1
|
Şeker M, Özerdem MS. Dementia rhythms: Unveiling the EEG dynamics for MCI detection through spectral and synchrony neuromarkers. J Neurosci Methods 2024; 409:110216. [PMID: 38964474 DOI: 10.1016/j.jneumeth.2024.110216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/20/2024] [Accepted: 06/29/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND Neurological disorders arise primarily from the dysfunction of brain cells, leading to various impairments. Electroencephalography (EEG) stands out as the most popular method in the discovery of neuromarkers indicating neurological disorders. The proposed study investigates the effectiveness of spectral and synchrony neuromarkers derived from resting state EEG in the detection of Mild Cognitive Impairment (MCI) with controls. NEW METHODS The dataset is composed of 10 MCI and 10 HC groups. Spectral features and synchrony measures are utilized to detect slowing patterns in MCI. Efficient neuro-markers are classified by 25 classification algorithm. Independent samples t-test and Pearson's Correlation Coefficients are applied to reveal group differences for spectral markers, and repeated measures ANOVA is tested for wPLI-based markers. RESULTS Lower peak amplitudes are prominent in MCI participants for high frequencies indicating slower physiological behavior of the demented EEG. The MCI and HC groups are correctly classified with 95 % acc. using peak amplitudes of beta band with LGBM classifier. Higher wPLI values are calculated for HC participants in high frequencies. The alpha wPLI values achieve a classification accuracy of 99 % using the LGBM algorithm for MCI detection. COMPARISON WITH EXISTING METHODS The neuro-markers including peak amplitudes, frequencies, and wPLIs with advanced machine learning techniques showcases the innovative nature of this research. CONCLUSION The findings suggest that peak amplitudes and wPLI in high frequency bands derived from resting state EEG are effective neuromarkers for detection of MCI. Spectral and synchrony neuro-markers hold great promise for accurate MCI detection.
Collapse
Affiliation(s)
- Mesut Şeker
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
| | - Mehmet Siraç Özerdem
- Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
| |
Collapse
|
2
|
Glimmerveen AB, Verhulst MMLH, de Kruijf NLM, van Gils P, Delnoij T, Bonnes J, van Heugten CM, Van Putten MJAM, Hofmeijer J. Resting state EEG relates to short- and long-term cognitive functioning after cardiac arrest. Resuscitation 2024; 201:110253. [PMID: 38797387 DOI: 10.1016/j.resuscitation.2024.110253] [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: 03/18/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Approximately half of cardiac arrest survivors have persistent cognitive impairment. Guidelines recommend early screening to identify patients at risk for cognitive impairment, but there is no consensus on the best screening method. We aimed to identify quantitative EEG measures relating with short- and long-term cognitive function after cardiac arrest for potential to cognitive outcome prediction. METHODS We analyzed data from a prospective longitudinal multicenter cohort study designed to develop a prediction model for cognitive outcome after cardiac arrest. For the current analysis, we used twenty-minute EEG registrations from 80 patients around one week after cardiac arrest. We calculated power spectral density, normalized alpha-to-theta ratio (nATR), peak frequency, and center of gravity (CoG) of this peak frequency. We related these with global cognitive functioning (scores on the Montreal Cognitive Assessment (MoCA)) at one week, three and twelve months follow-up with multivariate mixed effect models, and with performance on standard neuropsychological examination at twelve months using Pearson correlation coefficients. RESULTS Each individual EEG parameter related to MoCA at one week (βnATR = 7.36; P < 0.01; βpeak frequency = 1.73, P < 0.01; βCoG = -9.88, P < 0.01). The nATR also related with the MoCA at three months ((βnATR = 2.49; P 0.01). No EEG metrics significantly related to the MoCA score at twelve months. nATR and peak frequency related with memory performance at twelve months. Results were consistent in sensitivity analyses. CONCLUSION Early resting-state EEG parameters relate with short-term global cognitive functioning and with memory function at one year after cardiac arrest. Additional predictive values in multimodal prediction models need further study.
Collapse
Affiliation(s)
- A B Glimmerveen
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
| | - M M L H Verhulst
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - N L M de Kruijf
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - P van Gils
- Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Maastrich University, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht, The Netherlands; Maastricht University, Limburg Brain Injury Center, Maastricht, The Netherlands
| | - T Delnoij
- Department of Intensive Care Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - J Bonnes
- Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - C M van Heugten
- Maastricht University, Limburg Brain Injury Center, Maastricht, The Netherlands; Maastricht University, Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht, The Netherlands
| | - M J A M Van Putten
- Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, The Netherlands
| | - J Hofmeijer
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| |
Collapse
|
3
|
Jiang D, Yan L, Mayrand F. Emotion expressions and cognitive impairments in the elderly: review of the contactless detection approach. Front Digit Health 2024; 6:1335289. [PMID: 39040877 PMCID: PMC11260803 DOI: 10.3389/fdgth.2024.1335289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 06/20/2024] [Indexed: 07/24/2024] Open
Abstract
The aging population in Canada has been increasing continuously throughout the past decades. Amongst this demographic, around 11% suffer from some form of cognitive decline. While diagnosis through traditional means (i.e., Magnetic Resonance Imagings (MRIs), positron emission tomography (PET) scans, cognitive assessments, etc.) has been successful at detecting this decline, there remains unexplored measures of cognitive health that could reduce stress and cost for the elderly population, including approaches for early detection and preventive methods. Such efforts could additionally contribute to reducing the pressure and stress on the Canadian healthcare system, as well as improve the quality of life of the elderly population. Previous evidence has demonstrated emotional facial expressions being altered in individuals with various cognitive conditions such as dementias, mild cognitive impairment, and geriatric depression. This review highlights the commonalities among these cognitive health conditions, and research behind the contactless assessment methods to monitor the health and cognitive well-being of the elderly population through emotion expression. The contactless detection approach covered by this review includes automated facial expression analysis (AFEA), electroencephalogram (EEG) technologies and heart rate variability (HRV). In conclusion, a discussion of the potentials of the existing technologies and future direction of a novel assessment design through fusion of AFEA, EEG and HRV measures to increase detection of cognitive decline in a contactless and remote manner will be presented.
