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Voits T, DeLuca V, Hao J, Elin K, Abutalebi J, Duñabeitia JA, Berglund G, Gabrielsen A, Rook J, Thomsen H, Waagen P, Rothman J. Degree of multilingual engagement modulates resting state oscillatory activity across the lifespan. Neurobiol Aging 2024; 140:70-80. [PMID: 38735176 DOI: 10.1016/j.neurobiolaging.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/18/2024] [Accepted: 04/19/2024] [Indexed: 05/14/2024]
Abstract
Multilingualism has been demonstrated to lead to a more favorable trajectory of neurocognitive aging, yet our understanding of its effect on neurocognition across the lifespan remains limited. We collected resting state EEG recordings from a sample of multilingual individuals across a wide age range. Additionally, we obtained data on participant multilingual language use patterns alongside other known lifestyle enrichment factors. Language experience was operationalized via a modified multilingual diversity (MLD) score. Generalized additive modeling was employed to examine the effects and interactions of age and MLD on resting state oscillatory power and coherence. The data suggest an independent modulatory effect of individualized multilingual engagement on age-related differences in whole brain resting state power across alpha and theta bands, and an interaction between age and MLD on resting state coherence in alpha, theta, and low beta. These results provide evidence of multilingual engagement as an independent correlational factor related to differences in resting state EEG power, consistent with the claim that multilingualism can serve as a protective factor in neurocognitive aging.
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Affiliation(s)
- Toms Voits
- Department of Psychology, University of Gothenburg, Gothenburg, Sweden; UiT the Arctic University of Norway, Tromsø, Norway.
| | | | - Jiuzhou Hao
- UiT the Arctic University of Norway, Tromsø, Norway
| | - Kirill Elin
- UiT the Arctic University of Norway, Tromsø, Norway
| | - Jubin Abutalebi
- UiT the Arctic University of Norway, Tromsø, Norway; Centre for Neurolinguistics and Psycholinguistics (CNPL), Vita-Salute San Raffaele University, Milan, Italy
| | - Jon Andoni Duñabeitia
- UiT the Arctic University of Norway, Tromsø, Norway; Universidad Nebrija Research Center in Cognition (CINC), Nebrija University, Madrid, Spain
| | | | | | - Janine Rook
- Department of Applied Linguistics, University of Groningen, Groningen, the Netherlands
| | - Hilde Thomsen
- UiT the Arctic University of Norway, Tromsø, Norway; Université Côte d'Azur, Nice, France
| | | | - Jason Rothman
- UiT the Arctic University of Norway, Tromsø, Norway; Universidad Nebrija Research Center in Cognition (CINC), Nebrija University, Madrid, Spain
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Castellote-Caballero Y, Del Carmen Carcelén Fraile M, Aibar-Almazán A, Afanador-Restrepo DF, González-Martín AM. Effect of combined physical-cognitive training on the functional and cognitive capacity of older people with mild cognitive impairment: a randomized controlled trial. BMC Med 2024; 22:281. [PMID: 38972988 PMCID: PMC11229192 DOI: 10.1186/s12916-024-03469-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/05/2024] [Indexed: 07/09/2024] Open
Abstract
BACKGROUND The increase in population aging highlights the growing prevalence of mild cognitive impairment, prompting the adoption of interventions that combine physical exercise and cognitive training to improve health and cognitive performance in older adults. The aim of this study was to analyze the efficacy of a combined program on physical and cognitive health in older people with cognitive impairment. METHODS A 12-week randomized controlled clinical trial involving 95 participants (aged 72.12 ± 4.25 years), 47 individuals participated in a control group (CG) that only underwent cognitive stimulation, while 48 individuals were in an experimental group (EG) that participated in a combined program. Balance was measured using the Tinetti scale, upper body strength was assessed with the arm curl test, lower body strength was evaluated with the 30-s chair stand test, flexibility was tested using the back scratch test and chair sit-and-reach test, physical function was measured with the Timed Up and Go test, cognitive function was assessed using the Mini Mental State Examination, cognitive impairment was evaluated with the Montreal Cognitive Assessment, verbal fluency was tested with the Isaac test, and executive functions were assessed using the Trail Making Test. RESULTS The results of the study show significant improvements in both physical and cognitive aspects, such as balance, gait, upper and lower body strength, flexibility, physical function, cognitive function, cognitive impairment, verbal fluency, and executive functions in the group that carried out the intervention compared to the control group. CONCLUSION A combined program for older individuals with mild cognitive impairment leads to enhancements in physical and cognitive health. These improvements underscore the importance of integrating physical exercise with cognitive training as an effective strategy for enhancing overall health and quality of life in older adults. TRIAL REGISTRATION NCT05503641.
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Affiliation(s)
- Yolanda Castellote-Caballero
- Department of Health Sciences, Faculty of Health Sciences, University of Jaén, Jaén, 23071, Spain
- Department of Health Sciences, Faculty of Health Sciences, University of Atlántico Medio, Las Palmas de Gran Canaria, 35017, Spain
| | - María Del Carmen Carcelén Fraile
- Department of Education and Psychology, Faculty of Social Sciences, University of Atlántico Medio, Las Palmas de Gran Canaria, 35017, Spain.
| | - Agustín Aibar-Almazán
- Department of Health Sciences, Faculty of Health Sciences, University of Jaén, Jaén, 23071, Spain
- Department of Health Sciences, Faculty of Health Sciences, University of Atlántico Medio, Las Palmas de Gran Canaria, 35017, Spain
| | | | - Ana María González-Martín
- Department of Education and Psychology, Faculty of Social Sciences, University of Atlántico Medio, Las Palmas de Gran Canaria, 35017, Spain
- Department of Psychology, Centro de Educación Superior de Enseñanza e Investigación Educativa, Plaza de San Martín, 4, Madrid, 28013, Spain
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3
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Jiang D, Yan L, Mayrand F. Emotion expressions and cognitive impairments in the elderly: review of the contactless detection approach. Front Digit Health 2024; 6:1335289. [PMID: 39040877 PMCID: PMC11260803 DOI: 10.3389/fdgth.2024.1335289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 06/20/2024] [Indexed: 07/24/2024] Open
Abstract
The aging population in Canada has been increasing continuously throughout the past decades. Amongst this demographic, around 11% suffer from some form of cognitive decline. While diagnosis through traditional means (i.e., Magnetic Resonance Imagings (MRIs), positron emission tomography (PET) scans, cognitive assessments, etc.) has been successful at detecting this decline, there remains unexplored measures of cognitive health that could reduce stress and cost for the elderly population, including approaches for early detection and preventive methods. Such efforts could additionally contribute to reducing the pressure and stress on the Canadian healthcare system, as well as improve the quality of life of the elderly population. Previous evidence has demonstrated emotional facial expressions being altered in individuals with various cognitive conditions such as dementias, mild cognitive impairment, and geriatric depression. This review highlights the commonalities among these cognitive health conditions, and research behind the contactless assessment methods to monitor the health and cognitive well-being of the elderly population through emotion expression. The contactless detection approach covered by this review includes automated facial expression analysis (AFEA), electroencephalogram (EEG) technologies and heart rate variability (HRV). In conclusion, a discussion of the potentials of the existing technologies and future direction of a novel assessment design through fusion of AFEA, EEG and HRV measures to increase detection of cognitive decline in a contactless and remote manner will be presented.
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Affiliation(s)
- Di Jiang
- Medical Devices Research Centre, National Research Council of Canada, Boucherville, QC, Canada
| | - Luowei Yan
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - Florence Mayrand
- Department of Psychology, McGill University, Montreal, QC, Canada
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Holston EC. An Integrative Review about Electrophysiological Biomarkers of Cognitive Impairment in Alzheimer's Disease: A Developing Relationship. Issues Ment Health Nurs 2024; 45:746-757. [PMID: 38954497 DOI: 10.1080/01612840.2024.2352011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Background: Electrophysiological biomarkers are being examined as potential diagnostic measures of cognitive impairment and its manifestations for psychiatric nurses' use in the care of Alzheimer's disease (AD). However, there is no integrative review describing the themes from the current research about electrophysiological biomarkers and the developing relationship among the themes. Characterizing this developing relationship is imperative for any possible integration of biomarkers into the care of AD by psychiatric nurses. Objective: The purpose of this integrative review is to identify themes from the current research about electrophysiological biomarkers of AD and the developing relationship among the themes, the conceivable relational premise for psychiatric nurses to integrate electrophysiological biomarkers into the screening, assessment, diagnosis, and treatment of AD for the care of persons with AD. Methods: A literature search was executed with PUBMED (accessing Medline and Elsevier) and CINAHL databases that focused on studies about electrophysiological biomarkers of AD from 2015 to 2022. Twenty-seven peer-reviewed studies met this review's inclusion criteria. Results: Five themes emerged: (1) assessing/screening, (2) assessment differential, (3) diagnosing, (4) diagnostic accuracy, and (5) treating. These themes related sequentially and linearly, establishing a developing relationship about the risk, the onset, and the progression of AD. Discussion: Electrophysiological biomarkers associated to cognitive impairment in AD, supporting the accepted understanding of the symptoms of AD. Changes in behavior and functioning were not examined, limiting the possible integration of electrophysiological biomarkers. Further investigations are warranted with an expansion of the clinical symptoms and diverse study populations.
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Affiliation(s)
- Ezra C Holston
- Orvis School of Nursing, University of Nevada Reno, Reno, Nevada, USA
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5
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Pallathadka H, Gardanova ZR, Al-Tameemi AR, Al-Dhalimy AMB, Kadhum EH, Redhee AH. Investigating Cortical Complexity in Mixed Dementia through Nonlinear Dynamic Analyses: A Resting-State EEG Study. IRANIAN JOURNAL OF PSYCHIATRY 2024; 19:327-336. [PMID: 39055518 PMCID: PMC11267120 DOI: 10.18502/ijps.v19i3.15808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/05/2024] [Accepted: 05/05/2024] [Indexed: 07/27/2024]
Abstract
Objective: Dementia is a broad term referring to a decline in problem-solving abilities, language skills, memory, and other cognitive functions to a degree that it significantly disrupts everyday activities. The underlying cause of dementia is the impairment or loss of nerve cells and their connections within the brain. The particular symptoms experienced are contingent upon specific regions of the brain affected by this damage. In this research, we aimed to investigate the nonlinear dynamics of the mixed demented brain compared to healthy subjects using electroencephalogram (EEG) analysis. Method : For this purpose, EEG was recorded from 66 patients with mixed dementia and 65 healthy subjects during rest. After signal preprocessing, sample entropy and Katz fractal dimension analyses were applied to the preprocessed EEG data. Analysis of variance with repeated measures was utilized to compare the nonlinear dynamics of brain activity between dementia and healthy states and partial correlation analysis was employed to explore the relationship between EEG complexity measures and cognitive and neuropsychiatric symptoms of patients. Results: Based on repeated measures ANOVA, there was a significant main effect between groups for both Katz fractal dimension (F = 4.10, P = 0.01) and sample entropy (F = 4.81, P = 0.009) measures. Post hoc comparisons revealed that EEG complexity was significantly reduced in dementia mainly in the occipitoparietal and temporal areas (P < 0.05). MMSE scores were positively correlated with EEG complexity measures, while NPI scores were negatively correlated with EEG complexity measures, mainly in the occipitoparietal and temporal areas (P < 0.05). Moreover, using a KNN classifier, all significant complexity measures yielded the best classification performance with an accuracy of 98.05%, sensitivity of 97.03% and specificity of 99.16% in detecting dementia. Conclusion: This study demonstrated a unique dynamic system within the brain impacted by dementia that results in more predictable patterns of cortical activity mainly in the occipitoparietal and temporal areas. These abnormal patterns were associated with patients' cognitive capacity and neuropsychiatric symptoms.
