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Carrarini C, Nardulli C, Titti L, Iodice F, Miraglia F, Vecchio F, Rossini PM. Neuropsychological and electrophysiological measurements for diagnosis and prediction of dementia: a review on Machine Learning approach. Ageing Res Rev 2024; 100:102417. [PMID: 39002643 DOI: 10.1016/j.arr.2024.102417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/29/2024] [Accepted: 07/07/2024] [Indexed: 07/15/2024]
Abstract
INTRODUCTION Emerging and advanced technologies in the field of Artificial Intelligence (AI) represent promising methods to predict and diagnose neurodegenerative diseases, such as dementia. By using multimodal approaches, Machine Learning (ML) seems to provide a better understanding of the pathological mechanisms underlying the onset of dementia. The purpose of this review was to discuss the current ML application in the field of neuropsychology and electrophysiology, exploring its results in both prediction and diagnosis for different forms of dementia, such as Alzheimer's disease (AD), Vascular Dementia (VaD), Dementia with Lewy bodies (DLB), and Frontotemporal Dementia (FTD). METHODS Main ML-based papers focusing on neuropsychological assessments and electroencephalogram (EEG) studies were analyzed for each type of dementia. RESULTS An accuracy ranging between 70 % and 90 % or even more was observed in all neurophysiological and electrophysiological results trained by ML. Among all forms of dementia, the most significant findings were observed for AD. Relevant results were mostly related to diagnosis rather than prediction, because of the lack of longitudinal studies with appropriate follow-up duration. However, it remains unclear which ML algorithm performs better in diagnosing or predicting dementia. CONCLUSIONS Neuropsychological and electrophysiological measurements, together with ML analysis, may be considered as reliable instruments for early detection of dementia.
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Affiliation(s)
- Claudia Carrarini
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Neuroscience, Catholic University of Sacred Heart, Largo Agostino Gemelli 8, Rome 00168, Italy
| | - Cristina Nardulli
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Laura Titti
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Francesco Iodice
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Francesca Miraglia
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Theoretical and Applied Sciences, eCampus University, via Isimbardi 10, Novedrate 22060, Italy
| | - Fabrizio Vecchio
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Theoretical and Applied Sciences, eCampus University, via Isimbardi 10, Novedrate 22060, Italy
| | - Paolo Maria Rossini
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy.
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Mujib MD, Rao AZ, Hasan MA, Ikhlaq A, Shahid H, Bano N, Mustafa MU, Mukhtar F, Nisa M, Qazi SA. Comparative Neurological and Behavioral Assessment of Central and Peripheral Stimulation Technologies for Induced Pain and Cognitive Tasks. Biomedicines 2024; 12:1269. [PMID: 38927476 PMCID: PMC11201146 DOI: 10.3390/biomedicines12061269] [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: 04/22/2024] [Accepted: 05/03/2024] [Indexed: 06/28/2024] Open
Abstract
Pain is a multifaceted, multisystem disorder that adversely affects neuro-psychological processes. This study compares the effectiveness of central stimulation (transcranial direct current stimulation-tDCS over F3/F4) and peripheral stimulation (transcutaneous electrical nerve stimulation-TENS over the median nerve) in pain inhibition during a cognitive task in healthy volunteers and to observe potential neuro-cognitive improvements. Eighty healthy participants underwent a comprehensive experimental protocol, including cognitive assessments, the Cold Pressor Test (CPT) for pain induction, and tDCS/TENS administration. EEG recordings were conducted pre- and post-intervention across all conditions. The protocol for this study was categorized into four groups: G1 (control), G2 (TENS), G3 (anodal-tDCS), and G4 (cathodal-tDCS). Paired t-tests (p < 0.05) were conducted to compare Pre-Stage, Post-Stage, and neuromodulation conditions, with t-values providing insights into effect magnitudes. The result showed a reduction in pain intensity with TENS (p = 0.002, t-value = -5.34) and cathodal-tDCS (p = 0.023, t-value = -5.08) and increased pain tolerance with TENS (p = 0.009, t-value = 4.98) and cathodal-tDCS (p = 0.001, t-value = 5.78). Anodal-tDCS (p = 0.041, t-value = 4.86) improved cognitive performance. The EEG analysis revealed distinct neural oscillatory patterns across the groups. Specifically, G2 and G4 showed delta-power reductions, while G3 observed an increase. Moreover, G2 exhibited increased theta-power in the occipital region during CPT and Post-Stages. In the alpha-band, G2, G3, and G4 had reductions Post-Stage, while G1 and G3 increased. Additionally, beta-power increased in the frontal region for G2 and G3, contrasting with a reduction in G4. Furthermore, gamma-power globally increased during CPT1, with G1, G2, and G3 showing reductions Post-Stage, while G4 displayed a global decrease. The findings confirm the efficacy of TENS and tDCS as possible non-drug therapeutic alternatives for cognition with alleviation from pain.
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Affiliation(s)
- Muhammad Danish Mujib
- Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi 75270, Pakistan; (A.Z.R.); (M.A.H.)
| | - Ahmad Zahid Rao
- Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi 75270, Pakistan; (A.Z.R.); (M.A.H.)
| | - Muhammad Abul Hasan
- Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi 75270, Pakistan; (A.Z.R.); (M.A.H.)
- Neurocomputation Lab, National Centre of Artificial Intelligence, NED University of Engineering & Technology, Karachi 75270, Pakistan; (H.S.); (S.A.Q.)
| | - Ayesha Ikhlaq
- Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan; (A.I.); (M.U.M.); (F.M.)
| | - Hira Shahid
- Neurocomputation Lab, National Centre of Artificial Intelligence, NED University of Engineering & Technology, Karachi 75270, Pakistan; (H.S.); (S.A.Q.)
- Research Centre for Intelligent Healthcare, Coventry University, Coventry-CV1 2TU, UK
| | - Nargis Bano
- Department of Physics and Astronomy College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Muhammad Usman Mustafa
- Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan; (A.I.); (M.U.M.); (F.M.)
| | - Faisal Mukhtar
- Institute of Physics, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan; (A.I.); (M.U.M.); (F.M.)
| | - Mehrun Nisa
- Department of Physics, Govt. Sadiq College Women University, Bahawalpur 63100, Pakistan;
| | - Saad Ahmed Qazi
- Neurocomputation Lab, National Centre of Artificial Intelligence, NED University of Engineering & Technology, Karachi 75270, Pakistan; (H.S.); (S.A.Q.)
- Department of Electrical Engineering, NED University of Engineering & Technology, Karachi 75270, Pakistan
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Wyman-Chick KA, Chaudhury P, Bayram E, Abdelnour C, Matar E, Chiu SY, Ferreira D, Hamilton CA, Donaghy PC, Rodriguez-Porcel F, Toledo JB, Habich A, Barrett MJ, Patel B, Jaramillo-Jimenez A, Scott GD, Kane JPM. Differentiating Prodromal Dementia with Lewy Bodies from Prodromal Alzheimer's Disease: A Pragmatic Review for Clinicians. Neurol Ther 2024; 13:885-906. [PMID: 38720013 PMCID: PMC11136939 DOI: 10.1007/s40120-024-00620-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 04/05/2024] [Indexed: 05/12/2024] Open
Abstract
This pragmatic review synthesises the current understanding of prodromal dementia with Lewy bodies (pDLB) and prodromal Alzheimer's disease (pAD), including clinical presentations, neuropsychological profiles, neuropsychiatric symptoms, biomarkers, and indications for disease management. The core clinical features of dementia with Lewy bodies (DLB)-parkinsonism, complex visual hallucinations, cognitive fluctuations, and REM sleep behaviour disorder are common prodromal symptoms. Supportive clinical features of pDLB include severe neuroleptic sensitivity, as well as autonomic and neuropsychiatric symptoms. The neuropsychological profile in mild cognitive impairment attributable to Lewy body pathology (MCI-LB) tends to include impairment in visuospatial skills and executive functioning, distinguishing it from MCI due to AD, which typically presents with impairment in memory. pDLB may present with cognitive impairment, psychiatric symptoms, and/or recurrent episodes of delirium, indicating that it is not necessarily synonymous with MCI-LB. Imaging, fluid and other biomarkers may play a crucial role in differentiating pDLB from pAD. The current MCI-LB criteria recognise low dopamine transporter uptake using positron emission tomography or single photon emission computed tomography (SPECT), loss of REM atonia on polysomnography, and sympathetic cardiac denervation using meta-iodobenzylguanidine SPECT as indicative biomarkers with slowing of dominant frequency on EEG among others as supportive biomarkers. This review also highlights the emergence of fluid and skin-based biomarkers. There is little research evidence for the treatment of pDLB, but pharmacological and non-pharmacological treatments for DLB may be discussed with patients. Non-pharmacological interventions such as diet, exercise, and cognitive stimulation may provide benefit, while evaluation and management of contributing factors like medications and sleep disturbances are vital. There is a need to expand research across diverse patient populations to address existing disparities in clinical trial participation. In conclusion, an early and accurate diagnosis of pDLB or pAD presents an opportunity for tailored interventions, improved healthcare outcomes, and enhanced quality of life for patients and care partners.
