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Mathew J, Adhia DB, Hall M, De Ridder D, Mani R. EEG-Based Cortical Alterations in Individuals With Chronic Knee Pain Secondary to Osteoarthritis: A Cross-sectional Investigation. THE JOURNAL OF PAIN 2024; 25:104429. [PMID: 37989404 DOI: 10.1016/j.jpain.2023.11.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 11/05/2023] [Accepted: 11/10/2023] [Indexed: 11/23/2023]
Abstract
Chronic painful knee osteoarthritis (OA) is a disabling physical health condition. Alterations in brain responses to arthritic changes in the knee may explain persistent pain. This study investigated source localized, resting-state electroencephalography activity and functional connectivity in people with knee OA, compared to healthy controls. Adults aged 44 to 85 years with knee OA (n = 37) and healthy control (n = 39) were recruited. Resting-state electroencephalography was collected for 10 minutes and decomposed into infraslow frequency (ISF) to gamma frequency bands. Standard low-resolution electromagnetic brain tomography statistical nonparametric maps were conducted, current densities of regions of interest were compared between groups and correlation analyses were performed between electroencephalography (EEG) measures and clinical pain and functional outcomes in the knee OA group. Standard low-resolution electromagnetic brain tomography nonparametric maps revealed higher (P = .006) gamma band activity over the right insula (RIns) in the knee OA group. A significant (P < .0001) reduction in ISF band activity at the pregenual anterior cingulate cortex, whereas higher theta, alpha, beta, and gamma band activity at the dorsal anterior cingulate cortex, pregenual anterior cingulate cortex, the somatosensory cortex, and RIns in the knee OA group were identified. ISF activity of the dorsal anterior cingulate cortex was positively correlated with pain measures and psychological distress scores. Theta and alpha activity of RIns were negatively correlated with pain interference. In conclusion, aberrations in infraslow and faster frequency EEG oscillations at sensory discriminative, motivational-affective, and descending inhibitory cortical regions were demonstrated in people with chronic painful knee OA. Moreover, EEG oscillations were correlated with pain and functional outcome measures. PERSPECTIVE: This study confirms alterations in the rsEEG oscillations and its relationship with pain experience in people with knee OA. The study provides potential cortical targets and the EEG frequency bands for neuromodulatory interventions for managing chronic pain experience in knee OA.
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Affiliation(s)
- Jerin Mathew
- Centre for Health, Activity, and Rehabilitation Research, School of Physiotherapy, University of Otago, New Zealand; Department of Anatomy, School of Biomedical Sciences, University of Otago, New Zealand; Pain@Otago Research Theme, University of Otago, New Zealand
| | - Divya B Adhia
- Pain@Otago Research Theme, University of Otago, New Zealand; Division of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Matthew Hall
- Division of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Dirk De Ridder
- Pain@Otago Research Theme, University of Otago, New Zealand; Division of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Ramakrishnan Mani
- Centre for Health, Activity, and Rehabilitation Research, School of Physiotherapy, University of Otago, New Zealand; Pain@Otago Research Theme, University of Otago, New Zealand
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2
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Kang JH, Bae JH, Jeon YJ. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering (Basel) 2024; 11:418. [PMID: 38790286 PMCID: PMC11118246 DOI: 10.3390/bioengineering11050418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
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Affiliation(s)
- Jae-Hwan Kang
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jang-Han Bae
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Young-Ju Jeon
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
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Lawrence AJ, Lawrence-Wood E, Aidman EV, Spencer-Merris PL, Felmingham KL, McFarlane AC. Reduced pre-attentive threat versus nonthreat signal discrimination in clinically healthy military personnel with recurrent combat exposure history: A preliminary event-related potential (ERP) study. J Psychiatr Res 2024; 172:266-273. [PMID: 38417322 DOI: 10.1016/j.jpsychires.2024.02.033] [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: 06/03/2023] [Revised: 02/09/2024] [Accepted: 02/13/2024] [Indexed: 03/01/2024]
Abstract
Evidence now suggests that traumatic-stress impacts brain functions even in the absence of acute-onset post-traumatic stress disorder (PTSD) symptoms. These neurophysiological changes have also been suggested to account for increased risks of PTSD symptoms later developing in the aftermath of subsequent trauma. However, surprisingly few studies have explicitly examined brain function dynamics in high-risk populations, such as combat exposed military personnel without diagnosable PTSD. To extend available research, facial expression sensitive N170 event-related potential (ERP) amplitudes were examined in a clinically healthy sample of active service military personnel with recurrent combat exposure history. Consistent with several established theories of delayed-onset PTSD vulnerability, higher N170 amplitudes to backward-masked fearful and neutral facial expressions correlated with higher levels of past combat exposure. Significantly elevated amplitudes to nonthreatening neutral facial expressions also resulted in an absence of normal threat-versus-nonthreat signal processing specificity. While a modest sample size and cross-sectional design are key limitations here, ongoing prospective-longitudinal follow-ups may shed further light on the precise aetiology and prognostic utility of these preliminary findings in the near future.
