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Pascarella A, Manzo L, Ferlazzo E. Modern neurophysiological techniques indexing normal or abnormal brain aging. Seizure 2024:S1059-1311(24)00194-8. [PMID: 38972778 DOI: 10.1016/j.seizure.2024.07.001] [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: 04/09/2024] [Accepted: 07/01/2024] [Indexed: 07/09/2024] Open
Abstract
Brain aging is associated with a decline in cognitive performance, motor function and sensory perception, even in the absence of neurodegeneration. The underlying pathophysiological mechanisms remain incompletely understood, though alterations in neurogenesis, neuronal senescence and synaptic plasticity are implicated. Recent years have seen advancements in neurophysiological techniques such as electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP) and transcranial magnetic stimulation (TMS), offering insights into physiological and pathological brain aging. These methods provide real-time information on brain activity, connectivity and network dynamics. Integration of Artificial Intelligence (AI) techniques promise as a tool enhancing the diagnosis and prognosis of age-related cognitive decline. Our review highlights recent advances in these electrophysiological techniques (focusing on EEG, ERP, TMS and TMS-EEG methodologies) and their application in physiological and pathological brain aging. Physiological aging is characterized by changes in EEG spectral power and connectivity, ERP and TMS parameters, indicating alterations in neural activity and network function. Pathological aging, such as in Alzheimer's disease, is associated with further disruptions in EEG rhythms, ERP components and TMS measures, reflecting underlying neurodegenerative processes. Machine learning approaches show promise in classifying cognitive impairment and predicting disease progression. Standardization of neurophysiological methods and integration with other modalities are crucial for a comprehensive understanding of brain aging and neurodegenerative disorders. Advanced network analysis techniques and AI methods hold potential for enhancing diagnostic accuracy and deepening insights into age-related brain changes.
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Affiliation(s)
- Angelo Pascarella
- Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy.
| | - Lucia Manzo
- Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy
| | - Edoardo Ferlazzo
- Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan "Bianchi-Melacrino-Morelli Hospital", Reggio Calabria, Italy
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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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Hata M, Miyazaki Y, Mori K, Yoshiyama K, Akamine S, Kanemoto H, Gotoh S, Omori H, Hirashima A, Satake Y, Suehiro T, Takahashi S, Ikeda M. Utilizing portable electroencephalography to screen for pathology of Alzheimer's disease: a methodological advancement in diagnosis of neurodegenerative diseases. Front Psychiatry 2024; 15:1392158. [PMID: 38855641 PMCID: PMC11157607 DOI: 10.3389/fpsyt.2024.1392158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024] Open
Abstract
Background The current biomarker-supported diagnosis of Alzheimer's disease (AD) is hindered by invasiveness and cost issues. This study aimed to address these challenges by utilizing portable electroencephalography (EEG). We propose a novel, non-invasive, and cost-effective method for identifying AD, using a sample of patients with biomarker-verified AD, to facilitate early and accessible disease screening. Methods This study included 35 patients with biomarker-verified AD, confirmed via cerebrospinal fluid sampling, and 35 age- and sex-balanced healthy volunteers (HVs). All participants underwent portable EEG recordings, focusing on 2-minute resting-state EEG epochs with closed eyes state. EEG recordings were transformed into scalogram images, which were analyzed using "vision Transformer(ViT)," a cutting-edge deep learning model, to differentiate patients from HVs. Results The application of ViT to the scalogram images derived from portable EEG data demonstrated a significant capability to distinguish between patients with biomarker-verified AD and HVs. The method achieved an accuracy of 73%, with an area under the receiver operating characteristic curve of 0.80, indicating robust performance in identifying AD pathology using neurophysiological measures. Conclusions Our findings highlight the potential of portable EEG combined with advanced deep learning techniques as a transformative tool for screening of biomarker-verified AD. This study not only contributes to the neurophysiological understanding of AD but also opens new avenues for the development of accessible and non-invasive diagnostic methods. The proposed approach paves the way for future clinical applications, offering a promising solution to the limitations of advanced diagnostic practices for dementia.