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Affiliation(s)
- Kathryn A Wyman-Chick
- Struthers Parkinson's Center and Center for Memory and Aging, Department of Neurology, HealthPartners/Park Nicollet, Bloomington, USA.
| | - Parichita Chaudhury
- Cleo Roberts Memory and Movement Center, Banner Sun Health Research Institute, Sun City, USA
| | - Ece Bayram
- Parkinson and Other Movement Disorders Center, University of California San Diego, San Diego, USA
| | - Carla Abdelnour
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, USA
| | - Elie Matar
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Shannon Y Chiu
- Department of Neurology, Mayo Clinic Arizona, Phoenix, USA
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Solna, Sweden
- Department of Radiology, Mayo Clinic Rochester, Rochester, USA
- Facultad de Ciencias de la Salud, Universidad Fernando Pessoa Canarias, Las Palmas, Spain
| | - Calum A Hamilton
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - Paul C Donaghy
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK
| | | | - Jon B Toledo
- Nantz National Alzheimer Center, Stanley Appel Department of Neurology, Houston Methodist Hospital, Houston, USA
| | - Annegret Habich
- Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Solna, Sweden
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Matthew J Barrett
- Department of Neurology, Parkinson's and Movement Disorders Center, Virginia Commonwealth University, Richmond, USA
| | - Bhavana Patel
- Department of Neurology, College of Medicine, University of Florida, Gainesville, USA
- Norman Fixel Institute for Neurologic Diseases, University of Florida, Gainesville, USA
| | - Alberto Jaramillo-Jimenez
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
- School of Medicine, Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellín, Colombia
| | - Gregory D Scott
- Department of Pathology and Laboratory Services, VA Portland Medical Center, Portland, USA
| | - Joseph P M Kane
- Centre for Public Health, Queen's University Belfast, Belfast, UK
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Amin SN, Shaltout SA, El Gazzar WB, Abdel Latif NS, Al-Jussani GN, Alabdallat YJ, Albakri KA, Elberry DA. Impact of NMDA receptors block versus GABA-A receptors modulation on synaptic plasticity and brain electrical activity in metabolic syndrome. Adv Med Sci 2024; 69:176-189. [PMID: 38561071 DOI: 10.1016/j.advms.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 12/18/2023] [Accepted: 03/25/2024] [Indexed: 04/04/2024]
Abstract
PURPOSE Metabolic syndrome (MetS) is a common disorder associated with disturbed neurotransmitter homeostasis. Memantine, an N-methyl-d-aspartate receptor (NMDAR) antagonist, was first used in Alzheimer's disease. Allopregnanolone (Allo), a potent positive allosteric modulator of the Gamma-Amino-Butyric Acid (GABA)-A receptors, decreases in neurodegenerative diseases. The study investigated the impact of Memantine versus Allo administration on the animal model of MetS to clarify whether the mechanism of abnormalities is related more to excitatory or inhibitory neurotransmitter dysfunction. MATERIALS AND METHODS Fifty-six male rats were allocated into 7 groups: 4 control groups, 1 MetS group, and 2 treated MetS groups. They underwent assessment of cognition-related behavior by open field and forced swimming tests, electroencephalogram (EEG) recording, serum markers confirming the establishment of MetS model and hippocampal Glial Fibrillary Acidic Protein (GFAP) and Brain-Derived Neurotrophic Factor (BDNF). RESULTS Allo improved anxiety-like behavior and decreased grooming frequency compared to Memantine. Both drugs increased GFAP and BDNF expression, improving synaptic plasticity and cognition-related behaviors. The therapeutic effect of Allo was more beneficial regarding lipid profile and anxiety. We reported progressive slowing of EEG waves in the MetS group with Memantine and Allo treatment with increased relative theta and decreased relative delta rhythms. CONCLUSIONS Both Allo and Memantine boosted the outcome parameters in the animal model of MetS. Allo markedly improved the anxiety-like behavior in the form of significantly decreased grooming frequency compared to the Memantine-treated groups. Both drugs were associated with increased hippocampal GFAP and BDNF expression, indicating an improvement in synaptic plasticity and so, cognition-related behaviors.
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Affiliation(s)
- Shaimaa Nasr Amin
- Department of Anatomy, Physiology and Biochemistry, Faculty of Medicine, The Hashemite University, Zarqa, Jordan; Department of Physiology, Faculty of Medicine, Cairo University, Cairo, Egypt.
| | - Sherif Ahmed Shaltout
- Department of Pharmacology, Public Health, and Clinical Skills, Faculty of Medicine, The Hashemite University, Zarqa, Jordan; Department of Pharmacology, Faculty of Medicine, Benha University, Benha, Egypt
| | - Walaa Bayoumie El Gazzar
- Department of Anatomy, Physiology and Biochemistry, Faculty of Medicine, The Hashemite University, Zarqa, Jordan; Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Benha University, Benha, Egypt
| | - Noha Samir Abdel Latif
- Department of Medical Pharmacology, Faculty of Medicine, Cairo University Cairo, Egypt; Department of Medical Pharmacology, Armed Forces College of Medicine, Cairo, Egypt
| | - Ghadah Nazar Al-Jussani
- Department of Microbiology, Pathology and Forensic Medicine, Faculty of Medicine, The Hashemite University, Zarqa, Jordan
| | | | | | - Dalia Azmy Elberry
- Department of Physiology, Faculty of Medicine, Cairo University, Cairo, Egypt
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Simfukwe C, Han SH, Jeong HT, Youn YC. qEEG as Biomarker for Alzheimer's Disease: Investigating Relative PSD Difference and Coherence Analysis. Neuropsychiatr Dis Treat 2023; 19:2423-2437. [PMID: 37965528 PMCID: PMC10642578 DOI: 10.2147/ndt.s433207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/04/2023] [Indexed: 11/16/2023] Open
Abstract
Purpose Electroencephalography (EEG) is a non-intrusive technique that provides comprehensive insights into the electrical activities of the brain's cerebral cortex. The brain signals obtained from EEGs can be used as a neuropsychological biomarker to detect different stages of Alzheimer's disease (AD) through quantitative EEG (qEEG) analysis. This paper investigates the difference in the abnormalities of resting state EEG (rEEG) signals between eyes-open (EOR) and eyes-closed (ECR) in AD by analyzing 19-scalp electrode EEG signals and making a comparison with healthy controls (HC). Participants and Methods The rEEG data from 534 subjects (ages 40-90) consisting of 269 HC and 265 AD subjects in South Korea were used in this study. The qEEG for EOR and ECR states were performed separately for HC and AD subjects to measure the relative power spectrum density (PSD) and coherence with functional connectivity to evaluate abnormalities. The rEEG data were preprocessed and analyzed using EEGlab and Brainstorm toolboxes in MATLAB R2021a software, and statistical analyses were carried out using ANOVA. Results Based on the Welch method, the relative PSD of the EEG EOR and ECR states difference in the AD group showed a significant increase in the delta frequency band of 19 EEG channels, particularly in the frontal, parietal, and temporal, than the HC groups. The delta power band on the source level was increased for the AD group and decreased for the HC group. In contrast, the source activities of alpha, beta, and gamma frequency bands were significantly reduced in the AD group, with a high decrease in the beta frequency band in all brain areas. Furthermore, the coherence of rEEG among different EEG electrodes was analyzed in the beta frequency band. It showed that pair-wise coherence between different brain areas in the AD group is remarkably increased in the ECR state and decreased after subtracting out the EOR state. Conclusion The findings suggest that examining PSD and functional connectivity through coherence analysis could serve as a promising and comprehensive approach to differentiate individuals with AD from normal, which may benefit our understanding of the disease.
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Affiliation(s)
- Chanda Simfukwe
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Su-Hyun Han
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Ho Tae Jeong
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
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Etter G, Carmichael JE, Williams S. Linking temporal coordination of hippocampal activity to memory function. Front Cell Neurosci 2023; 17:1233849. [PMID: 37720546 PMCID: PMC10501408 DOI: 10.3389/fncel.2023.1233849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/01/2023] [Indexed: 09/19/2023] Open
Abstract
Oscillations in neural activity are widespread throughout the brain and can be observed at the population level through the local field potential. These rhythmic patterns are associated with cycles of excitability and are thought to coordinate networks of neurons, in turn facilitating effective communication both within local circuits and across brain regions. In the hippocampus, theta rhythms (4-12 Hz) could contribute to several key physiological mechanisms including long-range synchrony, plasticity, and at the behavioral scale, support memory encoding and retrieval. While neurons in the hippocampus appear to be temporally coordinated by theta oscillations, they also tend to fire in sequences that are developmentally preconfigured. Although loss of theta rhythmicity impairs memory, these sequences of spatiotemporal representations persist in conditions of altered hippocampal oscillations. The focus of this review is to disentangle the relative contribution of hippocampal oscillations from single-neuron activity in learning and memory. We first review cellular, anatomical, and physiological mechanisms underlying the generation and maintenance of hippocampal rhythms and how they contribute to memory function. We propose candidate hypotheses for how septohippocampal oscillations could support memory function while not contributing directly to hippocampal sequences. In particular, we explore how theta rhythms could coordinate the integration of upstream signals in the hippocampus to form future decisions, the relevance of such integration to downstream regions, as well as setting the stage for behavioral timescale synaptic plasticity. Finally, we leverage stimulation-based treatment in Alzheimer's disease conditions as an opportunity to assess the sufficiency of hippocampal oscillations for memory function.
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Affiliation(s)
| | | | - Sylvain Williams
- Department of Psychiatry, Douglas Mental Health Research Institute, McGill University, Montreal, QC, Canada
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Cappon D, Fox R, den Boer T, Yu W, LaGanke N, Cattaneo G, Perellón-Alfonso R, Bartrés-Faz D, Manor B, Pascual-Leone A. Tele-supervised home-based transcranial alternating current stimulation (tACS) for Alzheimer's disease: a pilot study. Front Hum Neurosci 2023; 17:1168673. [PMID: 37333833 PMCID: PMC10272342 DOI: 10.3389/fnhum.2023.1168673] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 04/27/2023] [Indexed: 06/20/2023] Open
Abstract
Background Over 55 million people worldwide are currently diagnosed with Alzheimer's disease (AD) and live with debilitating episodic memory deficits. Current pharmacological treatments have limited efficacy. Recently, transcranial alternating current stimulation (tACS) has shown memory improvement in AD by normalizing high-frequency neuronal activity. Here we investigate the feasibility, safety, and preliminary effects on episodic memory of an innovative protocol where tACS is administered within the homes of older adults with AD with the help of a study companion (HB-tACS). Methods Eight participants diagnosed with AD underwent multiple consecutive sessions of high-definition HB-tACS (40 Hz, 20-min) targeting the left angular gyrus (AG), a key node of the memory network. The Acute Phase comprised 14-weeks of HB-tACS with at least five sessions per week. Three participants underwent resting state electroencephalography (EEG) before and after the 14-week Acute Phase. Subsequently, participants completed a 2-3-month Hiatus Phase not receiving HB-tACS. Finally, in the Taper phase, participants received 2-3 sessions per week over 3-months. Primary outcomes were safety, as determined by the reporting of side effects and adverse events, and feasibility, as determined by adherence and compliance with the study protocol. Primary clinical outcomes were memory and global cognition, measured with the Memory Index Score (MIS) and Montreal Cognitive Assessment (MoCA), respectively. Secondary outcome was EEG theta/gamma ratio. Results reported as mean ± SD. Results All participants completed the study, with an average of 97 HB-tACS sessions completed by each participant; reporting mild side effects during 25% of sessions, moderate during 5%, and severe during 1%. Acute Phase adherence was 98 ± 6.8% and Taper phase was 125 ± 22.3% (rates over 100% indicates participants completed more than the minimum of 2/week). After the Acute Phase, all participants showed memory improvement, MIS of 7.25 ± 3.77, sustained during Hiatus 7.00 ± 4.90 and Taper 4.63 ± 2.39 Phases compared to baseline. For the three participants that underwent EEG, a decreased theta/gamma ratio in AG was observed. Conversely, participants did not show improvement in the MoCA, 1.13 ± 3.80 after the Acute Phase, and there was a modest decrease during the Hiatus -0.64 ± 3.28 and Taper -2.56 ± 5.03 Phases. Conclusion This pilot study shows that the home-based, remotely-supervised, study companion administered, multi-channel tACS protocol for older adults with AD was feasible and safe. Further, targeting the left AG, memory in this sample was improved. These are preliminary results that warrant larger more definite trials to further elucidate tolerability and efficacy of the HB-tACS intervention. NCT04783350. Clinical trial registration https://clinicaltrials.gov/ct2/show/NCT04783350?term=NCT04783350&draw=2&rank=1, identifier NCT04783350.