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Affiliation(s)
- Andrew J Lawrence
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, Australia.
| | - Ellie Lawrence-Wood
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, Australia; Phoenix Australia Centre for Posttraumatic Mental Health, University of Melbourne, Australia
| | - Eugene V Aidman
- Human and Decision Sciences Division, Defence Science and Technology Group (DSTG), Adelaide, Australia; School of Biomedical Sciences & Pharmacy, University of Newcastle, Newcastle, Australia
| | | | - Kim L Felmingham
- School of Psychological Science, University of Melbourne, Melbourne, Australia
| | - Alexander C McFarlane
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, Australia
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Benedetti GM, Guerriero RM, Press CA. Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG. Neurocrit Care 2023; 39:618-638. [PMID: 36949358 PMCID: PMC10033183 DOI: 10.1007/s12028-023-01686-5] [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: 07/28/2022] [Accepted: 01/30/2023] [Indexed: 03/24/2023]
Abstract
Critically ill children with acute neurologic dysfunction are at risk for a variety of complications that can be detected by noninvasive bedside neuromonitoring. Continuous electroencephalography (cEEG) is the most widely available and utilized form of neuromonitoring in the pediatric intensive care unit. In this article, we review the role of cEEG and the emerging role of quantitative EEG (qEEG) in this patient population. cEEG has long been established as the gold standard for detecting seizures in critically ill children and assessing treatment response, and its role in background assessment and neuroprognostication after brain injury is also discussed. We explore the emerging utility of both cEEG and qEEG as biomarkers of degree of cerebral dysfunction after specific injuries and their ability to detect both neurologic deterioration and improvement.
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Affiliation(s)
- Giulia M Benedetti
- Division of Pediatric Neurology, Department of Neurology, Seattle Children's Hospital and the University of Washington School of Medicine, Seattle, WA, USA.
- Division of Pediatric Neurology, Department of Pediatrics, C.S. Mott Children's Hospital and the University of Michigan, 1540 E Hospital Drive, Ann Arbor, MI, 48109-4279, USA.
| | - Rejéan M Guerriero
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Craig A Press
- Departments of Neurology and Pediatric, Children's Hospital of Philadelphia and Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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Jackson LE, Han YJ, Evans LH. The efficacy of electroencephalography neurofeedback for enhancing episodic memory in healthy and clinical participants: A systematic qualitative review and meta-analysis. Neurosci Biobehav Rev 2023; 155:105455. [PMID: 37926240 DOI: 10.1016/j.neubiorev.2023.105455] [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: 07/20/2023] [Revised: 10/09/2023] [Accepted: 10/30/2023] [Indexed: 11/07/2023]
Abstract
Several studies have examined whether electroencephalography neurofeedback (EEG-NF), a self-regulatory technique where an individual receives real-time feedback on a pattern of brain activity that is theoretically linked to a target behaviour, can enhance episodic memory. The aim of this research was to i) provide a qualitative overview of the literature, and ii) conduct a meta-analysis of appropriately controlled studies to determine whether EEG-NF can enhance episodic memory. The literature search returned 46 studies, with 21 studies (44 effect sizes) meeting the inclusion criteria for the meta-analysis. The qualitative overview revealed that, across EEG-NF studies on both healthy and clinical populations, procedures and protocols vary considerably and many studies were insufficiently powered with inadequate design features. The meta-analysis, conducted on studies with an active control, revealed a small-size, significant positive effect of EEG-NF on episodic memory performance (g = 0.31, p = 0.003), moderated by memory modality and EEG-NF self-regulation success. These results are discussed with a view towards optimising EEG-NF training and subsequent benefits to episodic memory.
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Affiliation(s)
- Lucy E Jackson
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, Wales, UK
| | - Yi-Jhong Han
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London SE5 9RJ, England, UK
| | - Lisa H Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff CF24 4HQ, Wales, UK.