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Affiliation(s)
| | - Zhanna R. Gardanova
- Pirogov Russian National Research Medical University, Moscow, Russia
- Medical University MGIMO-MED, Moscow, Russia
| | | | | | | | - Ahmed Huseen Redhee
- Medical Laboratory Technique College, the Islamic University, Najaf, Iraq
- Medical Laboratory Technique College, the Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq
- Medical Laboratory Technique College, the Islamic University of Babylon, Babylon, Iraq
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Krothapalli M, Buddendorff L, Yadav H, Schilaty ND, Jain S. From Gut Microbiota to Brain Waves: The Potential of the Microbiome and EEG as Biomarkers for Cognitive Impairment. Int J Mol Sci 2024; 25:6678. [PMID: 38928383 PMCID: PMC11203453 DOI: 10.3390/ijms25126678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/09/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder and a leading cause of dementia. Aging is a significant risk factor for AD, emphasizing the importance of early detection since symptoms cannot be reversed once the advanced stage is reached. Currently, there is no established method for early AD diagnosis. However, emerging evidence suggests that the microbiome has an impact on cognitive function. The gut microbiome and the brain communicate bidirectionally through the gut-brain axis, with systemic inflammation identified as a key connection that may contribute to AD. Gut dysbiosis is more prevalent in individuals with AD compared to their cognitively healthy counterparts, leading to increased gut permeability and subsequent systemic inflammation, potentially causing neuroinflammation. Detecting brain activity traditionally involves invasive and expensive methods, but electroencephalography (EEG) poses as a non-invasive alternative. EEG measures brain activity and multiple studies indicate distinct patterns in individuals with AD. Furthermore, EEG patterns in individuals with mild cognitive impairment differ from those in the advanced stage of AD, suggesting its potential as a method for early indication of AD. This review aims to consolidate existing knowledge on the microbiome and EEG as potential biomarkers for early-stage AD, highlighting the current state of research and suggesting avenues for further investigation.
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Affiliation(s)
- Mahathi Krothapalli
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
| | - Lauren Buddendorff
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
| | - Hariom Yadav
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
| | - Nathan D. Schilaty
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
- Center for Neuromusculoskeletal Research, University of South Florida, Tampa, FL 33612, USA
| | - Shalini Jain
- USF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL 33612, USA; (M.K.); (L.B.); (H.Y.)
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, FL 33612, USA;
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7
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Abdelmissih S, Hosny SA, Elwi HM, Sayed WM, Eshra MA, Shaker OG, Samir NF. Chronic Caffeine Consumption, Alone or Combined with Agomelatine or Quetiapine, Reduces the Maximum EEG Peak, As Linked to Cortical Neurodegeneration, Ovarian Estrogen Receptor Alpha, and Melatonin Receptor 2. Psychopharmacology (Berl) 2024:10.1007/s00213-024-06619-4. [PMID: 38842700 DOI: 10.1007/s00213-024-06619-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 05/16/2024] [Indexed: 06/07/2024]
Abstract
RATIONALE Evidence of the effects of chronic caffeine (CAFF)-containing beverages, alone or in combination with agomelatine (AGO) or quetiapine (QUET), on electroencephalography (EEG), which is relevant to cognition, epileptogenesis, and ovarian function, remains lacking. Estrogenic, adenosinergic, and melatonergic signaling is possibly linked to the dynamics of these substances. OBJECTIVES The brain and ovarian effects of CAFF were compared with those of AGO + CAFF and QUET + CAFF. The implications of estrogenic, adenosinergic, and melatonergic signaling and the brain-ovarian crosstalk were investigated. METHODS Adult female rats were administered AGO (10 mg/kg), QUET (10 mg/kg), CAFF, AGO + CAFF, or QUET + CAFF, once daily for 8 weeks. EEG, estrous cycle progression, and microstructure of the brain and ovaries were examined. Brain and ovarian 17β-estradiol (E2), antimullerian hormone (AMH), estrogen receptor alpha (E2Rα), adenosine receptor 2A (A2AR), and melatonin receptor 2 (MT2R) were assessed. RESULTS CAFF, alone or combined with AGO or QUET, reduced the maximum EEG peak, which was positively linked to ovarian E2Rα, negatively correlated to cortical neurodegeneration and ovarian MT2R, and associated with cystic ovaries. A large corpus luteum emerged with AGO + CAFF and QUET + CAFF, antagonizing the CAFF-mediated increased ovarian A2AR and reduced cortical E2Rα. AGO + CAFF provoked TTP delay and increased ovarian AMH, while QUET + CAFF slowed source EEG frequency to δ range and increased brain E2. CONCLUSIONS CAFF treatment triggered brain and ovarian derangements partially antagonized with concurrent AGO or QUET administration but with no overt affection of estrus cycle progression. Estrogenic, adenosinergic, and melatonergic signaling and brain-ovarian crosstalk may explain these effects.
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Affiliation(s)
- Sherine Abdelmissih
- Department of Medical Pharmacology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt.
| | - Sara Adel Hosny
- Department of Medical Histology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
| | - Heba M Elwi
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
| | - Walaa Mohamed Sayed
- Department of Anatomy and Embryology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
| | - Mohamed Ali Eshra
- Department of Medical Physiology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
| | - Olfat Gamil Shaker
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
| | - Nancy F Samir
- Department of Medical Physiology, Faculty of Medicine Kasr Al-Ainy, Cairo University, Cairo, Egypt
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8
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García-Agustin D, Rodríguez-Rodríguez V, Morgade-Fonte RM, Bobes MA, Galán-García L. Association between gait speed deterioration and EEG abnormalities. PLoS One 2024; 19:e0305074. [PMID: 38833443 PMCID: PMC11149873 DOI: 10.1371/journal.pone.0305074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 04/12/2024] [Indexed: 06/06/2024] Open
Abstract
Physical and cognitive decline at an older age is preceded by changes that accumulate over time until they become clinically evident difficulties. These changes, frequently overlooked by patients and health professionals, may respond better than fully established conditions to strategies designed to prevent disabilities and dependence in later life. The objective of this study was twofold; to provide further support for the need to screen for early functional changes in older adults and to look for an early association between decline in mobility and cognition. A cross-sectional cohort study was conducted on 95 active functionally independent community-dwelling older adults in Havana, Cuba. We measured their gait speed at the usual pace and the cognitive status using the MMSE. A value of 0.8 m/s was used as the cut-off point to decide whether they presented a decline in gait speed. A quantitative analysis of their EEG at rest was also performed to look for an associated subclinical decline in brain function. Results show that 70% of the sample had a gait speed deterioration (i.e., lower than 0.8 m/s), of which 80% also had an abnormal EEG frequency composition for their age. While there was no statistically significant difference in the MMSE score between participants with a gait speed above and below the selected cut-off, individuals with MMSE scores below 25 also had a gait speed<0.8 m/s and an abnormal EEG frequency composition. Our results provide further evidence of early decline in older adults-even if still independent and active-and point to the need for clinical pathways that incorporate screening and early intervention targeted at early deterioration to prolong the years of functional life in older age.
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Yao Y, Hasan WZW, Jiao W, Dong X, Ramli HR, Norsahperi NMH, Wen D. ChatGPT and BCI-VR: a new integrated diagnostic and therapeutic perspective for the accurate diagnosis and personalized treatment of mild cognitive impairment. Front Hum Neurosci 2024; 18:1426055. [PMID: 38895167 PMCID: PMC11183516 DOI: 10.3389/fnhum.2024.1426055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 05/17/2024] [Indexed: 06/21/2024] Open
Affiliation(s)
- Yiduo Yao
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Selangor, Malaysia
| | - W. Z. W. Hasan
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Selangor, Malaysia
| | - Wenlong Jiao
- Brain Computer Intelligence and Intelligent Health Institute, School of Intelligence Science and Technology, University of Science and Technology Beijing, Chengde, China
| | - Xianling Dong
- Hebei International Research Center of Medical Engineering, Chengde Medical University, Hebei, China
| | - H. R. Ramli
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Selangor, Malaysia
| | - N. M. H. Norsahperi
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Selangor, Malaysia
| | - Dong Wen
- Brain Computer Intelligence and Intelligent Health Institute, School of Intelligence Science and Technology, University of Science and Technology Beijing, Chengde, China
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Yu WY, Sun TH, Hsu KC, Wang CC, Chien SY, Tsai CH, Yang YW. Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information. Comput Biol Med 2024; 176:108621. [PMID: 38763067 DOI: 10.1016/j.compbiomed.2024.108621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/21/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.
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Affiliation(s)
- Wei-Yang Yu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Chia-Chun Wang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Chon-Haw Tsai
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan; Neuroscience Laboratory, Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Neuroscience and Brain Disease Center, College of Medicine, China Medical University, 40402, Taichung, Taiwan
| | - Yu-Wan Yang
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan.
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11
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Liu H, Wang J, Xin X, Wang P, Jiang W, Meng T. The relationship and pathways between resting-state EEG, physical function, and cognitive function in older adults. BMC Geriatr 2024; 24:463. [PMID: 38802730 PMCID: PMC11129501 DOI: 10.1186/s12877-024-05041-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 05/03/2024] [Indexed: 05/29/2024] Open
Abstract
OBJECTIVE Based on resting-state electroencephalography (EEG) evidence, this study aimed to explore the relationship and pathways between EEG-mediated physical function and cognitive function in older adults with cognitive impairment. METHODS A total of 140 older adults with cognitive impairment were recruited, and data on their physical function, cognitive function, and EEG were collected. Pearson correlation analysis, one-way analysis of variance, linear regression analysis, and structural equation modeling analysis were conducted to explore the relationships and pathways among variables. RESULTS FP1 theta (effect size = 0.136, 95% CI: 0.025-0.251) and T4 alpha2 (effect size = 0.140, 95% CI: 0.057-0.249) were found to significantly mediate the relationship. The direct effect (effect size = 0.866, 95% CI: 0.574-1.158) and total effect (effect size = 1.142, 95% CI: 0.848-1.435) of SPPB on MoCA were both significant. CONCLUSION Higher physical function scores in older adults with cognitive impairment were associated with higher cognitive function scores. Left frontal theta and right temporal alpha2, as key observed indicators, may mediate the relationship between physical function and cognitive function. It is suggested to implement personalized exercise interventions based on the specific physical function of older adults, which may delay the occurrence and progression of cognitive impairment in older adults with cognitive impairment.
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Affiliation(s)
- Hairong Liu
- Physical Education Department of Shanghai International Studies University, Shanghai, China
| | - Jing Wang
- School of Sports and Health of Shanghai Lixin University of Accounting and Finance Shanghai, Shanghai, 201620, China
| | - Xin Xin
- Shanghai University of Sport, Shanghai, China
| | - Peng Wang
- Shanghai University of Sport, Shanghai, China
| | | | - Tao Meng
- School of Sports and Health of Shanghai Lixin University of Accounting and Finance Shanghai, Shanghai, 201620, China.
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12
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Zunino L. Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen-Shannon Distance. ENTROPY (BASEL, SWITZERLAND) 2024; 26:432. [PMID: 38785681 PMCID: PMC11119498 DOI: 10.3390/e26050432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/10/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
Taking into account the complexity of the human brain dynamics, the appropriate characterization of any brain state is a challenge not easily met. Actually, even the discrimination of simple behavioral tasks, such as resting with eyes closed or eyes open, represents an intricate problem and many efforts have been and are being made to overcome it. In this work, the aforementioned issue is carefully addressed by performing multiscale analyses of electroencephalogram records with the permutation Jensen-Shannon distance. The influence that linear and nonlinear temporal correlations have on the discrimination is unveiled. Results obtained lead to significant conclusions that help to achieve an improved distinction between these resting brain states.