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Affiliation(s)
- Davide Cappon
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife, Boston, MA, United States
- Deanna and Sidney Wolk Center for Memory Health at Hebrew SeniorLife, Boston, MA, United States
- Department of Neurology, Harvard Medical School, Boston, MA, United States
| | - Rachel Fox
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife, Boston, MA, United States
- Deanna and Sidney Wolk Center for Memory Health at Hebrew SeniorLife, Boston, MA, United States
| | - Tim den Boer
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife, Boston, MA, United States
| | - Wanting Yu
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife, Boston, MA, United States
| | - Nicole LaGanke
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife, Boston, MA, United States
| | - Gabriele Cattaneo
- Institut Guttmann, Institut Universitari de Neurorehabilitació Adscrit a la UAB, Barcelona, Spain
- Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
- Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Badalona, Spain
| | - Ruben Perellón-Alfonso
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - David Bartrés-Faz
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Brad Manor
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife, Boston, MA, United States
- Deanna and Sidney Wolk Center for Memory Health at Hebrew SeniorLife, Boston, MA, United States
- Department of Neurology, Harvard Medical School, Boston, MA, United States
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Pais-Vieira C, Allahdad MK, Perrotta A, Peres AS, Kunicki C, Aguiar M, Oliveira M, Pais-Vieira M. Neurophysiological correlates of tactile width discrimination in humans. Front Hum Neurosci 2023; 17:1155102. [PMID: 37250697 PMCID: PMC10213448 DOI: 10.3389/fnhum.2023.1155102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/25/2023] [Indexed: 05/31/2023] Open
Abstract
Introduction Tactile information processing requires the integration of sensory, motor, and cognitive information. Width discrimination has been extensively studied in rodents, but not in humans. Methods Here, we describe Electroencephalography (EEG) signals in humans performing a tactile width discrimination task. The first goal of this study was to describe changes in neural activity occurring during the discrimination and the response periods. The second goal was to relate specific changes in neural activity to the performance in the task. Results Comparison of changes in power between two different periods of the task, corresponding to the discrimination of the tactile stimulus and the motor response, revealed the engagement of an asymmetrical network associated with fronto-temporo-parieto-occipital electrodes and across multiple frequency bands. Analysis of ratios of higher [Ratio 1: (0.5-20 Hz)/(0.5-45 Hz)] or lower frequencies [Ratio 2: (0.5-4.5 Hz)/(0.5-9 Hz)], during the discrimination period revealed that activity recorded from frontal-parietal electrodes was correlated to tactile width discrimination performance between-subjects, independently of task difficulty. Meanwhile, the dynamics in parieto-occipital electrodes were correlated to the changes in performance within-subjects (i.e., between the first and the second blocks) independently of task difficulty. In addition, analysis of information transfer, using Granger causality, further demonstrated that improvements in performance between blocks were characterized by an overall reduction in information transfer to the ipsilateral parietal electrode (P4) and an increase in information transfer to the contralateral parietal electrode (P3). Discussion The main finding of this study is that fronto-parietal electrodes encoded between-subjects' performances while parieto-occipital electrodes encoded within-subjects' performances, supporting the notion that tactile width discrimination processing is associated with a complex asymmetrical network involving fronto-parieto-occipital electrodes.
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Affiliation(s)
- Carla Pais-Vieira
- Centro de Investigação Interdisciplinar em Saúde (CIIS), Instituto de Ciências da Saúde (ICS), Universidade Católica Portuguesa, Porto, Portugal
| | - Mehrab K. Allahdad
- Centro de Investigação Interdisciplinar em Saúde (CIIS), Instituto de Ciências da Saúde (ICS), Universidade Católica Portuguesa, Porto, Portugal
| | - André Perrotta
- Centre for Informatics and Systems of the University of Coimbra (CISUC), Coimbra, Portugal
| | - André S. Peres
- Proaction Laboratory, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
- CINEICC, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal
| | - Carolina Kunicki
- Vasco da Gama Research Center (CIVG), Vasco da Gama University School (EUVG), Coimbra, Portugal
- Center for Neuroscience and Cell Biology (CNC), Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal
| | - Mafalda Aguiar
- Department of Medical Sciences, iBiMED-Institute of Biomedicine, Universidade de Aveiro, Aveiro, Portugal
| | - Manuel Oliveira
- Department of Medical Sciences, iBiMED-Institute of Biomedicine, Universidade de Aveiro, Aveiro, Portugal
| | - Miguel Pais-Vieira
- Department of Medical Sciences, iBiMED-Institute of Biomedicine, Universidade de Aveiro, Aveiro, Portugal
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9
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Hu YN, Hsieh TH, Tsai MT, Chien CY, Roan JN, Huang YC, Liang SF. Cognitive Function Deterioration After Cardiopulmonary Bypass: Can Intraoperative Optimal Cerebral Regional Tissue Oxygen Saturation Predict Postoperative Cognitive Function? J Cardiothorac Vasc Anesth 2023; 37:715-723. [PMID: 36813631 DOI: 10.1053/j.jvca.2023.01.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/26/2022] [Accepted: 01/23/2023] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Cognitive impairment is a common neurologic complication after cardiac surgery with cardiopulmonary bypass (CPB). This study evaluated postoperative cognitive function to determine predictors of cognitive dysfunction, including intraoperative cerebral regional tissue oxygen saturation (rSO2). DESIGN A prospective observational cohort study. SETTING At a single academic tertiary-care center. PARTICIPANTS A total of 60 adults undergoing cardiac surgery with CPB from January to August 2021. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS All patients underwent Mini-Mental State Examination (MMSE) and quantified electroencephalography (qEEG) 1 day before cardiac surgery, 7 days postoperatively (POD7), and POD60. Intraoperative cerebral rSO2 was monitored continuously. For MMSE, no significant decrease in MMSE score was found on POD7 versus preoperatively (p = 0.09), but POD60 scores showed significant improvement compared with both preoperative (p = 0.02) and POD7 scores (p < 0.001). On qEEG, relative theta power on POD7 was increased versus preoperatively (p < 0.001), but it was decreased on POD60 (POD7 versus POD60, p < 0.001), and was close to preoperative data (p > 0.99). Baseline rSO2 was an independent factor for postoperative MMSE. Both baseline and mean rSO2 showed a significant influence in postoperative relative theta activity, whereas mean rSO2 was the only predictor for the theta-gamma ratio (p = 0.04). CONCLUSIONS The MMSE in patients undergoing CPB declined at POD7 and recovered by POD60. Lower baseline rSO2 indicated a higher potential for MMSE decline at POD60. Inferior intraoperative mean rSO2 was related to higher postoperative relative theta activity and theta-gamma ratio, implying subclinical or further cognitive impairment.
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Affiliation(s)
- Yu-Ning Hu
- Division of Cardiovascular Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Tsung-Hao Hsieh
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan; Department of Psychology, National Cheng Kung University, Tainan, Taiwan
| | - Meng-Ta Tsai
- Division of Cardiovascular Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chung-Yao Chien
- Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Jun-Neng Roan
- Division of Cardiovascular Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan
| | - Yu-Ching Huang
- Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Sheng-Fu Liang
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan.
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10
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González-López M, Gonzalez-Moreira E, Areces-González A, Paz-Linares D, Fernández T. Who's driving? The default mode network in healthy elderly individuals at risk of cognitive decline. Front Neurol 2022; 13:1009574. [DOI: 10.3389/fneur.2022.1009574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/08/2022] [Indexed: 12/02/2022] Open
Abstract
IntroductionAge is the main risk factor for the development of neurocognitive disorders, with Alzheimer's disease being the most common. Its physiopathological features may develop decades before the onset of clinical symptoms. Quantitative electroencephalography (qEEG) is a promising and cost-effective tool for the prediction of cognitive decline in healthy older individuals that exhibit an excess of theta activity. The aim of the present study was to evaluate the feasibility of brain connectivity variable resolution electromagnetic tomography (BC-VARETA), a novel source localization algorithm, as a potential tool to assess brain connectivity with 19-channel recordings, which are common in clinical practice.MethodsWe explored differences in terms of functional connectivity among the nodes of the default mode network between two groups of healthy older participants, one of which exhibited an EEG marker of risk for cognitive decline.ResultsThe risk group exhibited increased levels of delta, theta, and beta functional connectivity among nodes of the default mode network, as well as reversed directionality patterns of connectivity among nodes in every frequency band when compared to the control group.DiscussionWe propose that an ongoing pathological process may be underway in healthy elderly individuals with excess theta activity in their EEGs, which is further evidenced by changes in their connectivity patterns. BC-VARETA implemented on 19-channels EEG recordings appears to be a promising tool to detect dysfunctions at the connectivity level in clinical settings.
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11
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Kamal F, Campbell K, Taler V. Effects of the Duration of a Resting-State EEG Recording in Healthy Aging and Mild Cognitive Impairment. Clin EEG Neurosci 2022; 53:443-451. [PMID: 33370162 DOI: 10.1177/1550059420983624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
INTRODUCTION The recording of resting-state EEG may provide a means to predict early cognitive decline associated with mild cognitive impairment (MCI). Previous studies have typically used very short recording times to avoid a confound with drowsiness that may occur in longer recordings. The effects of a longer recording have not however been systematically examined. METHODS Eyes-closed resting-state EEG activity was recorded in 40 older adult participants (20 healthy older adults and 20 people with MCI). The recording period was a relatively long 6 minutes, divided into two equal 3-minute halves to determine if drowsiness will be more apparent as the recording progresses. The participants also completed standardized neuropsychological tasks that assessed global cognition (Montreal Cognitive Assessment) and memory (California Verbal Learning Test, Second Edition). A spectral analysis was performed on both short (2 seconds) and long (8 seconds) segments in both 3-minute halves. RESULTS No differences in power density for any of the EEG frequency bands were found between the 2 halves of the study for either group. There was little evidence of increased drowsiness in the second half of the study even when frequency resolution was increased with the 8-second segmentation. Theta power density was overall larger for people with MCI compared to healthy older adults. A negative correlation was also observed between theta power and global cognition in healthy older adults. CONCLUSIONS The present results indicate that longer resting-state EEG recording can be reliably employed without increased risk of drowsiness.
