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Doval S, Nebreda A, Bruña R. Functional connectivity across the lifespan: a cross-sectional analysis of changes. Cereb Cortex 2024; 34:bhae396. [PMID: 39367726 DOI: 10.1093/cercor/bhae396] [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: 06/21/2024] [Revised: 09/11/2024] [Accepted: 09/13/2024] [Indexed: 10/06/2024] Open
Abstract
In the era of functional brain networks, our understanding of how they evolve across life in a healthy population remains limited. Here, we investigate functional connectivity across the human lifespan using magnetoencephalography in a cohort of 792 healthy individuals, categorized into young (13 to 30 yr), middle (31 to 54 yr), and late adulthood (55 to 80 yr). Employing corrected imaginary phase-locking value, we map the evolving landscapes of connectivity within delta, theta, alpha, beta, and gamma classical frequency bands among brain areas. Our findings reveal significant shifts in functional connectivity patterns across all frequency bands, with certain networks exhibiting increased connectivity and others decreased, dependent on the frequency band and specific age groups, showcasing the dynamic reorganization of neural networks as age increases. This detailed exploration provides, to our knowledge, the first all-encompassing view of how electrophysiological functional connectivity evolves at different life stages, offering new insights into the brain's adaptability and the intricate interplay of cognitive aging and network connectivity. This work not only contributes to the body of knowledge on cognitive aging and neurological health but also emphasizes the need for further research to develop targeted interventions for maintaining cognitive function in the aging population.
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Affiliation(s)
- Sandra Doval
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, C/ Ministro Ibañez, 4, 28015 Madrid, Spain
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Campus de Somosaguas, Ctra. de Húmera, s/n, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Alberto Nebreda
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, C/ Ministro Ibañez, 4, 28015 Madrid, Spain
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Campus de Somosaguas, Ctra. de Húmera, s/n, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, C/ Ministro Ibañez, 4, 28015 Madrid, Spain
- Department of Radiology, Rehabilitation and Physiotherapy, School of Medicine, Universidad Complutense de Madrid, Plaza de Ramón y Cajal, s/n, Ciudad Universitaria, 28040 Madrid, Spain
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Sato K, Hitomi T, Kobayashi K, Matsuhashi M, Shimotake A, Kuzuya A, Kinoshita A, Matsumoto R, Takechi H, Sugi T, Nishida S, Takahashi R, Ikeda A. Electroencephalography can Ubiquitously Delineate the Brain Dysfunction of Neurodegenerative Dementia by Both Visual and Automatic Analysis Methods: A Preliminary Study. Clin EEG Neurosci 2024:15500594241283512. [PMID: 39363628 DOI: 10.1177/15500594241283512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/05/2024]
Abstract
Introduction: The aim was to examine the differences in electroencephalography (EEG) findings by visual and automated quantitative analyses between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD). Methods: EEG data of 20 patients with AD and 24 with DLB/PDD (12 DLB and 12 PDD) were retrospectively analyzed. Based on the awake EEG, the posterior dominant rhythm frequency and proportion of patients who showed intermittent focal and diffuse slow waves (IDS) were visually and automatically compared between the AD and DLB/PDD groups. Results: On visual analysis, patients with DLB/PDD showed a lower PDR frequency than patients with AD. In patients with PDR <8 Hz and occipital slow waves or patients with PDR <8 Hz and IDS, DLB/PDD was highly suspected (PPV 100%) and AD was unlikely (PPV 0%). On automatic analysis, the findings of the PDR were similar to those on visual analysis. Comparisons between visual and automatic analysis showed an overlap in the focal slow wave commonly detected by both methods in 10 of 44 patients, and concordant presence or absence of IDS in 29 of 43 patients. With respect to PDR <8 Hz and the combination of PDR <8 Hz and IDS, PPV and NPV in DLB/PDD and AD were not different between visual and automatic analysis. Conclusions: As the noninvasive, widely available clinical tool of low expense, visual analysis of EEG findings provided highly sufficient information to delineate different brain dysfunction in AD and DLB/PDD, and automatic EEG analysis could support visual analysis especially about PD.
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Affiliation(s)
- Kei Sato
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Takefumi Hitomi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Clinical Laboratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Katsuya Kobayashi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masao Matsuhashi
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akihiro Shimotake
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akira Kuzuya
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ayae Kinoshita
- School of Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Riki Matsumoto
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Division of Neurology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Hajime Takechi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Geriatrics and Cognitive Disorders, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Takenao Sugi
- Department of Electrical and Electronic Engineering, Faculty of Science and Engineering, Saga University, Saga, Japan
| | - Shigeto Nishida
- Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Maiella M, Mencarelli L, Casula EP, Borghi I, Assogna M, di Lorenzo F, Bonnì S, Pezzopane V, Martorana A, Koch G. Breakdown of TMS evoked EEG signal propagation within the default mode network in Alzheimer's disease. Clin Neurophysiol 2024; 167:177-188. [PMID: 39332078 DOI: 10.1016/j.clinph.2024.09.007] [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: 03/13/2024] [Revised: 09/09/2024] [Accepted: 09/13/2024] [Indexed: 09/29/2024]
Abstract
BACKGROUND The neural activity of the Default Mode Network (DMN) is disrupted in patients with In Alzheimer's disease (AD). OBJECTIVES We used a novel multimodal approach to track neural signal propagation within the DMN in AD patients. METHODS Twenty mild to moderate AD patients were recruited. We used transcranial magnetic stimulation (TMS) pulses to probe with a millisecond time resolution the propagation of evoked electroencephalography (EEG) signal following the neural activation of the Precuneus (PC), which is a key hub area of the DMN. Moreover, functional and structural magnetic resonance imaging (MRI) data were collected to reconstruct individual features of the DMN. RESULTS In AD patients a probe TMS pulse applied over the PC evokes an increased local activity unmasking underlying hyperexcitability. In contrast, the EEG evoked neural signal did not propagate efficiently within the DMN showing a remarkable breakdown of signal propagation. fMRI and structural tractography showed that impaired signal propagation was related to the same connectivity matrices derived from DMN BOLD signal and transferred by specific white matter bundles forming the cingulum. These features were not detectable stimulating other areas (left dorsolateral prefrontal cortex) or for different networks (fronto-parietal network). Finally, connectivity breakdown was associated with cognitive impairment, as measured with the Clinical Dementia Rating Scale sum of boxes (CDR-SB). CONCLUSIONS TMS-EEG in AD shows both local hyperexcitability and a lack of signal propagation within the DMN. These neurophysiological features also correlate with structural and cognitive attributes of the patients. SIGNIFICANCE Neuronavigated TMS-EEG may be used as a novel neurophysiological biomarker of DMN connectivity in AD patients.
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Affiliation(s)
- Michele Maiella
- Department of Behavioural and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Lucia Mencarelli
- Department of Behavioural and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Elias P Casula
- Department of Behavioural and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Ilaria Borghi
- Department of Behavioural and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Department of Neuroscience and Rehabilitation, University of Ferrara, and Center for Translational Neurophysiology of Speech and Communication (CTNSC), Italian Institute of Technology (IIT), Ferrara, Italy
| | - Martina Assogna
- Department of Behavioural and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Francesco di Lorenzo
- Department of Behavioural and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Sonia Bonnì
- Department of Behavioural and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Valentina Pezzopane
- Department of Behavioural and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Department of Neuroscience and Rehabilitation, University of Ferrara, and Center for Translational Neurophysiology of Speech and Communication (CTNSC), Italian Institute of Technology (IIT), Ferrara, Italy
| | | | - Giacomo Koch
- Department of Behavioural and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy; Department of Neuroscience and Rehabilitation, University of Ferrara, and Center for Translational Neurophysiology of Speech and Communication (CTNSC), Italian Institute of Technology (IIT), Ferrara, Italy.
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Karimani F, Asgari Taei A, Abolghasemi-Dehaghani MR, Safari MS, Dargahi L. Impairment of entorhinal cortex network activity in Alzheimer's disease. Front Aging Neurosci 2024; 16:1402573. [PMID: 38882526 PMCID: PMC11176617 DOI: 10.3389/fnagi.2024.1402573] [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: 03/17/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
The entorhinal cortex (EC) stands out as a critical brain region affected in the early phases of Alzheimer's disease (AD), with some of the disease's pathological processes originating from this area, making it one of the most crucial brain regions in AD. Recent research highlights disruptions in the brain's network activity, characterized by heightened excitability and irregular oscillations, may contribute to cognitive impairment. These disruptions are proposed not only as potential therapeutic targets but also as early biomarkers for AD. In this paper, we will begin with a review of the anatomy and function of EC, highlighting its selective vulnerability in AD. Subsequently, we will discuss the disruption of EC network activity, exploring changes in excitability and neuronal oscillations in this region during AD and hypothesize that, considering the advancements in neuromodulation techniques, addressing the disturbances in the network activity of the EC could offer fresh insights for both the diagnosis and treatment of AD.
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Affiliation(s)
- Farnaz Karimani
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Afsaneh Asgari Taei
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Mir-Shahram Safari
- Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Leila Dargahi
- Neurobiology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Anjum MF, Espinoza AI, Cole RC, Singh A, May P, Uc EY, Dasgupta S, Narayanan NS. Resting-state EEG measures cognitive impairment in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:6. [PMID: 38172519 PMCID: PMC10764756 DOI: 10.1038/s41531-023-00602-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024] Open
Abstract
Cognitive dysfunction is common in Parkinson's disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value < 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value < 0.001) outperforming traditional spectral markers (rho = -0.30-0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value < 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.
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Affiliation(s)
- Md Fahim Anjum
- Department of Neurology, University of California San Francisco, San Francisco, CA, 94143, USA.
| | - Arturo I Espinoza
- Department of Neurology, The University of Iowa, Iowa city, IA, 52240, USA
| | - Rachel C Cole
- Department of Neurology, The University of Iowa, Iowa city, IA, 52240, USA
| | - Arun Singh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, South Dakota, SD, 57069, USA
| | - Patrick May
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa city, IA, 52240, USA
| | - Ergun Y Uc
- Department of Neurology, The University of Iowa, Iowa city, IA, 52240, USA
- Neurology Service, Iowa City VA Medical Center, Iowa city, IA, 52240, USA
| | - Soura Dasgupta
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa city, IA, 52240, USA
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Lin CY, Chang MC, Jhou HJ. Effect of Levetiracetam on Cognition: A Systematic Review and Meta-analysis of Double-Blind Randomized Placebo-Controlled Trials. CNS Drugs 2024; 38:1-14. [PMID: 38102532 DOI: 10.1007/s40263-023-01058-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/27/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND Studies have suggested that levetiracetam may help improve cognitive function in patients with epilepsy. Recently, its efficacy in improving cognitive function was reported in patients with amnestic mild cognitive impairment, schizophrenia, and Alzheimer's disease. However, the specific cognitive domains affected and the degree of evidence supporting these effects remain unclear. This systematic review and meta-analysis aimed to explore the effects of levetiracetam on different cognitive domains. METHODS This meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. We defined our inclusion criteria for the systematic review as: (1) randomized placebo-controlled trials (RCTs) involving human subjects, (2) double-blinded RCTs, and (3) RCTs evaluating the quantitative differences in cognitive function between levetiracetam and placebo. We excluded: (1) non-RCT studies, (2) open-label studies, and (3) RCTs lacking cognitive assessments for either intervention. Two authors independently searched electronic databases, including PubMed, Embase, Cochrane CENTRAL, and ClinicalTrials.gov, from inception until 2 July 2023. The methodological quality of the included studies was assessed using the Cochrane risk of bias tool. Meta-analytic techniques were applied to examine the impact of levetiracetam on cognitive domain tests, with Hedges' g facilitating the comparison with placebo. The domains analyzed comprised multi-domain, executive function, processing speed, working memory, verbal memory/learning (verbal ML), visuospatial memory/learning (visuospatial ML), and language. We used odds ratios to compare the incidence of treatment-emergent adverse events between the groups, including somnolence, fatigue, dizziness, headache, irritability, and cognitive adverse events. RESULTS A random-effects model was utilized to perform a meta-analysis of 16 RCTs including 545 participants. Compared with a placebo, levetiracetam was associated with improved executive function [Hedges'g = - 0.390, 95% confidence interval (CI) = - 0.609 to - 0.172, p < 0.001, I2 = 24.0%]. Subgroup analysis showed that levetiracetam outperformed placebo in patients without epilepsy (Hedges' g = - 0.419, 95% CI = - 0.647 to - 0.191, p < 0.001, I2 = 26.2%). Meanwhile, low-dose levetiracetam showed a moderate favorable effect over placebo (Hedges' g = -0.544, 95% CI = - 1.085 to - 0.003, p = 0.049, I2 = 65.3%). In patients without epilepsy, low-dose levetiracetam was associated with improved executive function (Hedges'g = - 0.544, 95% CI = - 1.085 to - 0.003, p = 0.049, I2 = 65.3%). Concurrently, levetiracetam was associated with more frequent somnolence than a placebo (odds ratio = 4.654, 95% CI = 1.533 to 14.124, p = 0.007, I2 = 32.9%). Potential publication bias was observed in the executive function domain. CONCLUSIONS This exploratory study suggests that levetiracetam might improve executive function in specific populations. However, the diversity in study populations and potential publication bias warrant caution.