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Modarres M, Cochran D, Kennedy DN, Frazier JA. Comparison of comprehensive quantitative EEG metrics between typically developing boys and girls in resting state eyes-open and eyes-closed conditions. Front Hum Neurosci 2023; 17:1237651. [PMID: 38021243 PMCID: PMC10659091 DOI: 10.3389/fnhum.2023.1237651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/09/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction A majority of published studies comparing quantitative EEG (qEEG) in typically developing (TD) children and children with neurodevelopmental or psychiatric disorders have used a control group (e.g., TD children) that combines boys and girls. This suggests a widespread supposition that typically developing boys and girls have similar brain activity at all locations and frequencies, allowing the data from TD boys and girls to be aggregated in a single group. Methods In this study, we have rigorously challenged this assumption by performing a comprehensive qEEG analysis on EEG recoding of TD boys (n = 84) and girls (n = 62), during resting state eyes-open and eyes-closed conditions (EEG recordings from Child Mind Institute's Healthy Brain Network (HBN) initiative). Our qEEG analysis was performed over narrow-band frequencies (e.g., separating low α from high α, etc.), included sex, age, and head size as covariates in the analysis, and encompassed computation of a wide range of qEEG metrics that included both absolute and relative spectral power levels, regional hemispheric asymmetry, and inter- and intra-hemispheric magnitude coherences as well as phase coherency among cortical regions. We have also introduced a novel compact yet comprehensive visual presentation of the results that allows comparison of the qEEG metrics of boys and girls for the entire EEG locations, pairs, and frequencies in a single graph. Results Our results show there are wide-spread EEG locations and frequencies where TD boys and girls exhibit differences in their absolute and relative spectral powers, hemispheric power asymmetry, and magnitude coherence and phase synchrony. Discussion These findings strongly support the necessity of including sex, age, and head size as covariates in the analysis of qEEG of children, and argue against combining data from boys and girls. Our analysis also supports the utility of narrow-band frequencies, e.g., dividing α, β, and γ band into finer sub-scales. The results of this study can serve as a comprehensive normative qEEG database for resting state studies in children containing both eyes open and eyes closed paradigms.
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Affiliation(s)
- Mo Modarres
- The Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - David Cochran
- The Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA, United States
- The Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Chan Medical School/UMass Memorial Health Care, Worcester, MA, United States
| | - David N. Kennedy
- The Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Jean A. Frazier
- The Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA, United States
- The Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Chan Medical School/UMass Memorial Health Care, Worcester, MA, United States
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Janiukstyte V, Owen TW, Chaudhary UJ, Diehl B, Lemieux L, Duncan JS, de Tisi J, Wang Y, Taylor PN. Normative brain mapping using scalp EEG and potential clinical application. Sci Rep 2023; 13:13442. [PMID: 37596291 PMCID: PMC10439201 DOI: 10.1038/s41598-023-39700-7] [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: 04/06/2023] [Accepted: 07/29/2023] [Indexed: 08/20/2023] Open
Abstract
A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.
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Affiliation(s)
- Vytene Janiukstyte
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK
| | - Thomas W Owen
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK
| | - Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Yujiang Wang
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE2 4HH, UK
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK
| | - Peter N Taylor
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle Upon Tyne, NE4 5DG, UK.
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE2 4HH, UK.
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.
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8
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Kim NH, Park U, Yang DW, Choi SH, Youn YC, Kang SW. PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer's disease. Sci Rep 2023; 13:10299. [PMID: 37365198 DOI: 10.1038/s41598-023-36713-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 06/08/2023] [Indexed: 06/28/2023] Open
Abstract
Developing reliable biomarkers is important for screening Alzheimer's disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical interpretation and quantification of signal information have limited its clinical application. There have been many research about machine learning (ML) adoption with EEG, but the accuracy of detecting AD is not so high or not validated with Aβ PET scan. We developed EEG-ML algorithm to detect brain Aβ pathology among subjective cognitive decline (SCD) or mild cognitive impairment (MCI) population, and validated it with Aβ PET. 19-channel resting-state EEG and Aβ PET were collected from 311 subjects: 196 SCD(36 Aβ +, 160 Aβ -), 115 MCI(54 Aβ +, 61Aβ -). 235 EEG data were used for training ML, and 76 for validation. EEG features were standardized for age and sex. Multiple important features sets were selected by 6 statistics analysis. Then, we trained 8 multiple machine learning for each important features set. Meanwhile, we conducted paired t-test to find statistically different features between amyloid positive and negative group. The best model showed 90.9% sensitivity, 76.7% specificity and 82.9% accuracy in MCI + SCD (33 Aβ +, 43 Aβ -). Limited to SCD, 92.3% sensitivity, 75.0% specificity, 81.1% accuracy (13 Aβ +, 24 Aβ -). 90% sensitivity, 78.9% specificity and 84.6% accuracy for MCI (20 Aβ +, 19 Aβ -). Similar trends of EEG power have been observed from the group comparison between Aβ + and Aβ -, and between MCI and SCD: enhancement of frontal/ frontotemporal theta; attenuation of mid-beta in centroparietal areas. The present findings suggest that accurate classification for beta-amyloid accumulation in the brain based on QEEG alone could be possible, which implies that QEEG is a promising biomarker for beta-amyloid. Since QEEG is more accessible, cost-effective, and safer than amyloid PET, QEEG-based biomarkers may play an important role in the diagnosis and treatment of AD. We expect specific patterns in QEEG could play an important role to predict future progression of cognitive impairment in the preclinical stage of AD. Further feature engineering and validation with larger dataset is recommended.