Collapse
Affiliation(s)
- Di Jiang
- Medical Devices Research Centre, National Research Council of Canada, Boucherville, QC, Canada
| | - Luowei Yan
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - Florence Mayrand
- Department of Psychology, McGill University, Montreal, QC, Canada
| |
Collapse
|
4
|
Krothapalli M, Buddendorff L, Yadav H, Schilaty ND, Jain S. From Gut Microbiota to Brain Waves: The Potential of the Microbiome and EEG as Biomarkers for Cognitive Impairment. Int J Mol Sci 2024; 25:6678. [PMID: 38928383 PMCID: PMC11203453 DOI: 10.3390/ijms25126678] [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: 04/22/2024] [Revised: 06/09/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder and a leading cause of dementia. Aging is a significant risk factor for AD, emphasizing the importance of early detection since symptoms cannot be reversed once the advanced stage is reached. Currently, there is no established method for early AD diagnosis. However, emerging evidence suggests that the microbiome has an impact on cognitive function. The gut microbiome and the brain communicate bidirectionally through the gut-brain axis, with systemic inflammation identified as a key connection that may contribute to AD. Gut dysbiosis is more prevalent in individuals with AD compared to their cognitively healthy counterparts, leading to increased gut permeability and subsequent systemic inflammation, potentially causing neuroinflammation. Detecting brain activity traditionally involves invasive and expensive methods, but electroencephalography (EEG) poses as a non-invasive alternative. EEG measures brain activity and multiple studies indicate distinct patterns in individuals with AD. Furthermore, EEG patterns in individuals with mild cognitive impairment differ from those in the advanced stage of AD, suggesting its potential as a method for early indication of AD. This review aims to consolidate existing knowledge on the microbiome and EEG as potential biomarkers for early-stage AD, highlighting the current state of research and suggesting avenues for further investigation.
Collapse
Affiliation(s)
- Mahathi Krothapalli
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
| | - Lauren Buddendorff
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
| | - Hariom Yadav
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
| | - Nathan D. Schilaty
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
- Center for Neuromusculoskeletal Research, University of South Florida, Tampa, FL 33612, USA
| | - Shalini Jain
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
| |
Collapse
|
5
|
Yu WY, Sun TH, Hsu KC, Wang CC, Chien SY, Tsai CH, Yang YW. Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information. Comput Biol Med 2024; 176:108621. [PMID: 38763067 DOI: 10.1016/j.compbiomed.2024.108621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/21/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.
Collapse
Affiliation(s)
- Wei-Yang Yu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Chia-Chun Wang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Chon-Haw Tsai
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan; Neuroscience Laboratory, Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Neuroscience and Brain Disease Center, College of Medicine, China Medical University, 40402, Taichung, Taiwan
| | - Yu-Wan Yang
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan.
| |
Collapse
|
6
|
Vecchio F, Miraglia F, Pappalettera C, Nucci L, Cacciotti A, Rossini PM. Small World derived index to distinguish Alzheimer's type dementia and healthy subjects. Age Ageing 2024; 53:afae121. [PMID: 38935531 PMCID: PMC11210397 DOI: 10.1093/ageing/afae121] [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: 12/27/2023] [Revised: 04/26/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND This article introduces a novel index aimed at uncovering specific brain connectivity patterns associated with Alzheimer's disease (AD), defined according to neuropsychological patterns. METHODS Electroencephalographic (EEG) recordings of 370 people, including 170 healthy subjects and 200 mild-AD patients, were acquired in different clinical centres using different acquisition equipment by harmonising acquisition settings. The study employed a new derived Small World (SW) index, SWcomb, that serves as a comprehensive metric designed to integrate the seven SW parameters, computed across the typical EEG frequency bands. The objective is to create a unified index that effectively distinguishes individuals with a neuropsychological pattern compatible with AD from healthy ones. RESULTS Results showed that the healthy group exhibited the lowest SWcomb values, while the AD group displayed the highest SWcomb ones. CONCLUSIONS These findings suggest that SWcomb index represents an easy-to-perform, low-cost, widely available and non-invasive biomarker for distinguishing between healthy individuals and AD patients.