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Affiliation(s)
- Chia-Yen Lin
- Department of Neurology, Neurological Institute, Taichung Veterans General Hospital, No. 1650, Taiwan Boulevard, Sect. 4, Taichung, 40705, Taiwan
| | - Meng-Chia Chang
- Department of Neurology, Neurological Institute, Taichung Veterans General Hospital, No. 1650, Taiwan Boulevard, Sect. 4, Taichung, 40705, Taiwan
| | - Hong-Jie Jhou
- Department of Neurology, Changhua Christian Hospital, 135 Nanhsiao Street, Changhua, 50006, Taiwan.
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Amato LG, Vergani AA, Lassi M, Fabbiani C, Mazzeo S, Burali R, Nacmias B, Sorbi S, Mannella R, Grippo A, Bessi V, Mazzoni A. Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12526. [PMID: 38371358 PMCID: PMC10870085 DOI: 10.1002/dad2.12526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 02/20/2024]
Abstract
INTRODUCTION Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern. METHODS We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. RESULTS Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50). DISCUSSION Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings. Highlights Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y.
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Affiliation(s)
- Lorenzo Gaetano Amato
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Alberto Arturo Vergani
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Michael Lassi
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Carlo Fabbiani
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Salvatore Mazzeo
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | | | - Benedetta Nacmias
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Sandro Sorbi
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | | | | | - Valentina Bessi
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Alberto Mazzoni
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
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Buzi G, Fornari C, Perinelli A, Mazza V. Functional connectivity changes in mild cognitive impairment: A meta-analysis of M/EEG studies. Clin Neurophysiol 2023; 156:183-195. [PMID: 37967512 DOI: 10.1016/j.clinph.2023.10.011] [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: 04/19/2023] [Revised: 08/31/2023] [Accepted: 10/22/2023] [Indexed: 11/17/2023]
Abstract
OBJECTIVE Early synchrony alterations have been observed through electrophysiological techniques in Mild Cognitive Impairment (MCI), which is considered the intermediate phase between healthy aging (HC) and Alzheimer's disease (AD). However, the documented direction (hyper/hypo-synchronization), regions and frequency bands affected are inconsistent. This meta-analysis intended to elucidate existing evidence linked to potential neurophysiological biomarkers of AD. METHODS We conducted a random-effects meta-analysis that entailed the unbiased inclusion of Non-statistically Significant Unreported Effect Sizes ("MetaNSUE") of electroencephalogram (EEG) and magnetoencephalogram (MEG) studies investigating functional connectivity changes at rest along the healthy-pathological aging continuum, searched through PubMed, Scopus, Web of Science and PsycINFO databases until June 2023. RESULTS Of the 3852 articles extracted, we analyzed 12 papers, and we found an alpha synchrony decrease in MCI compared to HC, specifically between temporal-parietal (d = -0.26) and frontal-parietal areas (d = -0.25). CONCLUSIONS Alterations of alpha synchrony are present even at MCI stage. SIGNIFICANCE Synchrony measures may be promising for the detection of the first hallmarks of connectivity alterations, even at the prodromal stages of the AD, before clinical symptoms occur.
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Affiliation(s)
- Giulia Buzi
- U1077 INSERM-EPHE-UNICAEN, Caen 14000, France
| | - Chiara Fornari
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy.
| | - Alessio Perinelli
- Department of Physics, University of Trento, Trento, Italy; INFN-TIFPA, Trento, Italy
| | - Veronica Mazza
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy.
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Anjum MF, Espinoza A, Cole R, Singh A, May P, Uc E, Dasgupta S, Narayanan N. Resting-state EEG measures cognitive impairment in Parkinson's disease. RESEARCH SQUARE 2023:rs.3.rs-2666578. [PMID: 36993450 PMCID: PMC10055637 DOI: 10.21203/rs.3.rs-2666578/v1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Background Cognitive dysfunction is common in Parkinson's disease (PD) and is diagnosed by complex, time-consuming psychometric tests which are affected by language and education, subject to learning effects, and not suitable for continuous monitoring of cognition. Objectives We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. Methods We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from the National Institutes of Health (NIH) Toolbox using cross-validation schemes, regression models, and randomization tests. Results We observed cognition-related changes in EEG activities over multiple spectral rhythms. Utilizing only 8 best-performing EEG electrodes, our proposed index strongly correlated with cognition (rho = 0.68, p value < 0.001 with MoCA; rho ≥ 0.56, p value < 0.001 with cognitive tests from the NIH Toolbox) outperforming traditional spectral markers (rho = -0.30 - 0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Conclusions Our approach is computationally efficient for real-time indexing of cognition across domains, implementable even in hardware with limited computing capabilities, making it potentially compatible with dynamic therapies such as closed-loop neurostimulation, and will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.
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Liu M, Liu B, Ye Z, Wu D. Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022. Front Neurosci 2023; 17:1128851. [PMID: 37021134 PMCID: PMC10067679 DOI: 10.3389/fnins.2023.1128851] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/06/2023] [Indexed: 03/22/2023] Open
Abstract
BackgroundElectroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI.MethodsRelated publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses.ResultsBetween 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were “EEG,” “mild cognitive impairment,” and “Alzheimer’s disease”.ConclusionThe application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI.
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Affiliation(s)
- Mingrui Liu
- Department of Rehabilitation, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Baohu Liu
- Department of Rehabilitation, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zelin Ye
- Department of Cardiovascular, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dongyu Wu
- Department of Rehabilitation, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- *Correspondence: Dongyu Wu,
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Jiao B, Li R, Zhou H, Qing K, Liu H, Pan H, Lei Y, Fu W, Wang X, Xiao X, Liu X, Yang Q, Liao X, Zhou Y, Fang L, Dong Y, Yang Y, Jiang H, Huang S, Shen L. Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology. Alzheimers Res Ther 2023; 15:32. [PMID: 36765411 PMCID: PMC9912534 DOI: 10.1186/s13195-023-01181-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/06/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Electroencephalogram (EEG) has emerged as a non-invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer's disease (AD). However, the effectiveness of EEG in the precise diagnosis and assessment of AD and its preclinical stage, amnestic mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD. METHODS A total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting-state EEG recordings for a three-level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of models by combining participants' EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for assessing the disease progression and individual's cognitive function. RESULTS The identified EEG biomarkers achieved over 70% accuracy in the three-level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD-linked neurodegeneration on EEG metrics were localized at parieto-occipital regions. In the cross-validation predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction. CONCLUSIONS Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.
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Affiliation(s)
- Bin Jiao
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China ,grid.216417.70000 0001 0379 7164National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China ,grid.216417.70000 0001 0379 7164Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China ,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China ,grid.216417.70000 0001 0379 7164Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Rihui Li
- grid.168010.e0000000419368956Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA USA ,Brainup Institute of Science and Technology, Chongqing, China
| | - Hui Zhou
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Kunqiang Qing
- Brainup Institute of Science and Technology, Chongqing, China
| | - Hui Liu
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Hefu Pan
- Brainup Institute of Science and Technology, Chongqing, China
| | - Yanqin Lei
- Brainup Institute of Science and Technology, Chongqing, China
| | - Wenjin Fu
- Brainup Institute of Science and Technology, Chongqing, China
| | - Xiaoan Wang
- Brainup Institute of Science and Technology, Chongqing, China
| | - Xuewen Xiao
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xixi Liu
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qijie Yang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xinxin Liao
- grid.216417.70000 0001 0379 7164Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Yafang Zhou
- grid.216417.70000 0001 0379 7164Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Liangjuan Fang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yanbin Dong
- Brainup Institute of Science and Technology, Chongqing, China
| | - Yuanhao Yang
- grid.1003.20000 0000 9320 7537Mater Research Institute, The University of Queensland, Woolloongabba, Queensland 4102 Australia
| | - Haiyan Jiang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Sha Huang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China. .,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China. .,Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China. .,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China. .,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China. .,Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China.
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12
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Lopez S, Del Percio C, Lizio R, Noce G, Padovani A, Nobili F, Arnaldi D, Famà F, Moretti DV, Cagnin A, Koch G, Benussi A, Onofrj M, Borroni B, Soricelli A, Ferri R, Buttinelli C, Giubilei F, Güntekin B, Yener G, Stocchi F, Vacca L, Bonanni L, Babiloni C. Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms. Front Aging Neurosci 2023; 15:780014. [PMID: 36776437 PMCID: PMC9908964 DOI: 10.3389/fnagi.2023.780014] [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: 09/20/2021] [Accepted: 01/05/2023] [Indexed: 01/28/2023] Open
Abstract
Introduction Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections). Methods Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used. Results Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants. Discussion In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.
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Affiliation(s)
- Susanna Lopez
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Claudio Del Percio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Roberta Lizio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | | | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Dario Arnaldi
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Francesco Famà
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Davide V. Moretti
- Alzheimer’s Disease Rehabilitation Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Giacomo Koch
- Non-Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
- Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, Rome, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University “G. D’Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy
- Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Türkiye
- Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Türkiye
| | - Görsev Yener
- Department of Neurology, Dokuz Eylül University Medical School, Izmir, Türkiye
- Faculty of Medicine, Izmir University of Economics, Izmir, Türkiye
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
- Telematic University San Raffaele, Rome, Italy
| | - Laura Vacca
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. D’Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
- San Raffaele of Cassino, Cassino, Italy
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13
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Wang C, Xu T, Yu W, Li T, Han H, Zhang M, Tao M. Early diagnosis of Alzheimer's disease and mild cognitive impairment based on electroencephalography: From the perspective of event related potentials and deep learning. Int J Psychophysiol 2022; 182:182-189. [DOI: 10.1016/j.ijpsycho.2022.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022]
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14
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Lee K, Choi KM, Park S, Lee SH, Im CH. Selection of the optimal channel configuration for implementing wearable EEG devices for the diagnosis of mild cognitive impairment. Alzheimers Res Ther 2022; 14:170. [DOI: 10.1186/s13195-022-01115-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/31/2022] [Indexed: 11/15/2022]
Abstract
Abstract
Background
Early diagnosis of mild cognitive impairment (MCI) is essential for timely treatment planning. With recent advances in the wearable technology, interest has increasingly shifted toward computer-aided self-diagnosis of MCI using wearable electroencephalography (EEG) devices in daily life. However, no study so far has investigated the optimal electrode configurations for the efficient diagnosis of MCI while considering the design factors of wearable EEG devices. In this study, we aimed to determine the optimal channel configurations of wearable EEG devices for the computer-aided diagnosis of MCI.
Method
We employed an EEG dataset collected from 21 patients with MCI and 21 healthy control subjects. After evaluating the classification accuracies for all possible electrode configurations for the two-, four-, six-, and eight-electrode conditions using a support vector machine, the optimal electrode configurations that provide the highest diagnostic accuracy were suggested for each electrode condition.
Results
The highest classification accuracies of 74.04% ± 4.82, 82.43% ± 6.14, 86.28% ± 2.81, and 86.85% ± 4.97 were achieved for the optimal two-, four-, six-, and eight-electrode configurations, respectively, which demonstrated the possibility of precise machine-learning-based diagnosis of MCI with a limited number of EEG electrodes. Additionally, further simulations with the EEG dataset revealed that the optimal electrode configurations had significantly higher classification accuracies than commercial EEG devices with the same number of electrodes, which suggested the importance of electrode configuration optimization for wearable EEG devices based on clinical EEG datasets.