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Affiliation(s)
- Farooq Kamal
- School of Psychology, University of Ottawa, Ottawa, Ontario, Canada.,Bruyère Research Institute, Ottawa, Ontario, Canada
| | - Kenneth Campbell
- School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
| | - Vanessa Taler
- School of Psychology, University of Ottawa, Ottawa, Ontario, Canada.,Bruyère Research Institute, Ottawa, Ontario, Canada
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12
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Suhail T, Indiradevi K, Suhara E, Poovathinal SA, Ayyappan A. Distinguishing cognitive states using electroencephalography local activation and functional connectivity patterns. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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13
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Smailovic U, Ferreira D, Ausén B, Ashton NJ, Koenig T, Zetterberg H, Blennow K, Jelic V. Decreased Electroencephalography Global Field Synchronization in Slow-Frequency Bands Characterizes Synaptic Dysfunction in Amnestic Subtypes of Mild Cognitive Impairment. Front Aging Neurosci 2022; 14:755454. [PMID: 35462693 PMCID: PMC9031731 DOI: 10.3389/fnagi.2022.755454] [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: 08/08/2021] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundMild cognitive impairment (MCI) is highly prevalent in a memory clinic setting and is heterogeneous regarding its clinical presentation, underlying pathophysiology, and prognosis. The most prevalent subtypes are single-domain amnestic MCI (sd-aMCI), considered to be a prodromal phase of Alzheimer’s disease (AD), and multidomain amnestic MCI (md-aMCI), which is associated with multiple etiologies. Since synaptic loss and dysfunction are the closest pathoanatomical correlates of AD-related cognitive impairment, we aimed to characterize it in patients with sd-aMCI and md-aMCI by means of resting-state electroencephalography (EEG) global field power (GFP), global field synchronization (GFS), and novel cerebrospinal fluid (CSF) synaptic biomarkers.MethodsWe included 52 patients with sd-aMCI (66.9 ± 7.3 years, 52% women) and 30 with md-aMCI (63.1 ± 7.1 years, 53% women). All patients underwent a detailed clinical assessment, resting-state EEG recordings and quantitative analysis (GFP and GFS in delta, theta, alpha, and beta bands), and analysis of CSF biomarkers of synaptic dysfunction, neurodegeneration, and AD-related pathology. Cognitive subtyping was based on a comprehensive neuropsychological examination. The Mini-Mental State Examination (MMSE) was used as an estimation of global cognitive performance. EEG and CSF biomarkers were included in a multivariate model together with MMSE and demographic variables, to investigate differences between sd-aMCI and md-aMCI.ResultsPatients with sd-aMCI had higher CSF phosphorylated tau, total tau and neurogranin levels, and lower values in GFS delta and theta. No differences were observed in GFP. The multivariate model showed that the most important synaptic measures for group separation were GFS theta, followed by GFS delta, GFP theta, CSF neurogranin, and GFP beta.ConclusionPatients with sd-aMCI when compared with those with md-aMCI have a neurophysiological and biochemical profile of synaptic damage, neurodegeneration, and amyloid pathology closer to that described in patients with AD. The most prominent signature in sd-aMCI was a decreased global synchronization in slow-frequency bands indicating that functional connectivity in slow frequencies is more specifically related to early effects of AD-specific molecular pathology.
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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, Huddinge, Sweden
- Department of Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Birgitta Ausén
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden
- Clinic for Cognitive Disorders, Karolinska University Hospital-Huddinge, Stockholm, Sweden
- Women’s Health and Allied Health Professionals Theme, Medical Unit Medical Psychology, Karolinska University Hospital, Huddinge, Sweden
| | - Nicholas James Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience Institute, London, United Kingdom
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, United Kingdom
| | - Thomas Koenig
- Psychiatric Electrophysiology Unit, Translational Research Center, University Hospital of Psychiatry, Bern, Switzerland
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London, United Kingdom
- UK Dementia Research Institute at UCL, London, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, Hong Kong SAR, China
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Vesna Jelic
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden
- Clinic for Cognitive Disorders, Karolinska University Hospital-Huddinge, Stockholm, Sweden
- *Correspondence: Vesna Jelic,
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Ha J, Park S, Im CH, Kim L. Classification of Gamers Using Multiple Physiological Signals: Distinguishing Features of Internet Gaming Disorder. Front Psychol 2021; 12:714333. [PMID: 34630223 PMCID: PMC8498337 DOI: 10.3389/fpsyg.2021.714333] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 08/26/2021] [Indexed: 11/13/2022] Open
Abstract
The proliferating and excessive use of internet games has caused various comorbid diseases, such as game addiction, which is now a major social problem. Recently, the American Psychiatry Association classified “Internet gaming disorder (IGD)” as an addiction/mental disorder. Although many studies have been conducted on the diagnosis, treatment, and prevention of IGD, screening studies for IGD are still scarce. In this study, we classified gamers using multiple physiological signals to contribute to the treatment and prevention of IGD. Participating gamers were divided into three groups based on Young’s Internet Addiction Test score and average game time as follows: Group A, those who rarely play games; Group B, those who enjoy and play games regularly; and Group C, those classified as having IGD. In our game-related cue-based experiment, we obtained self-reported craving scores and multiple physiological data such as electrooculogram (EOG), photoplethysmogram (PPG), and electroencephalogram (EEG) from the users while they watched neutral (natural scenery) or stimulating (gameplay) videos. By analysis of covariance (ANCOVA), 13 physiological features (vertical saccadic movement from EOG, standard deviation of N-N intervals, and PNN50 from PPG, and many EEG spectral power indicators) were determined to be significant to classify the three groups. The classification was performed using a 2-layers feedforward neural network. The fusion of three physiological signals showed the best result compared to other cases (combination of EOG and PPG or EEG only). The accuracy was 0.90 and F-1 scores were 0.93 (Group A), 0.89 (Group B), and 0.88 (Group C). However, the subjective self-reported scores did not show a significant difference among the three groups by ANCOVA analysis. The results indicate that the fusion of physiological signals can be an effective method to objectively classify gamers.
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Affiliation(s)
- Jihyeon Ha
- Center for Bionics, Korea Institute of Science and Technology, Seoul, South Korea.,Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Sangin Park
- Center for Bionics, Korea Institute of Science and Technology, Seoul, South Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Laehyun Kim
- Center for Bionics, Korea Institute of Science and Technology, Seoul, South Korea.,Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, South Korea
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15
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Al-Nuaimi AH, Blūma M, Al-Juboori SS, Eke CS, Jammeh E, Sun L, Ifeachor E. Robust EEG Based Biomarkers to Detect Alzheimer's Disease. Brain Sci 2021; 11:1026. [PMID: 34439645 PMCID: PMC8394244 DOI: 10.3390/brainsci11081026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 11/16/2022] Open
Abstract
Biomarkers to detect Alzheimer's disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).
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Affiliation(s)
- Ali H. Al-Nuaimi
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
- College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Al Adhamiya, Baghdad 10053, Iraq
| | - Marina Blūma
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy;
| | - Shaymaa S. Al-Juboori
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
- College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Al Adhamiya, Baghdad 10053, Iraq
| | - Chima S. Eke
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Emmanuel Jammeh
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Lingfen Sun
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
| | - Emmanuel Ifeachor
- School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK; (S.S.A.-J.); (C.S.E.); (E.J.); (L.S.); (E.I.)
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16
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Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment. PLoS One 2021; 16:e0244180. [PMID: 33544703 PMCID: PMC7864432 DOI: 10.1371/journal.pone.0244180] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/05/2020] [Indexed: 02/03/2023] Open
Abstract
In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18-40 (n = 129), 40-60 (n = 62) and 60-90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.
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17
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Dabaghian Y. From Topological Analyses to Functional Modeling: The Case of Hippocampus. Front Comput Neurosci 2021; 14:593166. [PMID: 33505262 PMCID: PMC7829363 DOI: 10.3389/fncom.2020.593166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 12/02/2020] [Indexed: 11/13/2022] Open
Abstract
Topological data analyses are widely used for describing and conceptualizing large volumes of neurobiological data, e.g., for quantifying spiking outputs of large neuronal ensembles and thus understanding the functions of the corresponding networks. Below we discuss an approach in which convergent topological analyses produce insights into how information may be processed in mammalian hippocampus—a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking data at different timescales and following the course of spatial learning at different levels of spatiotemporal granularity. This approach allows accounting for contributions from various physiological phenomena into spatial cognition—the neuronal spiking statistics, the effects of spiking synchronization by different brain waves, the roles played by synaptic efficacies and so forth. In particular, it is possible to demonstrate that networks with plastic and transient synaptic architectures can encode stable cognitive maps, revealing the characteristic timescales of memory processing.
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Affiliation(s)
- Yuri Dabaghian
- Department of Neurology, The University of Texas McGovern Medical School, Houston, TX, United States
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18
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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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Jafari Z, Kolb BE, Mohajerani MH. Neural oscillations and brain stimulation in Alzheimer's disease. Prog Neurobiol 2020; 194:101878. [PMID: 32615147 DOI: 10.1016/j.pneurobio.2020.101878] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 12/20/2019] [Accepted: 06/25/2020] [Indexed: 12/30/2022]
Abstract
Aging is associated with alterations in cognitive processing and brain neurophysiology. Whereas the primary symptom of amnestic mild cognitive impairment (aMCI) is memory problems greater than normal for age and education, patients with Alzheimer's disease (AD) show impairments in other cognitive domains in addition to memory dysfunction. Resting-state electroencephalography (rsEEG) studies in physiological aging indicate a global increase in low-frequency oscillations' power and the reduction and slowing of alpha activity. The enhancement of slow and the reduction of fast oscillations, and the disruption of brain functional connectivity, however, are characterized as major rsEEG changes in AD. Recent rodent studies also support human evidence of age- and AD-related changes in resting-state brain oscillations, and the neuroprotective effect of brain stimulation techniques through gamma-band stimulations. Cumulatively, current evidence moves toward optimizing rsEEG features as reliable predictors of people with aMCI at risk for conversion to AD and mapping neural alterations subsequent to brain stimulation therapies. The present paper reviews the latest evidence of changes in rsEEG oscillations in physiological aging, aMCI, and AD, as well as findings of various brain stimulation therapies from both human and non-human studies.
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Affiliation(s)
- Zahra Jafari
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, T1K 3M4, Canada
| | - Bryan E Kolb
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, T1K 3M4, Canada.
| | - Majid H Mohajerani
- Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, T1K 3M4, Canada.