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Affiliation(s)
- Luciano Zunino
- Centro de Investigaciones Ópticas (CONICET La Plata-CIC-UNLP), 1897 Gonnet, La Plata, Argentina;
- Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), 1900 La Plata, Argentina
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13
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Kim Y, Hwang J, Lee J, Jang S, Im Y, Yoon S, Lee SH. Clinical Implication of Maumgyeol Basic Biotypes-Electroencephalography- and Photoplethysmogram-Based Bwave State Inventory. Psychiatry Investig 2024; 21:528-538. [PMID: 38811002 PMCID: PMC11136575 DOI: 10.30773/pi.2023.0381] [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: 11/01/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 05/31/2024] Open
Abstract
OBJECTIVE The development of individual subtypes based on biomarkers offers a cost-effective and timely avenue to comprehending individual differences pertaining to mental health, independent from individuals' subjective insights. Incorporating 2-channel electroencephalography (EEG) and photoplethysmogram (PPG), we sought to establish a subtype classification system with clinical relevance. METHODS One hundred healthy participants and 99 patients with psychiatric disorders were recruited. Classification thresholds were determined using the EEG and PPG data from 2,278 individuals without mental disorders, serving to classify subtypes in our sample of 199 participants. Multivariate analysis of variance was applied to examine psychological distinctions among these subtypes. K-means clustering was employed to verify the classification system. RESULTS The distribution of subtypes differed between healthy participants and those with psychiatric disorders. Cognitive abilities were contingent upon brain subtypes, while mind subtypes exhibited significant differences in symptom severity, overall health, and cognitive stress. K-means clustering revealed that the results of our theory-based classification and data-driven classification are comparable. The synergistic assessment of both brain and mind subtypes was also explored. CONCLUSION Our subtype classification system offers a concise means to access individuals' mental health. The utilization of EEG and PPG signals for subtype classification offers potential for the future of digital mental healthcare.
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Affiliation(s)
- Yunsu Kim
- Department of Psychology, Sungkyunkwan University, Seoul, Republic of Korea
- Clinical Emotion and Cognition Research Laboratory, Department of Psychiatry, Inje University, Goyang, Republic of Korea
| | | | | | | | - Yumi Im
- Bwave Inc., Goyang, Republic of Korea
| | - Sunkyung Yoon
- Department of Psychology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Department of Psychiatry, Inje University, Goyang, Republic of Korea
- Bwave Inc., Goyang, Republic of Korea
- Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Republic of Korea
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14
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Lanfranco RC, Dos Santos Sousa F, Wessel PM, Rivera-Rei Á, Bekinschtein TA, Lucero B, Canales-Johnson A, Huepe D. Slow-wave brain connectivity predicts executive functioning and group belonging in socially vulnerable individuals. Cortex 2024; 174:201-214. [PMID: 38569258 DOI: 10.1016/j.cortex.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 01/19/2024] [Accepted: 03/05/2024] [Indexed: 04/05/2024]
Abstract
Important efforts have been made to describe the neural and cognitive features of healthy and clinical populations. However, the neural and cognitive features of socially vulnerable individuals remain largely unexplored, despite their proneness to developing neurocognitive disorders. Socially vulnerable individuals can be characterised as socially deprived, having a low socioeconomic status, suffering from chronic social stress, and exhibiting poor social adaptation. While it is known that such individuals are likely to perform worse than their peers on executive function tasks, studies on healthy but socially vulnerable groups are lacking. In the current study, we explore whether neural power and connectivity signatures can characterise executive function performance in healthy but socially vulnerable individuals, shedding light on the impairing effects that chronic stress and social disadvantages have on cognition. We measured resting-state electroencephalography and executive functioning in 38 socially vulnerable participants and 38 matched control participants. Our findings indicate that while neural power was uninformative, lower delta and theta phase synchrony are associated with worse executive function performance in all participants, whereas delta phase synchrony is higher in the socially vulnerable group compared to the control group. Finally, we found that delta phase synchrony and years of schooling are the best predictors for belonging to the socially vulnerable group. Overall, these findings suggest that exposure to chronic stress due to socioeconomic factors and a lack of education are associated with changes in slow-wave neural connectivity and executive functioning.
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Affiliation(s)
- Renzo C Lanfranco
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Center for Research in Cognition & Neurosciences, Université libre de Bruxelles, Brussels, Belgium
| | | | - Pierre Musa Wessel
- Department of Criminology, University of Cambridge, Cambridge, United Kingdom
| | - Álvaro Rivera-Rei
- Center for Social and Cognitive Neuroscience (SCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Tristán A Bekinschtein
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Boris Lucero
- The Neuropsychology and Cognitive Neurosciences Research Center, Faculty of Health Sciences, Universidad Católica del Maule, Talca, Chile
| | - Andrés Canales-Johnson
- Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, United Kingdom; The Neuropsychology and Cognitive Neurosciences Research Center, Faculty of Health Sciences, Universidad Católica del Maule, Talca, Chile.
| | - David Huepe
- Center for Social and Cognitive Neuroscience (SCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile.
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15
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Katwaroo AR, Adesh VS, Lowtan A, Umakanthan S. The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine. Postgrad Med J 2024; 100:289-296. [PMID: 38159301 DOI: 10.1093/postmj/qgad135] [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: 10/27/2023] [Accepted: 12/02/2023] [Indexed: 01/03/2024]
Abstract
In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.
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Affiliation(s)
- Arun Rabindra Katwaroo
- Department of Medicine, Trinidad Institute of Medical Technology, St Augustine, Trinidad and Tobago
| | | | - Amrita Lowtan
- Department of Preclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
| | - Srikanth Umakanthan
- Department of Paraclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St. Augustine, Trinidad and Tobago
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16
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Negrón-Oyarzo I, Dib T, Chacana-Véliz L, López-Quilodrán N, Urrutia-Piñones J. Large-scale coupling of prefrontal activity patterns as a mechanism for cognitive control in health and disease: evidence from rodent models. Front Neural Circuits 2024; 18:1286111. [PMID: 38638163 PMCID: PMC11024307 DOI: 10.3389/fncir.2024.1286111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 03/11/2024] [Indexed: 04/20/2024] Open
Abstract
Cognitive control of behavior is crucial for well-being, as allows subject to adapt to changing environments in a goal-directed way. Changes in cognitive control of behavior is observed during cognitive decline in elderly and in pathological mental conditions. Therefore, the recovery of cognitive control may provide a reliable preventive and therapeutic strategy. However, its neural basis is not completely understood. Cognitive control is supported by the prefrontal cortex, structure that integrates relevant information for the appropriate organization of behavior. At neurophysiological level, it is suggested that cognitive control is supported by local and large-scale synchronization of oscillatory activity patterns and neural spiking activity between the prefrontal cortex and distributed neural networks. In this review, we focus mainly on rodent models approaching the neuronal origin of these prefrontal patterns, and the cognitive and behavioral relevance of its coordination with distributed brain systems. We also examine the relationship between cognitive control and neural activity patterns in the prefrontal cortex, and its role in normal cognitive decline and pathological mental conditions. Finally, based on these body of evidence, we propose a common mechanism that may underlie the impaired cognitive control of behavior.
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Affiliation(s)
- Ignacio Negrón-Oyarzo
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Tatiana Dib
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Lorena Chacana-Véliz
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Nélida López-Quilodrán
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Jocelyn Urrutia-Piñones
- Instituto de Fisiología, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias Mención en Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
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17
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Power L, Friedman A, Bardouille T. Atypical paroxysmal slow cortical activity in healthy adults: Relationship to age and cognitive performance. Neurobiol Aging 2024; 136:44-57. [PMID: 38309051 DOI: 10.1016/j.neurobiolaging.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 02/05/2024]
Abstract
Paroxysmal patterns of slow cortical activity have been detected in EEG recordings from individuals with age-related neuropathology and have been shown to be correlated with cognitive dysfunction and blood-brain barrier disruption in these participants. The prevalence of these events in healthy participants, however, has not been studied. In this work, we inspect MEG recordings from 623 healthy participants from the Cam-CAN dataset for the presence of paroxysmal slow wave events (PSWEs). PSWEs were detected in approximately 20% of healthy participants in the dataset, and participants with PSWEs tended to be older and have lower cognitive performance than those without PSWEs. In addition, event features changed linearly with age and cognitive performance, resulting in longer and slower events in older adults, and more widespread events in those with low cognitive performance. These findings provide the first evidence of PSWEs in a subset of purportedly healthy adults. Going forward, these events may have utility as a diagnostic biomarker for atypical brain activity in older adults.
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Affiliation(s)
- Lindsey Power
- School of Biomedical Engineering, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Alon Friedman
- Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Timothy Bardouille
- Department of Physics & Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada.
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18
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Kudo K, Ranasinghe KG, Morise H, Syed F, Sekihara K, Rankin KP, Miller BL, Kramer JH, Rabinovici GD, Vossel K, Kirsch HE, Nagarajan SS. Neurophysiological trajectories in Alzheimer's disease progression. eLife 2024; 12:RP91044. [PMID: 38546337 PMCID: PMC10977971 DOI: 10.7554/elife.91044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2024] Open
Abstract
Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.
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Affiliation(s)
- Kiwamu Kudo
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
- Medical Imaging Business Center, Ricoh Company LtdKanazawaJapan
| | - Kamalini G Ranasinghe
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
| | - Hirofumi Morise
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
- Medical Imaging Business Center, Ricoh Company LtdKanazawaJapan
| | - Faatimah Syed
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
| | | | - Katherine P Rankin
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
| | - Bruce L Miller
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
| | - Joel H Kramer
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
| | - Gil D Rabinovici
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
- Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Keith Vossel
- Memory and Aging Center,UCSF Weill Institute for Neurosciences, University of California, San FranciscoSan FranciscoUnited States
- Mary S. Easton Center for Alzheimer’s Research and Care, Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Heidi E Kirsch
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
| | - Srikantan S Nagarajan
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San FranciscoSan FranciscoUnited States
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19
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Kudo K, Ranasinghe KG, Morise H, Syed F, Sekihara K, Rankin KP, Miller BL, Kramer JH, Rabinovici GD, Vossel K, Kirsch HE, Nagarajan SS. Neurophysiological trajectories in Alzheimer's disease progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.18.541379. [PMID: 37293044 PMCID: PMC10245777 DOI: 10.1101/2023.05.18.541379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.
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Affiliation(s)
- Kiwamu Kudo
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA
- Medical Imaging Business Center, Ricoh Company, Ltd., Kanazawa, 920-0177, Japan
| | - Kamalini G Ranasinghe
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
| | - Hirofumi Morise
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA
- Medical Imaging Business Center, Ricoh Company, Ltd., Kanazawa, 920-0177, Japan
| | - Faatimah Syed
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
| | | | - Katherine P Rankin
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
| | - Bruce L Miller
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
| | - Joel H Kramer
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
| | - Gil D Rabinovici
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA
| | - Keith Vossel
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA, 94158, USA
- Mary S. Easton Center for Alzheimer’s Research and Care, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Heidi E Kirsch
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA
| | - Srikantan S Nagarajan
- Biomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, 94143, USA
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20
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Kim SK, Kim H, Kim SH, Kim JB, Kim L. Electroencephalography-based classification of Alzheimer's disease spectrum during computer-based cognitive testing. Sci Rep 2024; 14:5252. [PMID: 38438453 PMCID: PMC10912091 DOI: 10.1038/s41598-024-55656-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/26/2024] [Indexed: 03/06/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive disease leading to cognitive decline, and to prevent it, researchers seek to diagnose mild cognitive impairment (MCI) early. Particularly, non-amnestic MCI (naMCI) is often mistaken for normal aging as the representative symptom of AD, memory decline, is absent. Subjective cognitive decline (SCD), an intermediate step between normal aging and MCI, is crucial for prediction or early detection of MCI, which determines the presence of AD spectrum pathology. We developed a computer-based cognitive task to classify the presence or absence of AD pathology and stage within the AD spectrum, and attempted to perform multi-stage classification through electroencephalography (EEG) during resting and memory encoding state. The resting and memory-encoding states of 58 patients (20 with SCD, 10 with naMCI, 18 with aMCI, and 10 with AD) were measured and classified into four groups. We extracted features that could reflect the phase, spectral, and temporal characteristics of the resting and memory-encoding states. For the classification, we compared nine machine learning models and three deep learning models using Leave-one-subject-out strategy. Significant correlations were found between the existing neurophysiological test scores and performance of our computer-based cognitive task for all cognitive domains. In all models used, the memory-encoding states realized a higher classification performance than resting states. The best model for the 4-class classification was cKNN. The highest accuracy using resting state data was 67.24%, while it was 93.10% using memory encoding state data. This study involving participants with SCD, naMCI, aMCI, and AD focused on early Alzheimer's diagnosis. The research used EEG data during resting and memory encoding states to classify these groups, demonstrating the significance of cognitive process-related brain waves for diagnosis. The computer-based cognitive task introduced in the study offers a time-efficient alternative to traditional neuropsychological tests, showing a strong correlation with their results and serving as a valuable tool to assess cognitive impairment with reduced bias.