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Affiliation(s)
- Nam Heon Kim
- iMediSync Inc, 15F, 411, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea
| | - Ukeob Park
- iMediSync Inc, 15F, 411, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea
| | - Dong Won Yang
- Department of Neurology, St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seong Hye Choi
- Department of Neurology, Inha University School of Medicine, Incheon, Republic of Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Republic of Korea
| | - Seung Wan Kang
- iMediSync Inc, 15F, 411, Teheran-ro, Gangnam-gu, Seoul, Republic of Korea.
- National Standard Reference Data Center for Korean EEG, Seoul National University College of Nursing, Seoul, Republic of Korea.
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9
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Janiukstyte V, Owen TW, Chaudhary UJ, Diehl B, Lemieux L, Duncan JS, de Tisi J, Wang Y, Taylor PN. Normative brain mapping using scalp EEG and potential clinical application. ARXIV 2023:arXiv:2304.03204v1. [PMID: 37064533 PMCID: PMC10104182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.
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Affiliation(s)
- Vytene Janiukstyte
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
| | - Thomas W Owen
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
| | - Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom, NE2 4HH
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Computing and Complex BioSystems Group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom, NE4 5DG
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom, NE2 4HH
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, United Kingdom, WC1N 3BG
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10
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The Resting State of Taiwan EEG Normative Database: Z-Scores of Patients with Major Depressive Disorder as the Cross-Validation. Brain Sci 2023; 13:brainsci13020351. [PMID: 36831893 PMCID: PMC9954681 DOI: 10.3390/brainsci13020351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/10/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
This study referred to the standard of electroencephalography (EEG) collection of normative databases and collected the Taiwan normative database to examine the reliability and validation of the Taiwan EEG normative database. We included 260 healthy participants and divided them into five groups in 10-year age-group segments and calculated the EEG means, standard deviation, and z-scores. Internal consistency reliability was verified at different frequencies between the three electrode locations in the Taiwan normative database. We recruited 221 major depressive disorder (MDD) patients for cross-validation between the Taiwan and NeuroGuide normative databases. There were high internal consistency reliabilities for delta, theta, alpha, beta, and high-beta at C3, Cz, and C4 in the HC group. There were high correlations between the two z-scores of the Taiwan and NeuroGuide normative databases in the frontal, central, parietal, temporal, and occipital lobes from MDD patients. The beta z-scores in the frontal lobe and central area, and the high-beta z-scores in the frontal, central, parietal, temporal, and occipital lobes were greater than one for MDD patients; in addition, the beta and high-beta absolute value z-scores in the whole brain were greater than the ones of MDD patients. The Taiwan EEG normative database has good psychometric characteristics of internal consistency reliability and cross-validation.
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11
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Jatupornpoonsub T, Thimachai P, Supasyndh O, Wongsawat Y. QEEG characteristics associated with malnutrition-inflammation complex syndrome. Front Hum Neurosci 2023; 17:944988. [PMID: 36825130 PMCID: PMC9941172 DOI: 10.3389/fnhum.2023.944988] [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: 05/16/2022] [Accepted: 01/19/2023] [Indexed: 02/09/2023] Open
Abstract
End-stage renal disease (ESRD) has been linked to cerebral complications due to the comorbidity of malnutrition and inflammation, which is referred to as malnutrition-inflammation complex syndrome (MICS). The severity of this condition is clinically assessed with the malnutrition-inflammation score (MIS), and a cutoff of five is used to optimally distinguish patients with and without MICS. However, this tool is still invasive and inconvenient, because it combines medical records, physical examination, and laboratory results. These steps require clinicians and limit MIS usage on a regular basis. Cerebral diseases in ESRD patients can be evaluated reliably and conveniently by using quantitative electroencephalogram (QEEG), which possibly reflects the severity of MICS likewise. Given the links between kidney and brain abnormalities, we hypothesized that some QEEG patterns might be associated with the severity of MICS and could be used to distinguish ESRD patients with and without MICS. Hence, we recruited 62 ESRD participants and divided them into two subgroups: ESRD with MICS (17 women (59%), age 60.31 ± 7.79 years, MIS < 5) and ESRD without MICS (20 women (61%), age 62.03 ± 9.29 years, MIS ≥ 5). These participants willingly participated in MIS and QEEG assessments. We found that MICS-related factors may alter QEEG characteristics, including the absolute power of the delta, theta, and beta 1 bands, the relative power of the theta and beta 3 subbands, the coherence of the delta and theta bands, and the amplitude asymmetry of the beta 1 band, in certain brain regions. Although most of these QEEG patterns are significantly correlated with MIS, the delta absolute power, beta 1 amplitude asymmetry, and theta coherence are the optimal inputs for the logistic regression model, which can accurately classify ESRD patients with and without MICS (90.0 ± 5.7% area under the receiver operating characteristic curve). We suggest that these QEEG features can be used not only to evaluate the severity of cerebral disorders in ESRD patients but also to noninvasively monitor MICS in clinical practice.