Conclusions
This study highlighted that the optimization of the electrode configuration, assuming the wearable EEG devices can potentially be utilized for daily life monitoring of MCI, is necessary to enhance the performance and portability.
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15
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Andrei Appelt P, Taciana Sisconetto A, Baldo Sucupira KSM, Neto EDM, Chagas TDJ, Bazan R, Moura Cabral A, Andrade ADO, de Souza LAPS, José Luvizutto G. Changes in Electrical Brain Activity and Cognitive Functions Following Mild to Moderate COVID-19: A one-Year Prospective Study After Acute Infection. Clin EEG Neurosci 2022; 53:543-557. [PMID: 35635280 PMCID: PMC9157278 DOI: 10.1177/15500594221103834] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The coronavirus disease 2019 (COVID-19) can disrupt various brain functions. Over a one-year period, we aimed to assess brain activity and cognitive function in 53 COVID-19 patients and 30 individuals without COVID-19 (or asymptomatic). The Montreal Cognitive Assessment, Trail Making Test Parts A and B (TMT-A and B), and Digit Span Test were used to assess cognitive function. Cognitive variables and electroencephalography (EEG) data (activity, mobility, and complexity) were compared between the groups at rest and during cognitive demand (F3-F7, Fz-F3, Fz-F4, and F4-F8). There was a reduction in F3-F7 activity during the TMT-B in the COVID-19 group at 6-12 months compared to the controls (p = 0.01) at baseline (p = 0.03), a reduction in signal complexity at F3-F7 at rest in the COVID-19 group at baseline and 6-12 months compared to the controls (p < 0.001), and a reduction in Fz-F4 activity at rest from 6-12 months in the post-COVID group compared to baseline (p = 0.02) and 3-6 months (p = 0.04). At 6-12 months, there was a time increase in TMT-A in the COVID-19 group compared to that in the controls (p = 0.04). Some correlations were found between EEG data and cognitive test in both groups. In conclusion, there was a reduction in brain activity at rest in the Fz-F4 areas and during high cognitive demands in the F3-F7 areas. A reduction in signal complexity in F3-F7 at rest was found in the COVID-19 group at 6-12 months after acute infection. Furthermore, individuals with COVID-19 experience long-term changes in cognitive function.
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Affiliation(s)
- Pablo Andrei Appelt
- Department of Applied Physical Therapy, 74348Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | - Angélica Taciana Sisconetto
- Department of Applied Physical Therapy, 74348Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | | | - Eduardo de Moura Neto
- Department of Sport Science, 74348Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | - Tatiane de Jesus Chagas
- Department of Sport Science, 74348Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | - Rodrigo Bazan
- Department of Neurology, Psychology and Psychiatry, Botucatu Medical School, Botucatu, São Paulo, Brazil
| | - Ariana Moura Cabral
- Centre for Innovation and Technology Assessment in Health, Faculty of Electrical Engineering, 28119Federal University of Uberlândia, Uberlândia, MG, Brazil
| | - Adriano de Oliveira Andrade
- Centre for Innovation and Technology Assessment in Health, Faculty of Electrical Engineering, 28119Federal University of Uberlândia, Uberlândia, MG, Brazil
| | | | - Gustavo José Luvizutto
- Department of Applied Physical Therapy, 74348Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
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16
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Ko H, Yoon SP. Optogenetic neuromodulation with gamma oscillation as a new strategy for Alzheimer disease: a narrative review. JOURNAL OF YEUNGNAM MEDICAL SCIENCE 2022; 39:269-277. [PMID: 35152662 PMCID: PMC9580057 DOI: 10.12701/jyms.2021.01683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/27/2022] [Accepted: 02/04/2022] [Indexed: 12/31/2022]
Abstract
The amyloid hypothesis has been considered a major explanation of the pathogenesis of Alzheimer disease. However, failure of phase III clinical trials with anti-amyloid-beta monoclonal antibodies reveals the need for other therapeutic approaches to treat Alzheimer disease. Compared to its relatively short history, optogenetics has developed considerably. The expression of microbial opsins in cells using genetic engineering allows specific control of cell signals or molecules. The application of optogenetics to Alzheimer disease research or clinical approaches is increasing. When applied with gamma entrainment, optogenetic neuromodulation can improve Alzheimer disease symptoms. Although safety problems exist with optogenetics such as the use of viral vectors, this technique has great potential for use in Alzheimer disease. In this paper, we review the historical applications of optogenetic neuromodulation with gamma entrainment to investigate the mechanisms involved in Alzheimer disease and potential therapeutic strategies.
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Affiliation(s)
- Haneol Ko
- Medical Course, Jeju National University School of Medicine, Jeju, Korea
| | - Sang-Pil Yoon
- Department of Anatomy, Jeju National University College of Medicine, Jeju, Korea
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17
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Identifying Patients with Epilepsy Having Depression/Anxiety Disorder Using Common Spatial Patterns of Functional EEG Networks. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00726-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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18
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Romero Milà B, Remakanthakurup Sindhu K, Mytinger JR, Shrey DW, Lopour BA. EEG biomarkers for the diagnosis and treatment of infantile spasms. Front Neurol 2022; 13:960454. [PMID: 35968272 PMCID: PMC9366674 DOI: 10.3389/fneur.2022.960454] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis and treatment are critical for young children with infantile spasms (IS), as this maximizes the possibility of the best possible child-specific outcome. However, there are major barriers to achieving this, including high rates of misdiagnosis or failure to recognize the seizures, medication failure, and relapse. There are currently no validated tools to aid clinicians in assessing objective diagnostic criteria, predicting or measuring medication response, or predicting the likelihood of relapse. However, the pivotal role of EEG in the clinical management of IS has prompted many recent studies of potential EEG biomarkers of the disease. These include both visual EEG biomarkers based on human visual interpretation of the EEG and computational EEG biomarkers in which computers calculate quantitative features of the EEG. Here, we review the literature on both types of biomarkers, organized based on the application (diagnosis, treatment response, prediction, etc.). Visual biomarkers include the assessment of hypsarrhythmia, epileptiform discharges, fast oscillations, and the Burden of AmplitudeS and Epileptiform Discharges (BASED) score. Computational markers include EEG amplitude and power spectrum, entropy, functional connectivity, high frequency oscillations (HFOs), long-range temporal correlations, and phase-amplitude coupling. We also introduce each of the computational measures and provide representative examples. Finally, we highlight remaining gaps in the literature, describe practical guidelines for future biomarker discovery and validation studies, and discuss remaining roadblocks to clinical implementation, with the goal of facilitating future work in this critical area.
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Affiliation(s)
- Blanca Romero Milà
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Barcelona, Spain
| | | | - John R. Mytinger
- Division of Pediatric Neurology, Department of Pediatrics, Nationwide Children's Hospital, The Ohio State University, Columbus, OH, United States
| | - Daniel W. Shrey
- Division of Neurology, Children's Hospital Orange County, Orange, CA, United States
- Department of Pediatrics, University of California, Irvine, Irvine, CA, United States
| | - Beth A. Lopour
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- *Correspondence: Beth A. Lopour
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19
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Lu Z, Wang H, Gu J, Gao F. Association between abnormal brain oscillations and cognitive performance in patients with bipolar disorder; Molecular mechanisms and clinical evidence. Synapse 2022; 76:e22247. [PMID: 35849784 DOI: 10.1002/syn.22247] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 05/23/2022] [Accepted: 06/20/2022] [Indexed: 11/10/2022]
Abstract
Brain oscillations have gained great attention in neuroscience during recent decades as functional building blocks of cognitive-sensory processes. Research has shown that oscillations in "alpha," "beta," "gamma," "delta," and "theta" frequency windows are highly modified in brain pathology, including in patients with cognitive impairment like bipolar disorder (BD). The study of changes in brain oscillations can provide fundamental knowledge for exploring neurophysiological biomarkers in cognitive impairment. The present article reviews findings from the role and molecular basis of abnormal neural oscillation and synchronization in the symptoms of patients with BD. An overview of the results clearly demonstrates that, in cognitive-sensory processes, resting and evoked/event-related electroencephalogram (EEG) spectra in the delta, theta, alpha, beta, and gamma bands are abnormally changed in patients with BD showing psychotic features. Abnormal oscillations have been found to be associated with several neural dysfunctions and abnormalities contributing to BD, including abnormal GABAergic neurotransmission signaling, hippocampal cell discharge, abnormal hippocampal neurogenesis, impaired cadherin and synaptic contact-based cell adhesion processes, extended lateral ventricles, decreased prefrontal cortical gray matter, and decreased hippocampal volume. Mechanistically, impairment in calcium voltage-gated channel subunit alpha1 I, neurotrophic tyrosine receptor kinase proteins, genes involved in brain neurogenesis and synaptogenesis like WNT3 and ACTG2, genes involved in the cell adhesion process like CDH12 and DISC1, and gamma-aminobutyric acid (GABA) signaling have been reported as the main molecular contributors to the abnormalities in resting-state low-frequency oscillations in BD patients. Findings also showed the association of impaired synaptic connections and disrupted membrane potential with abnormal beta/gamma oscillatory activity in patients with BD. Of note, the synaptic GABA neurotransmitter has been found to be a fundamental requirement for the occurrence of long-distance synchronous gamma oscillations necessary for coordinating the activity of neural networks between various brain regions. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zhou Lu
- Department of Neurosurgery, The Affiliated People's Hospital of NingBo University, NingBo, 315000, China
| | - Huixiao Wang
- Department of Neurosurgery, The Affiliated People's Hospital of NingBo University, NingBo, 315000, China
| | - Jiajie Gu
- Department of Neurosurgery, The Affiliated People's Hospital of NingBo University, NingBo, 315000, China
| | - Feng Gao
- Department of Neurosurgery, The Affiliated People's Hospital of NingBo University, NingBo, 315000, China
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20
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Kim JG, Kim H, Hwang J, Kang SH, Lee CN, Woo J, Kim C, Han K, Kim JB, Park KW. Differentiating amnestic from non-amnestic mild cognitive impairment subtypes using graph theoretical measures of electroencephalography. Sci Rep 2022; 12:6219. [PMID: 35418202 PMCID: PMC9008046 DOI: 10.1038/s41598-022-10322-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 04/06/2022] [Indexed: 12/12/2022] Open
Abstract
The purpose of this study was to explore different patterns of functional networks between amnestic mild cognitive impairment (aMCI) and non-aMCI (naMCI) using electroencephalography (EEG) graph theoretical analysis. The data of 197 drug-naïve individuals who complained cognitive impairment were reviewed. Resting-state EEG data was acquired. Graph analyses were performed and compared between aMCI and naMCI, as well as between early and late aMCI. Correlation analyses were conducted between the graph measures and neuropsychological test results. Machine learning algorithms were applied to determine whether the EEG graph measures could be used to distinguish aMCI from naMCI. Compared to naMCI, aMCI showed higher modularity in the beta band and lower radius in the gamma band. Modularity was negatively correlated with scores on the semantic fluency test, and the radius in the gamma band was positively correlated with visual memory, phonemic, and semantic fluency tests. The naïve Bayes algorithm classified aMCI and naMCI with 89% accuracy. Late aMCI showed inefficient and segregated network properties compared to early aMCI. Graph measures could differentiate aMCI from naMCI, suggesting that these measures might be considered as predictive markers for progression to Alzheimer’s dementia in patients with MCI.