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Sullivan BJ, Ammanuel S, Kipnis PA, Araki Y, Huganir RL, Kadam SD. Low-Dose Perampanel Rescues Cortical Gamma Dysregulation Associated With Parvalbumin Interneuron GluA2 Upregulation in Epileptic Syngap1 +/- Mice. Biol Psychiatry 2020; 87:829-842. [PMID: 32107006 PMCID: PMC7166168 DOI: 10.1016/j.biopsych.2019.12.025] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 12/19/2019] [Accepted: 12/19/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND Loss-of-function SYNGAP1 mutations cause a neurodevelopmental disorder characterized by intellectual disability and epilepsy. SYNGAP1 is a Ras GTPase-activating protein that underlies the formation and experience-dependent regulation of postsynaptic densities. The mechanisms that contribute to this proposed monogenic cause of intellectual disability and epilepsy remain unresolved. METHODS We established the phenotype of the epileptogenesis in a Syngap1+/- mouse model using 24-hour video electroencephalography (vEEG)/electromyography recordings at advancing ages. We administered an acute low dose of perampanel, a Food and Drug Administration-approved AMPA receptor (AMPAR) antagonist, during a follow-on 24-hour vEEG to investigate the role of AMPARs in Syngap1 haploinsufficiency. Immunohistochemistry was performed to determine the region- and location-specific differences in the expression of the GluA2 AMPAR subunit. RESULTS A progressive worsening of the epilepsy with emergence of multiple seizure phenotypes, interictal spike frequency, sleep dysfunction, and hyperactivity was identified in Syngap1+/- mice. Interictal spikes emerged predominantly during non-rapid eye movement sleep in 24-hour vEEG of Syngap1+/- mice. Myoclonic seizures occurred at behavioral-state transitions both in Syngap1+/- mice and during an overnight EEG from a child with SYNGAP1 haploinsufficiency. In Syngap1+/- mice, EEG spectral power analyses identified a significant loss of gamma power modulation during behavioral-state transitions. A significant region-specific increase of GluA2 AMPAR subunit expression in the somas of parvalbumin-positive interneurons was identified. CONCLUSIONS Acute dosing with perampanel significantly rescued behavioral state-dependent cortical gamma homeostasis, identifying a novel mechanism implicating Ca2+-impermeable AMPARs on parvalbumin-positive interneurons underlying circuit dysfunction in SYNGAP1 haploinsufficiency.
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Affiliation(s)
- Brennan J Sullivan
- Neuroscience Laboratory, Hugo Moser Research Institute, Kennedy Krieger Institute, Baltimore, Maryland
| | - Simon Ammanuel
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California
| | - Pavel A Kipnis
- Neuroscience Laboratory, Hugo Moser Research Institute, Kennedy Krieger Institute, Baltimore, Maryland
| | - Yoichi Araki
- Department of Neuroscience, Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Richard L Huganir
- Department of Neuroscience, Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Shilpa D Kadam
- Neuroscience Laboratory, Hugo Moser Research Institute, Kennedy Krieger Institute, Baltimore, Maryland; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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21
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Benwell CSY, Davila-Pérez P, Fried PJ, Jones RN, Travison TG, Santarnecchi E, Pascual-Leone A, Shafi MM. EEG spectral power abnormalities and their relationship with cognitive dysfunction in patients with Alzheimer's disease and type 2 diabetes. Neurobiol Aging 2020; 85:83-95. [PMID: 31727363 PMCID: PMC6942171 DOI: 10.1016/j.neurobiolaging.2019.10.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 09/30/2019] [Accepted: 10/07/2019] [Indexed: 12/13/2022]
Abstract
Rhythmic neural activity has been proposed to play a fundamental role in cognition. Both healthy and pathological aging are characterized by frequency-specific changes in oscillatory activity. However, the cognitive relevance of these changes across the spectrum from normal to pathological aging remains unknown. We examined electroencephalography (EEG) correlates of cognitive function in healthy aging and 2 of the most prominent and debilitating age-related disorders: type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD). Relative to healthy controls (HC), patients with AD were impaired on nearly every cognitive measure, whereas patients with T2DM performed worse mainly on learning and memory tests. A continuum of alterations in resting-state EEG was associated with pathological aging, generally characterized by reduced alpha (α) and beta (β) power (AD < T2DM < HC) and increased delta (δ) and theta (θ) power (AD > T2DM > HC), with some variations across different brain regions. There were also reductions in the frequency and power density of the posterior dominant rhythm in AD. The ratio of (α + β)/(δ + θ) was specifically associated with cognitive function in a domain- and diagnosis-specific manner. The results thus captured both similarities and differences in the pathophysiology of cerebral oscillations in T2DM and AD. Overall, pathological brain aging is marked by a shift in oscillatory power from higher to lower frequencies, which can be captured by a single cognitively relevant measure of the ratio of (α + β) over (δ + θ) power.
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Affiliation(s)
- Christopher S Y Benwell
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Psychology, School of Social Sciences, University of Dundee, Dundee, UK.
| | - Paula Davila-Pérez
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Neuroscience and Motor Control Group (NEUROcom), Institute for Biomedical Research (INIBIC), Universidade da Coruña, A Coruña, Spain
| | - Peter J Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Richard N Jones
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Butler Hospital, Providence, RI, USA
| | - Thomas G Travison
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA; Institut Guttman, Universitat Autonoma de Barcelona, Badalona, Barcelona, Spain; Center for Memory Health, Hebrew Senior Life, Boston, MA, USA
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA; Comprehensive Epilepsy Center, Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
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22
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Crouch B, Yeap JM, Pais B, Riedel G, Platt B. Of mice and motion: Behavioural-EEG phenotyping of Alzheimer’s disease mouse models. J Neurosci Methods 2019; 319:89-98. [DOI: 10.1016/j.jneumeth.2018.06.028] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 06/14/2018] [Accepted: 06/28/2018] [Indexed: 01/22/2023]
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23
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Effects of n-3 polyunsaturated fatty acid supplementation on cognitive functions, electrocortical activity and neurogenesis in a non-human primate, the grey mouse lemur (Microcebus murinus). Behav Brain Res 2018; 347:394-407. [DOI: 10.1016/j.bbr.2018.02.029] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 02/21/2018] [Accepted: 02/21/2018] [Indexed: 12/13/2022]
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24
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Useinović N, Vorkapić M, Leković A, Ademovič A, Šutulović N, Grubač Ž, Rašić-Marković A, Hrnčić D, Stanojlović O. Basic characteristics of epileptiform discharges triggered by lindane in rats. MEDICINSKI PODMLADAK 2018. [DOI: 10.5937/mp69-18552] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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25
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Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis. Med Biol Eng Comput 2017; 56:137-157. [DOI: 10.1007/s11517-017-1734-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 10/04/2017] [Indexed: 12/20/2022]
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26
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Michels L, Muthuraman M, Anwar AR, Kollias S, Leh SE, Riese F, Unschuld PG, Siniatchkin M, Gietl AF, Hock C. Changes of Functional and Directed Resting-State Connectivity Are Associated with Neuronal Oscillations, ApoE Genotype and Amyloid Deposition in Mild Cognitive Impairment. Front Aging Neurosci 2017; 9:304. [PMID: 29081745 PMCID: PMC5646353 DOI: 10.3389/fnagi.2017.00304] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 09/04/2017] [Indexed: 01/03/2023] Open
Abstract
The assessment of effects associated with cognitive impairment using electroencephalography (EEG) power mapping allows the visualization of frequency-band specific local changes in oscillatory activity. In contrast, measures of coherence and dynamic source synchronization allow for the study of functional and effective connectivity, respectively. Yet, these measures have rarely been assessed in parallel in the context of mild cognitive impairment (MCI) and furthermore it has not been examined if they are related to risk factors of Alzheimer’s disease (AD) such as amyloid deposition and apolipoprotein ε4 (ApoE) allele occurrence. Here, we investigated functional and directed connectivities with Renormalized Partial Directed Coherence (RPDC) in 17 healthy controls (HC) and 17 participants with MCI. Participants underwent ApoE-genotyping and Pittsburgh compound B positron emission tomography (PiB-PET) to assess amyloid deposition. We observed lower spectral source power in MCI in the alpha and beta bands. Coherence was stronger in HC than MCI across different neuronal sources in the delta, theta, alpha, beta and gamma bands. The directed coherence analysis indicated lower information flow between fronto-temporal (including the hippocampus) sources and unidirectional connectivity in MCI. In MCI, alpha and beta RPDC showed an inverse correlation to age and gender; global amyloid deposition was inversely correlated to alpha coherence, RPDC and beta and gamma coherence. Furthermore, the ApoE status was negatively correlated to alpha coherence and RPDC, beta RPDC and gamma coherence. A classification analysis of cognitive state revealed the highest accuracy using EEG power, coherence and RPDC as input. For this small but statistically robust (Bayesian power analyses) sample, our results suggest that resting EEG related functional and directed connectivities are sensitive to the cognitive state and are linked to ApoE and amyloid burden.
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Affiliation(s)
- Lars Michels
- Clinic of Neuroradiology, University Hospital of ZurichZurich, Switzerland.,MR-Center, University Children's Hospital ZurichZurich, Switzerland
| | - Muthuraman Muthuraman
- Clinic for Neurology, University of KielKiel, Germany.,Clinic for Neurology, University of MainzMainz, Germany
| | - Abdul R Anwar
- Clinic for Neurology, University of KielKiel, Germany
| | - Spyros Kollias
- Clinic of Neuroradiology, University Hospital of ZurichZurich, Switzerland
| | - Sandra E Leh
- Division of Psychiatry Research and Psychogeriatric Medicine, University of ZurichZurich, Switzerland
| | - Florian Riese
- Division of Psychiatry Research and Psychogeriatric Medicine, University of ZurichZurich, Switzerland
| | - Paul G Unschuld
- Division of Psychiatry Research and Psychogeriatric Medicine, University of ZurichZurich, Switzerland
| | - Michael Siniatchkin
- Institute of Medical Psychology and Medical Sociology, Christian-Albrechts-University of KielKiel, Germany
| | - Anton F Gietl
- Division of Psychiatry Research and Psychogeriatric Medicine, University of ZurichZurich, Switzerland
| | - Christoph Hock
- Division of Psychiatry Research and Psychogeriatric Medicine, University of ZurichZurich, Switzerland
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27
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Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks. SENSORS 2017; 17:s17061326. [PMID: 28594352 PMCID: PMC5492863 DOI: 10.3390/s17061326] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 04/25/2017] [Accepted: 05/04/2017] [Indexed: 01/31/2023]
Abstract
Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation XCorr and peak signal to noise ratio (PSNR) (ANOVA, p ˂ 0.05). The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, p ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing.