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Affiliation(s)
- Seul-Kee Kim
- Bionics Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Hayom Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sang Hee Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
| | - Laehyun Kim
- Bionics Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea.
- Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, Republic of Korea.
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21
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Easwaran K, Ramakrishnan K, Jeyabal SN. Classification of cognitive impairment using electroencephalography for clinical inspection. Proc Inst Mech Eng H 2024; 238:358-371. [PMID: 38366360 DOI: 10.1177/09544119241228912] [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] [Indexed: 02/18/2024]
Abstract
Impairment in cognitive skill though set-in due to various diseases, its progress is based on neuronal degeneration. In general, cognitive impairment (CI) is divided into three stages: mild, moderate and severe. Quantification of CI is important for deciding/changing therapy. Attempted in this work is to quantify electroencephalograph (EEG) signal and group it into four classes (controls and three stages of CI). After acquiring resting state EEG signal from the participants, non-local and local synchrony measures are derived from phase amplitude coupling and phase locking value. This totals to 160 features per individual for each task. Two types of classification networks are constructed. The first one is an artificial neural network (ANN) that takes derived features and gives a maximum accuracy of 85.11%. The second network is convolutional neural network (CNN) for which topographical images constructed from EEG features becomes the input dataset. The network is trained with 60% of data and then tested with remaining 40% of data. This process is performed in 5-fold technique, which yields an average accuracy of 94.75% with only 30 numbers of inputs for every individual. The result of the study shows that CNN outperforms ANN with a relatively lesser number of inputs. From this it can be concluded that this method proposes a simple task for acquiring EEG (which can be done by CI subjects) and quantifies CI stages with no overlapping between control and test group, thus making it possible for identifying early symptoms of CI.
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Affiliation(s)
- Karuppathal Easwaran
- Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India
| | - Kalpana Ramakrishnan
- Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India
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22
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Lin YR, Hsu TW, Hsu CW, Chen PY, Tseng PT, Liang CS. Effectiveness of Electroencephalography Neurofeedback for Improving Working Memory and Episodic Memory in the Elderly: A Meta-Analysis. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:369. [PMID: 38541096 PMCID: PMC10972127 DOI: 10.3390/medicina60030369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/10/2024] [Accepted: 02/19/2024] [Indexed: 07/23/2024]
Abstract
Background and Objective: Existing evidence indicates the potential benefits of electroencephalography neurofeedback (NFB) training for cognitive function. This study aims to comprehensively review all available evidence investigating the effectiveness of NFB on working memory (WM) and episodic memory (EM) in the elderly population. Material and Methods: A systematic search was conducted across five databases to identify clinical trials examining the impact of NFB on memory function in healthy elderly individuals or those with mild cognitive impairment (MCI). The co-primary outcomes focused on changes in WM and EM. Data synthesis was performed using a random-effects meta-analysis. Results: Fourteen clinical trials (n = 284) were included in the analysis. The findings revealed that NFB was associated with improved WM (k = 11, reported as Hedges' g = 0.665, 95% confidence [CI] = 0.473 to 0.858, p < 0.001) and EM (k = 12, 0.595, 0.333 to 0.856, p < 0.001) in the elderly, with moderate effect sizes. Subgroup analyses demonstrated that NFB had a positive impact on both WM and EM, not only in the healthy population (WM: k = 7, 0.495, 0.213 to 0.778, p = 0.001; EM: k = 6, 0.729, 0.483 to 0.976, p < 0.001) but also in those with MCI (WM: k = 6, 0.812, 0.549 to 1.074, p < 0.001; EM: k = 6, 0.503, 0.088 to 0.919, p = 0.018). Additionally, sufficient training time (totaling more than 300 min) was associated with a significant improvement in WM (k = 6, 0.743, 0.510 to 0.976, p < 0.001) and EM (k = 7, 0.516, 0.156 to 0.876, p = 0.005); however, such benefits were not observed in groups with inadequate training time. Conclusions: The results suggest that NFB is associated with enhancement of both WM and EM in both healthy and MCI elderly individuals, particularly when adequate training time (exceeding 300 min) is provided. These findings underscore the potential of NFB in dementia prevention or rehabilitation.
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Affiliation(s)
- Yu-Ru Lin
- Graduate Institute of Psychology, College of Humanities and Social Science, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Tien-Wei Hsu
- Department of Psychiatry, E-DA Dachang Hospital, I-Shou University, Kaohsiung 807, Taiwan
- Department of Psychiatry, E-DA Hospital, I-Shou University, Kaohsiung 807, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Che-Wei Hsu
- Department of Psychology, Kaohsiung Kai-Suan Psychiatric Hospital, Kaohsiung 807, Taiwan;
| | - Peng-Yu Chen
- Department of Psychology, Pingtung Veterans Hospital, Pingtung 900, Taiwan;
| | - Ping-Tao Tseng
- Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 807, Taiwan
- Department of Psychology, College of Medical and Health Science, Asia University, Taichung 413, Taiwan
- Prospect Clinic for Otorhinolaryngology & Neurology, Kaohsiung 807, Taiwan
- Institute of Precision Medicine, National Sun Yat-sen University, Kaohsiung 807, Taiwan
| | - Chih-Sung Liang
- Department of Psychiatry, Tri-Service General Hospital, Beitou Branch, Taipei 114, Taiwan
- Department of Psychiatry, National Defense Medical Centre, Taipei 114, Taiwan
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23
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Jensen KHR, Urdanibia-Centelles O, Dam VH, Köhler-Forsberg K, Frokjaer VG, Knudsen GM, Jørgensen MB, Ip CT. EEG abnormalities are not associated with poor antidepressant treatment outcome - A NeuroPharm study. Eur Neuropsychopharmacol 2024; 79:59-65. [PMID: 38128462 DOI: 10.1016/j.euroneuro.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/23/2023]
Abstract
EEG brain abnormalities, such as slowing and isolated epileptiform discharges (IEDs), has previously been associated with non-response to antidepressant treatment with escitalopram and venlafaxine, suggesting a potential need for treatment with anticonvulsant property in some patients. The current study aims to replicate the reported association of EEG abnormality and treatment outcomes in an open-label trial of escitalopram for major depressive disorder (MDD) and explore its relationship to mood and cognition. Pretreatment, 6 min eyes-closed resting-state 256-channel EEG was recorded in 91 patients with MDD (age 18-57) who were treated with 10-20 mg escitalopram for 12 weeks; patients could switch to duloxetine after four weeks. A certified clinical neurophysiologist rated the EEGs. IED and EEG slowing was seen in 13.2%, and in 6.6% there were findings with unclear significance (i.e., Wicket spikes and theta activity). We saw no group-difference in remission or response rates after 8 and 12 weeks of treatment or switching to duloxetine. Patients with EEG abnormalities had higher pretreatment mood disturbances driven by greater anger (p=.039) and poorer verbal memory (p=.012). However, EEG abnormality was not associated with improved mood or verbal memory after treatment. Our findings should be interpreted in light of the rarity of EEG abnormalities and the sample size. While we cannot confirm that EEG abnormalities are associated with non-response to treatment, including escitalopram, abnormal EEG activity is associated with poor mood and verbal memory. The clinical utility of EEG abnormality in antidepressant treatment selection needs careful evaluation before deciding if useful for clinical implementation.
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Affiliation(s)
- Kristian H Reveles Jensen
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Olalla Urdanibia-Centelles
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark
| | - Vibeke H Dam
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark
| | - Kristin Köhler-Forsberg
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gitte M Knudsen
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Martin B Jørgensen
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Cheng T Ip
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
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24
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Wang Z, Liu A, Yu J, Wang P, Bi Y, Xue S, Zhang J, Guo H, Zhang W. The effect of aperiodic components in distinguishing Alzheimer's disease from frontotemporal dementia. GeroScience 2024; 46:751-768. [PMID: 38110590 PMCID: PMC10828513 DOI: 10.1007/s11357-023-01041-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 12/07/2023] [Indexed: 12/20/2023] Open
Abstract
Distinguishing between Alzheimer's disease (AD) and frontotemporal dementia (FTD) presents a clinical challenge. Inexpensive and accessible techniques such as electroencephalography (EEG) are increasingly being used to address this challenge. In particular, the potential relevance between aperiodic components of EEG activity and these disorders has gained interest as our understanding evolves. This study aims to determine the differences in aperiodic activity between AD and FTD and evaluate its potential for distinguishing between the two disorders. A total of 88 participants, including 36 patients with AD, 23 patients with FTD, and 29 healthy controls (CN) underwent cognitive assessment and scalp EEG acquisition. Neuronal power spectra were parameterized to decompose the EEG spectrum, enabling comparison of group differences in different components. A support vector machine was employed to assess the impact of aperiodic parameters on the differential diagnosis. Compared with the CN group, both the AD and FTD groups showed varying degrees of increased alpha power (both periodic and raw power) and theta alpha power ratio. At the channel level, theta power (both periodic and raw power) in the frontal regions was higher in the AD group compared to the FTD group, and aperiodic parameters (both exponents and offsets) in the frontal, temporal, central, and parietal regions were higher in the AD group than in the FTD group. Importantly, the inclusion of aperiodic parameters led to improved performance in distinguishing between the two disorders. These findings highlight the significance of aperiodic components in discriminating dementia-related diseases.
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Affiliation(s)
- Zhuyong Wang
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Anyang Liu
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Jianshen Yu
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Pengfei Wang
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Yuewei Bi
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Sha Xue
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China
| | - Jiajun Zhang
- Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-Sen University, No. 135, Xingang Xi Road, Guangzhou, People's Republic of China.
| | - Hongbo Guo
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China.
| | - Wangming Zhang
- Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People's Republic of China.
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25
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Kopčanová M, Tait L, Donoghue T, Stothart G, Smith L, Flores-Sandoval AA, Davila-Perez P, Buss S, Shafi MM, Pascual-Leone A, Fried PJ, Benwell CSY. Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes. Neurobiol Dis 2024; 190:106380. [PMID: 38114048 DOI: 10.1016/j.nbd.2023.106380] [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: 07/13/2023] [Revised: 11/30/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasize the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.
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Affiliation(s)
- Martina Kopčanová
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK.
| | - Luke Tait
- Centre for Systems Modelling and Quantitative Biomedicine, School of Medical and Dental Sciences, University of Birmingham, UK; Cardiff University Brain Research Imaging Centre, Cardiff, UK
| | - Thomas Donoghue
- Department of Biomedical Engineering, Columbia University, New York, USA
| | | | - Laura Smith
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Aimee Arely Flores-Sandoval
- Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, 10117 Berlin, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Paula Davila-Perez
- Rey Juan Carlos University Hospital (HURJC), Department of Clinical Neurophysiology, Móstoles, Madrid, Spain; Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Stephanie Buss
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Department of Neurology, Harvard Medical School, Boston, MA, USA; Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston, MA, United States of America
| | - Peter J Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Christopher S Y Benwell
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
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26
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Amato LG, Vergani AA, Lassi M, Fabbiani C, Mazzeo S, Burali R, Nacmias B, Sorbi S, Mannella R, Grippo A, Bessi V, Mazzoni A. Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12526. [PMID: 38371358 PMCID: PMC10870085 DOI: 10.1002/dad2.12526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 02/20/2024]
Abstract
INTRODUCTION Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern. METHODS We developed a cortical model of AD-related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. RESULTS Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50). DISCUSSION Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non-invasive EEG recordings. Highlights Personalized cortical model estimating structural alterations from EEG recordings.Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%)Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%)Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y.