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Affiliation(s)
- Jae-Gyum Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hayom Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jihyeon Hwang
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sung Hoon Kang
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Chan-Nyoung Lee
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - JunHyuk Woo
- Laboratory of Computational Neurophysics, Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Chanjin Kim
- Laboratory of Computational Neurophysics, Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Kyungreem Han
- Laboratory of Computational Neurophysics, Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
| | - Kun-Woo Park
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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21
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Alotaibi N, Bakheet D, Konn D, Vollmer B, Maharatna K. Cognitive Outcome Prediction in Infants With Neonatal Hypoxic-Ischemic Encephalopathy Based on Functional Connectivity and Complexity of the Electroencephalography Signal. Front Hum Neurosci 2022; 15:795006. [PMID: 35153702 PMCID: PMC8830486 DOI: 10.3389/fnhum.2021.795006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/10/2021] [Indexed: 12/03/2022] Open
Abstract
Impaired neurodevelopmental outcome, in particular cognitive impairment, after neonatal hypoxic-ischemic encephalopathy is a major concern for parents, clinicians, and society. This study aims to investigate the potential benefits of using advanced quantitative electroencephalography analysis (qEEG) for early prediction of cognitive outcomes, assessed here at 2 years of age. EEG data were recorded within the first week after birth from a cohort of twenty infants with neonatal hypoxic-ischemic encephalopathy (HIE). A proposed regression framework was based on two different sets of features, namely graph-theoretical features derived from the weighted phase-lag index (WPLI) and entropies metrics represented by sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn). Both sets of features were calculated within the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain. Correlation analysis showed a significant association in the delta band between the proposed features, graph attributes (radius, transitivity, global efficiency, and characteristic path length) and entropy features (Pen and SpEn) from the neonatal EEG data and the cognitive development at age two years. These features were used to train and test the tree ensemble (boosted and bagged) regression models. The highest prediction performance was reached to 14.27 root mean square error (RMSE), 12.07 mean absolute error (MAE), and 0.45 R-squared using the entropy features with a boosted tree regression model. Thus, the results demonstrate that the proposed qEEG features show the state of brain function at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate identifying those who might benefit from early targeted intervention.
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Affiliation(s)
- Noura Alotaibi
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
- Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia
| | - Dalal Bakheet
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
- Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia
| | - Daniel Konn
- Clinical Neurophysiology, University Hospital Southampton, Southampton, United Kingdom
| | - Brigitte Vollmer
- Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Paediatric Neurology, Southampton Children’s Hospital, Southampton, United Kingdom
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
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22
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Park M, Hoang GM, Nguyen T, Lee E, Jung HJ, Choe Y, Lee MH, Hwang JY, Kim JG, Kim T. Effects of transcranial ultrasound stimulation pulsed at 40 Hz on Aβ plaques and brain rhythms in 5×FAD mice. Transl Neurodegener 2021; 10:48. [PMID: 34872618 PMCID: PMC8650290 DOI: 10.1186/s40035-021-00274-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/25/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common cause of dementia, and is characterized by amyloid-β (Aβ) plaques and tauopathy. Reducing Aβ has been considered a major AD treatment strategy in pharmacological and non-pharmacological approaches. Impairment of gamma oscillations, which play an important role in perception and cognitive function, has been shown in mouse AD models and human patients. Recently, the therapeutic effect of gamma entrainment in AD mouse models has been reported. Given that ultrasound is an emerging neuromodulation modality, we investigated the effect of ultrasound stimulation pulsed at gamma frequency (40 Hz) in an AD mouse model. METHODS We implanted electroencephalogram (EEG) electrodes and a piezo-ceramic disc ultrasound transducer on the skull surface of 6-month-old 5×FAD and wild-type control mice (n = 12 and 6, respectively). Six 5×FAD mice were treated with two-hour ultrasound stimulation at 40 Hz daily for two weeks, and the other six mice received sham treatment. Soluble and insoluble Aβ levels in the brain were measured by enzyme-linked immunosorbent assay. Spontaneous EEG gamma power was computed by wavelet analysis, and the brain connectivity was examined with phase-locking value and cross-frequency phase-amplitude coupling. RESULTS We found that the total Aβ42 levels, especially insoluble Aβ42, in the treatment group decreased in pre- and infra-limbic cortex (PIL) compared to that of the sham treatment group. A reduction in the number of Aβ plaques was also observed in the hippocampus. There was no increase in microbleeding in the transcranial ultrasound stimulation (tUS) group. In addition, the length and number of microglial processes decreased in PIL and hippocampus. Encelphalographic spontaneous gamma power was increased, and cross-frequency coupling was normalized, implying functional improvement after tUS stimulation. CONCLUSION These results suggest that the transcranial ultrasound-based gamma-band entrainment technique can be an effective therapy for AD by reducing the Aβ load and improving brain connectivity.
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Affiliation(s)
- Mincheol Park
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Gia Minh Hoang
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Thien Nguyen
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Eunkyung Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Hyun Jin Jung
- Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Youngshik Choe
- Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Moon Hwan Lee
- Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, 42988, Republic of Korea
| | - Jae Youn Hwang
- Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu, 42988, Republic of Korea
| | - Jae Gwan Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea.
| | - Tae Kim
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea.
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23
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Smailovic U, Johansson C, Koenig T, Kåreholt I, Graff C, Jelic V. Decreased Global EEG Synchronization in Amyloid Positive Mild Cognitive Impairment and Alzheimer's Disease Patients-Relationship to APOE ε4. Brain Sci 2021; 11:brainsci11101359. [PMID: 34679423 PMCID: PMC8533770 DOI: 10.3390/brainsci11101359] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/07/2021] [Accepted: 10/08/2021] [Indexed: 11/16/2022] Open
Abstract
The apolipoprotein E (APOE) ε4 allele is a risk factor for Alzheimer's disease (AD) that has been linked to changes in brain structure and function as well as to different biological subtypes of the disease. The present study aimed to investigate the association of APOE ε4 genotypes with brain functional impairment, as assessed by quantitative EEG (qEEG) in patients on the AD continuum. The study population included 101 amyloid positive patients diagnosed with mild cognitive impairment (MCI) (n = 50) and AD (n = 51) that underwent resting-state EEG recording and CSF Aβ42 analysis. In total, 31 patients were APOE ε4 non-carriers, 42 were carriers of one, and 28 were carriers of two APOE ε4 alleles. Quantitative EEG analysis included computation of the global field power (GFP) and global field synchronization (GFS) in conventional frequency bands. Amyloid positive patients who were carriers of APOE ε4 allele(s) had significantly higher GFP beta and significantly lower GFS in theta and beta bands compared to APOE ε4 non-carriers. Increased global EEG power in beta band in APOE ε4 carriers may represent a brain functional compensatory mechanism that offsets global EEG slowing in AD patients. Our findings suggest that decreased EEG measures of global synchronization in theta and beta bands reflect brain functional deficits related to the APOE ε4 genotype in patients that are on a biomarker-verified AD continuum.
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Affiliation(s)
- Una Smailovic
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 14152 Huddinge, Sweden;
- Department of Clinical Neurophysiology, Karolinska University Hospital, 14186 Huddinge, Sweden
- Correspondence:
| | - Charlotte Johansson
- Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 14152 Huddinge, Sweden; (C.J.); (C.G.)
- Clinic for Cognitive Disorders, Karolinska University Hospital, 14186 Huddinge, Sweden
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, 3012 Bern, Switzerland;
| | - Ingemar Kåreholt
- Aging Research Centre, Karolinska Institutet and Stockholm University, 17165 Solna, Sweden;
- School of Health and Welfare, Aging Research Network—Jönköping (ARN-J), Institute for Gerontology, Jönköping University, 55111 Jönköping, Sweden
| | - Caroline Graff
- Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 14152 Huddinge, Sweden; (C.J.); (C.G.)
- Unit for Hereditary Dementia, Karolinska University Hospital-Solna, 17176 Solna, Sweden
| | - Vesna Jelic
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 14152 Huddinge, Sweden;
- Clinic for Cognitive Disorders, Karolinska University Hospital, 14186 Huddinge, Sweden
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24
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Influence of PICALM and CLU risk variants on beta EEG activity in Alzheimer's disease patients. Sci Rep 2021; 11:20465. [PMID: 34650147 PMCID: PMC8516883 DOI: 10.1038/s41598-021-99589-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 09/27/2021] [Indexed: 11/09/2022] Open
Abstract
PICALM and CLU genes have been linked to alterations in brain biochemical processes that may have an impact on Alzheimer’s disease (AD) development and neurophysiological dynamics. The aim of this study is to analyze the relationship between the electroencephalographic (EEG) activity and the PICALM and CLU alleles described as conferring risk or protective effects on AD patients and healthy controls. For this purpose, EEG activity was acquired from: 18 AD patients and 12 controls carrying risk alleles of both PICALM and CLU genes, and 35 AD patients and 12 controls carrying both protective alleles. Relative power (RP) in the conventional EEG frequency bands (delta, theta, alpha, beta, and gamma) was computed to quantify the brain activity at source level. In addition, spatial entropy (SE) was calculated in each band to characterize the regional distribution of the RP values throughout the brain. Statistically significant differences in global RP and SE at beta band (p-values < 0.05, Mann–Whitney U-test) were found between genotypes in the AD group. Furthermore, RP showed statistically significant differences in 58 cortical regions out of the 68 analyzed in AD. No statistically significant differences were found in the control group at any frequency band. Our results suggest that PICALM and CLU AD-inducing genotypes are involved in physiological processes related to disruption in beta power, which may be associated with physiological disturbances such as alterations in beta-amyloid and neurotransmitter metabolism.
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25
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Monllor P, Cervera-Ferri A, Lloret MA, Esteve D, Lopez B, Leon JL, Lloret A. Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules? Int J Mol Sci 2021; 22:10889. [PMID: 34639229 PMCID: PMC8509134 DOI: 10.3390/ijms221910889] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/26/2021] [Accepted: 10/05/2021] [Indexed: 12/12/2022] Open
Abstract
Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.
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Affiliation(s)
- Paloma Monllor
- CIBERFES, Department of Physiology, Institute INCLIVA, Faculty of Medicine, Health Research University of Valencia, Avda. Blasco Ibanez 17, 46010 Valencia, Spain; (P.M.); (D.E.)
| | - Ana Cervera-Ferri
- Department of Human Anatomy and Embryology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain;
| | - Maria-Angeles Lloret
- Department of Clinical Neurophysiology, University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain;
| | - Daniel Esteve
- CIBERFES, Department of Physiology, Institute INCLIVA, Faculty of Medicine, Health Research University of Valencia, Avda. Blasco Ibanez 17, 46010 Valencia, Spain; (P.M.); (D.E.)
| | - Begoña Lopez
- Department of Neurology, University Clinic Hospital of Valencia, Avda. Blasco Ibanez, 19, 46010 Valencia, Spain;
| | - Jose-Luis Leon
- Ascires Biomedical Group, Department of Neuroradiology, Hospital Clinico Universitario, 46010 Valencia, Spain;
| | - Ana Lloret
- CIBERFES, Department of Physiology, Institute INCLIVA, Faculty of Medicine, Health Research University of Valencia, Avda. Blasco Ibanez 17, 46010 Valencia, Spain; (P.M.); (D.E.)
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26
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Fodor Z, Horváth A, Hidasi Z, Gouw AA, Stam CJ, Csukly G. EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance. Front Aging Neurosci 2021; 13:680200. [PMID: 34690735 PMCID: PMC8529331 DOI: 10.3389/fnagi.2021.680200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 09/20/2021] [Indexed: 12/18/2022] Open
Abstract
Background: While decreased alpha and beta-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer's disease, it is not yet entirely known whether these differences can mark cognitive decline in the early stages of the disease. Our study aimed to analyze electroencephalography (EEG) FC and network differences in the alpha and beta frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints. Methods: Functional connectivity and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task with varying memory load. FC between EEG channels was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree (MST) approach, which reconstructs the critical backbone of the original network. Results: Memory load (increasing number of to-be-learned items) enhanced the mean AEC-c in the control group in both frequency bands. In contrast to that, after an initial increase, the MCI group showed significantly (p < 0.05) diminished FC in the alpha band in the highest memory load condition, while in the beta band this modulation was absent. Moreover, mean alpha and beta AEC-c correlated significantly with the size of medial temporal lobe structures in the entire sample. The network analysis revealed increased maximum degree, betweenness centrality, and degree divergence, and decreased diameter and eccentricity in the MCI group compared to the control group in both frequency bands independently of the memory load. This suggests a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution. Conclusion: Alpha- and beta-band FC measured by AEC-c correlates with cognitive load-related modulation, with subtle medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the "hub overload and failure" framework and might be part of a compensatory mechanism or a consequence of neural disinhibition.
