool Children with Specific Language Impairment. Dev Neurosci 2024:1-14. [PMID: 38723615 DOI: 10.1159/000539135] [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: 09/03/2023] [Accepted: 04/18/2024] [Indexed: 06/19/2024] Open
Abstract
INTRODUCTION Children with specific language impairment (SLI) have difficulties in different speech and language domains. Electrophysiological studies have documented that auditory processing in children with SLI is atypical and probably caused by delayed and abnormal auditory maturation. During the resting state, or different auditory tasks, children with SLI show low or high beta spectral power, which could be a clinical correlate for investigating brain rhythms. METHODS The aim of this study was to examine the electrophysiological cortical activity of the beta rhythm while listening to words and nonwords in children with SLI in comparison to typical development (TD) children. The participants were 50 children with SLI, aged 4 and 5 years, and 50 age matched TD children. The children were divided into two subgroups according to age: (1) children 4 years of age; (2) children 5 years of age. RESULTS The older group differed from the younger group in beta auditory processing, with increased values of beta spectral power in the right frontal, temporal, and parietal regions. In addition, children with SLI have higher beta spectral power than TD children in the bilateral temporal regions. CONCLUSION Complex beta auditory activation in TD and SLI children indicates the presence of early changes in functional brain connectivity.
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Affiliation(s)
- Saška Fatić
- Cognitive Neuroscience Department, Research and Development Institute "Life Activities Advancement Institute,", Belgrade, Serbia
- Department of Speech, Language, and Hearing Sciences, Institute for Experimental Phonetics and Speech Pathology, Belgrade, Serbia
| | - Nina Stanojević
- Cognitive Neuroscience Department, Research and Development Institute "Life Activities Advancement Institute,", Belgrade, Serbia
- Department of Speech, Language, and Hearing Sciences, Institute for Experimental Phonetics and Speech Pathology, Belgrade, Serbia
| | - Ljiljana Jeličić
- Cognitive Neuroscience Department, Research and Development Institute "Life Activities Advancement Institute,", Belgrade, Serbia
- Department of Speech, Language, and Hearing Sciences, Institute for Experimental Phonetics and Speech Pathology, Belgrade, Serbia
| | - Ružica Bilibajkić
- Cognitive Neuroscience Department, Research and Development Institute "Life Activities Advancement Institute,", Belgrade, Serbia
| | - Maša Marisavljević
- Cognitive Neuroscience Department, Research and Development Institute "Life Activities Advancement Institute,", Belgrade, Serbia
- Department of Speech, Language, and Hearing Sciences, Institute for Experimental Phonetics and Speech Pathology, Belgrade, Serbia
| | - Slavica Maksimović
- Cognitive Neuroscience Department, Research and Development Institute "Life Activities Advancement Institute,", Belgrade, Serbia
- Department of Speech, Language, and Hearing Sciences, Institute for Experimental Phonetics and Speech Pathology, Belgrade, Serbia
| | - Aleksandar Gavrilović
- Faculty of Medical Sciences, Department of Neurology, University of Kragujevac, Kragujevac, Serbia
- Clinic of Neurology, Clinical Center Kragujevac, Kragujevac, Serbia
| | - Miško Subotić
- Cognitive Neuroscience Department, Research and Development Institute "Life Activities Advancement Institute,", Belgrade, Serbia
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Iyer KK, Roberts JA, Waak M, Vogrin SJ, Kevat A, Chawla J, Haataja LM, Lauronen L, Vanhatalo S, Stevenson NJ. A growth chart of brain function from infancy to adolescence based on EEG. EBioMedicine 2024; 102:105061. [PMID: 38537603 PMCID: PMC11026939 DOI: 10.1016/j.ebiom.2024.105061] [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: 07/28/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND In children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function. METHODS We developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analysed a 10- to 15-min segment of 18-channel EEG recorded during light sleep (N1 and N2 states). FINDINGS The FBA had a weighted mean absolute error (wMAE) of 0.85 years (95% CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95% CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95% CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohen's d = 0.36, p = 0.028). INTERPRETATION A FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts. FUNDING This research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation.
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Affiliation(s)
- Kartik K Iyer
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Faculty of Medicine, The University of Queensland, Brisbane, Australia.
| | - James A Roberts
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Michaela Waak
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
| | | | - Ajay Kevat
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
| | - Jasneek Chawla
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Queensland Children's Hospital, Brisbane, Australia
| | - Leena M Haataja
- Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Leena Lauronen
- Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sampsa Vanhatalo
- Departments of Physiology and Clinical Neurophysiology, BABA Center, Paediatric Research Center, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Nathan J Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
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Bruns N, Joist CA, Joist CM, Daniels A, Felderhoff-Müser U, Dohna-Schwake C, Tschiedel E. Correlation of Comfort Score and Narcotrend Index during Procedural Sedation with Midazolam and Propofol in Children. J Clin Med 2024; 13:1483. [PMID: 38592307 PMCID: PMC10932229 DOI: 10.3390/jcm13051483] [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: 02/09/2024] [Revised: 02/28/2024] [Accepted: 03/02/2024] [Indexed: 04/10/2024] Open
Abstract
Background/Objectives: Precise assessment of hypnotic depth in children during procedural sedation with preserved spontaneous breathing is challenging. The Narcotrendindex (NI) offers uninterrupted information by continuous electrocortical monitoring without the need to apply a stimulus with the risk of assessment-induced arousal. This study aimed to explore the correlation between NI and the Comfort Scale (CS) during procedural sedation with midazolam and propofol and to identify an NI target range for deep sedation. Methods: A prospective observational study was conducted on 176 children (6 months to 17.9 years) undergoing procedural sedation with midazolam premedication and continuous propofol infusion. Statistical analyses included Pearson correlation of NI and CS values, logistic regression, and receiver operating curves. Results: Median NI values varied with CS and age. The correlation coefficient between CS and NI was 0.50 and slightly higher in procedure-specific subgroup analyses. The optimal NI cut-off for deep sedation was between 50 and 60 depending on the analyzed subgroup and displayed high positive predictive values for sufficient sedation throughout. Conclusion: Our study found a moderate correlation between NI and CS, demonstrating reliable identification of adequately sedated patients.