Collapse
Affiliation(s)
- Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
| | - Alessia Cacciotti
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, 00166 Rome, Italy
| |
Collapse
|
7
|
Lacaux C, Strauss M, Bekinschtein TA, Oudiette D. Embracing sleep-onset complexity. Trends Neurosci 2024; 47:273-288. [PMID: 38519370 DOI: 10.1016/j.tins.2024.02.002] [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: 09/06/2023] [Revised: 01/17/2024] [Accepted: 02/07/2024] [Indexed: 03/24/2024]
Abstract
Sleep is crucial for many vital functions and has been extensively studied. By contrast, the sleep-onset period (SOP), often portrayed as a mere prelude to sleep, has been largely overlooked and remains poorly characterized. Recent findings, however, have reignited interest in this transitional period and have shed light on its neural mechanisms, cognitive dynamics, and clinical implications. This review synthesizes the existing knowledge about the SOP in humans. We first examine the current definition of the SOP and its limits, and consider the dynamic and complex electrophysiological changes that accompany the descent to sleep. We then describe the interplay between internal and external processing during the wake-to-sleep transition. Finally, we discuss the putative cognitive benefits of the SOP and identify novel directions to better diagnose sleep-onset disorders.
Collapse
Affiliation(s)
- Célia Lacaux
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Institut du Cerveau (Paris Brain Institute), Institut du Cerveau et de la Moelle Épinière (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Sorbonne Université, Paris 75013, France.
| | - Mélanie Strauss
- Neuropsychology and Functional Neuroimaging Research Group (UR2NF), Center for Research in Cognition and Neurosciences (CRCN), Université Libre de Bruxelles, B-1050 Brussels, Belgium; Departments of Neurology, Psychiatry, and Sleep Medicine, Hôpital Universitaire de Bruxelles, Site Erasme, Université Libre de Bruxelles, B-1070 Brussels, Belgium
| | - Tristan A Bekinschtein
- Cambridge Consciousness and Cognition Laboratory, Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
| | - Delphine Oudiette
- Institut du Cerveau (Paris Brain Institute), Institut du Cerveau et de la Moelle Épinière (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Sorbonne Université, Paris 75013, France; Assistance Publique - Hopitaux de Paris (AP-HP), Hôpital Pitié-Salpêtrière, Service des Pathologies du Sommeil, National Reference Centre for Narcolepsy, Paris 75013, France.
| |
Collapse
|
8
|
Power L, Friedman A, Bardouille T. Atypical paroxysmal slow cortical activity in healthy adults: Relationship to age and cognitive performance. Neurobiol Aging 2024; 136:44-57. [PMID: 38309051 DOI: 10.1016/j.neurobiolaging.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 02/05/2024]
Abstract
Paroxysmal patterns of slow cortical activity have been detected in EEG recordings from individuals with age-related neuropathology and have been shown to be correlated with cognitive dysfunction and blood-brain barrier disruption in these participants. The prevalence of these events in healthy participants, however, has not been studied. In this work, we inspect MEG recordings from 623 healthy participants from the Cam-CAN dataset for the presence of paroxysmal slow wave events (PSWEs). PSWEs were detected in approximately 20% of healthy participants in the dataset, and participants with PSWEs tended to be older and have lower cognitive performance than those without PSWEs. In addition, event features changed linearly with age and cognitive performance, resulting in longer and slower events in older adults, and more widespread events in those with low cognitive performance. These findings provide the first evidence of PSWEs in a subset of purportedly healthy adults. Going forward, these events may have utility as a diagnostic biomarker for atypical brain activity in older adults.
Collapse
Affiliation(s)
- Lindsey Power
- School of Biomedical Engineering, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Alon Friedman
- Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Timothy Bardouille
- Department of Physics & Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada.
| |
Collapse
|
9
|
Andrillon T, Taillard J, Strauss M. Sleepiness and the transition from wakefulness to sleep. Neurophysiol Clin 2024; 54:102954. [PMID: 38460284 DOI: 10.1016/j.neucli.2024.102954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/02/2024] [Accepted: 02/03/2024] [Indexed: 03/11/2024] Open
Abstract
The transition from wakefulness to sleep is a progressive process that is reflected in the gradual loss of responsiveness, an alteration of cognitive functions, and a drastic shift in brain dynamics. These changes do not occur all at once. The sleep onset period (SOP) refers here to this period of transition between wakefulness and sleep. For example, although transitions of brain activity at sleep onset can occur within seconds in a given brain region, these changes occur at different time points across the brain, resulting in a SOP that can last several minutes. Likewise, the transition to sleep impacts cognitive and behavioral levels in a graded and staged fashion. It is often accompanied and preceded by a sensation of drowsiness and the subjective feeling of a need for sleep, also associated with specific physiological and behavioral signatures. To better characterize fluctuations in vigilance and the SOP, a multidimensional approach is thus warranted. Such a multidimensional approach could mitigate important limitations in the current classification of sleep, leading ultimately to better diagnoses and treatments of individuals with sleep and/or vigilance disorders. These insights could also be translated in real-life settings to either facilitate sleep onset in individuals with sleep difficulties or, on the contrary, prevent or control inappropriate sleep onsets.