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28
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Cassani R, Falk TH, Fraga FJ, Cecchi M, Moore DK, Anghinah R. Towards automated electroencephalography-based Alzheimer’s disease diagnosis using portable low-density devices. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.12.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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29
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Lees T, Lal S. Stress and its Impact on the Neurocognitive Performance of Australian Nurses. Stress Health 2017; 33:45-54. [PMID: 26916210 DOI: 10.1002/smi.2672] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2015] [Revised: 01/16/2016] [Accepted: 01/18/2016] [Indexed: 11/10/2022]
Abstract
Nurses function inside a particularly stressful occupation that requires the provision of continuous care to individuals who are often in great need. Stress has been shown to impair performance and specifically shown to impair nursing quality. However, we do not yet know how stress influences the cognitive performance of nurses, and hence, the present study investigated the associations between stress and cognitive performance in nurses using electroencephalography and administered cognitive assessments. Thirty-six nurses (34 women) of mean age 37.77 ± 11.40 years were recruited. Stress was examined using the Lifestyle Appraisal Questionnaire. Broad spectrum electroencephalogram activity at positions Fp1, Fp2, C3 and C4 was recorded for a 5-min baseline and active phase to physiologically assess cognitive performance. Additionally, the Mini-Mental State Exam and Cognistat were also used to measure cognitive performance. Assessed cognitive performance was not associated to stress, however, lifestyle factors, as well as a number of the examined cognitive electroencephalographic variables including changes in theta, alpha activity and gamma reactivity were. Definitively determining how stress affects the cognitive performance of nurses requires additional research; the present study forms a foundation from which future research can further expand the examination of stress exposure in nurses. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Ty Lees
- Neuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia
| | - Sara Lal
- Neuroscience Research Unit, School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia
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30
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Fu H, Rodriguez GA, Herman M, Emrani S, Nahmani E, Barrett G, Figueroa HY, Goldberg E, Hussaini SA, Duff KE. Tau Pathology Induces Excitatory Neuron Loss, Grid Cell Dysfunction, and Spatial Memory Deficits Reminiscent of Early Alzheimer's Disease. Neuron 2017; 93:533-541.e5. [PMID: 28111080 DOI: 10.1016/j.neuron.2016.12.023] [Citation(s) in RCA: 163] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 10/20/2016] [Accepted: 12/15/2016] [Indexed: 11/18/2022]
Abstract
The earliest stages of Alzheimer's disease (AD) are characterized by the formation of mature tangles in the entorhinal cortex and disorientation and confusion when navigating familiar places. The medial entorhinal cortex (MEC) contains specialized neurons called grid cells that form part of the spatial navigation system. Here we show in a transgenic mouse model expressing mutant human tau predominantly in the EC that the formation of mature tangles in old mice was associated with excitatory cell loss and deficits in grid cell function, including destabilized grid fields and reduced firing rates, as well as altered network activity. Overt tau pathology in the aged mice was accompanied by spatial memory deficits. Therefore, tau pathology initiated in the entorhinal cortex could lead to deficits in grid cell firing and underlie the deterioration of spatial cognition seen in human AD.
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Affiliation(s)
- Hongjun Fu
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY 10032, USA
| | - Gustavo A Rodriguez
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY 10032, USA
| | - Mathieu Herman
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY 10032, USA
| | - Sheina Emrani
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY 10032, USA
| | - Eden Nahmani
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY 10032, USA
| | - Geoffrey Barrett
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY 10032, USA
| | - Helen Y Figueroa
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY 10032, USA
| | - Eliana Goldberg
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY 10032, USA
| | - S Abid Hussaini
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY 10032, USA; Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA.
| | - Karen E Duff
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Medical Center, New York, NY 10032, USA; Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA; Department of Integrative Neuroscience, New York State Psychiatric Institute, New York, NY 10032, USA.
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31
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Univariate and Multivariate Generalized Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease. ENTROPY 2017. [DOI: 10.3390/e19010031] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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32
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Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer's disease. Cogn Neurodyn 2016; 11:217-231. [PMID: 28559952 DOI: 10.1007/s11571-016-9418-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 10/13/2016] [Accepted: 11/09/2016] [Indexed: 10/20/2022] Open
Abstract
The complexity change of brain activity in Alzheimer's disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.
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33
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Cao B, Wang J, Mu L, Poon DCH, Li Y. Impairment of decision making associated with disruption of phase-locking in the anterior cingulate cortex in viscerally hypersensitive rats. Exp Neurol 2016; 286:21-31. [PMID: 27664369 DOI: 10.1016/j.expneurol.2016.09.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Revised: 09/14/2016] [Accepted: 09/19/2016] [Indexed: 12/18/2022]
Abstract
Visceral hypersensitivity (VH) is a key factor of irritable bowel syndrome (IBS). Previous studies have identified an enhanced response of anterior cingulate cortex (ACC) to colorectal distension in VH rats, which can be observed up to 7weeks following colonic anaphylaxis, independent of colonic inflammation. The induction of VH produces a change in the ability to induce subsequent synaptic plasticity at the ACC circuitry. In clinical practice, a positive link between IBS and cognitive impairments has been noted for years, but no animal model has been reported. Decision-making is a valuable model for monitoring higher-order cognitive functions in animals, which depends on the integrated function of several sub-regions of the ACC and amygdala. Using rat gambling task (RGT) in the present study, we observed an impairment of decision-making behavior in VH rats. Electrophysiological study showed a reduction of long-term potentiation in the basolateral amygdala (BLA)-ACC synapses in VH rats. Multiple-electrode array recordings of local field potential (LFP) in both BLA and ACC were also performed in freely behaving rats. Spike-field coherence (SFC) analysis revealed chronic visceral pain led to disruption of ACC spike timing and BLA local theta oscillation. Finally, cross-correlation analysis revealed that VH was associated with suppressed synchronization of theta oscillation between the BLA and ACC, indicating reduced neuronal communications between these two regions under the VH state. The present results demonstrate that functional disturbances in BLA-ACC neural circuitry may be relevant causes for the deficits in decision-making in chronic pain state.
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Affiliation(s)
- Bing Cao
- Department of Biomedical Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Kowloon, Hong Kong
| | - Jun Wang
- Department of Biomedical Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Kowloon, Hong Kong
| | - Li Mu
- Department of Biomedical Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Kowloon, Hong Kong
| | - David Chun-Hei Poon
- Department of Biomedical Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Kowloon, Hong Kong
| | - Ying Li
- Department of Biomedical Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong; Centre for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Kowloon, Hong Kong; School of Veterinary Medicine, City University of Hong Kong, Kowloon, Hong Kong.
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34
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Basso E, Arai M, Dabaghian Y. Gamma Synchronization Influences Map Formation Time in a Topological Model of Spatial Learning. PLoS Comput Biol 2016; 12:e1005114. [PMID: 27636199 PMCID: PMC5026372 DOI: 10.1371/journal.pcbi.1005114] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2016] [Accepted: 08/20/2016] [Indexed: 12/30/2022] Open
Abstract
The mammalian hippocampus plays a crucial role in producing a cognitive map of space-an internalized representation of the animal's environment. We have previously shown that it is possible to model this map formation using a topological framework, in which information about the environment is transmitted through the temporal organization of neuronal spiking activity, particularly those occasions in which the firing of different place cells overlaps. In this paper, we discuss how gamma rhythm, one of the main components of the extracellular electrical field potential affects the efficiency of place cell map formation. Using methods of algebraic topology and the maximal entropy principle, we demonstrate that gamma modulation synchronizes the spiking of dynamical cell assemblies, which enables learning a spatial map at faster timescales.
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Affiliation(s)
- Edward Basso
- Department of Physics, Rice University, Houston, Texas, United States of America
| | - Mamiko Arai
- Department of Mathematics, Tokyo Women’s Christian University, 2-6-1 Zempukuji, Suginami-ku, Tokyo, Japan
| | - Yuri Dabaghian
- Jan and Dan Duncan Neurological Research Institute, Baylor College of Medicine, Houston, Texas, United States of America
- Department of Computational and Applied Mathematics, Rice University, Houston, Texas, United States of America
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35
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Moretti DV. Electroencephalography-driven approach to prodromal Alzheimer's disease diagnosis: from biomarker integration to network-level comprehension. Clin Interv Aging 2016; 11:897-912. [PMID: 27462146 PMCID: PMC4939982 DOI: 10.2147/cia.s103313] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Decay of the temporoparietal cortex is associated with prodromal Alzheimer's disease (AD). Additionally, shrinkage of the temporoparietal cerebral area has been connected with an increase in α3/α2 electroencephalogram (EEG) power ratio in prodromal AD. Furthermore, a lower regional blood perfusion has been exhibited in patients with a higher α3/α2 proportion when contrasted with low α3/α2 proportion. Furthermore, a lower regional blood perfusion and reduced hippocampal volume has been exhibited in patients with higher α3/α2 when contrasted with lower α3/α2 EEG power ratio. Neuropsychological evaluation, EEG recording, and magnetic resonance imaging were conducted in 74 patients with mild cognitive impairment (MCI). Estimation of cortical thickness and α3/α2 frequency power ratio was conducted for each patient. A subgroup of 27 patients also underwent single-photon emission computed tomography evaluation. In view of α3/α2 power ratio, the patients were divided into three groups. The connections among cortical decay, cerebral perfusion, and memory loss were evaluated by Pearson's r coefficient. Results demonstrated that higher α3/α2 frequency power ratio group was identified with brain shrinkage and cutdown perfusion inside the temporoparietal projections. In addition, decay and cutdown perfusion rate were connected with memory shortfalls in patients with MCI. MCI subgroup with higher α3/α2 EEG power ratio are at a greater risk to develop AD dementia.
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Affiliation(s)
- Davide Vito Moretti
- Rehabilitation in Alzheimer’s Disease Operative Unit, IRCCS San Giovanni di Dio, Fatebenefratelli, Brescia, Italy
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Lees T, Khushaba R, Lal S. Electroencephalogram associations to cognitive performance in clinically active nurses. Physiol Meas 2016; 37:968-80. [PMID: 27244262 DOI: 10.1088/0967-3334/37/7/968] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Cognitive impairment is traditionally identified via cognitive screening tools that have limited ability in detecting early or transitional stages of impairment. The dynamic nature of physiological variables such as the electroencephalogram (EEG) may provide alternate means for detecting these transitions. However, previous research examining EEG and cognitive performance is largely confined to samples with diagnosed cognitive impairments, and research examining non-impaired, and occupation specific samples, is limited. The present study aimed to investigate the associations between frontal pole and central EEG and cognitive performance in a sample of male and female nurses, and to determine the significance of these associations. Fifty seven nurses participated in the study, in which two lead bipolar EEG was recorded at positions Fp1 (frontal polar), Fp2, C3 (central) and C4 during a baseline and an active phase involving the common neuropsychological Stroop test. Participants' cognitive performance was assessed using the mini-mental state exam (MMSE) and Cognistat screening tools. Significant correlations between EEG beta activity and the outcome of MMSE and Cognistat were revealed, where an increased beta activity was associated to an increased global cognitive performance. Additionally, domain specific cognitive performance was also significantly associated to various EEG variables. The study identified potential EEG biomarkers for global and domain specific cognitive performance, and provides initial groundwork for the development of future EEG based biomarkers for detection of cognitive pathologies.