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Affiliation(s)
- Nora Bruns
- Department of Pediatrics I, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany (E.T.)
- Center for Translational Neuro- and Behavioural Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Carolina A. Joist
- Department of Pediatrics I, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany (E.T.)
- Center for Translational Neuro- and Behavioural Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Constantin M. Joist
- Department of Pediatrics I, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany (E.T.)
- Center for Translational Neuro- and Behavioural Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Anna Daniels
- Department of Pediatrics I, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany (E.T.)
- Center for Translational Neuro- and Behavioural Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Ursula Felderhoff-Müser
- Department of Pediatrics I, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany (E.T.)
- Center for Translational Neuro- and Behavioural Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Christian Dohna-Schwake
- Department of Pediatrics I, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany (E.T.)
- Center for Translational Neuro- and Behavioural Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
| | - Eva Tschiedel
- Department of Pediatrics I, University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany (E.T.)
- Center for Translational Neuro- and Behavioural Sciences (C-TNBS), University Hospital Essen, University of Duisburg-Essen, 45147 Essen, Germany
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Mastrangelo M, Manti F, Ricciardi G, Bove R, Greco C, Tolve M, Pisani F. The burden of epilepsy on long-term outcome of genetic developmental and epileptic encephalopathies: A single tertiary center longitudinal retrospective cohort study. Epilepsy Behav 2024; 152:109670. [PMID: 38335860 DOI: 10.1016/j.yebeh.2024.109670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/11/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND This retrospective cohort analysis highlighted neurodevelopmental outcome predictors of genetic developmental and epileptic encephalopathies (DEE). PATIENTS AND METHODS Patients' demographic, clinical and molecular genetics data were collected. All patients underwent clinical, developmental, and neuropsychological assessments. RESULTS We recruited 100 participants (53 males, 47 females) with a mean follow-up lasting 10.46 ± 8.37 years. Age at epilepsy-onset was predictive of poor adaptive and cognitive functions (VABS-II score, r = 0.350, p = 0.001; BRIEF control subscale, r = -0.253; p = 0.031). Duration of epilepsy correlated negatively with IQ (r = -0.234, p = 0.019) and VABS-II score (r = -0.367, p = 0.001). Correlations were found between delayed/lacking EEG maturation/organization and IQ (r = 0.587, p = 0.001), VABS-II score (r = 0.658, p = 0.001), BRIEF-MI and BRIEF-GEC scores (r = -0.375, p = 0.001; r = -0.236, p = 0.033), ASEBA anxiety (r = -0.220, p = 0.047) and ADHD (r = -0.233, p = 0.035) scores. The number of antiseizure medications (ASMs) correlated with IQ (r = -0.414, p = 0.001), VABS-II (r = -0.496, p = 0.001), and BRIEF-MI (r = 0.294, p = 0.012) scores; while age at the beginning of therapy with ASEBA anxiety score (r = 0.272, p = 0.013). The occurrence of status epilepticus was associated with worse adaptive performances. The linear regression analysis model showed that delayed/lacking EEG maturation/organization had a significant influence on the IQ (R2 = 0.252, p < 0.001) and the BRIEF-GEC variability (R2 = 0.042, p = 0.036). The delayed/lacking EEG maturation/organization and the duration of epilepsy also had a significant influence on the VABS-II score (R2 = 0.455, p = 0.005). CONCLUSIONS Age at seizure-onset, EEG maturation/organization, duration of epilepsy, occurrence of status epilepticus, age at the introduction and number of ASMs used are reliable predictors of long-term outcomes in patients with genetic DEE.
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Affiliation(s)
- Mario Mastrangelo
- Department of Women/Child Health and Urological Science, Sapienza University of Rome, Rome, Italy; Unit of Child Neurology and Psychiatry, Azienda Ospedaliero Universitaria Policlinico Umberto, Rome, Italy.
| | - Filippo Manti
- Unit of Child Neurology and Psychiatry, Azienda Ospedaliero Universitaria Policlinico Umberto, Rome, Italy; Unit of Child Neurology and Psychiatry, Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Giacomina Ricciardi
- Unit of Child Neurology and Psychiatry, Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Rossella Bove
- Unit of Child Neurology and Psychiatry, Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Carlo Greco
- Unit of Child Neurology and Psychiatry, Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Manuela Tolve
- Unit of Child Neurology and Psychiatry, Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy; Department of Experimental Medicine, Sapienza University of Rome, Italy
| | - Francesco Pisani
- Unit of Child Neurology and Psychiatry, Azienda Ospedaliero Universitaria Policlinico Umberto, Rome, Italy; Unit of Child Neurology and Psychiatry, Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
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Caffarelli M, Karukonda V, Aghaeeaval M, McQuillen PS, Numis AL, Mackay MT, Press CA, Wintermark M, Fox CK, Amorim E. A quantitative EEG index for the recognition of arterial ischemic stroke in children. Clin Neurophysiol 2023; 156:113-124. [PMID: 37918222 DOI: 10.1016/j.clinph.2023.10.001] [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: 11/08/2022] [Revised: 09/22/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023]
Abstract
OBJECTIVE To describe and assess performance of the Correlate Of Injury to the Nervous system (COIN) index, a quantitative electroencephalography (EEG) metric designed to identify areas of cerebral dysfunction concerning for stroke. METHODS Case-control study comparing continuous EEG data from children with acute ischemic stroke to children without stroke, with or without encephalopathy. COIN is calculated continuously and compares EEG power between cerebral hemispheres. Stroke relative infarct volume (RIV) was calculated from quantitative neuroimaging analysis. Significance was determined using a two-sample t-test. Sensitivity, specificity, and accuracy were measured using logistic regression. RESULTS Average COIN values were -34.7 in the stroke cohort compared to -9.5 in controls without encephalopathy (p = 0.003) and -10.5 in controls with encephalopathy (p = 0.006). The optimal COIN cutoff to discriminate stroke from controls was -15 in non-encephalopathic and -18 in encephalopathic controls with >92% accuracy in strokes with RIV > 5%. A COIN cutoff of -20 allowed discrimination between strokes with <5% and >5% RIV (p = 0.027). CONCLUSIONS We demonstrate that COIN can identify children with acute ischemic stroke. SIGNIFICANCE COIN may be a valuable tool for stroke identification in children. Additional studies are needed to determine utility as a monitoring technique for children at risk for stroke.