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Affiliation(s)
- Tirapoot Jatupornpoonsub
- Brain-Computer Interface Laboratory, Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand
| | - Paramat Thimachai
- Division of Nephrology, Department of Medicine, Phramongkutklao Hospital, Bangkok, Thailand
| | - Ouppatham Supasyndh
- Division of Nephrology, Department of Medicine, Phramongkutklao Hospital, Bangkok, Thailand
| | - Yodchanan Wongsawat
- Brain-Computer Interface Laboratory, Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand,*Correspondence: Yodchanan Wongsawat ✉
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12
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Normative Structure of Resting-State EEG in Bipolar Derivations for Daily Clinical Practice: A Pilot Study. Brain Sci 2023; 13:brainsci13020167. [PMID: 36831710 PMCID: PMC9953767 DOI: 10.3390/brainsci13020167] [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: 12/29/2022] [Revised: 01/12/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
We used numerical methods to define the normative structure of resting-state EEG (rsEEG) in a pilot study of 37 healthy subjects (10-74 years old), using a double-banana bipolar montage. Artifact-free 120-200 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum was calculated and used to compute the area for delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands and was log-transformed. Furthermore, Shannon's spectral entropy (SSE) and coherence by bands were computed. Finally, we also calculated the main frequency and amplitude of the posterior dominant rhythm. According to the age-dependent distribution of the bands, we divided the patients in the following three groups: younger than 20; between 21 and 50; and older than 51 years old. The distribution of bands and coherence was different for the three groups depending on the brain lobes. We described the normative equations for the three age groups and for every brain lobe. We showed the feasibility of a normative structure of rsEEG picked up with a double-banana montage.
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13
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Yoon JE, Mo H, Kim DW, Im HJ. Quantitative electroencephalographic analysis of delirium tremens development following alcohol-withdrawal seizure based on a small number of male cases. Brain Behav 2022; 12:e2804. [PMID: 36306397 PMCID: PMC9759131 DOI: 10.1002/brb3.2804] [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: 06/20/2022] [Revised: 08/29/2022] [Accepted: 10/08/2022] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Seizures and delirium tremens (DTs) are recognized as severe alcohol-withdrawal symptoms. Prolonged admission and serious complications associated with alcohol withdrawal are responsible for increased costs and use of medical and social resources. This study investigated the predictive value of quantitative electroencephalography (QEEG) for developing alcohol-related DTs after alcohol-withdrawal seizure (AWS). METHODS We compared differences in QEEG in patients after AWS (n = 13). QEEG was performed in the intensive care unit within 48 h of admission, including in age- and sex-matched healthy controls. We also investigated the prognostic value of QEEG for the development of alcohol DTs after AWS in a retrospective, case-control study. The spectral power of each band frequency and the ratio of the theta to alpha band (TAR) in the electroencephalogram were analyzed using iSyncBrain® (iMediSync, Inc., Korea). RESULTS The beta frequency and the alpha frequency band power were significantly higher and lower, respectively, in patients than in age- and sex-matched healthy controls. In AWS patients with DTs, the relative beta-3 power was lower, particularly in the left frontal area, and the TAR was significantly higher in the central channel than in those without DTs. CONCLUSION Quantitative EEG showed neuronal excitability and decreased cognitive activities characteristic of AWS associated with alcohol-withdrawal state, and we demonstrated that quantitative EEG might be a helpful tool for detecting patients at a high risk of developing DTs during an alcohol-dependence period.