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Affiliation(s)
- Ty Lees
- Neuroscience Research Unit, School of Life Sciences, University of Technology, Sydney PO Box 123, Broadway NSW 2007, Australia
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Liu X, Zhang C, Ji Z, Ma Y, Shang X, Zhang Q, Zheng W, Li X, Gao J, Wang R, Wang J, Yu H. Multiple characteristics analysis of Alzheimer's electroencephalogram by power spectral density and Lempel-Ziv complexity. Cogn Neurodyn 2016; 10:121-33. [PMID: 27066150 PMCID: PMC4805689 DOI: 10.1007/s11571-015-9367-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Revised: 10/26/2015] [Accepted: 11/05/2015] [Indexed: 10/22/2022] Open
Abstract
To investigate the electroencephalograph (EEG) background activity in patients with Alzheimer's disease (AD), power spectrum density (PSD) and Lempel-Ziv (LZ) complexity analysis are proposed to extract multiple effective features of EEG signals from AD patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared with the control group, the relative PSD of AD group is significantly higher in the theta frequency band while lower in the alpha frequency bands. In order to explore the nonlinear information, Lempel-Ziv complexity (LZC) and multi-scale LZC is further applied to all electrodes for the four frequency bands. Analysis results demonstrate that the group difference is significant in the alpha frequency band by LZC and multi-scale LZC analysis. However, the group difference of multi-scale LZC is much more remarkable, manifesting as more channels undergo notable changes, particularly in electrodes O1 and O2 in the occipital area. Moreover, the multi-scale LZC value provided a better classification between the two groups with an accuracy of 85.7 %. In addition, we combine both features of the relative PSD and multi-scale LZC to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature, reaching 91.4 %. The obtained results show that analysis of PSD and multi-scale LZC can be taken as a potential comprehensive measure to distinguish AD patients from the normal controls, which may benefit our understanding of the disease.
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Affiliation(s)
- Xiaokun Liu
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Chunlai Zhang
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Zheng Ji
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Yi Ma
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Xiaoming Shang
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Qi Zhang
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Wencheng Zheng
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Xia Li
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Jun Gao
- />Department of Cardiology, Tangshan Gongren Hospital, Hebei Medical University, Tangshan, 063000 Hebei People’s Republic of China
| | - Ruofan Wang
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
| | - Jiang Wang
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
| | - Haitao Yu
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
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Reichert JL, Kober SE, Witte M, Neuper C, Wood G. Age-related effects on verbal and visuospatial memory are mediated by theta and alpha II rhythms. Int J Psychophysiol 2016; 99:67-78. [DOI: 10.1016/j.ijpsycho.2015.11.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Revised: 10/16/2015] [Accepted: 11/11/2015] [Indexed: 11/30/2022]
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Moretti DV. Association of EEG, MRI, and regional blood flow biomarkers is predictive of prodromal Alzheimer's disease. Neuropsychiatr Dis Treat 2015; 11:2779-91. [PMID: 26604762 PMCID: PMC4629965 DOI: 10.2147/ndt.s93253] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Thinning in the temporoparietal cortex, hippocampal atrophy, and a lower regional blood perfusion is connected with prodromal stage of Alzheimer's disease (AD). Of note, an increase of electroencephalography (EEG) upper/low alpha frequency power ratio has also been associated with these major landmarks of prodromal AD. METHODS Clinical and neuropsychological assessment, EEG recording, and high-resolution three-dimensional magnetic resonance imaging were done in 74 grown up subjects with mild cognitive impairment. This information was gathered and has been assessed 3 years postliminary. EEG recording and perfusion single-photon emission computed tomography assessment was done in 27 subjects. Alpha3/alpha2 frequency power ratio, including cortical thickness, was figured for every subject. Contrasts in cortical thickness among the groups were assessed. Pearson's r relationship coefficient was utilized to evaluate the quality of the relationship between cortical thinning, brain perfusion, and EEG markers. RESULTS The higher alpha3/alpha2 frequency power ratio group corresponded with more prominent cortical decay and a lower perfusional rate in the temporoparietal cortex. In a subsequent meetup after 3 years, these patients had AD. CONCLUSION High EEG upper/low alpha power ratio was connected with cortical diminishing and lower perfusion in the temporoparietal brain area. The increase in EEG upper/low alpha frequency power ratio could be helpful in recognizing people in danger of conversion to AD dementia and this may be quality information in connection with clinical assessment.
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Cao Y, Cai L, Wang J, Wang R, Yu H, Cao Y, Liu J. Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy. CHAOS (WOODBURY, N.Y.) 2015; 25:083116. [PMID: 26328567 DOI: 10.1063/1.4929148] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.
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Affiliation(s)
- Yuzhen Cao
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
| | - Lihui Cai
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Ruofan Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Haitao Yu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Yibin Cao
- Tangshan Gongren Hospital, Tangshan Medical College of Hebei Medical University, Tangshan 063000, Hebei, People's Republic of China
| | - Jing Liu
- Tangshan Gongren Hospital, Tangshan Medical College of Hebei Medical University, Tangshan 063000, Hebei, People's Republic of China
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Moretti DV. Mild Cognitive Impairment: Structural, Metabolical, and Neurophysiological Evidence of a Novel EEG Biomarker. Front Neurol 2015. [PMID: 26217299 PMCID: PMC4491619 DOI: 10.3389/fneur.2015.00152] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Recent studies demonstrate that the alpha3/alpha2 power ratio correlates with cortical atrophy, regional hypoperfusion, and memory impairment in subjects with mild cognitive impairment (MCI). METHODS Evidences were reviewed in subjects with MCI, who underwent EEG recording, magnetic resonance imaging (MRI) scans, and memory evaluation. Alpha3/alpha2 power ratio (alpha2 8.9-10.9 Hz range; alpha3 10.9-12.9 Hz range), cortical thickness, linear EEG coherence, and memory impairment have been evaluated in a large group of 74 patients. A subset of 27 subjects within the same group also underwent single photon emission computed tomography (SPECT) evaluation. RESULTS In MCI subjects with higher EEG upper/low alpha power ratio, a greater temporo-parietal and hippocampal atrophy was found as well as a decrease in regional blood perfusion and memory impairment. In this group, an increase of theta oscillations is associated with a greater interhemispheric coupling between temporal areas. CONCLUSION The increase of alpha3/alpha2 power ratio is a promising novel biomarker in identifying MCI subjects at risk for Alzheimer's disease.
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Moretti DV. Electroencephalography reveals lower regional blood perfusion and atrophy of the temporoparietal network associated with memory deficits and hippocampal volume reduction in mild cognitive impairment due to Alzheimer's disease. Neuropsychiatr Dis Treat 2015; 11:461-70. [PMID: 25750526 PMCID: PMC4348123 DOI: 10.2147/ndt.s78830] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND An increased electroencephalographic (EEG) upper/lower alpha power ratio has been associated with less regional blood perfusion, atrophy of the temporoparietal region of the brain, and reduction of hippocampal volume in subjects affected by mild cognitive impairment due to Alzheimer's disease as compared with subjects who do not develop the disease. Moreover, EEG theta frequency activity is quite different in these groups. This study investigated the correlation between biomarkers and memory performance. METHODS EEG α3/α2 power ratio and cortical thickness were computed in 74 adult subjects with prodromal Alzheimer's disease. Twenty of these subjects also underwent assessment of blood perfusion by single-photon emission computed tomography (SPECT). Pearson's r was used to assess the correlation between cortical thinning, brain perfusion, and memory impairment. RESULTS In the higher α3/α2 frequency power ratio group, greater cortical atrophy and lower regional perfusion in the temporoparietal cortex was correlated with an increase in EEG theta frequency. Memory impairment was more pronounced in the magnetic resonance imaging group and SPECT groups. CONCLUSION A high EEG upper/low alpha power ratio was associated with cortical thinning and less perfusion in the temporoparietal area. Moreover, atrophy and less regional perfusion were significantly correlated with memory impairment in subjects with prodromal Alzheimer's disease. The EEG upper/lower alpha frequency power ratio could be useful for identifying individuals at risk for progression to Alzheimer's dementia and may be of value in the clinical context.
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Affiliation(s)
- Davide Vito Moretti
- National Institute for the research and cure of Alzheimer’s disease, S. John of God, Fatebenefratelli, Brescia, Italy
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Moretti DV. Understanding early dementia: EEG, MRI, SPECT and memory evaluation. Transl Neurosci 2015; 6:32-46. [PMID: 28123789 PMCID: PMC4936613 DOI: 10.1515/tnsci-2015-0005] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Accepted: 12/01/2014] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND An increase in the EEG upper/low α power ratio has been associated with mild cognitive impairment (MCI) due to Alzheimer's disease (AD) and to the atrophy of temporoparietal brain areas. Subjects with a higher α3/α2 frequency power ratio showed lower brain perfusion than in the low α3/α2 group. The two groups show significantly different hippocampal volumes and correlation with θ frequency activity. METHODS Seventy-four adult subjects with MCI underwent clinical and neuropsychological evaluation, electroencephalogram (EEG) recording, and high resolution 3D magnetic resonance imaging (MRI). Twenty-seven of them underwent EEG recording and perfusion single-photon emission computed tomography (SPECT) evaluation. The α3/α2 power ratio and cortical thickness were computed for each subject. The difference in cortical thickness between the groups was estimated. RESULTS In the higher upper/low α group, memory impairment was more pronounced in both the MRI group and the SPECT MCI groups. An increase in the production of θ oscillations was associated with greater interhemisperic coupling between temporal areas. It also correlated with greater cortical atrophy and lower perfusional rate in the temporoparietal cortex. CONCLUSION High EEG upper/low α power ratio was associated with cortical thinning and lower perfusion in temporoparietal areas. Moreover, both atrophy and lower perfusion rate significantly correlated with memory impairment in MCI subjects. Therefore, the increase in the EEG upper/low α frequency power ratio could be useful in identifying individuals at risk for progression to AD dementia in a clinical context.