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Affiliation(s)
- Mauro Caffarelli
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA.
| | - Vishnu Karukonda
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Mahsa Aghaeeaval
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Patrick S McQuillen
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adam L Numis
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Mark T Mackay
- Royal Children's Hospital, Melbourne, Victoria, Australia; The Murdoch Children's Research Institute Melbourne, Victoria, Australia; The Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - Craig A Press
- Departments of Pediatrics and Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Max Wintermark
- Department of Neuroradiology, University of Texas MD Anderson Center, Houston, TX, USA
| | - Christine K Fox
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Edilberto Amorim
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
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Jiang H, Chen P, Sun Z, Liang C, Xue R, Zhao L, Wang Q, Li X, Deng W, Gao Z, Huang F, Huang S, Zhang Y, Li T. Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study. Neuropsychopharmacology 2023; 48:1920-1930. [PMID: 37491671 PMCID: PMC10584957 DOI: 10.1038/s41386-023-01658-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 05/24/2023] [Accepted: 07/07/2023] [Indexed: 07/27/2023]
Abstract
Schizophrenia (SCZ) is a chronic and serious mental disorder with a high mortality rate. At present, there is a lack of objective, cost-effective and widely disseminated diagnosis tools to address this mental health crisis globally. Clinical electroencephalogram (EEG) is a noninvasive technique to measure brain activity with high temporal resolution, and accumulating evidence demonstrates that clinical EEG is capable of capturing abnormal SCZ neuropathology. Although EEG-based automated diagnostic tools have obtained impressive performance on individual datasets, the transportability of potential EEG biomarkers in cross-site real-world application is still an open question. To address the challenges of small sample sizes and population heterogeneity, we develop an advanced interpretable deep learning model using multimodal clinical EEG features and demographic information as inputs to graph neural networks, and further propose different transfer learning strategies to adapt to different clinical scenarios. Taking the disease discrimination of health control (HC) and SCZ with 1030 participants as a use case, our model is trained on a small clinical dataset (N = 188, Chinese) and enhanced using a large-scale public dataset (N = 508, American) of adult participants. Cross-site validation from an independent dataset of adult participants (N = 157, Chinese) produced stable performance, with AUCs of 0.793-0.852 and accuracies of 0.786-0.858 for different SCZ prevalence, respectively. In addition, cross-site validation from another dataset of adolescent boys (N = 84, Russian) yielded an AUC of 0.702 and an accuracy of 0.690. Moreover, feature visualization further revealed that the ranking of feature importance varied significantly among different datasets, and that EEG theta and alpha band power appeared to be the most significant and translational biomarkers of SCZ pathology. Overall, our promising results demonstrate the feasibility of SCZ discrimination using EEG biomarkers in multiple clinical settings.
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Affiliation(s)
- Haiteng Jiang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Peiyin Chen
- Alibaba Damo Academy, 969 West Wen Yi Road, Yu Hang District, Hangzhou, Zhejiang, China
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Zhaohong Sun
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chengqian Liang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Rui Xue
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Liansheng Zhao
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiang Wang
- Psychiatric Laboratory and Mental Health Center, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaojing Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Deng
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongke Gao
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Fei Huang
- Alibaba Damo Academy, 969 West Wen Yi Road, Yu Hang District, Hangzhou, Zhejiang, China
| | - Songfang Huang
- Alibaba Damo Academy, 969 West Wen Yi Road, Yu Hang District, Hangzhou, Zhejiang, China
| | - Yaoyun Zhang
- Alibaba Damo Academy, 969 West Wen Yi Road, Yu Hang District, Hangzhou, Zhejiang, China.
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China.
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9
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Wong SB, Tsao Y, Tsai WH, Wang TS, Wu HC, Wang SS. Application of bidirectional long short-term memory network for prediction of cognitive age. Sci Rep 2023; 13:20197. [PMID: 37980387 PMCID: PMC10657465 DOI: 10.1038/s41598-023-47606-7] [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: 04/04/2023] [Accepted: 11/16/2023] [Indexed: 11/20/2023] Open
Abstract
Electroencephalography (EEG) measures changes in neuronal activity and can reveal significant changes from infancy to adulthood concomitant with brain maturation, making it a potential physiological marker of brain maturation and cognition. To investigate a promising deep learning tool for EEG classification, we applied the bidirectional long short-term memory (BLSTM) algorithm to analyze EEG data from the pediatric EEG laboratory of Taipei Tzu Chi Hospital. The trained BLSTM model was 86% accurate when identifying EEGs from young children (8 months-6 years) and adolescents (12-20 years). However, there was only a modest classification accuracy (69.3%) when categorizing EEG samples into three age groups (8 months-6 years, 6-12 years, and 12-20 years). For EEG samples from patients with intellectual disability, the prediction accuracy of the trained BLSTM model was 46.4%, which was significantly lower than its accuracy for EEGs from neurotypical patients, indicating that the individual's intelligence plays a major role in the age prediction. This study confirmed that scalp EEG can reflect brain maturation and the BLSTM algorithm is a feasible deep learning tool for the identification of cognitive age. The trained model can potentially be applied to clinical services as a supportive measurement of neurodevelopmental status.