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Affiliation(s)
- Lorenzo Gaetano Amato
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Alberto Arturo Vergani
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Michael Lassi
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
| | - Carlo Fabbiani
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Salvatore Mazzeo
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | | | - Benedetta Nacmias
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Sandro Sorbi
- IRCSS Fondazione Don Carlo GnocchiFlorenceItaly
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | | | | | - Valentina Bessi
- Department of NeurosciencePsychology, Drug Research and Child HealthCareggi University HospitalFlorenceItaly
| | - Alberto Mazzoni
- The BioRobotics InstituteSant'Anna School of Advanced StudiesPisaItaly
- Department of Excellence in Robotics and AISant'Anna School of Advanced StudiesPisaItaly
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Deng J, Sun B, Kavcic V, Liu M, Giordani B, Li T. Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG. Alzheimers Dement 2024; 20:145-158. [PMID: 37496373 PMCID: PMC10811294 DOI: 10.1002/alz.13411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Early discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency. METHODS Our research is based on resting-state electroencephalography (EEG) and the current dataset includes 137 consensus-diagnosed, community-dwelling Black Americans (ages 60-90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time-varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity-based score. RESULTS The leave-one-out cross-validation accuracy is 91.97% and 3-fold accuracy is 91.17%. The 9 to 18 months' progression trend prediction accuracy over an availability-limited subset sample is 84.61%. CONCLUSION The EEG-based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.
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Affiliation(s)
- Jinxian Deng
- Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingMichiganUSA
| | - Boxin Sun
- Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingMichiganUSA
| | - Voyko Kavcic
- Institute of GerontologyWayne State UniversityDetroitMichiganUSA
- International Institute of Applied GerontologyLjubljanaSlovenia
| | - Mingyan Liu
- Department of Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborMichiganUSA
| | - Bruno Giordani
- Departments of PsychiatryNeurologyPsychology and School of NursingUniversity of MichiganAnn ArborMichiganUSA
- Michigan Alzheimer's Disease Research CenterAnn ArborMichiganUSA
| | - Tongtong Li
- Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingMichiganUSA
- Michigan Alzheimer's Disease Research CenterAnn ArborMichiganUSA
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28
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Buzi G, Fornari C, Perinelli A, Mazza V. Functional connectivity changes in mild cognitive impairment: A meta-analysis of M/EEG studies. Clin Neurophysiol 2023; 156:183-195. [PMID: 37967512 DOI: 10.1016/j.clinph.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 08/31/2023] [Accepted: 10/22/2023] [Indexed: 11/17/2023]
Abstract
OBJECTIVE Early synchrony alterations have been observed through electrophysiological techniques in Mild Cognitive Impairment (MCI), which is considered the intermediate phase between healthy aging (HC) and Alzheimer's disease (AD). However, the documented direction (hyper/hypo-synchronization), regions and frequency bands affected are inconsistent. This meta-analysis intended to elucidate existing evidence linked to potential neurophysiological biomarkers of AD. METHODS We conducted a random-effects meta-analysis that entailed the unbiased inclusion of Non-statistically Significant Unreported Effect Sizes ("MetaNSUE") of electroencephalogram (EEG) and magnetoencephalogram (MEG) studies investigating functional connectivity changes at rest along the healthy-pathological aging continuum, searched through PubMed, Scopus, Web of Science and PsycINFO databases until June 2023. RESULTS Of the 3852 articles extracted, we analyzed 12 papers, and we found an alpha synchrony decrease in MCI compared to HC, specifically between temporal-parietal (d = -0.26) and frontal-parietal areas (d = -0.25). CONCLUSIONS Alterations of alpha synchrony are present even at MCI stage. SIGNIFICANCE Synchrony measures may be promising for the detection of the first hallmarks of connectivity alterations, even at the prodromal stages of the AD, before clinical symptoms occur.
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Affiliation(s)
- Giulia Buzi
- U1077 INSERM-EPHE-UNICAEN, Caen 14000, France
| | - Chiara Fornari
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy.
| | - Alessio Perinelli
- Department of Physics, University of Trento, Trento, Italy; INFN-TIFPA, Trento, Italy
| | - Veronica Mazza
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy.
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29
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Winchester LM, Harshfield EL, Shi L, Badhwar A, Khleifat AA, Clarke N, Dehsarvi A, Lengyel I, Lourida I, Madan CR, Marzi SJ, Proitsi P, Rajkumar AP, Rittman T, Silajdžić E, Tamburin S, Ranson JM, Llewellyn DJ. Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia. Alzheimers Dement 2023; 19:5860-5871. [PMID: 37654029 PMCID: PMC10840606 DOI: 10.1002/alz.13390] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/13/2023] [Accepted: 06/19/2023] [Indexed: 09/02/2023]
Abstract
With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.
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Affiliation(s)
| | - Eric L Harshfield
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, Cambridge, UK
| | - Liu Shi
- Novo Nordisk Research Centre Oxford (NNRCO), Headington, UK
| | - AmanPreet Badhwar
- Département de Pharmacologie et Physiologie, Institut de Génie Biomédical, Faculté de Médecine, Université de Montréal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Natasha Clarke
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Amir Dehsarvi
- School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Imre Lengyel
- Wellcome-Wolfson Institute of Experimental Medicine, Queen's University, Belfast, UK
| | - Ilianna Lourida
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute at Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Petroula Proitsi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anto P Rajkumar
- Institute of Mental Health, Mental Health and Clinical Neurosciences academic unit, University of Nottingham, Nottingham, UK, Mental health services of older people, Nottinghamshire healthcare NHS foundation trust, Nottingham, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Edina Silajdžić
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Janice M Ranson
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | - David J Llewellyn
- Health and Community Sciences, University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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30
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Si Y, He R, Jiang L, Yao D, Zhang H, Xu P, Ma X, Yu L, Li F. Differentiating Between Alzheimer's Disease and Frontotemporal Dementia Based on the Resting-State Multilayer EEG Network. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4521-4527. [PMID: 37922187 DOI: 10.1109/tnsre.2023.3329174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2023]
Abstract
Frontotemporal dementia (FTD) is frequently misdiagnosed as Alzheimer's disease (AD) due to similar clinical symptoms. In this study, we constructed frequency-based multilayer resting-state electroencephalogram (EEG) networks and extracted representative network features to improve the differentiation between AD and FTD. When compared with healthy controls (HC), AD showed primarily stronger delta-alpha cross-couplings and weaker theta-sigma cross-couplings. Notably, when comparing the AD and FTD groups, we found that the AD exhibited stronger delta-alpha and delta-beta connectivity than the FTD. Thereafter, by extracting the representative network features and then applying these features in the classification between AD and FTD, an accuracy of 81.1% was achieved. Finally, a multivariable linear regressive model was built, based on the differential topologies, and then adopted to predict the scores of the Mini-Mental State Examination (MMSE) scale. Accordingly, the predicted and actual measured scores were indeed significantly correlated with each other ( r = 0.274, p = 0.036). These findings consistently suggest that frequency-based multilayer resting-state networks can be utilized for classifying AD and FTD and have potential applications for clinical diagnosis.
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31
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Mei X, Zou C, Si Z, Xu T, Hu J, Wu X, Zheng C. Antidepressant effect of bright light therapy on patients with Alzheimer's disease and their caregivers. Front Pharmacol 2023; 14:1235406. [PMID: 38034990 PMCID: PMC10684929 DOI: 10.3389/fphar.2023.1235406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 11/06/2023] [Indexed: 12/02/2023] Open
Abstract
Background: As a non-pharmacologic treatment, bright light therapy (BLT) is often used to improve affective disorders and memory function. In this study, we aimed to determine the effect of BLT on depression and electrophysiological features of the brain in patients with Alzheimer's disease (AD) and their caregivers using a light-emitting diode device of 14000 lux. Methods: A 4-week case-control trial was conducted. Neuropsychiatric and electroencephalogram (EEG) examination were evaluated at baseline and after 4 weeks. EEG power in delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz) bands was calculated for our main analysis. Demographic and clinical variables were analyzed using Student's t test and the chi-square test. Pearson's correlation was used to determine the correlation between electrophysiological features, blood biochemical indicators, and cognitive assessment scale scores. Results: In this study, 22 in-patients with AD and 23 caregivers were recruited. After BLT, the Hamilton depression scale score decreased in the fourth week. Compared with the age-matched controls of their caregivers, a higher spectral power at the lower delta and theta frequencies was observed in the AD group. After BLT, the EEG power of the delta and theta frequencies in the AD group decreased. No change was observed in blood amyloid concentrations before and after BLT. Conclusion: In conclusion, a 4-week course of BLT significantly suppressed depression in patients with AD and their caregivers. Moreover, changes in EEG power were also significant in both groups.
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Affiliation(s)
- Xi Mei
- Key Lab, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Chenjun Zou
- Department of Geriatric, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Zizhen Si
- Medical College, Ningbo University, Ningbo, Zhejiang, China
| | - Ting Xu
- Department of Geriatric, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Jun Hu
- Department of Geriatric, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Xiangping Wu
- Key Lab, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
| | - Chengying Zheng
- Department of Geriatric, Ningbo Kangning Hospital, Ningbo, Zhejiang, China
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Finn CE, Han GT, Naples AJ, Wolf JM, McPartland JC. Development of peak alpha frequency reflects a distinct trajectory of neural maturation in autistic children. Autism Res 2023; 16:2077-2089. [PMID: 37638733 DOI: 10.1002/aur.3017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 08/05/2023] [Indexed: 08/29/2023]
Abstract
Electroencephalographic peak alpha frequency (PAF) is a marker of neural maturation that increases with age throughout childhood. Distinct maturation of PAF is observed in children with autism spectrum disorder such that PAF does not increase with age and is instead positively associated with cognitive ability. The current study clarifies and extends previous findings by characterizing the effects of age and cognitive ability on PAF between diagnostic groups in a sample of children and adolescents with and without autism spectrum disorder. Resting EEG data and behavioral measures were collected from 45 autistic children and 34 neurotypical controls aged 8 to 18 years. Utilizing generalized additive models to account for nonlinear relations, we examined differences in the joint effect of age and nonverbal IQ by diagnosis as well as bivariate relations between age, nonverbal IQ, and PAF across diagnostic groups. Age was positively associated with PAF among neurotypical children but not among autistic children. In contrast, nonverbal IQ but not age was positively associated with PAF among autistic children. Models accounting for nonlinear relations revealed different developmental trajectories as a function of age and cognitive ability based on diagnostic status. Results align with prior evidence indicating that typical age-related increases in PAF are absent in autistic children and that PAF instead increases with cognitive ability in these children. Findings suggest the potential of PAF to index distinct trajectories of neural maturation in autistic children.
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Affiliation(s)
- Caroline E Finn
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
| | - Gloria T Han
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam J Naples
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
| | - Julie M Wolf
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
| | - James C McPartland
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
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33
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Tobeh NS, Bruce KD. Emerging Alzheimer's disease therapeutics: promising insights from lipid metabolism and microglia-focused interventions. Front Aging Neurosci 2023; 15:1259012. [PMID: 38020773 PMCID: PMC10630922 DOI: 10.3389/fnagi.2023.1259012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
More than 55 million people suffer from dementia, with this number projected to double every 20 years. In the United States, 1 in 3 aged individuals dies from Alzheimer's disease (AD) or another type of dementia and AD kills more individuals than breast cancer and prostate cancer combined. AD is a complex and multifactorial disease involving amyloid plaque and neurofibrillary tangle formation, glial cell dysfunction, and lipid droplet accumulation (among other pathologies), ultimately leading to neurodegeneration and neuronal death. Unfortunately, the current FDA-approved therapeutics do not reverse nor halt AD. While recently approved amyloid-targeting antibodies can slow AD progression to improve outcomes for some patients, they are associated with adverse side effects, may have a narrow therapeutic window, and are expensive. In this review, we evaluate current and emerging AD therapeutics in preclinical and clinical development and provide insight into emerging strategies that target brain lipid metabolism and microglial function - an approach that may synergistically target multiple mechanisms that drive AD neuropathogenesis. Overall, we evaluate whether these disease-modifying emerging therapeutics hold promise as interventions that may be able to reverse or halt AD progression.