Collapse
Affiliation(s)
- Thomas Andrillon
- Paris Brain Institute, Sorbonne Université, Inserm-CNRS, Paris 75013, France; Monash Centre for Consciousness & Contemplative Studies, Monash University, Melbourne, VIC 3800, Australia
| | - Jacques Taillard
- Univ. Bordeaux, CNRS, SANPSY, UMR 6033, F-33000 Bordeaux, France
| | - Mélanie Strauss
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), CUB Hôpital Érasme, Services de Neurologie, Psychiatrie et Laboratoire du sommeil, Route de Lennik 808 1070 Bruxelles, Belgium; Neuropsychology and Functional Neuroimaging Research Group (UR2NF), Center for Research in Cognition and Neurosciences (CRCN), Université Libre de Bruxelles, B-1050 Brussels, Belgium.
| |
Collapse
|
10
|
Lal U, Chikkankod AV, Longo L. A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer's Disease with Electroencephalography in Resting-State Adults. Brain Sci 2024; 14:335. [PMID: 38671987 PMCID: PMC11048688 DOI: 10.3390/brainsci14040335] [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: 02/20/2024] [Revised: 03/20/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Early-stage Alzheimer's disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% and 93%, and 91.5% and 91% for discriminating AD and HC, FTD and HC, and AD and FTD, respectively. The feature importance array, an explainable AI feature, highlighted the brain lobes that contributed to identifying and distinguishing AD and FTD biomarkers. This research introduces a novel framework for detecting and discriminating AD and FTD using EEG signals, addressing the need for accurate early-stage diagnostics. Furthermore, a comparative evaluation of sliding windowing, multiple feature extraction, and machine learning methods on AD/FTD detection and discrimination is documented.
Collapse
Affiliation(s)
- Utkarsh Lal
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal 576104, Karnataka, India;
| | - Arjun Vinayak Chikkankod
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India;
- Artificial Intelligence and Cognitive Load Lab, the Applied Intelligence Research Centre, Technological University Dublin, D07 H6K8 Dublin, Ireland
| | - Luca Longo
- Artificial Intelligence and Cognitive Load Lab, the Applied Intelligence Research Centre, Technological University Dublin, D07 H6K8 Dublin, Ireland
| |
Collapse
|
11
|
Wang L, Sheng J, Duan S, Lin S, Li Y, Li Z, Li S, Sataer Y, Chen J. How Society Anxiety Influences Attention Control in College Students: The Moderated Mediation Effect of Cognitive Flexibility and Resting-state Electroencephalography Activity. J Cogn Neurosci 2024; 36:327-339. [PMID: 38060259 DOI: 10.1162/jocn_a_02095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Social anxiety is a prevalent issue among college students, adversely affecting their overall well-being. Drawing from the cognitive model of social anxiety and attention control theory, heightened levels of social anxiety may correspond to poorer attention control ability. However, little is known about the underlying cognitive mechanisms of the relationship between social anxiety and attention control. To address this research gap, the current study recruited a sample of 156 college students (56 women) who underwent self-report measures of social anxiety, cognitive flexibility, and attention control, followed by a resting-state EEG recording. The results revealed a significant negative predictive effect of social anxiety on attention control, with cognitive flexibility partially mediating this relationship. Furthermore, resting-state theta power emerged as a significant moderator, accentuating the negative impact of social anxiety on cognitive flexibility among individuals with lower theta power. In addition, frontal alpha asymmetry (FAA) demonstrated a moderating effect, with lower FAA intensifying the predictive influence of cognitive flexibility on attention control. Taken together, these results suggested that social anxiety can predict attention control either directly or indirectly via the mediating role of cognitive flexibility, and lower theta power and FAA has a risk amplification effect, which provide novel insights into the treatment and prevention of social anxiety and its negative impact on college students.
Collapse
Affiliation(s)
- Li Wang
- South China Normal University
| | | | | | | | | | - Zhe Li
- South China Normal University
| | | | | | | |
Collapse
|
12
|
Chang J, Chang C. Quantitative Electroencephalography Markers for an Accurate Diagnosis of Frontotemporal Dementia: A Spectral Power Ratio Approach. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:2155. [PMID: 38138258 PMCID: PMC10744364 DOI: 10.3390/medicina59122155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/28/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
Background and Objectives: Frontotemporal dementia (FTD) is the second most common form of presenile dementia; however, its diagnosis has been poorly investigated. Previous attempts to diagnose FTD using quantitative electroencephalography (qEEG) have yielded inconsistent results in both spectral and functional connectivity analyses. This study aimed to introduce an accurate qEEG marker that could be used to diagnose FTD and other neurological abnormalities. Materials and Methods: We used open-access electroencephalography data from OpenNeuro to investigate the power ratio between the frontal and temporal lobes in the resting state of 23 patients with FTD and 29 healthy controls. Spectral data were extracted using a fast Fourier transform in the delta (0.5 ≤ 4 Hz), theta (4 ≤ 8 Hz), alpha (8-13 Hz), beta (>13-30 Hz), and gamma (>30-45 Hz) bands. Results: We found that the spectral power ratio between the frontal and temporal lobes is a promising qEEG marker of FTD. Frontal (F)-theta/temporal (T)-alpha, F-alpha/T-theta, F-theta/F-alpha, and T-beta/T-gamma showed a consistently high discrimination score for the diagnosis of FTD for different parameters and referencing methods. Conclusions: The study findings can serve as reference for future research focused on diagnosing FTD and other neurological anomalies.