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Affiliation(s)
- Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - András Horváth
- Department of Neurology, National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Zoltán Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Alida A. Gouw
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
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27
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Joseph S, Knezevic D, Zomorrodi R, Blumberger DM, Daskalakis ZJ, Mulsant BH, Pollock BG, Voineskos A, Wang W, Rajji TK, Kumar S. Dorsolateral prefrontal cortex excitability abnormalities in Alzheimer's Dementia: Findings from transcranial magnetic stimulation and electroencephalography study. Int J Psychophysiol 2021; 169:55-62. [PMID: 34499960 DOI: 10.1016/j.ijpsycho.2021.08.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/04/2021] [Accepted: 08/30/2021] [Indexed: 02/06/2023]
Abstract
There is some evidence of cortical hyper-excitability in Alzheimer's Dementia (AD) but its relationship with cognition is not clear. In this study, we assessed dorsolateral prefrontal cortex (DLPFC) excitability and its relationship with cognition in AD. Twenty-four participants with AD (mean [SD] age = 74.1 [7.2] years) and eleven elderly healthy controls (HC) (mean [SD] age = 68.8 [7.3] years) were recruited. Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) was used to assess cortical excitability. Cortical evoked activity (CEA) between 25 and 80 ms post-TMS stimulus was calculated as the primary measure of cortical excitability. TMS-evoked potential peak (TEP) amplitudes (P30, N45 and P60) were also calculated. Cognition was assessed using Montreal Cognitive Assessment (MoCA), Executive Interview (EXIT) and Cambridge Neuropsychological Test Automated Battery Stockings of Cambridge (SOC). There was no difference in TMS stimulus intensity between the groups. DLPFC-CEA was higher in the AD (mean [SD] = 134.64 [90.22] μV) than the HC group (mean [SD] = 82.65 [40.28] μV; t33 = 2.357, p = 0.025). There were no differences in TEP peak amplitudes between the groups. Further, DLPFC-CEA was inversely associated with MoCA and SOC, and positively associated with EXIT scores in AD. These results suggest increased DLPFC excitability in AD, and its inverse associations with global cognition and executive function. Future studies should examine these findings in larger samples and longitudinally, and could also assess these markers of cortical excitability in relation to other established markers of AD and in response to interventions.
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Affiliation(s)
- Shaylyn Joseph
- Centre for Addiction and Mental Health, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | | | - Reza Zomorrodi
- Centre for Addiction and Mental Health, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Daniel M Blumberger
- Centre for Addiction and Mental Health, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Toronto Dementia Research Alliance, Toronto, Canada
| | | | - Benoit H Mulsant
- Centre for Addiction and Mental Health, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Toronto Dementia Research Alliance, Toronto, Canada
| | - Bruce G Pollock
- Centre for Addiction and Mental Health, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Toronto Dementia Research Alliance, Toronto, Canada
| | - Aristotle Voineskos
- Centre for Addiction and Mental Health, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Toronto Dementia Research Alliance, Toronto, Canada
| | - Wei Wang
- Centre for Addiction and Mental Health, Toronto, Canada; University of South Florida, FL, United States
| | - Tarek K Rajji
- Centre for Addiction and Mental Health, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Toronto Dementia Research Alliance, Toronto, Canada
| | - Sanjeev Kumar
- Centre for Addiction and Mental Health, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Toronto Dementia Research Alliance, Toronto, Canada.
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28
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Dimitriadis SI, Lyssoudis C, Tsolaki AC, Lazarou E, Kozori M, Tsolaki M. Greek High Phenolic Early Harvest Extra Virgin Olive Oil Reduces the Over-Excitation of Information-Flow Based on Dominant Coupling Mode (DoCM) Model in Patients with Mild Cognitive Impairment: An EEG Resting-State Validation Approach. J Alzheimers Dis 2021; 83:191-207. [PMID: 34308906 DOI: 10.3233/jad-210454] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Extra virgin olive oil (EVOO) constitutes a natural compound with high protection over cognitive function that could positively alter brain dynamics and the mixture of within and between-frequency connectivity. OBJECTIVE The balance of cross-frequency coupling over within-frequency coupling can build a nonlinearity index (NI) that encapsulates the over-excitation of information flow between brain areas and across experimental time. The present study investigated for the very first time how the Greek High Phenolic Early Harvest Extra Virgin Olive Oil (HP-EH-EVOO) versus Moderate Phenolic (MP-EVOO) and Mediterranean Diet (MeDi) intervention in people with mild cognitive impairment (MCI) could affect their spontaneous EEG dynamic connectivity. METHODS Forty-three subjects (14 in MeDi, 16 in MP-EVOO, and 13 in HP-EH-EVOO) followed an EEG resting-state recording session (eyes-open and closed) before and after the treatment. Following our dominant coupling mode model, we built a dynamic integrated dynamic functional connectivity graph that tabulates the functional strength and the dominant coupling mode model of every pair of brain areas. RESULTS Signal spectrum within 1-13 Hz and theta/beta ratio have decreased in the HP-EH-EVOO group in the eyes-open condition. The intervention improved the FIDoCM across groups and conditions but was more prominent in the HP-EH-EVOO group (p < 0.001). Finally, we revealed a significant higher post-intervention reduction of NI (ΔNITotal and α) for the HP-EH-EVOO compared to the MP-EVOO and MeDi groups (p < 0.0001). CONCLUSION Long-term intervention with HP-EH-EVOO reduced the over-excitation of information flow in spontaneous brain activity and altered the signal spectrum of EEG rhythms.
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Affiliation(s)
- Stavros I Dimitriadis
- 1st Department of Neurology, G.H. "AHEPA, " School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece.,Integrative Neuroimaging Lab, Thessaloniki, Greece.,Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, United Kingdom.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Christos Lyssoudis
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| | - Anthoula C Tsolaki
- 1st Department of Neurology, G.H. "AHEPA, " School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Eftychia Lazarou
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| | - Mahi Kozori
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| | - Magda Tsolaki
- 1st Department of Neurology, G.H. "AHEPA, " School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
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29
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Joseph S, Patterson R, Wang W, Blumberger DM, Rajji T, Kumar S. Quantitative Assessment of Cortical Excitability in Alzheimer's Dementia and Its Association with Clinical Symptoms: A Systematic Review and Meta-Analyses. J Alzheimers Dis 2021; 88:867-891. [PMID: 34219724 DOI: 10.3233/jad-210311] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is characterized by cognitive and neuropsychiatric symptoms (NPS) due to underlying neurodegenerative pathology. Some studies using electroencephalography (EEG) have shown increased epileptiform and epileptic activity in AD. OBJECTIVE This review and meta-analyses aims to synthesize the existing evidence for quantitative abnormalities of cortical excitability in AD and their relationship with clinical symptoms. METHODS We systematically searched and reviewed publications that quantitatively assessed cortical excitability, using transcranial magnetic stimulation (TMS) resting motor threshold (rMT), active motor threshold (aMT), motor evoked potential (MEP) or directly from the cortex using TMS-EEG via TMS-evoked potential (TEP). We meta-analyzed studies that assessed rMT and aMT using random effects model. RESULTS We identified 895 publications out of which 37 were included in the qualitative review and 30 studies using rMT or aMT were included in the meta-analyses. The AD group had reduced rMT (Hedges' g = -0.99, 95%CI [-1.29, -0.68], p < 0.00001) and aMT (Hedges' g = -0.87, 95%CI [-1.50, -0.24], p < 0.00001) as compared with control groups, indicative of higher cortical excitability. Qualitative review found some evidence of increased MEP amplitude, whereas findings related to TEP were inconsistent. There was some evidence supporting an inverse association between cortical excitability and global cognition. No publications reported on the relationship between cortical excitability and NPS. CONCLUSION There is strong evidence of increased motor cortex excitability in AD and some evidence of an inverse association between excitability and cognition. Future studies should assess cortical excitability from non-motor areas using TMS-EEG and examine its relationship with cognition and NPS.
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Affiliation(s)
- Shaylyn Joseph
- Centre for Addiction and Mental Health, Toronto, Canada.,University of Toronto, Toronto, Canada
| | - Rachel Patterson
- Centre for Addiction and Mental Health, Toronto, Canada.,University of Toronto, Toronto, Canada
| | - Wei Wang
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Daniel M Blumberger
- Centre for Addiction and Mental Health, Toronto, Canada.,University of Toronto, Toronto, Canada
| | - Tarek Rajji
- Centre for Addiction and Mental Health, Toronto, Canada.,University of Toronto, Toronto, Canada.,Toronto Dementia Research Alliance, Toronto, Canada
| | - Sanjeev Kumar
- Centre for Addiction and Mental Health, Toronto, Canada.,University of Toronto, Toronto, Canada
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30
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Rea RC, Berlot R, Martin SL, Craig CE, Holmes PS, Wright DJ, Bon J, Pirtošek Z, Ray NJ. Quantitative EEG and cholinergic basal forebrain atrophy in Parkinson's disease and mild cognitive impairment. Neurobiol Aging 2021; 106:37-44. [PMID: 34233212 DOI: 10.1016/j.neurobiolaging.2021.05.023] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 05/22/2021] [Accepted: 05/31/2021] [Indexed: 02/08/2023]
Abstract
Cholinergic degeneration is a key feature of dementia in neurodegenerative conditions including Alzheimer's disease (AD) and Parkinson's disease (PD). Quantitative electro-encephalography (EEG) metrics are altered in both conditions from early stages, and recent research in people with Lewy body and AD dementia suggests these changes may be associated with atrophy in cholinergic basal forebrain nuclei (cBF). To determine if these relationships exist in predementia stages of neurodegenerative conditions, we studied resting-state EEG and in vivo cBF volumes in 31 people with PD (without dementia), 21 people with mild cognitive impairment (MCI), and 21 age-matched controls. People with PD showed increased power in slower frequencies and reduced alpha reactivity compared to controls. Volumes of cholinergic cell clusters corresponding to the medial septum and vertical and horizontal limb of the diagonal band, and the posterior nucleus basalis of Meynert, correlated positively with; alpha reactivity in people with PD (p< 0.01); and pre-alpha power in people with MCI (p< 0.05). These results suggest that alpha reactivity and pre-alpha power are related to changes in cBF volumes in MCI and PD without dementia.
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Affiliation(s)
- River C Rea
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK.
| | - Rok Berlot
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Sarah L Martin
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK
| | - Chesney E Craig
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK
| | - Paul S Holmes
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK
| | - David J Wright
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK
| | - Jurij Bon
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia; University Psychiatric Clinic Ljubljana, Ljubljana, Slovenia
| | - Zvezdan Pirtošek
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Nicola J Ray
- Department of Psychology, Health, Psychology, and Communities Research Centre, Manchester Metropolitan University, Manchester, UK
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31
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Moghadami M, Moghimi S, Moghimi A, Malekzadeh GR, Fadardi JS. The Investigation of Simultaneous EEG and Eye Tracking Characteristics During Fixation Task in Mild Alzheimer's Disease. Clin EEG Neurosci 2021; 52:211-220. [PMID: 32539459 DOI: 10.1177/1550059420932752] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that occurs many years before the first clinical symptoms. Finding more exact, significant, and valuable criteria or indices for the diagnosis of the mild form of Alzheimer's disease is very important for clinical and research purposes. Electroencephalography (EEG) and eye tracking biomarkers would provide noninvasive tools for the early detection of AD. Due to the advantages of EEG and eye tracking, in this study, we employed them simultaneously to conduct research on the mild AD. For this purpose, 19 patients with mild AD were compared with 19 gender- and age-matched normal subjects who did not have any history of cognitive or neurological disorders. EEG and eye-tracking data were concurrently collected in both groups in a fixation task. Our results revealed that the total fixation duration was significantly shorter for the AD patients, but their fixation frequency was more than that of the controls. In addition, increased theta power and decreased alpha power were observed in the AD group. Interestingly, there was a statistically significant correlation between fixation frequency and alpha power in the parietal area in the control group. However, this connection was not statistically significant in the AD group. The findings also indicated an elevated coherence in the AD patients in the parieto-occipital area. It is assumed that the AD patients might use the neural compensational processes for the fixation state. This study provides evidence for the simultaneously EEG and eye-tracking changes in the areas, which are involved in the control of the fixational eye movements.