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Affiliation(s)
- Nour S Tobeh
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Kimberley D Bruce
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
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Scheijbeler EP, de Haan W, Stam CJ, Twisk JWR, Gouw AA. Longitudinal resting-state EEG in amyloid-positive patients along the Alzheimer's disease continuum: considerations for clinical trials. Alzheimers Res Ther 2023; 15:182. [PMID: 37858173 PMCID: PMC10585755 DOI: 10.1186/s13195-023-01327-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND To enable successful inclusion of electroencephalography (EEG) outcome measures in Alzheimer's disease (AD) clinical trials, we retrospectively mapped the progression of resting-state EEG measures over time in amyloid-positive patients with mild cognitive impairment (MCI) or dementia due to AD. METHODS Resting-state 21-channel EEG was recorded in 148 amyloid-positive AD patients (MCI, n = 88; dementia due to AD, n = 60). Two or more EEG recordings were available for all subjects. We computed whole-brain and regional relative power (i.e., theta (4-8 Hz), alpha1 (8-10 Hz), alpha2 (10-13 Hz), beta (13-30 Hz)), peak frequency, signal variability (i.e., theta permutation entropy), and functional connectivity values (i.e., alpha and beta corrected amplitude envelope correlation, theta phase lag index, weighted symbolic mutual information, inverted joint permutation entropy). Whole-group linear mixed effects models were used to model the development of EEG measures over time. Group-wise analysis was performed to investigate potential differences in change trajectories between the MCI and dementia subgroups. Finally, we estimated the minimum sample size required to detect different treatment effects (i.e., 50% less deterioration, stabilization, or 50% improvement) on the development of EEG measures over time, in hypothetical clinical trials of 1- or 2-year duration. RESULTS Whole-group analysis revealed significant regional and global oscillatory slowing over time (i.e., increased relative theta power, decreased beta power), with strongest effects for temporal and parieto-occipital regions. Disease severity at baseline influenced the EEG measures' rates of change, with fastest deterioration reported in MCI patients. Only AD dementia patients displayed a significant decrease of the parieto-occipital peak frequency and theta signal variability over time. We estimate that 2-year trials, focusing on amyloid-positive MCI patients, require 36 subjects per arm (2 arms, 1:1 randomization, 80% power) to detect a stabilizing treatment effect on temporal relative theta power. CONCLUSIONS Resting-state EEG measures could facilitate early detection of treatment effects on neuronal function in AD patients. Their sensitivity depends on the region-of-interest and disease severity of the study population. Conventional spectral measures, particularly recorded from temporal regions, present sensitive AD treatment monitoring markers.
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Affiliation(s)
- Elliz P Scheijbeler
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands.
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands.
| | - Willem de Haan
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Jos W R Twisk
- Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
| | - Alida A Gouw
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
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Smith AE, Chau A, Greaves D, Keage HAD, Feuerriegel D. Resting EEG power spectra across middle to late life: associations with age, cognition, APOE-ɛ4 carriage, and cardiometabolic burden. Neurobiol Aging 2023; 130:93-102. [PMID: 37494844 DOI: 10.1016/j.neurobiolaging.2023.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 05/25/2023] [Accepted: 06/03/2023] [Indexed: 07/28/2023]
Abstract
We investigated how resting electroencephalography (EEG) measures are associated with risk factors for late-life cognitive impairment and dementia, including age, apolipoprotein E ɛ4 (APOE-ɛ4) carriage, and cardiometabolic burden. Resting EEG was recorded from 86 adults (50-80 years of age). Participants additionally completed the Addenbrooke's Cognitive Examination (ACE) III and had blood drawn to assess APOE-ɛ4 carriage status and cardiometabolic burden. EEG power spectra were decomposed into sources of periodic and aperiodic activity to derive measures of aperiodic component slope and alpha (7-14 Hz) and beta (15-30 Hz) peak power and peak frequency. Alpha and beta peak power measures were corrected for aperiodic activity. The aperiodic component slope was correlated with ACE-III scores but not age. Alpha peak frequency decreased with age. Individuals with higher cardiometabolic burden had lower alpha peak frequencies and lower beta peak power. APOE-ɛ4 carriers had lower beta peak frequencies. Our findings suggest that the slope of the aperiodic component of resting EEG power spectra is more closely associated with measures of cognitive performance rather than chronological age in older adults.
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Affiliation(s)
- Ashleigh E Smith
- Alliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
| | - Anson Chau
- Alliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia; Medical Radiation Science, Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
| | - Danielle Greaves
- Alliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia; Cognitive Ageing and Impairment Neurosciences (CAIN), Justice and Society, University of South Australia, Adelaide, South Australia, Australia; UniSA Online, University of South Australia, Adelaide, South Australia, Australia
| | - Hannah A D Keage
- Cognitive Ageing and Impairment Neurosciences (CAIN), Justice and Society, University of South Australia, Adelaide, South Australia, Australia
| | - Daniel Feuerriegel
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria, Australia.
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Gimenez-Aparisi G, Guijarro-Estelles E, Chornet-Lurbe A, Ballesta-Martinez S, Pardo-Hernandez M, Ye-Lin Y. Early detection of Parkinson's disease: Systematic analysis of the influence of the eyes on quantitative biomarkers in resting state electroencephalography. Heliyon 2023; 9:e20625. [PMID: 37829809 PMCID: PMC10565694 DOI: 10.1016/j.heliyon.2023.e20625] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 07/24/2023] [Accepted: 10/02/2023] [Indexed: 10/14/2023] Open
Abstract
While resting state electroencephalography (EEG) provides relevant information on pathological changes in Parkinson's disease, most studies focus on the eyes-closed EEG biomarkers. Recent evidence has shown that both eyes-open EEG and reactivity to eyes-opening can also differentiate Parkinson's disease from healthy aging, but no consensus has been reached on a discriminatory capability benchmark. The aim of this study was to determine the resting-state EEG biomarkers suitable for real-time application that can differentiate Parkinson's patients from healthy subjects under both eyes closed and open. For this, we analysed and compared the quantitative EEG analyses of 13 early-stage cognitively normal Parkinson's patients with an age and sex-matched healthy group. We found that Parkinson's disease exhibited abnormal excessive theta activity in eyes-closed, which was reflected by a significantly higher relative theta power, a higher time percentage with a frequency peak in the theta band and a reduced alpha/theta ratio, while Parkinson's patients showed a significantly steeper non-oscillatory spectral slope activity than that of healthy subjects. We also found considerably less alpha and beta reactivity to eyes-opening in Parkinson's disease plus a significant moderate correlation between these EEG-biomarkers and the MDS-UPDRS score, used to assesses the clinical symptoms of Parkinson's Disease. Both EEG recordings with the eyes open and reactivity to eyes-opening provided additional information to the eyes-closed condition. We thus strongly recommend that both eyes open and closed be used in clinical practice recording protocols to promote EEG as a complementary non-invasive screening method for the early detection of Parkinson's disease, which would allow clinicians to design patient-oriented treatment and improve the patient's quality of life.
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Affiliation(s)
- G. Gimenez-Aparisi
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain
| | - E. Guijarro-Estelles
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain
| | - A. Chornet-Lurbe
- Servicio de Neurofisiología Clínica, Hospital Lluís Alcanyís, departamento de salud Xàtiva-Ontinyent, 46800, Xàtiva, València, Spain
| | - S. Ballesta-Martinez
- Servicio de Neurofisiología Clínica, Hospital Lluís Alcanyís, departamento de salud Xàtiva-Ontinyent, 46800, Xàtiva, València, Spain
| | - M. Pardo-Hernandez
- Servicio de Neurofisiología Clínica, Hospital Lluís Alcanyís, departamento de salud Xàtiva-Ontinyent, 46800, Xàtiva, València, Spain
| | - Y. Ye-Lin
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain
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Kim SE, Shin C, Yim J, Seo K, Ryu H, Choi H, Park J, Min BK. Resting-state electroencephalographic characteristics related to mild cognitive impairments. Front Psychiatry 2023; 14:1231861. [PMID: 37779609 PMCID: PMC10539934 DOI: 10.3389/fpsyt.2023.1231861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Alzheimer's disease (AD) causes a rapid deterioration in cognitive and physical functions, including problem-solving, memory, language, and daily activities. Mild cognitive impairment (MCI) is considered a risk factor for AD, and early diagnosis and treatment of MCI may help slow the progression of AD. Electroencephalography (EEG) analysis has become an increasingly popular tool for developing biomarkers for MCI and AD diagnosis. Compared with healthy elderly, patients with AD showed very clear differences in EEG patterns, but it is inconclusive for MCI. This study aimed to investigate the resting-state EEG features of individuals with MCI (n = 12) and cognitively healthy controls (HC) (n = 13) with their eyes closed. EEG data were analyzed using spectral power, complexity, functional connectivity, and graph analysis. The results revealed no significant difference in EEG spectral power between the HC and MCI groups. However, we observed significant changes in brain complexity and networks in individuals with MCI compared with HC. Patients with MCI exhibited lower complexity in the middle temporal lobe, lower global efficiency in theta and alpha bands, higher local efficiency in the beta band, lower nodal efficiency in the frontal theta band, and less small-world network topology compared to the HC group. These observed differences may be related to underlying neuropathological alterations associated with MCI progression. The findings highlight the potential of network analysis as a promising tool for the diagnosis of MCI.
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Affiliation(s)
- Seong-Eun Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Chanwoo Shin
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Junyeop Yim
- Department of Applied Mathematics, Kongju National University, Gongju-si, Republic of Korea
| | - Kyoungwon Seo
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Hokyoung Ryu
- Graduate School of Technology and Innovation Management, Hanyang University, Seoul, Republic of Korea
| | - Hojin Choi
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Jinseok Park
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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38
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Kumar WS, Ray S. Healthy ageing and cognitive impairment alter EEG functional connectivity in distinct frequency bands. Eur J Neurosci 2023; 58:3432-3449. [PMID: 37559505 DOI: 10.1111/ejn.16114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023]
Abstract
Functional connectivity (FC) indicates the interdependencies between brain signals recorded from spatially distinct locations in different frequency bands, which is modulated by cognitive tasks and is known to change with ageing and cognitive disorders. Recently, the power of narrow-band gamma oscillations induced by visual gratings have been shown to reduce with both healthy ageing and in subjects with mild cognitive impairment (MCI). However, the impact of ageing/MCI on stimulus-induced gamma FC has not been well studied. We recorded electroencephalogram (EEG) from a large cohort (N = 229) of elderly subjects (>49 years) while they viewed large cartesian gratings to induce gamma oscillations and studied changes in alpha and gamma FC with healthy ageing (N = 218) and MCI (N = 11). Surprisingly, we found distinct differences across age and MCI groups in power and FC. With healthy ageing, alpha power did not change but FC decreased significantly. MCI reduced gamma but not alpha FC significantly compared with age and gender matched controls, even when power was matched between the two groups. Overall, our results suggest distinct effects of ageing and disease on EEG power and FC, suggesting different mechanisms underlying ageing and cognitive disorders.