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Affiliation(s)
- Jee-Eun Yoon
- Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu, Republic of Korea
| | - Heejung Mo
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea
| | - Dong Wook Kim
- Department of Neurology, School of Medicine, Konkuk University, Seoul, Republic of Korea
| | - Hee-Jin Im
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Republic of Korea
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14
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Jeong T, Park U, Kang SW. Novel quantitative electroencephalogram feature image adapted for deep learning: Verification through classification of Alzheimer’s disease dementia. Front Neurosci 2022; 16:1033379. [PMID: 36408393 PMCID: PMC9670114 DOI: 10.3389/fnins.2022.1033379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022] Open
Abstract
Quantitative electroencephalography (QEEG) analysis is commonly adopted for the investigation of various neurological disorders, revealing electroencephalogram (EEG) features associated with specific dysfunctions. Conventionally, topographies are widely utilized for spatial representation of EEG characteristics at specific frequencies or frequency bands. However, multiple topographies at various frequency bands are required for a complete description of brain activity. In consequence, use of topographies for the training of deep learning algorithms is often challenging. The present study describes the development and application of a novel QEEG feature image that integrates all required spatial and spectral information within a single image, overcoming conventional obstacles. EEG powers recorded at 19 channels defined by the international 10–20 system were pre-processed using the EEG auto-analysis system iSyncBrain®, removing the artifact components selected through independent component analysis (ICA) and rejecting bad epochs. Hereafter, spectral powers computed through fast Fourier transform (FFT) were standardized into Z-scores through iMediSync, Inc.’s age- and sex-specific normative database. The standardized spectral powers for each channel were subsequently rearranged and concatenated into a rectangular feature matrix, in accordance with their spatial location on the scalp surface. Application of various feature engineering techniques on the established feature matrix yielded multiple types of feature images. Such feature images were utilized in the deep learning classification of Alzheimer’s disease dementia (ADD) and non-Alzheimer’s disease dementia (NADD) data, in order to validate the use of our novel feature images. The resulting classification accuracy was 97.4%. The Classification criteria were further inferred through an explainable artificial intelligence (XAI) algorithm, which complied with the conventionally known EEG characteristics of AD. Such outstanding classification performance bolsters the potential of our novel QEEG feature images in broadening QEEG utility.
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Affiliation(s)
| | | | - Seung Wan Kang
- iMediSync, Inc., Seoul, South Korea
- National Standard Reference Data Center for Korean EEG, College of Nursing, Seoul National University, Seoul, South Korea
- *Correspondence: Seung Wan Kang, ,
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15
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Jeong T, Park U, Kang SW. Novel quantitative electroencephalogram feature image adapted for deep learning: Verification through classification of Alzheimer's disease dementia. Front Neurosci 2022; 16:1033379. [PMID: 36408393 PMCID: PMC9670114 DOI: 10.3389/fnins.2022.1033379;256,183-194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/14/2022] [Indexed: 06/28/2023] Open
Abstract
Quantitative electroencephalography (QEEG) analysis is commonly adopted for the investigation of various neurological disorders, revealing electroencephalogram (EEG) features associated with specific dysfunctions. Conventionally, topographies are widely utilized for spatial representation of EEG characteristics at specific frequencies or frequency bands. However, multiple topographies at various frequency bands are required for a complete description of brain activity. In consequence, use of topographies for the training of deep learning algorithms is often challenging. The present study describes the development and application of a novel QEEG feature image that integrates all required spatial and spectral information within a single image, overcoming conventional obstacles. EEG powers recorded at 19 channels defined by the international 10-20 system were pre-processed using the EEG auto-analysis system iSyncBrain®, removing the artifact components selected through independent component analysis (ICA) and rejecting bad epochs. Hereafter, spectral powers computed through fast Fourier transform (FFT) were standardized into Z-scores through iMediSync, Inc.'s age- and sex-specific normative database. The standardized spectral powers for each channel were subsequently rearranged and concatenated into a rectangular feature matrix, in accordance with their spatial location on the scalp surface. Application of various feature engineering techniques on the established feature matrix yielded multiple types of feature images. Such feature images were utilized in the deep learning classification of Alzheimer's disease dementia (ADD) and non-Alzheimer's disease dementia (NADD) data, in order to validate the use of our novel feature images. The resulting classification accuracy was 97.4%. The Classification criteria were further inferred through an explainable artificial intelligence (XAI) algorithm, which complied with the conventionally known EEG characteristics of AD. Such outstanding classification performance bolsters the potential of our novel QEEG feature images in broadening QEEG utility.
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Affiliation(s)
| | | | - Seung Wan Kang
- iMediSync, Inc., Seoul, South Korea
- National Standard Reference Data Center for Korean EEG, College of Nursing, Seoul National University, Seoul, South Korea
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16
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Jung KH, Kang DJ, Lee WJ, Son HS, Kim S, Kang SW. Pathophysiological insight into transient global amnesia from quantitative electroencephalography. Neurobiol Dis 2022; 170:105778. [PMID: 35636647 DOI: 10.1016/j.nbd.2022.105778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/04/2022] [Accepted: 05/23/2022] [Indexed: 11/29/2022] Open
Abstract
Transient global amnesia (TGA) is recognized as a benign memory disorder, with characteristic clinical and imaging features. However, the pathophysiology of TGA remains elusive. This study aims to elucidate the pathophysiological changes underlying TGA by exploring the brain activities. In total, 215 patients with TGA (age: 61.8 ± 7.8 years; women: 146) with MRI (within 7 days) and EEG studies (within 90 days) were recruited. Quantitative EEG (QEEG) power spectra and network analysis were performed by the artificial intelligence EEG analysis platform (iSyncBrain®). Subgroup analyses were conducted for different clinical groups, based on symptom duration, EEG timing after onset, and cytotoxic lesions on the MRI. Compared with 252 age- and sex-matched subjects (age: 64.5 ± 8.3 years, women: 182), TGA patients showed a global decrease in absolute power in all band waves, a relative decrease in alpha waves, a relative increase in theta waves, and atypical compensation activity. These QEEG changes were observed regardless of having cytotoxic lesions in MRI and they were significant up to 1 week after symptom onset. Network analysis showed that TGA was more activated than normal controls in alpha1 band-waves, exhibiting a compensatory process. TGA results in prolonged and widespread alterations of brain activity and connectivity. QEEG provide insight into pathophysiology of TGA.