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Affiliation(s)
- Malihe Moghadami
- Department of Biology, Faculty of Science, 48440Ferdowsi University of Mashhad, Mashhad, Khorassan Razavi, Iran
| | - Sahar Moghimi
- Department of Electrical Engineering, 108847Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Razavi Khorasan, Iran
| | - Ali Moghimi
- Rayan Center for Neuroscience and Behavior, Ferdowsi University of Mashhad, Mashhad, Khorassan Razavi, Iran
| | - Gholam Reza Malekzadeh
- Faculty of Medical Sciences, 125639Islamic Azad University, Mashhad Branch, Mashhad, Razavi Khorasan, Iran
| | - Javad Salehi Fadardi
- Department of Psychology, Faculty of Education Sciences & Psychology, Ferdowsi University of Mashhad, Mashhad, Razavi Khorasan, Iran.,School of Community and Global Health, Claremont Graduate University, Claremont, CA, USA.,School of Psychology, Bangor University, Bangor, UK
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32
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EEG-Single-Channel Envelope Synchronisation and Classification for Seizure Detection and Prediction. Brain Sci 2021; 11:brainsci11040516. [PMID: 33921588 PMCID: PMC8073763 DOI: 10.3390/brainsci11040516] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/02/2021] [Accepted: 04/14/2021] [Indexed: 01/23/2023] Open
Abstract
This paper tackles the complex issue of detecting and classifying epileptic seizures whilst maintaining the total calculations at a minimum. Where many systems depend on the coupling between multiple sources, leading to hundreds of combinations of electrodes, our method calculates the instantaneous phase between non-identical upper and lower envelopes of a single-electroencephalography channel reducing the workload to the total number of electrode points. From over 600 h of simulations, our method shows a sensitivity and specificity of 100% for high false-positive rates and 83% and 75%, respectively, for moderate to low false positive rates, which compares well to both single- and multi-channel-based methods. Furthermore, pre-ictal variations in synchronisation were detected in over 90% of patients implying a possible prediction system.
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33
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Babiloni C, Arakaki X, Azami H, Bennys K, Blinowska K, Bonanni L, Bujan A, Carrillo MC, Cichocki A, de Frutos-Lucas J, Del Percio C, Dubois B, Edelmayer R, Egan G, Epelbaum S, Escudero J, Evans A, Farina F, Fargo K, Fernández A, Ferri R, Frisoni G, Hampel H, Harrington MG, Jelic V, Jeong J, Jiang Y, Kaminski M, Kavcic V, Kilborn K, Kumar S, Lam A, Lim L, Lizio R, Lopez D, Lopez S, Lucey B, Maestú F, McGeown WJ, McKeith I, Moretti DV, Nobili F, Noce G, Olichney J, Onofrj M, Osorio R, Parra-Rodriguez M, Rajji T, Ritter P, Soricelli A, Stocchi F, Tarnanas I, Taylor JP, Teipel S, Tucci F, Valdes-Sosa M, Valdes-Sosa P, Weiergräber M, Yener G, Guntekin B. Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel. Alzheimers Dement 2021; 17:1528-1553. [PMID: 33860614 DOI: 10.1002/alz.12311] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 12/28/2020] [Accepted: 01/01/2021] [Indexed: 12/25/2022]
Abstract
The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy.,San Raffaele of Cassino, Cassino (FR), Italy
| | | | - Hamed Azami
- Department of Neurology and Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Karim Bennys
- Centre Mémoire de Ressources et de Recherche (CMRR), Centre Hospitalier, Universitaire de Montpellier, Montpellier, France
| | - Katarzyna Blinowska
- Institute of Biocybernetics, Warsaw, Poland.,Faculty of Physics University of Warsaw and Nalecz, Warsaw, Poland
| | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University "G. D'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Ana Bujan
- Psychological Neuroscience Lab, School of Psychology, University of Minho, Minho, Portugal
| | - Maria C Carrillo
- Division of Medical & Scientific Relations, Alzheimer's Association, Chicago, Illinois, USA
| | - Andrzej Cichocki
- Skolkowo Institute of Science and Technology (SKOLTECH), Moscow, Russia.,Systems Research Institute PAS, Warsaw, Poland.,Nicolaus Copernicus University (UMK), Torun, Poland
| | - Jaisalmer de Frutos-Lucas
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Bruno Dubois
- Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Institute of Memory and Alzheimer's Disease (IM2A), Paris, France.,ICM, INSERM U1127, CNRS UMR 7225, Sorbonne Université, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Rebecca Edelmayer
- Division of Medical & Scientific Relations, Alzheimer's Association, Chicago, Illinois, USA
| | - Gary Egan
- Foundation Director of the Monash Biomedical Imaging (MBI) Research Facilities, Monash University, Clayton, Australia
| | - Stephane Epelbaum
- Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l'hôpital, Institute of Memory and Alzheimer's Disease (IM2A), Paris, France.,ICM, INSERM U1127, CNRS UMR 7225, Sorbonne Université, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Javier Escudero
- School of Engineering, Institute for Digital Communications, The University of Edinburgh, Edinburgh, UK
| | - Alan Evans
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Francesca Farina
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Keith Fargo
- Division of Medical & Scientific Relations, Alzheimer's Association, Chicago, Illinois, USA
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Giovanni Frisoni
- IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Harald Hampel
- GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l'hôpital, Sorbonne University, Paris, France
| | | | - Vesna Jelic
- Division of Clinical Geriatrics, NVS Department, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Jaeseung Jeong
- Department of Bio and Brain Engineering/Program of Brain and Cognitive Engineering Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Yang Jiang
- Department of Behavioral Science, College of Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Maciej Kaminski
- Faculty of Physics University of Warsaw and Nalecz, Warsaw, Poland
| | - Voyko Kavcic
- Institute of Gerontology, Wayne State University, Detroit, Michigan, USA
| | - Kerry Kilborn
- School of Psychology, University of Glasgow, Glasgow, UK
| | - Sanjeev Kumar
- Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Alice Lam
- MGH Epilepsy Service, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lew Lim
- Vielight Inc., Toronto, Ontario, Canada
| | | | - David Lopez
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | - Brendan Lucey
- Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - William J McGeown
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, UK
| | - Ian McKeith
- Newcastle upon Tyne, Translational and Clinical Research Institute, Newcastle University, UK
| | | | - Flavio Nobili
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy.,Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - John Olichney
- UC Davis Department of Neurology and Center for Mind and Brain, Davis, California, USA
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University "G. D'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Ricardo Osorio
- Center for Brain Health, Department of Psychiatry, NYU Langone Medical Center, New York, New York, USA
| | | | - Tarek Rajji
- Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology, Charité Universitätsmedizin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Andrea Soricelli
- IRCCS SDN, Napoli, Italy.,Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Ioannis Tarnanas
- Global Brain Health Institute, University of California San Francisco, San Francisco, USA.,Global Brain Health Institute, Trinity College Dublin, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - John Paul Taylor
- Newcastle upon Tyne, Translational and Clinical Research Institute, Newcastle University, UK
| | - Stefan Teipel
- Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany.,German Center for Neurodegenerative Diseases (DZNE) - Rostock/Greifswald, Rostock, Germany
| | - Federico Tucci
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | | | - Pedro Valdes-Sosa
- Cuban Neuroscience Center, Havana, Cuba.,Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Marco Weiergräber
- Experimental Neuropsychopharmacology, BfArM), Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte, Bonn, Germany
| | - Gorsev Yener
- Departments of Neurosciences and Department of Neurology, Dokuz Eylül University Medical School, Izmir, Turkey
| | - Bahar Guntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey.,REMER, Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab, Istanbul Medipol University, Istanbul, Turkey
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34
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Lin Y, Du P, Sun H, Liang Y, Wang Z, Cui Y, Chen K, Xia Y, Yao D, Yu L, Guo D. Identifying Refractory Epilepsy Without Structural Abnormalities by Fusing the Common Spatial Patterns of Functional and Effective EEG Networks. IEEE Trans Neural Syst Rehabil Eng 2021; 29:708-717. [PMID: 33830925 DOI: 10.1109/tnsre.2021.3071785] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Drug refractory epilepsy (RE) is believed to be associated with structural lesions, but some RE patients show no significant structural abnormalities (RE-no-SA) on conventional magnetic resonance imaging scans. Since most of the medically controlled epilepsy (MCE) patients also do not exhibit structural abnormalities, a reliable assessment needs to be developed to differentiate RE-no-SA patients and MCE patients to avoid misdiagnosis and inappropriate treatment. Using resting-state scalp electroencephalogram (EEG) datasets, we extracted the spatial pattern of network (SPN) features from the functional and effective EEG networks of both RE-no-SA patients and MCE patients. Compared to the performance of traditional resting-state EEG network properties, the SPN features exhibited remarkable superiority in classifying these two groups of epilepsy patients, and accuracy values of 90.00% and 80.00% were obtained for the SPN features of the functional and effective EEG networks, respectively. By further fusing the SPN features of functional and effective networks, we demonstrated that the highest accuracy value of 96.67% could be reached, with a sensitivity of 100% and specificity of 92.86%. Overall, these findings not only indicate that the fused functional and effective SPN features are promising as reliable measurements for distinguishing RE-no-SA patients and MCE patients but also may provide a new perspective to explore the complex neurophysiology of refractory epilepsy.
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35
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Gutierrez Nuno RA, Chung CHR, Maharatna K. Hardware architecture for real-time EEG-based functional brain connectivity parameter extraction. J Neural Eng 2020; 18. [PMID: 33326940 DOI: 10.1088/1741-2552/abd462] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 12/16/2020] [Indexed: 11/11/2022]
Abstract
In this work, we proposed a novel architecture for real-time quantitative characterization of functional brain connectivity networks derived from Electroencephalogram (EEG). It consists of two main parts - calculation of Phase Lag Index (PLI) to form the functional connectivity networks and the extraction of a set of graph-theoretic parameters to quantitatively characterize these networks. The architecture was developed for a 19-channel EEG system. The system can calculate all the functional connectivity parameters in a total time of 131µs, utilizes 71% logic resources, and shows 51.84 mW dynamic power consumption at 22.16 MHz operation frequency when implemented in a Stratix IV EP4SGX230K FPGA. Our analysis also showed that the system occupies an area equivalent to approximately 937K 2-input NAND gates, with an estimated power consumption of 39.3 mW at 0.9 V supply using a 90 nm CMOS Application Specific Integrated Circuit (ASIC) technology.
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Affiliation(s)
- Rafael Angel Gutierrez Nuno
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Chi Hang Raphael Chung
- University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Koushik Maharatna
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, University of Southampton, Southampton, SO17 1BJ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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36
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Lejko N, Larabi DI, Herrmann CS, Aleman A, Ćurčić-Blake B. Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis. J Alzheimers Dis 2020; 78:1047-1088. [PMID: 33185607 PMCID: PMC7739973 DOI: 10.3233/jad-200962] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background: Mild cognitive impairment (MCI) is a stage between expected age-related cognitive decline and dementia. Dementias have been associated with changes in neural oscillations across the frequency spectrum, including the alpha range. Alpha is the most prominent rhythm in human EEG and is best detected during awake resting state (RS). Though several studies measured alpha power and synchronization in MCI, findings have not yet been integrated. Objective: To consolidate findings on power and synchronization of alpha oscillations across stages of cognitive decline. Methods: We included studies published until January 2020 that compared power or functional connectivity between 1) people with MCI and cognitively healthy older adults (OA) or people with a neurodegenerative dementia, and 2) people with progressive and stable MCI. Random-effects meta-analyses were performed when enough data was available. Results: Sixty-eight studies were included in the review. Global RS alpha power was lower in AD than in MCI (ES = –0.30; 95% CI = –0.51, –0.10; k = 6), and in MCI than in OA (ES = –1.49; 95% CI = –2.69, –0.29; k = 5). However, the latter meta-analysis should be interpreted cautiously due to high heterogeneity. The review showed lower RS alpha power in progressive than in stable MCI, and lower task-related alpha reactivity in MCI than in OA. People with MCI had both lower and higher functional connectivity than OA. Publications lacked consistency in MCI diagnosis and EEG measures. Conclusion: Research indicates that RS alpha power decreases with increasing impairment, and could—combined with measures from other frequency bands—become a biomarker of early cognitive decline.