Collapse
Affiliation(s)
- Jinwon Chang
- Korean Minjok Leadership Academy, Hoengseong 25268, Republic of Korea
| | - Chul Chang
- College of Medicine, Catholic University of Korea, Seoul 06591, Republic of Korea;
| |
Collapse
|
13
|
Bae H, Kang MJ, Ha SW, Jeong DE, Lee K, Lim S, Min JY, Min KB. Association of plasma amyloid-β oligomerization with theta/beta ratio in older adults. Front Aging Neurosci 2023; 15:1291881. [PMID: 38106526 PMCID: PMC10722169 DOI: 10.3389/fnagi.2023.1291881] [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: 09/10/2023] [Accepted: 11/06/2023] [Indexed: 12/19/2023] Open
Abstract
Background Oligomeric Aβ (OAβ) is a promising candidate marker for Alzheimer's disease (AD) diagnosis. Electroencephalography (EEG) is a potential tool for early detection of AD. Still, whether EEG power ratios, particularly the theta/alpha ratio (TAR) and theta/beta ratio (TBR), reflect Aβ burden-a primary mechanism underlying cognitive impairment and AD. This study investigated the association of TAR and TBR with amyloid burden in older adults based on MDS-OAβ levels. Methods 529 individuals (aged ≥60 years) were recruited. All participants underwent EEG (MINDD SCAN, Ybrain Inc., South Korea) and AlzOn™ test (PeopleBio Inc., Gyeonggi-do, Republic of Korea) for quantifying MDS-OAβ values in the plasma. EEG variables were log-transformed to normalize the data distribution. Using the MDS-OAβ cutoff value (0.78 ng/mL), all participants were classified into two groups: high MDS-OAβ and low MDS-OAβ. Results Participants with high MDS-OAβ levels had significantly higher TARs and TBRs than those with low MDS-OAβ levels. The log-transformed TBRs in the central lobe (β = 0.161, p = 0.0026), frontal lobe (β = 0.145, p = 0.0044), parietal lobe (β = 0.166, p = 0.0028), occipital lobe (β = 0.158, p = 0.0058), and temporal lobe (beta = 0.162, p = 0.0042) were significantly and positively associated with increases in MDS-OAβ levels. After adjusting for the Bonferroni correction, the TBRs in all lobe regions, except the occipital lobe, were significantly associated with increased MDS-OAβ levels. Conclusion We found a significant association of MDS-OAβ with TBR in older adults. This finding indicates that an increase in amyloid burden may be associated with an increase in the low-frequency band and a decrease in the relatively high-frequency band.
Collapse
Affiliation(s)
- Heewon Bae
- Veterans Medical Research Institute, Veterans Health Service Medical Center, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Min Ju Kang
- Department of Neurology, Veterans Health Service Medical Center, Seoul, Republic of Korea
| | - Sang-Won Ha
- Department of Neurology, Veterans Health Service Medical Center, Seoul, Republic of Korea
| | - Da-Eun Jeong
- Department of Neurology, Veterans Health Service Medical Center, Seoul, Republic of Korea
| | - Kiwon Lee
- Ybrain Research Institute, Seongnam-si, Republic of Korea
| | - Seungui Lim
- Ybrain Research Institute, Seongnam-si, Republic of Korea
| | - Jin-Young Min
- Veterans Medical Research Institute, Veterans Health Service Medical Center, Seoul, Republic of Korea
| | - Kyoung-Bok Min
- Department of Preventive Medicine, College of Medicine, Seoul National University, Seoul, Republic of Korea
- Medical Research Center, Institute of Health Policy and Management, Seoul National University, Seoul, Republic of Korea
| |
Collapse
|
14
|
Mei X, Zou C, Si Z, Xu T, Hu J, Wu X, Zheng C. Antidepressant effect of bright light therapy on patients with Alzheimer's disease and their caregivers. Front Pharmacol 2023; 14:1235406. [PMID: 38034990 PMCID: PMC10684929 DOI: 10.3389/fphar.2023.1235406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 11/06/2023] [Indexed: 12/02/2023] Open
Abstract
Background: As a non-pharmacologic treatment, bright light therapy (BLT) is often used to improve affective disorders and memory function. In this study, we aimed to determine the effect of BLT on depression and electrophysiological features of the brain in patients with Alzheimer's disease (AD) and their caregivers using a light-emitting diode device of 14000 lux. Methods: A 4-week case-control trial was conducted. Neuropsychiatric and electroencephalogram (EEG) examination were evaluated at baseline and after 4 weeks. EEG power in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz) bands was calculated for our main analysis. Demographic and clinical variables were analyzed using Student's t test and the chi-square test. Pearson's correlation was used to determine the correlation between electrophysiological features, blood biochemical indicators, and cognitive assessment scale scores. Results: In this study, 22 in-patients with AD and 23 caregivers were recruited. After BLT, the Hamilton depression scale score decreased in the fourth week. Compared with the age-matched controls of their caregivers, a higher spectral power at the lower delta and theta frequencies was observed in the AD group. After BLT, the EEG power of the delta and theta frequencies in the AD group decreased. No change was observed in blood amyloid concentrations before and after BLT. Conclusion: In conclusion, a 4-week course of BLT significantly suppressed depression in patients with AD and their caregivers. Moreover, changes in EEG power were also significant in both groups.