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Affiliation(s)
- Shi-Bing Wong
- Department of Pediatrics, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan.
- School of Medicine, Tzu Chi University, Hualien, Taiwan.
| | - Yu Tsao
- Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan
| | - Wen-Hsin Tsai
- Department of Pediatrics, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Tzong-Shi Wang
- School of Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Psychiatry, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan
| | - Hsin-Chi Wu
- School of Medicine, Tzu Chi University, Hualien, Taiwan
- Department of Physical Medicine and Rehabilitation, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan
| | - Syu-Siang Wang
- Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan.
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Sun F, Wang S, Wang Y, Sun J, Li Y, Li Y, Xu Y, Wang X. Differences in generation and maintenance between ictal and interictal generalized spike-and-wave discharges in childhood absence epilepsy: A magnetoencephalography study. Epilepsy Behav 2023; 148:109440. [PMID: 37748416 DOI: 10.1016/j.yebeh.2023.109440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/05/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023]
Abstract
PURPOSE Childhood absence epilepsy (CAE) is characterized by impaired consciousness and distinct electroencephalogram (EEG) patterns. However, interictal epileptiform discharges (IEDs) do not lead to noticeable symptoms. This study examines the disparity between ictal and interictal generalized spike-and-wave discharges (GSWDs) to determine the mechanisms behind CAE and consciousness. METHODS We enrolled 24 patients with ictal and interictal GSWDs in the study. The magnetoencephalography (MEG) data were recorded before and during GSWDs at a sampling rate of 6000 Hz and analyzed across six frequency bands. The absolute and relative spectral power were estimated with the Minimum Norm Estimate (MNE) combined with the Welch technique. All the statistical analyses were performed using paired-sample tests. RESULTS During GSWDs, the right lateral occipital cortex indicated a significant difference in the theta band (5-7 Hz) with stronger power (P = 0.027). The interictal group possessed stronger spectral power in the delta band (P < 0.01) and weaker power in the alpha band (P < 0.01) as early as 10 s before GSWDs in absolute and relative spectral power. Additionally, the ictal group revealed enhanced spectral power inside the occipital cortex in the alpha band and stronger spectral power in the right frontal regions within beta (15-29 Hz), gamma 1 (30-59 Hz), and gamma 2 (60-90 Hz) bands. CONCLUSIONS GSWDs seem to change gradually, with local neural activity changing even 10 s before discharge. During GSWDs, visual afferent stimulus insensitivity could be related to the impaired response state in CAE. The inhibitory signal in the low-frequency band can shorten GSWD duration, thereby achieving seizure control through inhibitory effect strengthening.
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Affiliation(s)
- Fangling Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Siyi Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yingfan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jintao Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yihan Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yanzhang Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yue Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoshan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
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11
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Carvalho MDCG, Ximenes RAA, Andrade-Valença LPA, Montarroyos UR, Diniz GTN, Rodrigues LC, Brickley EB, Eickmann SH, de Araujo TVB, Martelli CMT, da Silva PFS, Miranda-Filho DDB. Longitudinal evolution of electroencephalogram (EEG): Findings over five years of follow-up in children with Zika-related microcephaly from the Microcephaly Epidemic Research Group Pediatric Cohort (2015-2020). Seizure 2023; 110:28-41. [PMID: 37302158 DOI: 10.1016/j.seizure.2023.05.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 05/26/2023] [Accepted: 05/28/2023] [Indexed: 06/13/2023] Open
Abstract
OBJECTIVE To assess the longitudinal evolution of EEG findings in children with Zika related-microcephaly (ZRM) and to evaluate the associations of these patterns with the children's clinical and neuroimaging characteristics. METHODS As part of the follow-up of the Microcephaly Epidemic Research Group Pediatric Cohort (MERG-PC) in Recife, Brazil, we performed serial EEG recordings in a subgroup of children with ZRM to evaluate changes in background rhythms and epileptiform activity (EA). Latent class analysis was used to identify patterns in the evolution of EA over time; clinical and neuroimaging findings were compared across the identified groups. RESULTS Out of the 72 children with ZRM who were evaluated during 190 EEGs/videoEEGs, all participants presented with abnormal background activity, 37.5% presented with an alpha-theta rhythmic activity, and 25% presented with sleep spindles, which were less commonly observed in children with epilepsy. EA changed over time in 79.2% of children, and three distinct trajectories were identified: (i) multifocal EA over time, (ii) no discharges/focal EA evolving to focal/multifocal EA, and (iii) focal/multifocal EA evolving to epileptic encephalopathy patterns (e.g., hypsarrhythmia or continuous EA in sleep). The multifocal EA over time trajectory was associated with periventricular and thalamus/basal ganglia calcifications, brainstem and corpus callosum atrophy and had less focal epilepsy, whereas the children in the trajectory which evolved to epileptic encephalopathy patterns had more frequently focal epilepsy. SIGNIFICANCE These findings suggest that, in most children with ZRM, trajectories of changes in EA can be identified and associated with neuroimaging and clinical features.