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Affiliation(s)
- Nena Lejko
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands
| | - Daouia I Larabi
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - André Aleman
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands
| | - Branislava Ćurčić-Blake
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands
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37
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Wang Z. Brain Entropy Mapping in Healthy Aging and Alzheimer's Disease. Front Aging Neurosci 2020; 12:596122. [PMID: 33240080 PMCID: PMC7683386 DOI: 10.3389/fnagi.2020.596122] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/06/2020] [Indexed: 12/18/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease, for which aging remains the major risk factor. Aging is under a consistent pressure of increasing brain entropy (BEN) due to the progressive brain deteriorations. Noticeably, the brain constantly consumes a large amount of energy to maintain its functional integrity, likely creating or maintaining a big "reserve" to counteract the high entropy. Malfunctions of this latent reserve may indicate a critical point of disease progression. The purpose of this study was to characterize BEN in aging and AD and to test an inverse-U-shape BEN model: BEN increases with age and AD pathology in normal aging but decreases in the AD continuum. BEN was measured with resting state fMRI and compared across aging and the AD continuum. Associations of BEN with age, education, clinical symptoms, and pathology were examined by multiple regression. The analysis results highlighted resting BEN in the default mode network, medial temporal lobe, and prefrontal cortex and showed that: (1) BEN increased with age and pathological deposition in normal aging but decreased with age and pathological deposition in the AD continuum; (2) AD showed catastrophic BEN reduction, which was related to more severe cognitive impairment and daily function disability; and (3) BEN decreased with education years in normal aging, but not in the AD continuum. BEN evolution follows an inverse-U trajectory when AD progresses from normal aging to AD dementia. Education is beneficial for suppressing the entropy increase potency in normal aging.
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Affiliation(s)
- Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, United States
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38
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Maturana-Candelas A, Gómez C, Poza J, Ruiz-Gómez SJ, Hornero R. Inter-band Bispectral Analysis of EEG Background Activity to Characterize Alzheimer's Disease Continuum. Front Comput Neurosci 2020; 14:70. [PMID: 33100999 PMCID: PMC7554631 DOI: 10.3389/fncom.2020.00070] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 06/25/2020] [Indexed: 11/13/2022] Open
Abstract
The aim of this study was to characterize the EEG alterations in inter-band interactions along the Alzheimer's disease (AD) continuum. For this purpose, EEG background activity from 51 healthy control subjects, 51 mild cognitive impairment patients, 50 mild AD patients, 50 moderate AD patients, and 50 severe AD patients was analyzed by means of bispectrum. Three inter-band features were extracted from bispectrum matrices: bispectral relative power (BispRP), cubic bispectral entropy (BispEn), and bispectral median frequency (BispMF). BispRP results showed an increase of delta and theta interactions with other frequency bands and the opposite behavior for alpha, beta-1, and beta-2. Delta and theta interactions, along with the rest of the spectrum, also experimented a decrease of BispEn with disease progression, suggesting these bands interact with a reduced variety of components in advanced stages of dementia. Finally, BispMF showed a consistent reduction along the AD continuum in all bands, which is reflective of an interaction of the global spectrum with lower frequency bands as the disease develops. Our results indicate a progressive decrease in inter-band interactions with the severity of the disease, especially those involving high frequency components. Since inter-band coupling oscillations are related to complex and multi-scaled brain processes, these alterations likely reflect the neurodegeneration associated with the AD continuum.
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Affiliation(s)
- Aarón Maturana-Candelas
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
| | - Saúl J Ruiz-Gómez
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, Escuela Técnica Superior de Ingenieros, de Telecomunicación, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid, Spain.,Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, Valladolid, Spain
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39
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Lees T, Maharaj S, Kalatzis G, Nassif NT, Newton PJ, Lal S. Electroencephalographic prediction of global and domain specific cognitive performance of clinically active Australian Nurses. Physiol Meas 2020; 41:095001. [PMID: 33021231 DOI: 10.1088/1361-6579/abb12a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To investigate the relationship between EEG activity and the global and domain specific cognitive performance of healthy nurses, and determine the predictive capabilities of these relationships. APPROACH Sixty-four nurses were recruited for the present study, and data from 61 were utilised in the present analysis. Global and domain specific cognitive performance of each participant was assessed psychometrically using the Mini-mental state exam and the Cognistat, and a 32-lead monopolar EEG was recorded during a resting baseline phase and an active phase in which participants completed the Stroop test. MAIN RESULTS Global cognitive performance was successfully predicted (81%-85% of variance) by a combination of fast wave activity variables in the alpha, beta and theta frequency bands. Interestingly, predicting domain specific performance had varying degrees of success (42%-99% of the variance predicted) and relied on combinations of both slow and fast wave activity, with delta and gamma activity predicting attention performance; delta, theta, and gamma activity predicting memory performance; and delta and beta variables predicting judgement performance. SIGNIFICANCE Global and domain specific cognitive performance of Australian nurses may be predicted with varying degrees of success by a unique combination of EEG variables. These proposed models image transitory cognitive declines and as such may prove useful in the prediction of early cognitive impairment, and may enable better diagnosis, and management of cognitive impairment.
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Affiliation(s)
- Ty Lees
- Edna Bennett Pierce Prevention Research Center, Pennsylvania State University, 115 Health & Human Development Building, University Park, PA 16802, United States of America
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Arnts H, van Erp WS, Boon LI, Bosman CA, Admiraal MM, Schrantee A, Pennartz CMA, Schuurman R, Stam CJ, van Rootselaar AF, Hillebrand A, van den Munckhof P. Awakening after a sleeping pill: Restoring functional brain networks after severe brain injury. Cortex 2020; 132:135-146. [PMID: 32979847 DOI: 10.1016/j.cortex.2020.08.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/09/2020] [Accepted: 08/11/2020] [Indexed: 12/11/2022]
Abstract
Some patients with severe brain injury show short-term neurological improvements, such as recovery of consciousness, motor function, or speech after administering zolpidem, a GABA receptor agonist. The working mechanism of this paradoxical phenomenon remains unknown. In this study, we used electroencephalography and magnetoencephalography to investigate a spectacular zolpidem-induced awakening, including the recovery of functional communication and the ability to walk in a patient with severe hypoxic-ischemic brain injury. We show that cognitive deficits, speech loss, and motor impairments after severe brain injury are associated with stronger beta band connectivity throughout the brain and suggest that neurological recovery after zolpidem occurs with the restoration of beta band connectivity. This exploratory work proposes an essential role for beta rhythms in goal-directed behavior and cognition. It advocates further fundamental and clinical research on the role of increased beta band connectivity in the development of neurological deficits after severe brain injury.
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Affiliation(s)
- Hisse Arnts
- Amsterdam UMC, University of Amsterdam, Department of Neurosurgery, Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - Willemijn S van Erp
- Department of Primary and Community Care, Centre for Family Medicine, Geriatric Care and Public Health, Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Lennard I Boon
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Conrado A Bosman
- Cognitive and Systems Neuroscience Group, Swammerdam Institute, Center for Neuroscience, University of Amsterdam, Amsterdam, the Netherlands
| | - Marjolein M Admiraal
- Amsterdam UMC, University of Amsterdam, Department of Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Anouk Schrantee
- Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands
| | - Cyriel M A Pennartz
- Cognitive and Systems Neuroscience Group, Swammerdam Institute, Center for Neuroscience, University of Amsterdam, Amsterdam, the Netherlands
| | - Rick Schuurman
- Amsterdam UMC, University of Amsterdam, Department of Neurosurgery, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Anne-Fleur van Rootselaar
- Amsterdam UMC, University of Amsterdam, Department of Neurology and Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Pepijn van den Munckhof
- Amsterdam UMC, University of Amsterdam, Department of Neurosurgery, Amsterdam Neuroscience, Amsterdam, the Netherlands
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Moezzi B, Pratti LM, Hordacre B, Graetz L, Berryman C, Lavrencic LM, Ridding MC, Keage HAD, McDonnell MD, Goldsworthy MR. Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis. Neuroscience 2020; 422:230-239. [PMID: 31806080 DOI: 10.1016/j.neuroscience.2019.08.038] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 08/15/2019] [Accepted: 08/22/2019] [Indexed: 01/01/2023]
Abstract
Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in delta, theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 93% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in delta, theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and more than half of connections involving occipital electrodes, showed decreased connectivity with older age. Slightly less than half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age were not significantly different in electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with aging during a resting state.
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Affiliation(s)
- Bahar Moezzi
- Cognitive Ageing and Impairment Neurosciences Laboratory, School of Psychology, Social Work and Social Policy, University of South Australia, Australia.
| | | | - Brenton Hordacre
- School of Health Sciences, University of South Australia, Australia
| | - Lynton Graetz
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Australia
| | - Carolyn Berryman
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Australia
| | - Louise M Lavrencic
- Cognitive Ageing and Impairment Neurosciences Laboratory, School of Psychology, Social Work and Social Policy, University of South Australia, Australia; Neuroscience Research of Australia, Australia
| | - Michael C Ridding
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Australia
| | - Hannah A D Keage
- Cognitive Ageing and Impairment Neurosciences Laboratory, School of Psychology, Social Work and Social Policy, University of South Australia, Australia
| | - Mark D McDonnell
- Computational Learning Systems Laboratory, School of Information Technology and Mathematical Sciences, University of South Australia, Australia
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Ioulietta L, Kostas G, Spiros N, Vangelis OP, Anthoula T, Ioannis K, Magda T, Dimitris K. A Novel Connectome-Based Electrophysiological Study of Subjective Cognitive Decline Related to Alzheimer's Disease by Using Resting-State High-Density EEG EGI GES 300. Brain Sci 2020; 10:brainsci10060392. [PMID: 32575641 PMCID: PMC7349850 DOI: 10.3390/brainsci10060392] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/17/2020] [Accepted: 06/18/2020] [Indexed: 12/17/2022] Open
Abstract
Aim: To investigate for the first time the brain network in the Alzheimer’s disease (AD) spectrum by implementing a high-density electroencephalography (HD-EEG - EGI GES 300) study with 256 channels in order to seek if the brain connectome can be effectively used to distinguish cognitive impairment in preclinical stages. Methods: Twenty participants with AD, 30 with mild cognitive impairment (MCI), 20 with subjective cognitive decline (SCD) and 22 healthy controls (HC) were examined with a detailed neuropsychological battery and 10 min resting state HD-EEG. We extracted correlation matrices by using Pearson correlation coefficients for each subject and constructed weighted undirected networks for calculating clustering coefficient (CC), strength (S) and betweenness centrality (BC) at global (256 electrodes) and local levels (29 parietal electrodes). Results: One-way ANOVA presented a statistically significant difference among the four groups at local level in CC [F (3, 88) = 4.76, p = 0.004] and S [F (3, 88) = 4.69, p = 0.004]. However, no statistically significant difference was found at a global level. According to the independent sample t-test, local CC was higher for HC [M (SD) = 0.79 (0.07)] compared with SCD [M (SD) = 0.72 (0.09)]; t (40) = 2.39, p = 0.02, MCI [M (SD) = 0.71 (0.09)]; t (50) = 0.41, p = 0.004 and AD [M (SD) = 0.68 (0.11)]; t (40) = 3.62, p = 0.001 as well, while BC showed an increase at a local level but a decrease at a global level as the disease progresses. These findings provide evidence that disruptions in brain networks in parietal organization may potentially represent a key factor in the ability to distinguish people at early stages of the AD continuum. Conclusions: The above findings reveal a dynamically disrupted network organization of preclinical stages, showing that SCD exhibits network disorganization with intermediate values between MCI and HC. Additionally, these pieces of evidence provide information on the usefulness of the 256 HD-EEG in network construction.
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Affiliation(s)
- Lazarou Ioulietta
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), 57001 Thessaloniki, Greece; (G.K.); (N.S.); (O.V.P.); (T.A.); (K.I.); (T.M.)
- 1st Department of Neurology, G.H. “AHEPA”, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
- Correspondence:
| | - Georgiadis Kostas
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), 57001 Thessaloniki, Greece; (G.K.); (N.S.); (O.V.P.); (T.A.); (K.I.); (T.M.)