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Affiliation(s)
- Davide Vito Moretti
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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Wang R, Wang J, Li S, Yu H, Deng B, Wei X. Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum. CHAOS (WOODBURY, N.Y.) 2015; 25:013110. [PMID: 25637921 DOI: 10.1063/1.4906038] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of features obtained by discriminant analysis may improve the classification accuracy. These results demonstrate the great promise for scape EEG spectral and bispectral features as a potential effective method for detection of AD, which may facilitate our understanding of the pathological mechanism of the disease.
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Affiliation(s)
- Ruofan Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Jiang Wang
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Shunan Li
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Haitao Yu
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Bin Deng
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
| | - Xile Wei
- School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
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Power spectral density and coherence analysis of Alzheimer's EEG. Cogn Neurodyn 2014; 9:291-304. [PMID: 25972978 DOI: 10.1007/s11571-014-9325-x] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Revised: 12/03/2014] [Accepted: 12/10/2014] [Indexed: 10/24/2022] Open
Abstract
In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.
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Yener GG, Emek-Savaş DD, Güntekin B, Başar E. The visual cognitive network, but not the visual sensory network, is affected in amnestic mild cognitive impairment: a study of brain oscillatory responses. Brain Res 2014; 1585:141-9. [PMID: 25152459 DOI: 10.1016/j.brainres.2014.08.038] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Revised: 08/13/2014] [Accepted: 08/14/2014] [Indexed: 11/17/2022]
Abstract
Mild Cognitive Impairment (MCI) is considered in many as prodromal stage of Alzheimer's disease (AD). Event-related oscillations (ERO) reflect cognitive responses of brain whereas sensory-evoked oscillations (SEO) inform about sensory responses. For this study, we compared visual SEO and ERO responses in MCI to explore brain dynamics (BACKGROUND). Forty-three patients with MCI (mean age=74.0 year) and 41 age- and education-matched healthy-elderly controls (HC) (mean age=71.1 year) participated in the study. The maximum peak-to-peak amplitudes for each subject's averaged delta response (0.5-3.0 Hz) were measured from two conditions (simple visual stimulation and classical visual oddball paradigm target stimulation) (METHOD). Overall, amplitudes of target ERO responses were higher than SEO amplitudes. The preferential location for maximum amplitude values was frontal lobe for ERO and occipital lobe for SEO. The ANOVA for delta responses showed significant results for the group Xparadigm. Post-hoc tests indicated that (1) the difference between groups were significant for target delta responses, but not for SEO, (2) ERO elicited higher responses for HC than MCI patients, and (3) females had higher target ERO than males and this difference was pronounced in the control group (RESULTS). Overall, cognitive responses display almost double the amplitudes of sensory responses over frontal regions. The topography of oscillatory responses differs depending on stimuli: visualsensory responses are highest over occipitals and -cognitive responses over frontal regions. A group effect is observed in MCI indicating that visual sensory and cognitive circuits behave differently indicating preserved visual sensory responses, but decreased cognitive responses (CONCLUSION).
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Affiliation(s)
- Görsev G Yener
- Brain Dynamics Multidisciplinary Research Center, Dokuz Eylül University, Izmir 35340, Turkey; Department of Neurosciences, Dokuz Eylül University, Izmir 35340, Turkey; Department of Neurology, Dokuz Eylül University Medical School, Balçova, Izmir 35340, Turkey; Brain Dynamics, Cognition and Complex Systems Research Center, Istanbul Kultur University, Istanbul 34156, Turkey.
| | - Derya Durusu Emek-Savaş
- Department of Neurosciences, Dokuz Eylül University, Izmir 35340, Turkey; Brain Dynamics, Cognition and Complex Systems Research Center, Istanbul Kultur University, Istanbul 34156, Turkey
| | - Bahar Güntekin
- Brain Dynamics, Cognition and Complex Systems Research Center, Istanbul Kultur University, Istanbul 34156, Turkey
| | - Erol Başar
- Brain Dynamics, Cognition and Complex Systems Research Center, Istanbul Kultur University, Istanbul 34156, Turkey
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Tsolaki A, Kazis D, Kompatsiaris I, Kosmidou V, Tsolaki M. Electroencephalogram and Alzheimer's disease: clinical and research approaches. Int J Alzheimers Dis 2014; 2014:349249. [PMID: 24868482 PMCID: PMC4020452 DOI: 10.1155/2014/349249] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Accepted: 03/16/2014] [Indexed: 01/08/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled "Cognitive Signal Processing Lab," which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.
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Affiliation(s)
- Anthoula Tsolaki
- Medical Physics Laboratory, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Dimitrios Kazis
- 3rd Department of Neurology, Aristotle University of Thessaloniki, Exochi, 57010 Thessaloniki, Greece
| | - Ioannis Kompatsiaris
- Centre of Research and Technology, Information Technologies Institute, 6th Klm Charilaou-Thermi Road, P.O. Box 60361, Thermi, 57001 Thessaloniki, Greece
| | - Vasiliki Kosmidou
- Centre of Research and Technology, Information Technologies Institute, 6th Klm Charilaou-Thermi Road, P.O. Box 60361, Thermi, 57001 Thessaloniki, Greece
| | - Magda Tsolaki
- 3rd Department of Neurology, Aristotle University of Thessaloniki, Exochi, 57010 Thessaloniki, Greece
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Cassani R, Falk TH, Fraga FJ, Kanda PAM, Anghinah R. The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis. Front Aging Neurosci 2014; 6:55. [PMID: 24723886 PMCID: PMC3971195 DOI: 10.3389/fnagi.2014.00055] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2013] [Accepted: 03/06/2014] [Indexed: 11/13/2022] Open
Abstract
Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.
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Affiliation(s)
- Raymundo Cassani
- Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, University of Quebec Montreal, QC, Canada
| | - Tiago H Falk
- Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, University of Quebec Montreal, QC, Canada
| | - Francisco J Fraga
- Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, University of Quebec Montreal, QC, Canada ; Engineering, Modelling and Applied Social Sciences Center, Universidade Federal do ABC São Paulo, Brazil
| | - Paulo A M Kanda
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, Universidade de São Paulo São Paulo, Brazil
| | - Renato Anghinah
- Reference Center of Behavioural Disturbances and Dementia, School of Medicine, Universidade de São Paulo São Paulo, Brazil
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Rodriguez R, Lopera F, Alvarez A, Fernandez Y, Galan L, Quiroz Y, Bobes MA. Spectral Analysis of EEG in Familial Alzheimer's Disease with E280A Presenilin-1 Mutation Gene. Int J Alzheimers Dis 2014; 2014:180741. [PMID: 24551475 PMCID: PMC3914466 DOI: 10.1155/2014/180741] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Accepted: 10/13/2013] [Indexed: 11/17/2022] Open
Abstract
To evaluate the hypothesis that quantitative EEG (qEEG) analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD) preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects), an asymptomatic carrier (ACr) group (21 subjects), with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D (2)) was calculated between groups. To evaluate the diagnostic efficiency of this statistic D (2), the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D (2) using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89) and between AD probable and ACr groups (area ROC = 0.91). This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function.
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Affiliation(s)
- Rene Rodriguez
- Clinical Neurophysiology Department, Cuban Neuroscience Center, Havana, CP 10400, Cuba
| | | | - Alfredo Alvarez
- Clinical Neurophysiology Department, Cuban Neuroscience Center, Havana, CP 10400, Cuba
| | - Yuriem Fernandez
- Cognitive Department, Cuban Neuroscience Center, Havana, CP 10400, Cuba
| | - Lidice Galan
- Cognitive Department, Cuban Neuroscience Center, Havana, CP 10400, Cuba
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Garcés P, Vicente R, Wibral M, Pineda-Pardo JÁ, López ME, Aurtenetxe S, Marcos A, de Andrés ME, Yus M, Sancho M, Maestú F, Fernández A. Brain-wide slowing of spontaneous alpha rhythms in mild cognitive impairment. Front Aging Neurosci 2013; 5:100. [PMID: 24409145 PMCID: PMC3873508 DOI: 10.3389/fnagi.2013.00100] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2013] [Accepted: 12/15/2013] [Indexed: 11/13/2022] Open
Abstract
The neurophysiological changes associated with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) include an increase in low frequency activity, as measured with electroencephalography or magnetoencephalography (MEG). A relevant property of spectral measures is the alpha peak, which corresponds to the dominant alpha rhythm. Here we studied the spatial distribution of MEG resting state alpha peak frequency and amplitude values in a sample of 27 MCI patients and 24 age-matched healthy controls. Power spectra were reconstructed in source space with linearly constrained minimum variance beamformer. Then, 88 Regions of Interest (ROIs) were defined and an alpha peak per ROI and subject was identified. Statistical analyses were performed at every ROI, accounting for age, sex and educational level. Peak frequency was significantly decreased (p < 0.05) in MCIs in many posterior ROIs. The average peak frequency over all ROIs was 9.68 ± 0.71 Hz for controls and 9.05 ± 0.90 Hz for MCIs and the average normalized amplitude was (2.57 ± 0.59)·10−2 for controls and (2.70 ± 0.49)·10−2 for MCIs. Age and gender were also found to play a role in the alpha peak, since its frequency was higher in females than in males in posterior ROIs and correlated negatively with age in frontal ROIs. Furthermore, we examined the dependence of peak parameters with hippocampal volume, which is a commonly used marker of early structural AD-related damage. Peak frequency was positively correlated with hippocampal volume in many posterior ROIs. Overall, these findings indicate a pathological alpha slowing in MCI.
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Affiliation(s)
- Pilar Garcés
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain ; Department of Applied Physics III, Faculty of Physics, Complutense University of Madrid Madrid, Spain
| | - Raul Vicente
- MEG Unit, Brain Imaging Center, Goethe University Frankfurt, Germany ; Max-Planck Institute for Brain Research Frankfurt, Germany ; Institute of Computer Science, Faculty of Mathematics and Computer Science, University of Tartu Tartu, Estonia
| | - Michael Wibral
- MEG Unit, Brain Imaging Center, Goethe University Frankfurt, Germany
| | - Jose Ángel Pineda-Pardo
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain
| | - Maria Eugenia López
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Madrid, Spain
| | - Sara Aurtenetxe
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Madrid, Spain
| | - Alberto Marcos
- Neurology Department, San Carlos Clinical Hospital Madrid, Spain
| | | | - Miguel Yus
- Radiology Department, San Carlos Clinical Hospital Madrid, Spain
| | - Miguel Sancho
- Department of Applied Physics III, Faculty of Physics, Complutense University of Madrid Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain ; Department of Basic Psychology II, Complutense University of Madrid Madrid, Spain
| | - Alberto Fernández
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology Madrid, Spain ; Department of Psychiatry and Medical Psychology, Faculty of Medicine, Complutense University of Madrid Madrid, Spain
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