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Affiliation(s)
| | - Ricardo A A Ximenes
- University of Pernambuco, Recife Brazil; Federal University of Pernambuco, Recife, Brazil
| | | | | | | | - Laura C Rodrigues
- London School of Hygiene & Tropical Medicine, London, United Kingdom
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Iyer KK, Roberts JA, Waak M, Kevat A, Chawla J, Lauronen L, Vanhatalo S, Stevenson NJ. Optimization of time series features to estimate brain age in children from electroencephalography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082782 DOI: 10.1109/embc40787.2023.10340663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Functional brain age measures in children, derived from the electroencephalogram (EEG), offer direct and objective measures in assessing neurodevelopmental status. Here we explored the effectiveness of 32 preselected 'handcrafted' EEG features in predicting brain age in children. These features were benchmarked against a large library of highly comparative multivariate time series features (>7000 features). Results showed that age predictors based on handcrafted EEG features consistently outperformed a generic set of time series features. These findings suggest that optimization of brain age estimation in children benefits from careful preselection of EEG features that are related to age and neurodevelopmental trajectory. This approach shows potential for clinical translation in the future.Clinical Relevance-Handcrafted EEG features provide an accurate functional neurodevelopmental biomarker that tracks brain function maturity in children.
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13
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Nogales A, García-Tejedor ÁJ, Chazarra P, Ugalde-Canitrot A. Discriminating and understanding brain states in children with epileptic spasms using deep learning and graph metrics analysis of brain connectivity. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 232:107427. [PMID: 36870168 DOI: 10.1016/j.cmpb.2023.107427] [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: 09/01/2022] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Epilepsy is a brain disorder consisting of abnormal electrical discharges of neurons resulting in epileptic seizures. The nature and spatial distribution of these electrical signals make epilepsy a field for the analysis of brain connectivity using artificial intelligence and network analysis techniques since their study requires large amounts of data over large spatial and temporal scales. For example, to discriminate states that would otherwise be indistinguishable from the human eye. This paper aims to identify the different brain states that appear concerning the intriguing seizure type of epileptic spasms. Once these states have been differentiated, an attempt is made to understand their corresponding brain activity. METHODS The representation of brain connectivity can be done by graphing the topology and intensity of brain activations. Graph images from different instants within and outside the actual seizure are used as input to a deep learning model for classification purposes. This work uses convolutional neural networks to discriminate the different states of the epileptic brain based on the appearance of these graphs at different times. Next, we apply several graph metrics as an aid to interpret what happens in the brain regions during and around the seizure. RESULTS Results show that the model consistently finds distinctive brain states in children with epilepsy with focal onset epileptic spasms that are indistinguishable under the expert visual inspection of EEG traces. Furthermore, differences are found in brain connectivity and network measures in each of the different states. CONCLUSIONS Computer-assisted discrimination using this model can detect subtle differences in the various brain states of children with epileptic spasms. The research reveals previously undisclosed information regarding brain connectivity and networks, allowing for a better understanding of the pathophysiology and evolving characteristics of this particular seizure type. From our data, we speculate that the prefrontal, premotor, and motor cortices could be more involved in a hypersynchronized state occurring in the few seconds immediately preceding the visually evident EEG and clinical ictal features of the first spasm in a cluster. On the other hand, a disconnection in centro-parietal areas seems a relevant feature in the predisposition and repetitive generation of epileptic spasms within clusters.
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Affiliation(s)
- Alberto Nogales
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain.
| | - Álvaro J García-Tejedor
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain
| | - Pedro Chazarra
- CEIEC Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800, Pozuelo de Alarcón 28223, Spain
| | - Arturo Ugalde-Canitrot
- School of Medicine. Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda km. 1,800. Pozuelo de Alarcón 28223, Spain; Epilepsy Unit, Neurology and Clinical Neurophysiology Service, Hospital Universitario La Paz, Paseo de la Castellana, 261, Madrid 28046, Spain
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14
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Fingelkurts AA, Fingelkurts AA. Turning Back the Clock: A Retrospective Single-Blind Study on Brain Age Change in Response to Nutraceuticals Supplementation vs. Lifestyle Modifications. Brain Sci 2023; 13:520. [PMID: 36979330 PMCID: PMC10046544 DOI: 10.3390/brainsci13030520] [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: 02/20/2023] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND There is a growing consensus that chronological age (CA) is not an accurate indicator of the aging process and that biological age (BA) instead is a better measure of an individual's risk of age-related outcomes and a more accurate predictor of mortality than actual CA. In this context, BA measures the "true" age, which is an integrated result of an individual's level of damage accumulation across all levels of biological organization, along with preserved resources. The BA is plastic and depends upon epigenetics. Brain state is an important factor contributing to health- and lifespan. METHODS AND OBJECTIVE Quantitative electroencephalography (qEEG)-derived brain BA (BBA) is a suitable and promising measure of brain aging. In the present study, we aimed to show that BBA can be decelerated or even reversed in humans (N = 89) by using customized programs of nutraceutical compounds or lifestyle changes (mean duration = 13 months). RESULTS We observed that BBA was younger than CA in both groups at the end of the intervention. Furthermore, the BBA of the participants in the nutraceuticals group was 2.83 years younger at the endpoint of the intervention compared with their BBA score at the beginning of the intervention, while the BBA of the participants in the lifestyle group was only 0.02 years younger at the end of the intervention. These results were accompanied by improvements in mental-physical health comorbidities in both groups. The pre-intervention BBA score and the sex of the participants were considered confounding factors and analyzed separately. CONCLUSIONS Overall, the obtained results support the feasibility of the goal of this study and also provide the first robust evidence that halting and reversal of brain aging are possible in humans within a reasonable (practical) timeframe of approximately one year.