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Affiliation(s)
| | - Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bengaluru, India
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39
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Bjerkan J, Lancaster G, Meglič B, Kobal J, Crawford TJ, McClintock PVE, Stefanovska A. Aging affects the phase coherence between spontaneous oscillations in brain oxygenation and neural activity. Brain Res Bull 2023; 201:110704. [PMID: 37451471 DOI: 10.1016/j.brainresbull.2023.110704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023]
Abstract
The risk of neurodegenerative disorders increases with age, due to reduced vascular nutrition and impaired neural function. However, the interactions between cardiovascular dynamics and neural activity, and how these interactions evolve in healthy aging, are not well understood. Here, the interactions are studied by assessment of the phase coherence between spontaneous oscillations in cerebral oxygenation measured by fNIRS, the electrical activity of the brain measured by EEG, and cardiovascular functions extracted from ECG and respiration effort, all simultaneously recorded. Signals measured at rest in 21 younger participants (31.1 ± 6.9 years) and 24 older participants (64.9 ± 6.9 years) were analysed by wavelet transform, wavelet phase coherence and ridge extraction for frequencies between 0.007 and 4 Hz. Coherence between the neural and oxygenation oscillations at ∼ 0.1 Hz is significantly reduced in the older adults in 46/176 fNIRS-EEG probe combinations. This reduction in coherence cannot be accounted for in terms of reduced power, thus indicating that neurovascular interactions change with age. The approach presented promises a noninvasive means of evaluating the efficiency of the neurovascular unit in aging and disease.
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Affiliation(s)
- Juliane Bjerkan
- Lancaster University, Department of Physics, LA1 4YB, Lancaster, United Kingdom
| | - Gemma Lancaster
- Lancaster University, Department of Physics, LA1 4YB, Lancaster, United Kingdom
| | - Bernard Meglič
- University of Ljubljana Medical Centre, Department of Neurology, 1525, Ljubljana, Slovenia
| | - Jan Kobal
- University of Ljubljana Medical Centre, Department of Neurology, 1525, Ljubljana, Slovenia
| | - Trevor J Crawford
- Lancaster University, Department of Psychology, LA1 4YF, Lancaster, United Kingdom
| | | | - Aneta Stefanovska
- Lancaster University, Department of Physics, LA1 4YB, Lancaster, United Kingdom.
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40
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Lee SH, Hwang HH, Kim S, Hwang J, Park J, Park S. Clinical Implication of Maumgyeol Basic Service-the 2 Channel Electroencephalography and a Photoplethysmogram-based Mental Health Evaluation Software. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2023; 21:583-593. [PMID: 37424425 PMCID: PMC10335898 DOI: 10.9758/cpn.23.1062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/24/2023] [Accepted: 03/29/2023] [Indexed: 07/11/2023]
Abstract
Objective Maumgyeol Basic service is a mental health evaluation and grade scoring software using the 2 channels EEG and photoplethysmogram (PPG). This service is supposed to assess potential at-risk groups with mental illness more easily, rapidly, and reliably. This study aimed to evaluate the clinical implication of the Maumgyeol Basic service. Methods One hundred one healthy controls and 103 patients with a psychiatric disorder were recruited. Psychological evaluation (Mental Health Screening for Depressive Disorders [MHS-D], Mental Health Screening for Anxiety Disorders [MHS-A], cognitive stress response scale [CSRS], 12-item General Health Questionnaire [GHQ-12], Clinical Global Impression [CGI]) and digit symbol substitution test (DSST) were applied to all participants. Maumgyeol brain health score and Maumgyeol mind health score were calculated from 2 channel frontal EEG and PPG, respectively. Results Participants were divided into three groups: Maumgyeol Risky, Maumgyeol Good, and Maumgyeol Usual. The Maumgyeol mind health scores, but not brain health scores, were significantly lower in the patients group compared to healthy controls. Maumgyeol Risky group showed significantly lower psychological and cognitive ability evaluation scores than Maumgyeol Usual and Good groups. Maumgyel brain health score showed significant correlations with CSRS and DSST. Maumgyeol mind health score showed significant correlations with CGI and DSST. About 20.6% of individuals were classified as the No Insight group, who had mental health problems but were unaware of their illnesses. Conclusion This study suggests that the Maumgyeol Basic service can provide important clinical information about mental health and be used as a meaningful digital mental healthcare monitoring solution to prevent symptom aggravation.
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Affiliation(s)
- Seung-Hwan Lee
- Bwave Inc., Goyang, Korea
- Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
- Clinical Emotion and Cognition Research Laboratory, Department of Psychiatry, Inje University, Goyang, Korea
| | - Hyeon-Ho Hwang
- Clinical Emotion and Cognition Research Laboratory, Department of Psychiatry, Inje University, Goyang, Korea
- Department of Human-Computer Interaction, Hanyang University, Ansan, Korea
| | - Sungkean Kim
- Department of Human-Computer Interaction, Hanyang University, Ansan, Korea
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Abramova O, Zorkina Y, Ushakova V, Gryadunov D, Ikonnikova A, Fedoseeva E, Emelyanova M, Ochneva A, Morozova I, Pavlov K, Syunyakov T, Andryushchenko A, Savilov V, Kurmishev M, Andreuyk D, Shport S, Gurina O, Chekhonin V, Kostyuk G, Morozova A. Alteration of Blood Immune Biomarkers in MCI Patients with Different APOE Genotypes after Cognitive Training: A 1 Year Follow-Up Cohort Study. Int J Mol Sci 2023; 24:13395. [PMID: 37686198 PMCID: PMC10488004 DOI: 10.3390/ijms241713395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Many studies aim to detect the early phase of dementia. One of the major ways to achieve this is to identify corresponding biomarkers, particularly immune blood biomarkers. The objective of this study was to identify such biomarkers in patients with mild cognitive impairment (MCI) in an experiment that included cognitive training. A group of patients with MCI diagnoses over the age of 65 participated in the study (n = 136). Measurements of cognitive functions (using the Mini-Mental State Examination scale and Montreal Cognitive Assessment) and determination of 27 serum biomarkers were performed twice: on the first visit and on the second visit, one year after the cognitive training. APOE genotypes were also determined. Concentrations of EGF (F = 17; p = 0.00007), Eotaxin (F = 7.17; p = 0.008), GRO (F = 13.42; p = 0.0004), IL-8 (F = 8.16; p = 0.005), MCP-1 (F = 13.46; p = 0.0001) and MDC (F = 5.93; p = 0.016) increased after the cognitive training in MCI patients. All these parameters except IL-8 demonstrated a weak correlation with other immune parameters and were poorly represented in the principal component analysis. Differences in concentrations of IP-10, FGF-2, TGFa and VEGF in patients with MCI were associated with APOE genotype. Therefore, the study identified several immune blood biomarkers that could potentially be associated with changes in cognitive function.
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Affiliation(s)
- Olga Abramova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Yana Zorkina
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Valeriya Ushakova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
- Biological Faculty, M.V. Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Dmitry Gryadunov
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Anna Ikonnikova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Elena Fedoseeva
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Marina Emelyanova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Aleksandra Ochneva
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Irina Morozova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
| | - Konstantin Pavlov
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Timur Syunyakov
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
- International Centre for Education and Research in Neuropsychiatry (ICERN), Samara State Medical University, 443016 Samara, Russia
| | - Alisa Andryushchenko
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
| | - Victor Savilov
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
| | - Marat Kurmishev
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
| | - Denis Andreuyk
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
- Biological Faculty, M.V. Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Svetlana Shport
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Olga Gurina
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Vladimir Chekhonin
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
- Department of Medical Nanobiotechnology, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Georgy Kostyuk
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
- Department of Psychiatry, Federal State Budgetary Educational Institution of Higher Education “Moscow State University of Food Production”, Volokolamskoye Highway 11, 125080 Moscow, Russia
| | - Anna Morozova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (O.A.); (Y.Z.); (V.U.); (A.O.); (I.M.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
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Mazzeo S, Lassi M, Padiglioni S, Vergani AA, Moschini V, Scarpino M, Giacomucci G, Burali R, Morinelli C, Fabbiani C, Galdo G, Amato LG, Bagnoli S, Emiliani F, Ingannato A, Nacmias B, Sorbi S, Grippo A, Mazzoni A, Bessi V. PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With machine learning: the PREVIEW study protocol. BMC Neurol 2023; 23:300. [PMID: 37573339 PMCID: PMC10422810 DOI: 10.1186/s12883-023-03347-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 07/28/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia. METHODS We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ42, t-tau, and p-tau concentration and Aβ42/Aβ40 ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD. DISCUSSION This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD. TRIAL REGISTRATION NUMBER (TRN) NCT05569083.
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Affiliation(s)
- Salvatore Mazzeo
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
- Research and Innovation Centre for Dementia-CRIDEM, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Michael Lassi
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Sonia Padiglioni
- Research and Innovation Centre for Dementia-CRIDEM, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
- Regional Referral Centre for Relational Criticalities - Tuscany Region, Florence, Italy
| | - Alberto Arturo Vergani
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Valentina Moschini
- Research and Innovation Centre for Dementia-CRIDEM, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | | | - Giulia Giacomucci
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
| | | | - Carmen Morinelli
- Research and Innovation Centre for Dementia-CRIDEM, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | | | - Giulia Galdo
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
| | - Lorenzo Gaetano Amato
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Silvia Bagnoli
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
| | - Filippo Emiliani
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
| | - Assunta Ingannato
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | | | - Alberto Mazzoni
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Valentina Bessi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Azienda Ospedaliera-Universitaria Careggi, Largo Brambilla 3, Florence, 50134, Italy.
- Research and Innovation Centre for Dementia-CRIDEM, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.
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Martínez‐Cañada P, Perez‐Valero E, Minguillon J, Pelayo F, López‐Gordo MA, Morillas C. Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12477. [PMID: 37662693 PMCID: PMC10474329 DOI: 10.1002/dad2.12477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 09/05/2023]
Abstract
INTRODUCTION Accumulation and interaction of amyloid-beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, for example, magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the '1/f slope', has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging. METHODS Using 1/f slopes from resting-state spectral data and computational modeling, we developed a new method for inferring E/I alterations in AD. RESULTS We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters. DISCUSSION Our results provide a general framework for investigating circuit-level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.
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Affiliation(s)
- Pablo Martínez‐Cañada
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
| | - Eduardo Perez‐Valero
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
| | - Jesus Minguillon
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
- Department of Signal TheoryTelematics and CommunicationsUniversity of GranadaGranadaSpain
| | - Francisco Pelayo
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
| | - Miguel A. López‐Gordo
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
- Department of Signal TheoryTelematics and CommunicationsUniversity of GranadaGranadaSpain
| | - Christian Morillas
- Department of Computer EngineeringAutomation and RoboticsUniversity of GranadaGranadaSpain
- Research Centre for Information and Communications Technologies (CITIC)University of GranadaGranadaSpain
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Gelbard-Sagiv H, Pardo S, Getter N, Guendelman M, Benninger F, Kraus D, Shriki O, Ben-Sasson S. Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:5805. [PMID: 37447653 DOI: 10.3390/s23135805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/15/2023] [Accepted: 06/19/2023] [Indexed: 07/15/2023]
Abstract
Epilepsy, a prevalent neurological disorder, profoundly affects patients' quality of life due to the unpredictable nature of seizures. The development of a reliable and user-friendly wearable EEG system capable of detecting and predicting seizures has the potential to revolutionize epilepsy care. However, optimizing electrode configurations for such systems, which is crucial for balancing accuracy and practicality, remains to be explored. This study addresses this gap by developing a systematic approach to optimize electrode configurations for a seizure detection machine-learning algorithm. Our approach was applied to an extensive database of prolonged annotated EEG recordings from 158 epilepsy patients. Multiple electrode configurations ranging from one to eighteen were assessed to determine the optimal number of electrodes. Results indicated that the performance was initially maintained as the number of electrodes decreased, but a drop in performance was found to have occurred at around eight electrodes. Subsequently, a comprehensive analysis of all eight-electrode configurations was conducted using a computationally intensive workflow to identify the optimal configurations. This approach can inform the mechanical design process of an EEG system that balances seizure detection accuracy with the ease of use and portability. Additionally, this framework holds potential for optimizing hardware in other machine learning applications. The study presents a significant step towards the development of an efficient wearable EEG system for seizure detection.