- Informatics Department, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
| | - Nikolopoulos Spiros
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), 57001 Thessaloniki, Greece; (G.K.); (N.S.); (O.V.P.); (T.A.); (K.I.); (T.M.)
| | - Oikonomou P. Vangelis
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), 57001 Thessaloniki, Greece; (G.K.); (N.S.); (O.V.P.); (T.A.); (K.I.); (T.M.)
| | - Tsolaki Anthoula
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), 57001 Thessaloniki, Greece; (G.K.); (N.S.); (O.V.P.); (T.A.); (K.I.); (T.M.)
- Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD), 54643 Thessaloniki, Greece
| | - Kompatsiaris Ioannis
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), 57001 Thessaloniki, Greece; (G.K.); (N.S.); (O.V.P.); (T.A.); (K.I.); (T.M.)
| | - Tsolaki Magda
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), 57001 Thessaloniki, Greece; (G.K.); (N.S.); (O.V.P.); (T.A.); (K.I.); (T.M.)
- 1st Department of Neurology, G.H. “AHEPA”, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
- Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD), 54643 Thessaloniki, Greece
| | - Kugiumtzis Dimitris
- Department of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
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Sahoo B, Pathak A, Deco G, Banerjee A, Roy D. Lifespan associated global patterns of coherent neural communication. Neuroimage 2020; 216:116824. [PMID: 32289459 DOI: 10.1016/j.neuroimage.2020.116824] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 02/27/2020] [Accepted: 03/24/2020] [Indexed: 11/19/2022] Open
Abstract
Healthy ageing is accompanied by changes to spontaneous electromagnetic oscillations. At the macroscopic scale, previous studies have quantified the basic features, e.g., power and frequencies in rhythms of interest from the perspective of attention, perception, learning and memory. On the other hand, signatures and modes of neural communication have recently been argued to be identifiable from global measures applied on neuro-electromagnetic data such as global coherence that quantifies the degree of togetherness of distributed neural oscillations and metastability that parametrizes the transient dynamics of the network switching between successive stable states. Here, we demonstrate that global coherence and metastability can be informative measures to track healthy ageing dynamics over lifespan and together with the traditional spectral measures provides an attractive explanation of neuronal information processing. Finding normative patterns of brain rhythms in resting state MEG would naturally pave the way for tracking task relevant metrics that could crucially determine cognitive flexibility and performance. While previously reported observations of a reduction in peak alpha frequency and increased beta power in older adults are reflective of changes at individual sensors (during rest and task), global coherence and metastability pinpoint the underlying coordination dynamics over multiple brain areas across the entire lifespan. In addition to replication of the previous observations in a substantially larger lifespan cohort than what was previously reported, we also demonstrate, for the first time to the best of our knowledge, age related changes in coherence and metastability in signals over time scales of neuronal processing. Furthermore, we observed a marked frequency dependence in changes in global coordination dynamics, which, coupled with the long-held view of specific frequency bands subserving different aspects of cognition, hints at differential functional processing roles for slower and faster brain dynamics.
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Affiliation(s)
- Bikash Sahoo
- Cognitive Brain Dynamics Lab National Brain Research Centre (NBRC), NH8 Nainwal Mode, 122051, Manesar, Haryana, India
| | - Anagh Pathak
- Cognitive Brain Dynamics Lab National Brain Research Centre (NBRC), NH8 Nainwal Mode, 122051, Manesar, Haryana, India
| | - Gustavo Deco
- Institució Catalana de la Recerc Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís, Companys 23, Barcelona, 08010, Spain
| | - Arpan Banerjee
- Cognitive Brain Dynamics Lab National Brain Research Centre (NBRC), NH8 Nainwal Mode, 122051, Manesar, Haryana, India.
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab National Brain Research Centre (NBRC), NH8 Nainwal Mode, 122051, Manesar, Haryana, India.
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Núñez P, Poza J, Gómez C, Barroso-García V, Maturana-Candelas A, Tola-Arribas MA, Cano M, Hornero R. Characterization of the dynamic behavior of neural activity in Alzheimer's disease: exploring the non-stationarity and recurrence structure of EEG resting-state activity. J Neural Eng 2020; 17:016071. [PMID: 32000144 DOI: 10.1088/1741-2552/ab71e9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
OBJECTIVE Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to induce perturbations to normal neuronal behavior and disrupt neuronal networks. Recent work suggests that the dynamic properties of resting-state neuronal activity could be affected by MCI and AD-induced neurodegeneration. The aim of the study was to characterize these properties from different perspectives: (i) using the Kullback-Leibler divergence (KLD), a measure of non-stationarity derived from the continuous wavelet transform; and (ii) using the entropy of the recurrence point density ([Formula: see text]) and the median of the recurrence point density ([Formula: see text]), two novel metrics based on recurrence quantification analysis. APPROACH KLD, [Formula: see text] and [Formula: see text] were computed for 49 patients with dementia due to AD, 66 patients with MCI due to AD and 43 cognitively healthy controls from 60 s electroencephalographic (EEG) recordings with a 10 s sliding window with no overlap. Afterwards, we tested whether the measures reflected alterations to normal neuronal activity induced by MCI and AD. MAIN RESULTS Our results showed that frequency-dependent alterations to normal dynamic behavior can be found in patients with MCI and AD, both in non-stationarity and recurrence structure. Patients with MCI showed signs of patterns of abnormal state recurrence in the theta (4-8 Hz) and beta (13-30 Hz) frequency bands that became more marked in AD. Moreover, abnormal non-stationarity patterns were found in MCI patients, but not in patients with AD in delta (1-4 Hz), alpha (8-13 Hz), and gamma (30-70 Hz). SIGNIFICANCE The alterations in normal levels of non-stationarity in patients with MCI suggest an initial increase in cortical activity during the development of AD. This increase could possibly be due to an impairment in neuronal inhibition that is not present during later stages. MCI and AD induce alterations to the recurrence structure of cortical activity, suggesting that normal state switching during rest may be affected by these pathologies.
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Affiliation(s)
- Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain. Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina CIBER-BBN, Valladolid, Spain. Author to whom any correspondence should be addressed
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Ruiz-Gomez SJ, Gomez C, Poza J, Maturana-Candelas A, Rodriguez-Gonzalez V, Garcia M, Tola-Arribas MA, Cano M, Hornero R. Analysis of Volume Conduction Effects on Different Functional Connectivity Metrics: Application to Alzheimer's Disease EEG Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:6434-6437. [PMID: 31947315 DOI: 10.1109/embc.2019.8856548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The aim of this study was to evaluate the effect of volume conduction on different connectivity metrics: Amplitude Envelope Correlation (AEC), Phase Lag Index (PLI), and Magnitude Squared Coherence (MSCOH). These measures were applied to: (i) a synthetic model of 64 coupled oscillators; and (ii) a resting-state EEG database of 72 patients with dementia due to Alzheimer's disease (AD) and 37 cognitively healthy controls. Our results revealed that AEC and PLI are weakly influenced by the simulated volume conduction compared to MSCOH, although the three metrics are not immune to this effect. Furthermore, results with real EEG recordings showed that AD patients are characterized by an AEC increase in δ frequency band and widespread connectivity decreases in α and β1 bands. These coupling changes reflect the abnormalities in spontaneous EEG activity of AD patients and might provide further insights into the underlying brain dynamics associated with this disorder.
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Briels C, Stam C, Scheltens P, Bruins S, Lues I, Gouw A. In pursuit of a sensitive EEG functional connectivity outcome measure for clinical trials in Alzheimer’s disease. Clin Neurophysiol 2020; 131:88-95. [DOI: 10.1016/j.clinph.2019.09.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 07/19/2019] [Accepted: 09/15/2019] [Indexed: 01/01/2023]
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47
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Cooray GK, Sundgren M, Brismar T. Mechanism of visual network dysfunction in relapsing-remitting multiple sclerosis and its relation to cognition. Clin Neurophysiol 2019; 131:361-367. [PMID: 31864125 DOI: 10.1016/j.clinph.2019.10.029] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 09/10/2019] [Accepted: 10/31/2019] [Indexed: 01/31/2023]
Abstract
OBJECTIVE To investigate if changes in brain network function and connectivity contribute to the abnormalities in visual event related potentials (ERP) in relapsing-remitting multiple sclerosis (RRMS), and explore their relation to a decrease in cognitive performance. METHODS We evaluated 72 patients with RRMS and 89 healthy control subjects in a cross-sectional study. Visual ERP were generated using illusory and non-illusory stimuli and recorded using 21 EEG scalp electrodes. The measured activity was modelled using Dynamic Causal Modelling. The model network consisted of 4 symmetric nodes including the primary visual cortex (V1/V2) and the Lateral Occipital Complex. Patients and controls were tested with a neuropsychological test battery consisting of 18 cognitive tests covering six cognitive domains. RESULTS We found reduced cortical connectivity in bottom-up and interhemispheric connections to the right lateral occipital complex in patients (p < 0.001). Furthermore, interhemispherical connections were related to cognitive dysfunction in several domains (attention, executive function, visual perception and organization, processing speed and global cognition) for patients (p < 0.05). No relation was seen between cortical network connectivity and cognitive function in the healthy control subjects. CONCLUSION Changes in the functional connectivity to higher cortical regions provide a neurobiological explanation for the changes of the visual ERP in RRMS. SIGNIFICANCE This study suggests that changes in connectivity to higher cortical regions partly explain visual network dysfunction in RRMS where a lower interhemispheric connectivity may contribute to impaired cognitive function.
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Affiliation(s)
- Gerald K Cooray
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden.
| | - Mathias Sundgren
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Neuro Department, Karolinska University Hospital, Stockholm, Sweden
| | - Tom Brismar
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden; Department of Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden
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Abstract
Currently established and employed biomarkers of Alzheimer's disease (AD) predominantly mirror AD-associated molecular and structural brain changes. While they are necessary for identifying disease-specific neuropathology, they lack a clear and robust relationship with the clinical presentation of dementia; they can be altered in healthy individuals, while they often inadequately mirror the degree of cognitive and functional deficits in affected subjects. There is growing evidence that synaptic loss and dysfunction are early events during the trajectory of AD pathogenesis that best correlate with the clinical symptoms, suggesting measures of brain functional deficits as candidate early markers of AD. Resting-state electroencephalography (EEG) is a widely available and noninvasive diagnostic method that provides direct insight into brain synaptic activity in real time. Quantitative EEG (qEEG) analysis additionally provides information on physiologically meaningful frequency components, dynamic alterations and topography of EEG signal generators, i.e. neuronal signaling. Numerous studies have shown that qEEG measures can detect disruptions in activity, topographical distribution and synchronization of neuronal (synaptic) activity such as generalized EEG slowing, reduced global synchronization and anteriorization of neuronal generators of fast-frequency resting-state EEG activity in patients along the AD continuum. Moreover, qEEG measures appear to correlate well with surrogate markers of AD neuropathology and discriminate between different types of dementia, making them promising low-cost and noninvasive markers of AD. Future large-scale longitudinal clinical studies are needed to elucidate the diagnostic and prognostic potential of qEEG measures as early functional markers of AD on an individual subject level.
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Affiliation(s)
- Una Smailovic
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden.
| | - Vesna Jelic
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Clinic for Cognitive Disorders, Theme Aging, Karolinska University Hospital, Huddinge, Sweden
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Ruiz-Gómez SJ, Hornero R, Poza J, Maturana-Candelas A, Pinto N, Gómez C. Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer’s disease continuum. J Neural Eng 2019; 16:066019. [DOI: 10.1088/1741-2552/ab4024] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Pusil S, López ME, Cuesta P, Bruña R, Pereda E, Maestú F. Hypersynchronization in mild cognitive impairment: the ‘X’ model. Brain 2019; 142:3936-3950. [DOI: 10.1093/brain/awz320] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 08/06/2019] [Accepted: 08/13/2019] [Indexed: 12/21/2022] Open
Abstract
Hypersynchronization has been considered as a biomarker of synaptic dysfunction along the Alzheimeŕs disease continuum. In a longitudinal MEG study, Pusil et al. reveal changes in functional connectivity upon progression from MCI to Alzheimer’s disease. They propose the ‘X’ model to explain their findings, and suggest that hypersynchronization predicts conversion.
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Affiliation(s)
- Sandra Pusil
- Laboratory of Neuropsychology, University of the Balearic Islands, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - María Eugenia López
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE Universidad de La Laguna, Tenerife, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE Universidad de La Laguna, Tenerife, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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