Collapse
Affiliation(s)
- Xi Mei
- Key Lab, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Chenjun Zou
- Department of Geriatric, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Zizhen Si
- Medical College, Ningbo University, Ningbo, Zhejiang, China
| | - Ting Xu
- Department of Geriatric, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Jun Hu
- Department of Geriatric, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Xiangping Wu
- Key Lab, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Chengying Zheng
- Department of Geriatric, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| |
Collapse
|
15
|
Chang J, Choi Y. Depression diagnosis based on electroencephalography power ratios. Brain Behav 2023; 13:e3173. [PMID: 37479962 PMCID: PMC10454346 DOI: 10.1002/brb3.3173] [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: 04/30/2023] [Revised: 06/14/2023] [Accepted: 07/08/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Depression is a common mental disorder that impacts millions of people across the world. However, its diagnosis is difficult due to the dependence on subjective testing. Although quantitative electroencephalography (EEG) has been investigated as a promising diagnostic tool for depression, the associated results have proven contradictory. The current study determines whether the alpha/beta (ABR), alpha/theta (ATR), and theta/beta (TBR) ratios can serve as biological markers of depression. METHODS We used open-access EEG data from OpenNeuro to investigate power ratios in the resting state of 46 patients with depression and 75 healthy controls. Spectral data were extracted by fast Fourier transform at the theta band (4-8 Hz), alpha band (8-13 Hz), and beta band (13-32 Hz). Neural network, logistic regression, and receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracies of each suggested index. Additionally, the cutoff point, sensitivity, specificity, positive predictive value, and negative predictive value at the maximized Youden index were compared for each variable. RESULTS Decreased anterior frontal, frontal, central, parietal, occipital, and temporal ABR and decreased central and parietal TBR were observed in the depression group. The area under the curve of the ROC curves further revealed that these ratios could all effectively differentiate depression. In particular, the central, frontal, and parietal ABR exhibited high discrimination scores. Multiple logistic regression analysis demonstrated that the Beck Depression Inventory and Spielberger Trait Anxiety Inventory scores, as well as the probability of depression, increased with a decrease in the central ABR. Moreover, neural network analysis revealed that the global ABR was the most effective index for diagnosing depression among the three global EEG power ratios. CONCLUSIONS The central, frontal, and parietal ABR represent potential biomarkers to differentiate patients with depression from healthy controls.
Collapse
Affiliation(s)
- Jinwon Chang
- Korean Minjok Leadership AcademyHoengseong‐gunGangwon‐doRepublic of Korea
| | - Yuha Choi
- Korean Minjok Leadership AcademyHoengseong‐gunGangwon‐doRepublic of Korea
| |
Collapse
|
16
|
Pease M, Elmer J, Shahabadi AZ, Mallela AN, Ruiz-Rodriguez JF, Sexton D, Barot N, Gonzalez-Martinez JA, Shutter L, Okonkwo DO, Castellano JF. Predicting posttraumatic epilepsy using admission electroencephalography after severe traumatic brain injury. Epilepsia 2023; 64:1842-1852. [PMID: 37073101 PMCID: PMC11293840 DOI: 10.1111/epi.17622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 04/20/2023]
Abstract
OBJECTIVE Posttraumatic epilepsy (PTE) develops in as many as one third of severe traumatic brain injury (TBI) patients, often years after injury. Analysis of early electroencephalographic (EEG) features, by both standardized visual interpretation (viEEG) and quantitative EEG (qEEG) analysis, may aid early identification of patients at high risk for PTE. METHODS We performed a case-control study using a prospective database of severe TBI patients treated at a single center from 2011 to 2018. We identified patients who survived 2 years postinjury and matched patients with PTE to those without using age and admission Glasgow Coma Scale score. A neuropsychologist recorded outcomes at 1 year using the Expanded Glasgow Outcomes Scale (GOSE). All patients underwent continuous EEG for 3-5 days. A board-certified epileptologist, blinded to outcomes, described viEEG features using standardized descriptions. We extracted 14 qEEG features from an early 5-min epoch, described them using qualitative statistics, then developed two multivariable models to predict long-term risk of PTE (random forest and logistic regression). RESULTS We identified 27 patients with and 35 without PTE. GOSE scores were similar at 1 year (p = .93). The median time to onset of PTE was 7.2 months posttrauma (interquartile range = 2.2-22.2 months). None of the viEEG features was different between the groups. On qEEG, the PTE cohort had higher spectral power in the delta frequencies, more power variance in the delta and theta frequencies, and higher peak envelope (all p < .01). Using random forest, combining qEEG and clinical features produced an area under the curve of .76. Using logistic regression, increases in the delta:theta power ratio (odds ratio [OR] = 1.3, p < .01) and peak envelope (OR = 1.1, p < .01) predicted risk for PTE. SIGNIFICANCE In a cohort of severe TBI patients, acute phase EEG features may predict PTE. Predictive models, as applied to this study, may help identify patients at high risk for PTE, assist early clinical management, and guide patient selection for clinical trials.