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Affiliation(s)
| | - Snir Pardo
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
| | - Nir Getter
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Miriam Guendelman
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
| | - Felix Benninger
- Department of Neurology, Rabin Medical Center, Beilinson Hospital, Petach Tikva 4941492, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Dror Kraus
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Pediatric Neurology, Schneider Children's Medical Center of Israel, Petach Tikva 4920235, Israel
| | - Oren Shriki
- NeuroHelp Ltd., Ramat-Gan 5252181, Israel
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
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Davoudi S, Schwartz T, Labbe A, Trainor L, Lippé S. Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants. Cereb Cortex 2023; 33:8734-8747. [PMID: 37143183 PMCID: PMC10321121 DOI: 10.1093/cercor/bhad154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/06/2023] Open
Abstract
Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.
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Affiliation(s)
- Saeideh Davoudi
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Neuroscience, Université de Montréal, Montréal H3T 1J4, Canada
| | - Tyler Schwartz
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Aurélie Labbe
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Laurel Trainor
- Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton L8S 4K1, Canada
| | - Sarah Lippé
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Psychology, Université de Montréal, Montréal H2V 2S9, Canada
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Ulbl J, Rakusa M. The Importance of Subjective Cognitive Decline Recognition and the Potential of Molecular and Neurophysiological Biomarkers-A Systematic Review. Int J Mol Sci 2023; 24:10158. [PMID: 37373304 DOI: 10.3390/ijms241210158] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/01/2023] [Accepted: 06/10/2023] [Indexed: 06/29/2023] Open
Abstract
Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are early stages of Alzheimer's disease (AD). Neurophysiological markers such as electroencephalography (EEG) and event-related potential (ERP) are emerging as alternatives to traditional molecular and imaging markers. This paper aimed to review the literature on EEG and ERP markers in individuals with SCD. We analysed 30 studies that met our criteria, with 17 focusing on resting-state or cognitive task EEG, 11 on ERPs, and two on both EEG and ERP parameters. Typical spectral changes were indicative of EEG rhythm slowing and were associated with faster clinical progression, lower education levels, and abnormal cerebrospinal fluid biomarkers profiles. Some studies found no difference in ERP components between SCD subjects, controls, or MCI, while others reported lower amplitudes in the SCD group compared to controls. Further research is needed to explore the prognostic value of EEG and ERP in relation to molecular markers in individuals with SCD.
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Affiliation(s)
- Janina Ulbl
- Division of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia
- Faculty of Medicine, University of Maribor, 2000 Maribor, Slovenia
| | - Martin Rakusa
- Division of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia
- Faculty of Medicine, University of Maribor, 2000 Maribor, Slovenia
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Wijaya A, Setiawan NA, Ahmad AH, Zakaria R, Othman Z. Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA). AIMS Neurosci 2023; 10:154-171. [PMID: 37426780 PMCID: PMC10323261 DOI: 10.3934/neuroscience.2023012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/27/2023] [Accepted: 06/12/2023] [Indexed: 07/11/2023] Open
Abstract
Mild cognitive impairment (MCI) is often considered a precursor to Alzheimer's disease (AD) and early diagnosis may help improve treatment effectiveness. To identify accurate MCI biomarkers, researchers have utilized various neuroscience techniques, with electroencephalography (EEG) being a popular choice due to its low cost and better temporal resolution. In this scoping review, we analyzed 2310 peer-reviewed articles on EEG and MCI between 2012 and 2022 to track the research progress in this field. Our data analysis involved co-occurrence analysis using VOSviewer and a Patterns, Advances, Gaps, Evidence of Practice, and Research Recommendations (PAGER) framework. We found that event-related potentials (ERP), EEG, epilepsy, quantitative EEG (QEEG), and EEG-based machine learning were the primary research themes. The study showed that ERP/EEG, QEEG, and EEG-based machine learning frameworks provide high-accuracy detection of seizure and MCI. These findings identify the main research themes in EEG and MCI and suggest promising avenues for future research in this field.
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Affiliation(s)
- Adi Wijaya
- Department of Health Information Management, Universitas Indonesia Maju, Jakarta, Indonesia
| | - Noor Akhmad Setiawan
- Department of Electrical and Information Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Asma Hayati Ahmad
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
| | - Rahimah Zakaria
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
| | - Zahiruddin Othman
- School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kota Bharu, Malaysia
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Kopčanová M, Tait L, Donoghue T, Stothart G, Smith L, Sandoval AAF, Davila-Perez P, Buss S, Shafi MM, Pascual-Leone A, Fried PJ, Benwell CS. Resting-state EEG signatures of Alzheimer's disease are driven by periodic but not aperiodic changes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.11.544491. [PMID: 37398162 PMCID: PMC10312609 DOI: 10.1101/2023.06.11.544491] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Electroencephalography (EEG) has shown potential for identifying early-stage biomarkers of neurocognitive dysfunction associated with dementia due to Alzheimer's disease (AD). A large body of evidence shows that, compared to healthy controls (HC), AD is associated with power increases in lower EEG frequencies (delta and theta) and decreases in higher frequencies (alpha and beta), together with slowing of the peak alpha frequency. However, the pathophysiological processes underlying these changes remain unclear. For instance, recent studies have shown that apparent shifts in EEG power from high to low frequencies can be driven either by frequency specific periodic power changes or rather by non-oscillatory (aperiodic) changes in the underlying 1/f slope of the power spectrum. Hence, to clarify the mechanism(s) underlying the EEG alterations associated with AD, it is necessary to account for both periodic and aperiodic characteristics of the EEG signal. Across two independent datasets, we examined whether resting-state EEG changes linked to AD reflect true oscillatory (periodic) changes, changes in the aperiodic (non-oscillatory) signal, or a combination of both. We found strong evidence that the alterations are purely periodic in nature, with decreases in oscillatory power at alpha and beta frequencies (AD < HC) leading to lower (alpha + beta) / (delta + theta) power ratios in AD. Aperiodic EEG features did not differ between AD and HC. By replicating the findings in two cohorts, we provide robust evidence for purely oscillatory pathophysiology in AD and against aperiodic EEG changes. We therefore clarify the alterations underlying the neural dynamics in AD and emphasise the robustness of oscillatory AD signatures, which may further be used as potential prognostic or interventional targets in future clinical investigations.
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Affiliation(s)
- Martina Kopčanová
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
| | - Luke Tait
- Centre for Systems Modelling and Quantitative Biomedicine, School of Medical and Dental Sciences, University of Birmingham, UK
- Cardiff University Brain Research Imaging Centre, Cardiff, UK
| | - Thomas Donoghue
- Department of Biomedical Engineering, Columbia University, New York, USA
| | | | - Laura Smith
- School of Psychology, University of Kent, Kent, UK
| | - Aimee Arely Flores Sandoval
- Charité – Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, 10117, Berlin, Germany
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Paula Davila-Perez
- Rey Juan Carlos University Hospital (HURJC), Department of Clinical Neurophysiology, Móstoles, Madrid, Spain
- Health Research Institute-Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain
| | - Stephanie Buss
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Mouhsin M. Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Alvaro Pascual-Leone
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston MA
| | - Peter J. Fried
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| | - Christopher S.Y. Benwell
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
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Tomasello L, Carlucci L, Laganà A, Galletta S, Marinelli CV, Raffaele M, Zoccolotti P. Neuropsychological Evaluation and Quantitative EEG in Patients with Frontotemporal Dementia, Alzheimer's Disease, and Mild Cognitive Impairment. Brain Sci 2023; 13:930. [PMID: 37371408 DOI: 10.3390/brainsci13060930] [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: 04/22/2023] [Revised: 05/25/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
This study analyzed the efficacy of EEG resting state and neuropsychological performances in discriminating patients with different forms of dementia, or mild cognitive impairment (MCI), compared with control subjects. Forty-four patients with dementia (nineteen patients with AD, and seven with FTD), eighteen with MCI, and nineteen healthy subjects, matched for age and gender, underwent an extensive neuropsychological test battery and an EEG resting state recording. Results showed greater theta activation in posterior areas in the Alzheimer's disease (AD) and Fronto-Temporal Dementia (FTD) groups compared with the MCI and control groups. AD patients also showed more delta band activity in the temporal-occipital areas than controls and MCI patients. By contrast, the alpha and beta bands did not discriminate among groups. A hierarchical clustering analysis based on neuropsychological and EEG data yielded a three-factor solution. The clusters differed for several neuropsychological measures, as well as for beta and theta bands. Neuropsychological tests were most sensitive in capturing an initial cognitive decline, while increased theta activity was uniquely associated with a substantial worsening of the clinical picture, representing a negative prognostic factor. In line with the Research Domains Framework (RDoC) perspective, the joint use of cognitive and neurophysiological data may provide converging evidence to document the evolution of cognitive skills in at-risk individuals.
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Affiliation(s)
- Letteria Tomasello
- Department of Clinical and Experimental Medicine, University of Messina, 98122 Messina, Italy
- Faculty of Medicine and Dentistry, Sapienza University of Rome, 00185 Rome, Italy
| | - Leonardo Carlucci
- Learning Sciences Hub, Department of Humanities, Letters, Cultural Heritage and Educational Studies, Foggia University, 71121 Foggia, Italy
| | - Angelina Laganà
- Department of Biomedical and Dental Sciences, Morphological and Functional Images, 98122 Messina, Italy
| | - Santi Galletta
- Réseau Hospitalier Neuchâtelois (RHNe), Service de Neurologie et Neuroréadaptation, 2000 Neuchâtel, Switzerland
| | - Chiara Valeria Marinelli
- Learning Sciences Hub, Department of Humanities, Letters, Cultural Heritage and Educational Studies, Foggia University, 71121 Foggia, Italy
| | - Massimo Raffaele
- Department of Clinical and Experimental Medicine, University of Messina, 98122 Messina, Italy
| | - Pierluigi Zoccolotti
- Tuscany Rehabilitation Clinic, 52025 Montevarchi, Italy
- Department of Psychology, Sapienza University of Rome, 00185 Rome, Italy
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Poikonen H, Zaluska T, Wang X, Magno M, Kapur M. Nonlinear and machine learning analyses on high-density EEG data of math experts and novices. Sci Rep 2023; 13:8012. [PMID: 37198273 DOI: 10.1038/s41598-023-35032-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 05/11/2023] [Indexed: 05/19/2023] Open
Abstract
Current trend in neurosciences is to use naturalistic stimuli, such as cinema, class-room biology or video gaming, aiming to understand the brain functions during ecologically valid conditions. Naturalistic stimuli recruit complex and overlapping cognitive, emotional and sensory brain processes. Brain oscillations form underlying mechanisms for such processes, and further, these processes can be modified by expertise. Human cortical functions are often analyzed with linear methods despite brain as a biological system is highly nonlinear. This study applies a relatively robust nonlinear method, Higuchi fractal dimension (HFD), to classify cortical functions of math experts and novices when they solve long and complex math demonstrations in an EEG laboratory. Brain imaging data, which is collected over a long time span during naturalistic stimuli, enables the application of data-driven analyses. Therefore, we also explore the neural signature of math expertise with machine learning algorithms. There is a need for novel methodologies in analyzing naturalistic data because formulation of theories of the brain functions in the real world based on reductionist and simplified study designs is both challenging and questionable. Data-driven intelligent approaches may be helpful in developing and testing new theories on complex brain functions. Our results clarify the different neural signature, analyzed by HFD, of math experts and novices during complex math and suggest machine learning as a promising data-driven approach to understand the brain processes in expertise and mathematical cognition.
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Affiliation(s)
- Hanna Poikonen
- Learning Sciences and Higher Education, ETH Zurich, Clausiusstrasse 59 RZ J2, 8092, Zurich, Switzerland.
| | - Tomasz Zaluska
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
| | - Xiaying Wang
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
| | - Michele Magno
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
| | - Manu Kapur
- Learning Sciences and Higher Education, ETH Zurich, Clausiusstrasse 59 RZ J2, 8092, Zurich, Switzerland
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