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Саракаева ЛР, Рыжкова ДВ, Митрофанова ЛБ, Баиров ВГ, Сухоцкая АА, Смородин АП, Ефтич ЕА, Кельмансон ИА, Никитина ИЛ. [Electroencephalogram features in children with congenital hyperinsulinism treated according to the international protocol in Russian Federation]. PROBLEMY ENDOKRINOLOGII 2023; 69:68-75. [PMID: 36842080 PMCID: PMC9978872 DOI: 10.14341/probl13174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/03/2022] [Indexed: 02/27/2023]
Abstract
BACKGROUND Congenital hyperinsulinism (CHI) is a rare life-threatening disease characterised by persistent hypoglycaemia as a result of inappropriate insulin secretion, which can lead to irreversible neurological defects in infants. AIM To evaluate neurophysiological characteristics of central nervous system in children with congenital hyperinsulinism treated according to the international protocol in Russian Federation. MATERIALS AND METHODS Our retrospective, prospective cohort study included 73 patients who received treatment for CHI according to the current international protocol at different departments of the Almazov National Medical Research Centre from 2017 to 2022. All patients underwent a comprehensive examination, including electroencephalography (EEG). RESULTS Among 73 patients with CHI, 35% (23) had focal form of the disease, 65% had non-focal form (49% (39) - diffuse form, 16% (11) - atypical form). All patients with focal form of CHI had a recovery as an outcome.Analysing the EEG data we found that paroxysmal activity was recorded in 23 patients (32%), 50 patients did not have paroxysmal activity (68%). Diffuse changes were observed in 47 patients (64%), whereas 26 patients (36%) were absent of it. By constructing Kaplan-Meier curves we found that the alpha rhythm is formed significantly (p=0.026) earlier in patients with a focal form of CHI. CONCLUSION CHI patients treated according to the international guidelines in Russian Federation show rather positive neurological outcome. We established that alpha rhythm earliest formation is associated with focal form of CHI.
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Affiliation(s)
- Л. Р. Саракаева
- Национальный медицинский исследовательский центр им. В.А. Алмазова
| | - Д. В. Рыжкова
- Национальный медицинский исследовательский центр им. В.А. Алмазова
| | | | - В. Г. Баиров
- Национальный медицинский исследовательский центр им. В.А. Алмазова
| | - А. А. Сухоцкая
- Национальный медицинский исследовательский центр им. В.А. Алмазова
| | - А. П. Смородин
- Национальный медицинский исследовательский центр им. В.А. Алмазова
| | - Е. А. Ефтич
- Национальный медицинский исследовательский центр им. В.А. Алмазова
| | - И. А. Кельмансон
- Национальный медицинский исследовательский центр им. В.А. Алмазова
| | - И. Л. Никитина
- Национальный медицинский исследовательский центр им. В.А. Алмазова
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Bauer W, Dylag KA, Lysiak A, Wieczorek-Stawinska W, Pelc M, Szmajda M, Martinek R, Zygarlicki J, Bańdo B, Stomal-Slowinska M, Kawala-Sterniuk A. Initial study on quantitative electroencephalographic analysis of bioelectrical activity of the brain of children with fetal alcohol spectrum disorders (FASD) without epilepsy. Sci Rep 2023; 13:109. [PMID: 36596841 PMCID: PMC9810692 DOI: 10.1038/s41598-022-26590-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 12/16/2022] [Indexed: 01/04/2023] Open
Abstract
Fetal alcohol spectrum disorders (FASD) are spectrum of neurodevelopmental conditions associated with prenatal alcohol exposure. The FASD manifests mostly with facial dysmorphism, prenatal and postnatal growth retardation, and selected birth defects (including central nervous system defects). Unrecognized and untreated FASD leads to severe disability in adulthood. The diagnosis of FASD is based on clinical criteria and neither biomarkers nor imaging tests can be used in order to confirm the diagnosis. The quantitative electroencephalography (QEEG) is a type of EEG analysis, which involves the use of mathematical algorithms, and which has brought new possibilities of EEG signal evaluation, among the other things-the analysis of a specific frequency band. The main objective of this study was to identify characteristic patterns in QEEG among individuals affected with FASD. This study was of a pilot prospective study character with experimental group consisting of patients with newly diagnosed FASD and of the control group consisting of children with gastroenterological issues. The EEG recordings of both groups were obtained, than analyzed using a commercial QEEG module. As a results we were able to establish the dominance of the alpha rhythm over the beta rhythm in FASD-participants compared to those from the control group, mostly in frontal and temporal regions. Second important finding is an increased theta/beta ratio among patients with FASD. These findings are consistent with the current knowledge on the pathological processes resulting from the prenatal alcohol exposure. The obtained results and conclusions were promising, however, further research is necessary (and planned) in order to validate the use of QEEG tools in FASD diagnostics.