Collapse
Affiliation(s)
- Matthew Pease
- Department of Neurological Surgery, University of Pittsburgh Medical Center Healthcare System, Pittsburgh, Pennsylvania, USA
| | - Jonathan Elmer
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Critical Care, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Emergency Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Ameneh Zare Shahabadi
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Arka N. Mallela
- Department of Neurological Surgery, University of Pittsburgh Medical Center Healthcare System, Pittsburgh, Pennsylvania, USA
| | | | - Daniel Sexton
- Department of Neurosurgery, Duke University, Durham, North Carolina, USA
| | - Niravkumar Barot
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Jorge A. Gonzalez-Martinez
- Department of Neurological Surgery, University of Pittsburgh Medical Center Healthcare System, Pittsburgh, Pennsylvania, USA
| | - Lori Shutter
- Department of Neurological Surgery, University of Pittsburgh Medical Center Healthcare System, Pittsburgh, Pennsylvania, USA
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Critical Care, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - David O. Okonkwo
- Department of Neurological Surgery, University of Pittsburgh Medical Center Healthcare System, Pittsburgh, Pennsylvania, USA
| | - James F. Castellano
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| |
Collapse
|
17
|
Del Percio C, Lopez S, Noce G, Lizio R, Tucci F, Soricelli A, Ferri R, Nobili F, Arnaldi D, Famà F, Buttinelli C, Giubilei F, Marizzoni M, Güntekin B, Yener G, Stocchi F, Vacca L, Frisoni GB, Babiloni C. What a Single Electroencephalographic (EEG) Channel Can Tell us About Alzheimer's Disease Patients With Mild Cognitive Impairment. Clin EEG Neurosci 2023; 54:21-35. [PMID: 36413420 DOI: 10.1177/15500594221125033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abnormalities in cortical sources of resting-state eyes closed electroencephalographic (rsEEG) rhythms recorded by hospital settings (10-20 montage) with 19 scalp electrodes characterized Alzheimer's disease (AD) from preclinical to dementia stages. An intriguing rsEEG application is the monitoring and evaluation of AD progression in large populations with few electrodes in low-cost devices. Here we evaluated whether the above-mentioned abnormalities can be observed from fewer scalp electrodes in patients with mild cognitive impairment due to AD (ADMCI). Clinical and rsEEG data acquired in hospital settings (10-20 montage) from 75 ADMCI participants and 70 age-, education-, and sex-matched normal elderly controls (Nold) were available in an Italian-Turkish archive (PDWAVES Consortium; www.pdwaves.eu). Standard spectral fast fourier transform (FFT) analysis of rsEEG data for individual delta, theta, and alpha frequency bands was computed from 6 monopolar scalp electrodes to derive bipolar C3-P3, C4-P4, P3-O1, and P4-O2 markers. The ADMCI group showed increased delta and decreased alpha power density at the C3-P3, C4-P4, P3-O1, and P4-O2 bipolar channels compared to the Nold group. Increased theta power density for ADMCI patients was observed only at the C3-P3 bipolar channel. Best classification accuracy between the ADMCI and Nold individuals reached 81% (area under the receiver operating characteristic curve) using Alpha2/Theta power density computed at the C3-P3 bipolar channel. Standard rsEEG power density computed from six posterior bipolar channels characterized ADMCI status. These results may pave the way toward diffuse clinical applications in health monitoring of dementia using low-cost EEG systems with a strict number of electrodes in lower- and middle-income countries.
Collapse
Affiliation(s)
- Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | | | | | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Flavio Nobili
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), 27212Università di Genova, Italy
| | - Dario Arnaldi
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy.,Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), 27212Università di Genova, Italy
| | - Francesco Famà
- Clinica neurologica, 9246IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, 9311Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, 9311Sapienza University of Rome, Rome, Italy
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, 218502Istanbul Medipol University, Istanbul, Turkey.,REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab., 218502Istanbul Medipol University, Istanbul, Turkey
| | - Görsev Yener
- Izmir University of Economics, Faculty of Medicine, Izmir, Turkey
| | | | | | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and 27212University of Geneva, Geneva, Switzerland
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", 9311Sapienza University of Rome, Rome, Italy.,Hospital San Raffaele Cassino, Cassino (FR), Italy
| |
Collapse
|
18
|
What a single electroencephalographic (EEG) channel can tell us about patients with dementia due to Alzheimer's disease. Int J Psychophysiol 2022; 182:169-181. [DOI: 10.1016/j.ijpsycho.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
|
19
|
Otaiku AI. Association of sleep abnormalities in older adults with risk of developing Parkinson's disease. Sleep 2022; 45:zsac206. [PMID: 36037514 PMCID: PMC9644115 DOI: 10.1093/sleep/zsac206] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/16/2022] [Indexed: 11/30/2022] Open
Abstract
STUDY OBJECTIVES Parkinson's disease (PD) is associated with abnormalities of sleep macro- and microstructure as measured using polysomnography (PSG). Whether these abnormalities precede the development of PD is unknown. This study investigated the association between PSG measured sleep abnormalities in older adults and the risk of incident PD. METHODS A total of 2,770 men from the ancillary sleep study of the Osteoporotic Fractures in Men Study (MrOS), a population-based cohort from the United States, who were free from PD baseline and underwent overnight PSG, were included in this longitudinal analysis. Incident PD was based on a clinical diagnosis from a medical professional. Multivariable logistic regression was used to estimate odds ratios (OR) for incident PD by quartiles of PSG measures, with adjustment for sociodemographic characteristics, medical comorbidities, and lifestyle factors. RESULTS During a median follow-up of 9.8 years, 70 (2.5%) cases of incident PD were identified. Longer total sleep time, lower rapid eye movement sleep (REM) percentage, a lower α/θ ratio during non-REM sleep and higher minimum oxygen saturations during REM sleep, were each associated with an increased risk of developing PD. Conversely, a higher awakening index was associated with a decreased risk of developing PD. The OR for the highest risk quartiles compared to the lowest risk quartiles, ranged from 2.1 to 3.7 (p's < .05). The associations remained significant when cases occurring within the first two years of follow-up were excluded from the analyses. CONCLUSIONS Macro- and micro-structural sleep abnormalities precede the development of PD by several years and can identify individuals at high risk of developing PD in the future.