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Affiliation(s)
- Keun-Hwa Jung
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea; Program in Neuroscience, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | | | - Woo-Jin Lee
- Department of Neurology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Hyo-Shin Son
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sohyun Kim
- Department of Neurology, Seoul National University Hospital, Seoul, 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, Republic of Korea.
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17
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Ye S, Han Y, Pan X, Niu K, Liao Y, Meng X. Association of CT-Based Delta Radiomics Biomarker With Progression-Free Survival in Patients With Colorectal Liver Metastases Undergo Chemotherapy. Front Oncol 2022; 12:843991. [PMID: 35692757 PMCID: PMC9184515 DOI: 10.3389/fonc.2022.843991] [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: 12/27/2021] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
Predicting the prognosis of patients in advance is conducive to providing personalized treatment for patients. Our aim was to predict the therapeutic efficacy and progression free survival (PFS) of patients with liver metastasis of colorectal cancer according to the changes of computed tomography (CT) radiomics before and after chemotherapy. Methods This retrospective study included 139 patients (397 lesions) with colorectal liver metastases who underwent neoadjuvant chemotherapy from April 2015 to April 2020. We divided the lesions into training cohort and testing cohort with a ratio of 7:3. Two - dimensional region of interest (ROI) was obtained by manually delineating the largest layers of each metastasis lesion. The expanded ROI (3 mm and 5 mm) were also included in the study to characterize microenvironment around tumor. For each of the ROI, 1,316 radiomics features were extracted from delineated plain scan, arterial, and venous phase CT images before and after neoadjuvant chemotherapy. Delta radiomics features were constructed by subtracting the radiomics features after treatment from the radiomics features before treatment. Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were applied in the training cohort to select the valuable features. Based on clinical characteristics and radiomics features, 7 Cox proportional-hazards model were constructed to predict the PFS of patients. C-index value and Kaplan Meier (KM) analysis were used to evaluate the efficacy of predicting PFS of these models. Moreover, the prediction performance of one-year PFS was also evaluated by area under the curve (AUC). Results Compared with the PreRad (Radiomics form pre-treatment CT images; C-index [95% confidence interval (CI)] in testing cohort: 0.614(0.552-0.675) and PostRad models (Radiomics form post-treatment CT images; 0.642(0.578-0.707), the delta model has better PFS prediction performance (Delta radiomics; 0.688(0.627-0.749). By incorporating clinical characteristics, CombDeltaRad obtains the best performance in both training cohort [C-index (95% CI): 0.802(0.772-0.832)] and the testing cohort (0.744(0.686-0.803). For 1-year PFS prediction, CombDeltaRad model obtained the best performance with AUC (95% CI) of 0.871(0.828-0.914) and 0.745 (0.651-0.838) in training cohort and testing cohort, respectively. Conclusion CT radiomics features have the potential to predict PFS in patients with colorectal cancer and liver metastasis who undergo neoadjuvant chemotherapy. By combining pre-treatment radiomics features, post-treatment radiomics features, and clinical characteristics better prediction results can be achieved.
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Affiliation(s)
- Shuai Ye
- The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yu Han
- The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - XiMin Pan
- The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - KeXin Niu
- The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - YuTing Liao
- GE Healthcare Pharmaceutical Diagnostics, Guangzhou, China
| | - XiaoChun Meng
- The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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18
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Li M, Wang Y, Lopez-Naranjo C, Hu S, Reyes RCG, Paz-Linares D, Areces-Gonzalez A, Hamid AIA, Evans AC, Savostyanov AN, Calzada-Reyes A, Villringer A, Tobon-Quintero CA, Garcia-Agustin D, Yao D, Dong L, Aubert-Vazquez E, Reza F, Razzaq FA, Omar H, Abdullah JM, Galler JR, Ochoa-Gomez JF, Prichep LS, Galan-Garcia L, Morales-Chacon L, Valdes-Sosa MJ, Tröndle M, Zulkifly MFM, Abdul Rahman MRB, Milakhina NS, Langer N, Rudych P, Koenig T, Virues-Alba TA, Lei X, Bringas-Vega ML, Bosch-Bayard JF, Valdes-Sosa PA. Harmonized-Multinational qEEG norms (HarMNqEEG). Neuroimage 2022; 256:119190. [PMID: 35398285 DOI: 10.1016/j.neuroimage.2022.119190] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/23/2022] [Accepted: 04/05/2022] [Indexed: 12/14/2022] Open
Abstract
This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.