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Affiliation(s)
- Waldemar Bauer
- grid.9922.00000 0000 9174 1488Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland
| | - Katarzyna Anna Dylag
- St. Louis Children Hospital in Krakow, 30-663 Kraków, Poland ,grid.5522.00000 0001 2162 9631Department of Pathophysiology, Jagiellonian University in Krakow – Collegium Medicum, 31-121 Kraków, Poland
| | - Adam Lysiak
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | | | - Mariusz Pelc
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland ,grid.36316.310000 0001 0806 5472School of Computing and Mathematical Sciences, University of Greenwich, London, SE10 9LS UK
| | - Miroslaw Szmajda
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Radek Martinek
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland ,grid.440850.d0000 0000 9643 2828Department of Cybernetics and Biomedical Engineering, VSB—Technical University Ostrava—FEECS, 708 00 Ostrava-Poruba, Czech Republic
| | - Jaroslaw Zygarlicki
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
| | - Bożena Bańdo
- St. Louis Children Hospital in Krakow, 30-663 Kraków, Poland
| | | | - Aleksandra Kawala-Sterniuk
- grid.440608.e0000 0000 9187 132XFaculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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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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Ukhinov EB, Madaeva IM, Berdina ON, Rychkova LV, Kolesnikova LI, Kolesnikov SI. Features of the EEG Pattern of Sleep Spindles and Its Diagnostic Significance in Ontogeny. Bull Exp Biol Med 2022; 173:399-408. [DOI: 10.1007/s10517-022-05557-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Indexed: 11/30/2022]
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19
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Schultz B, Schultz M, Boehne M, Dennhardt N. EEG monitoring during anesthesia in children aged 0 to 18 months: amplitude-integrated EEG and age effects. BMC Pediatr 2022; 22:156. [PMID: 35346111 PMCID: PMC8962600 DOI: 10.1186/s12887-022-03180-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 02/28/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The amplitude-integrated EEG (aEEG) is a widely used monitoring tool in neonatology / pediatric intensive care. It takes into account the amplitudes, but not the frequency composition, of the EEG. Advantages of the aEEG are clear criteria for interpretation and time compression. During the first year of life, the electroencephalogram (EEG) during sedation / anesthesia changes from a low-differentiated to a differentiated EEG; higher-frequency waves develop increasingly. There are few studies on the use of aEEG during pediatric anesthesia. A systematic evaluation of the aEEG in defined EEG stages during anesthesia / sedation is not yet available. Parameters of pediatric EEGs (power, median frequency, spectral edge frequency) recorded during anesthesia and of the corresponding aEEGs (upper and lower value of the aEEG trace) should be examined for age-related changes. Furthermore, it should be examined whether the aEEG can distinguish EEG stages of sedation / anesthesia in differentiated EEGs.
Methods
In a secondary analysis of a prospective observational study EEGs and aEEGs (1-channel recordings, electrode positions on forehead) of 50 children (age: 0–18 months) were evaluated. EEG stages: A (awake), Slow EEG, E2, F0, and F1 in low-differentiated EEGs and A (awake), B0–2, C0–2, D0–2, E0–2, F0–1 in differentiated EEGs.
Results
Median and spectral edge frequency increased significantly with age (p < 0.001 each). In low-differentiated EEGs, the power of the Slow EEG increased significantly with age (p < 0.001). In differentiated EEGs, the power increased significantly with age in each of the EEG stages B1 to E1 (p = 0.04, or less), and the upper and lower values of the aEEG trace increased with age (p < 0.001). A discriminant analysis using the upper and lower values of the aEEG showed that EEG epochs from the stages B1 to E1 were assigned to the original EEG stage in only 19.3% of the cases. When age was added as the third variable, the rate of correct reclassifications was 28.5%.
Conclusions
The aEEG was not suitable for distinguishing EEG stages above the burst suppression range. For this purpose, the frequency composition of the EEG should be taken into account.
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Ji Y, Choi TY, Lee J, Yoon S, Won GH, Jeong H, Kang SW, Kim JW. Characteristics of Attention-Deficit/Hyperactivity Disorder Subtypes in Children Classified Using Quantitative Electroencephalography. Neuropsychiatr Dis Treat 2022; 18:2725-2736. [PMID: 36437880 PMCID: PMC9697401 DOI: 10.2147/ndt.s386774] [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: 08/18/2022] [Accepted: 11/11/2022] [Indexed: 11/22/2022] Open
Abstract
PURPOSE This study used quantitative electroencephalography (QEEG) to investigate the characteristics of attention-deficit/hyperactivity disorder (ADHD) subtypes in children. PATIENTS AND METHODS There were 69 subjects (42 with ADHD and 27 neurotypical (NT)) in this study. A semi-structured interview was conducted with each participant for psychiatric diagnostic evaluation. We measured the absolute and relative power in 19 channels and analyzed QEEG using the following frequency ranges: delta (1-4 Hz), theta (4-8 Hz), alpha 1 (8-10 Hz), alpha 2 (10-12 Hz), beta 1 (12-15 Hz), beta 2 (15-20 Hz), beta 3 (20-30 Hz), and gamma (30-45 Hz). Group analyses and EEG noise preprocessing were conducted using iSyncBrain, a cloud-based, artificial intelligence EEG analysis platform. Analysis of covariance adjusted for IQ, age, and sex was used. RESULTS QEEG analysis revealed three ADHD subtypes, characterized by (A) elevated relative fast alpha and beta power, (B) elevated absolute slow frequency (delta and theta power), or (C) elevated absolute and relative beta power. A significant difference was found in the Korean ADHD Rating Scale (K-ARS) among the four groups (df=3, F=8.004, p<0.001); group C had the highest score (25.31±11.16), followed by group A (21.67±13.18). The score of group B (12.64±7.84) was similar to that of the NT group (11.07±6.12) and did not reach the cut-off point of the K-ARS. In the Wender-Utah Rating Scale (WURS), group B score (55.82±23.17) was significantly higher than the NT group score (42.81±13.26). CONCLUSION These results indicate that children with ADHD do not constitute a neurophysiologically homogenous group. Children with QEEG subtype B (elevated slow frequency) may be difficult to distinguish from normal children using the K-ARS, which is the most common screening tool for ADHD. Moreover, parents of children with this subtype may be less sensitive to observing ADHD symptoms.