Collapse
Affiliation(s)
- Abidemi I Otaiku
- Department of Neurology, Birmingham City Hospital, Birmingham, UK
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| |
Collapse
|
20
|
Vallarino E, Sommariva S, Famà F, Piana M, Nobili F, Arnaldi D. Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data. Hum Brain Mapp 2022; 43:5095-5110. [PMID: 35770938 PMCID: PMC9812240 DOI: 10.1002/hbm.25995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/05/2022] [Indexed: 01/15/2023] Open
Abstract
A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.
Collapse
Affiliation(s)
| | - Sara Sommariva
- Dipartimento di Matematica (DIMA)Università degli Studi di GenovaGenoaItaly,CNR‐SPINGenoaItaly
| | - Francesco Famà
- Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno‐Infantili (DINOGMI)Università degli Studi di GenovaGenoaItaly,IRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Michele Piana
- Dipartimento di Matematica (DIMA)Università degli Studi di GenovaGenoaItaly,IRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Flavio Nobili
- Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno‐Infantili (DINOGMI)Università degli Studi di GenovaGenoaItaly,IRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Dario Arnaldi
- Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno‐Infantili (DINOGMI)Università degli Studi di GenovaGenoaItaly,IRCCS Ospedale Policlinico San MartinoGenoaItaly
| |
Collapse
|
21
|
Korkmaz A, Rhyman L, Ramasami P. Synthesis, characterization, DFT and molecular docking studies of acetone O-((2,5-dichlorophenyl)sulfonyl) oxime. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2021-0230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Acetone O-((2,5-dichlorophenyl)sulfonyl) oxime was prepared from 2,5-dichlorophenylsulfonyl chloride and acetone oxime using triethylamine. The compound was characterized using 1H NMR and 13C NMR spectra. Molecular docking was performed with the compound and cholinesterase enzymes. The average affinity of the compound with the acetylcholinesterase and butyrylcholinesterase was calculated at −7.46 ± 0.14 and −6.70 ± 0.00 kcal/mol, respectively. The density functional theory method was also used to complement the experimental study. The findings of this work might be useful towards the applications of the compound studied.
Collapse
Affiliation(s)
- Adem Korkmaz
- Faculty of Health Science, MuşAlparslan University , Mush , Turkey
| | - Lydia Rhyman
- Department of Chemistry , Computational Chemistry Group, Faculty of Science, University of Mauritius , Reduit 80837 , Mauritius
- Department of Chemical Sciences , Center for Natural Product Research, University of Johannesburg, Doornfontein Campus , Johannesburg 2028 , South Africa
| | - Ponnadurai Ramasami
- Department of Chemistry , Computational Chemistry Group, Faculty of Science, University of Mauritius , Reduit 80837 , Mauritius
- Department of Chemical Sciences , Center for Natural Product Research, University of Johannesburg, Doornfontein Campus , Johannesburg 2028 , South Africa
| |
Collapse
|
22
|
Cheron G, Ristori D, Petieau M, Simar C, Zarka D, Cebolla AM. Effects of Pulsed-Wave Chromotherapy and Guided Relaxation on the Theta-Alpha Oscillation During Arrest Reaction. Front Psychol 2022; 13:792872. [PMID: 35310269 PMCID: PMC8929400 DOI: 10.3389/fpsyg.2022.792872] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 01/13/2022] [Indexed: 12/31/2022] Open
Abstract
The search for the best wellness practice has promoted the development of devices integrating different technologies and guided meditation. However, the final effects on the electrical activity of the brain remain relatively sparse. Here, we have analyzed of the alpha and theta electroencephalographic oscillations during the realization of the arrest reaction (AR; eyes close/eyes open transition) when a chromotherapy session performed in a dedicated room [Rebalance (RB) device], with an ergonomic bed integrating pulsed-wave light (PWL) stimulation, guided breathing, and body scan exercises. We demonstrated that the PWL induced an evoked-related potential characterized by the N2-P3 components maximally recorded on the fronto-central areas and accompanied by an event-related synchronization (ERS) of the delta–theta–alpha oscillations. The power of the alpha and theta oscillations was analyzed during repeated ARs testing realized along with the whole RB session. We showed that the power of the alpha and theta oscillations was significantly increased during the session in comparison to their values recorded before. Of the 14 participants, 11 and 6 showed a significant power increase of the alpha and theta oscillations, respectively. These increased powers were not observed in two different control groups (n = 28) who stayed passively outside or inside the RB room but without any type of stimulation. These preliminary results suggest that PWL chromotherapy and guided relaxation induce measurable electrical brain changes that could be beneficial under neuropsychiatric perspectives.
Collapse
Affiliation(s)
- Guy Cheron
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.,ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium.,Laboratory of Neuroscience, Université de Mons, Mons, Belgium
| | - Dominique Ristori
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.,ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Mathieu Petieau
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.,ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Cédric Simar
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.,ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium.,Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Brussels, Belgium
| | - David Zarka
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.,ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Ana-Maria Cebolla
- Laboratory of Neurophysiology and Movement Biomechanics, Université Libre de Bruxelles, Brussels, Belgium.,ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| |
Collapse
|