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Affiliation(s)
- Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Carlos Lopez-Naranjo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shiang Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei 230601, China
| | | | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Río, Cuba
| | - Aini Ismafairus Abd Hamid
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Alan C Evans
- McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Alexander N Savostyanov
- Humanitarian Institute, Novosibirsk State University, Novosibirsk 630090, Russia; Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia
| | | | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany; Center for Stroke Research, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Carlos A Tobon-Quintero
- Grupo Neuropsicología y Conducta - GRUNECO, Faculty of Medicine, Universidad de Antioquia, Colombia; Research Department, Institución Prestadora de Servicios de Salud IPS Universitaria, Colombia
| | - Daysi Garcia-Agustin
- Cuban Center for Neurocience, La Habana, Cuba; The Cuban center aging longevity and health, Havana Cuba
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, China
| | | | - Faruque Reza
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Fuleah Abdul Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hazim Omar
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Jafri Malin Abdullah
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Universiti Sains Malaysia Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Janina R Galler
- Division of Pediatric Gastroenterology and Nutrition, Massachusetts General Hospital for Children, Boston, MA, United States Massachusetts General Hospital for Children, Boston, MA, United States
| | - John F Ochoa-Gomez
- Grupo Neuropsicología y Conducta - GRUNECO, Faculty of Medicine, Universidad de Antioquia, Colombia; Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Leslie S Prichep
- Research & Development, BrainScope Company, Inc. Bethesda, MD, United States; Department of Psychiatry (Ret.), Brain Research Laboratories, NYU School of Medicine, New York, NY, United States
| | | | - Lilia Morales-Chacon
- Department of Clinical Neurophysiology, International Center for Neurological Restoration, Playa, Havana 11300, Cuba
| | | | - Marius Tröndle
- Department of Methods of Plasticity Research, Institute of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program "Dynamic of Healthy Aging", University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Mohd Faizal Mohd Zulkifly
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada
| | - Muhammad Riddha Bin Abdul Rahman
- Brain and Behaviour Cluster, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; Hospital Universiti Sains Malaysia, Universiti Sains Malaysia, Health Campus, Kota Bharu, Kelantan 16150, Malaysia; School of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, Kuala Nerus 21300, Malaysia
| | - Natalya S Milakhina
- Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia
| | - Nicolas Langer
- Department of Methods of Plasticity Research, Institute of Psychology, University of Zurich, Zurich, Switzerland; University Research Priority Program "Dynamic of Healthy Aging", University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Pavel Rudych
- Laboratory of Psychophysiology of Individual Differences, Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine, Novosibirsk 630117, Russia; Department of Information Technologies Novosibirsk State University, Novosibirsk 630090, Russia; Federal Research Center for Information and Computational Technologies, Biomedical Data Processing Lab, Novosibirsk 630090, Russia
| | - Thomas Koenig
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | | | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba.
| | - Jorge F Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba; McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Canada.
| | - Pedro Antonio Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Cuban Center for Neurocience, La Habana, Cuba.
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19
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Kim T, Park U, Kang SW. Prediction model for potential depression using sex and age-reflected quantitative EEG biomarkers. Front Psychiatry 2022; 13:913890. [PMID: 36159938 PMCID: PMC9490263 DOI: 10.3389/fpsyt.2022.913890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
Depression is a prevalent mental disorder in modern society, causing many people to suffer or even commit suicide. Psychiatrists and psychologists typically diagnose depression using representative tests, such as the Beck's Depression Inventory (BDI) and the Hamilton Depression Rating Scale (HDRS), in conjunction with patient consultations. Traditional tests, however, are time-consuming, can be trained on patients, and entailed a lot of clinician subjectivity. In the present study, we trained the machine learning models using sex and age-reflected z-score values of quantitative EEG (QEEG) indicators based on data from the National Standard Reference Data Center for Korean EEG, with 116 potential depression subjects and 80 healthy controls. The classification model has distinguished potential depression groups and normal groups, with a test accuracy of up to 92.31% and a 10-cross-validation loss of 0.13. This performance proposes a model with z-score QEEG metrics, considering sex and age as objective and reliable biomarkers for early screening for the potential depression.
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Affiliation(s)
| | | | - Seung Wan Kang
- iMediSync Inc., Seoul, South Korea.,National Standard Reference Data Center for Korean EEG, Seoul National University College of Nursing, Seoul, South Korea
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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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