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Affiliation(s)
- Yoonmi Ji
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
| | - Tae Young Choi
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
| | - Jonghun Lee
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
| | - Seoyoung Yoon
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
| | - Geun Hui Won
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
| | | | - Seung Wan Kang
- iMediSync Inc, Seoul, Republic of Korea.,National Standard Reference Data Center for Korean EEG, Seoul National University College of Nursing, Seoul, Republic of Korea
| | - Jun Won Kim
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
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EEG Assessment in a 2-Year-Old Child with Prolonged Disorders of Consciousness: 3 Years' Follow-up. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8826238. [PMID: 33293944 PMCID: PMC7718066 DOI: 10.1155/2020/8826238] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 10/21/2020] [Accepted: 10/31/2020] [Indexed: 11/25/2022]
Abstract
A 2-year-old girl, diagnosed with traumatic brain injury and epilepsy following car trauma, was followed up for 3 years (a total of 15 recordings taken at 0, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, 19, 26, and 35 months). There is still no clear guidance on the diagnosis, treatment, and prognosis of children with disorders of consciousness. At each appointment, recordings included the child's height, weight, pediatric Glasgow Coma Scale (pGCS), Coma Recovery Scale-Revised (CRS-R), Gesell Developmental Schedule, computed tomography or magnetic resonance imaging, electroencephalogram, frequency of seizures, oral antiepileptic drugs, stimulation with subject's own name (SON), and median nerve electrical stimulation (MNS). Growth and development were deemed appropriate for the age of the child. The pGCS and Gesell Developmental Schedule provided a comprehensive assessment of consciousness and mental development; the weighted Phase Lag Index (wPLI ) in the β-band (13–25 Hz) can distinguish unresponsive wakefulness syndrome from minimally conscious state and confirm that the SON and MNS were effective. The continuous increase of delta-band power indicates a poor prognosis. Interictal epileptiform discharges (IEDs) have a cumulative effect and seizures seriously affect the prognosis.
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Byeon J, Choi TY, Won GH, Lee J, Kim JW. A novel quantitative electroencephalography subtype with high alpha power in ADHD: ADHD or misdiagnosed ADHD? PLoS One 2020; 15:e0242566. [PMID: 33201920 PMCID: PMC7671485 DOI: 10.1371/journal.pone.0242566] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Accepted: 11/04/2020] [Indexed: 11/24/2022] Open
Abstract
This study investigated quantitative electroencephalography (QEEG) subtypes as auxiliary tools to assess Attention Deficit Hyperactivity Disorder (ADHD). A total of 74 subjects (58 male and 16 female) were assessed using the Korean version of the Diagnostic Interview Schedule for Children Version IV and were assigned to one of three groups: ADHD, ADHD-Not Otherwise specified (NOS), and Neurotypical (NT). We measured absolute and relative EEG power in 19 channels and conducted an auditory continuous performance test. We analyzed QEEG according to the frequency range: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), and beta (13.5–30 Hz). The subjects were then grouped by Ward’s method of cluster analysis using the squared Euclidian distance to measure dissimilarities. We discovered four QEEG clusters, which were characterized by: (a) elevated delta power with less theta activity, (b) elevated slow alpha relative power, (c) elevated theta with deficiencies of alpha and beta relative power, and (d) elevated fast alpha and beta absolute power. The largest proportion of participants in clusters (a) and (c) were from the ADHD group (48% and 47%, respectively). Conversely, group (b) mostly consisted of the participants from the NOS group (59%), while group (d) had the largest proportion of participants from the NT group (62%). These results indicate that children with ADHD does not neurophysiologically constitute a homogenous group. We also identified a new subtype with increased alpha power in addition to those commonly reported in ADHD. Given the QEEG characteristics with increased alpha power, we should consider the possibility that this subtype may be caused by childhood depression. In conclusion, we believe that these QEEG subtypes of ADHD are expected to provide valuable information for accurately diagnosing ADHD.
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Affiliation(s)
- Jun Byeon
- Department of Psychiatry, Catholic University of Daegu School of Medicine, Daegu, Republic of Korea
| | - Tae Young Choi
- Department of Psychiatry, Catholic University of Daegu School of Medicine, Daegu, Republic of Korea
| | - Geun Hui Won
- Department of Psychiatry, Catholic University of Daegu School of Medicine, Daegu, Republic of Korea
| | - Jaewon Lee
- Department of Psychiatry, Easybrain Center, Seoul, Republic of Korea
| | - Jun Won Kim
- Department of Psychiatry, Catholic University of Daegu School of Medicine, Daegu, Republic of Korea
- * E-mail:
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Mesin L, Valerio M, Capizzi G. Automated diagnosis of encephalitis in pediatric patients using EEG rhythms and slow biphasic complexes. Phys Eng Sci Med 2020; 43:997-1006. [PMID: 32696434 DOI: 10.1007/s13246-020-00893-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 06/29/2020] [Indexed: 11/25/2022]
Abstract
Slow biphasic complexes (SBC) have been identified in the EEG of patients suffering for inflammatory brain diseases. Their amplitude, location and frequency of appearance were found to correlate with the severity of encephalitis. Other characteristics of SBCs and of EEG traces of patients could reflect the grade of pathology. Here, EEG rhythms are investigated together with SBCs for a better characterization of encephalitis. EEGs have been acquired from pediatric patients: ten controls and ten encephalitic patients. They were split by neurologists into five classes of different severity of the pathology. The relative power of EEG rhythms was found to change significantly in EEGs labeled with different severity scores. Moreover, a significant variation was found in the last seconds before the appearance of an SBC. This information and quantitative indexes characterizing the SBCs were used to build a binary classification decision tree able to identify the classes of severity. True classification rate of the best model was 76.1% (73.5% with leave-one-out test). Moreover, the classification errors were among classes with similar severity scores (precision higher than 80% was achieved considering three instead of five classes). Our classification method may be a promising supporting tool for clinicians to diagnose, assess and make the follow-up of patients with encephalitis.
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Affiliation(s)
- Luca Mesin
- Mathematical Biology and Physiology, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Turin, Italy.
| | - Massimo Valerio
- Mathematical Biology and Physiology, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Turin, Italy
| | - Giorgio Capizzi
- Ospedale Infantile Regina Margherita, Department of Child Neuropsychiatry, Universitá di Torino, Turin, Italy
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