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Affiliation(s)
- Masahiro Hata
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yuki Miyazaki
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kohji Mori
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kenji Yoshiyama
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shoshin Akamine
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hideki Kanemoto
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shiho Gotoh
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hisaki Omori
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Psychiatry, Esaka Hospital, Osaka, Japan
| | - Atsuya Hirashima
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Psychiatry, Esaka Hospital, Osaka, Japan
| | - Yuto Satake
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Takashi Suehiro
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shun Takahashi
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan
- Clinical Research and Education Center, Asakayama General Hospital, Osaka, Japan
- Department of Neuropsychiatry, Wakayama Medical University, Wakayama, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan
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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: 0] [Impact Index Per Article: 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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Chang J, Chang C. Quantitative Electroencephalography Markers for an Accurate Diagnosis of Frontotemporal Dementia: A Spectral Power Ratio Approach. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:2155. [PMID: 38138258 PMCID: PMC10744364 DOI: 10.3390/medicina59122155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/28/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
Background and Objectives: Frontotemporal dementia (FTD) is the second most common form of presenile dementia; however, its diagnosis has been poorly investigated. Previous attempts to diagnose FTD using quantitative electroencephalography (qEEG) have yielded inconsistent results in both spectral and functional connectivity analyses. This study aimed to introduce an accurate qEEG marker that could be used to diagnose FTD and other neurological abnormalities. Materials and Methods: We used open-access electroencephalography data from OpenNeuro to investigate the power ratio between the frontal and temporal lobes in the resting state of 23 patients with FTD and 29 healthy controls. Spectral data were extracted using a fast Fourier transform in the delta (0.5 ≤ 4 Hz), theta (4 ≤ 8 Hz), alpha (8-13 Hz), beta (>13-30 Hz), and gamma (>30-45 Hz) bands. Results: We found that the spectral power ratio between the frontal and temporal lobes is a promising qEEG marker of FTD. Frontal (F)-theta/temporal (T)-alpha, F-alpha/T-theta, F-theta/F-alpha, and T-beta/T-gamma showed a consistently high discrimination score for the diagnosis of FTD for different parameters and referencing methods. Conclusions: The study findings can serve as reference for future research focused on diagnosing FTD and other neurological anomalies.
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Affiliation(s)
- Jinwon Chang
- Korean Minjok Leadership Academy, Hoengseong 25268, Republic of Korea
| | - Chul Chang
- College of Medicine, Catholic University of Korea, Seoul 06591, Republic of Korea;
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Schröder S, Bönig L, Proskynitopoulos PJ, Janke E, Heck J, Mahmoudi N, Groh A, Berding G, Wedegärtner F, Deest-Gaubatz S, Maier HB, Bleich S, Frieling H, Schulze Westhoff M. Bifrontal electroconvulsive therapy leads to improvement of cerebral glucose hypometabolism in frontotemporal dementia with comorbid psychotic depression - a case report. BMC Psychiatry 2023; 23:279. [PMID: 37081424 PMCID: PMC10120124 DOI: 10.1186/s12888-023-04759-z] [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: 01/15/2023] [Accepted: 04/07/2023] [Indexed: 04/22/2023] Open
Abstract
BACKGROUND Differentiating depression and dementia in elderly patients represents a major clinical challenge for psychiatrists. Pharmacological and non-pharmacological treatment options for both conditions are often used cautiously due to fear of adverse effects. If a clinically indicated therapy is not initiated due to fear of adverse effects, the quality of life of affected patients may significantly be reduced. CASE PRESENTATION Here, we describe the case of a 65-year-old woman who presented to the department of psychiatry of a university hospital with depressed mood, pronounced anxiety, and nihilistic thoughts. While several pharmacological treatments remained without clinical response, further behavioral observation in conjunction with 18F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) revealed the diagnosis of frontotemporal dementia (FTD). To counter the pharmacological treatment resistance of psychotic depression, we decided to perform electroconvulsive therapy (ECT). Remarkably, ten sessions of ECT yielded an almost complete remission of depressive symptoms. In addition, the patient's delusional ideas disappeared. A follow-up 18F-FDG PET/CT after the ECT series still showed a frontally and parieto-temporally accentuated hypometabolism, albeit with a clear regression compared to the previous image. The follow-up 18F-FDG PET/CT thus corroborated the diagnosis of FTD, while on the other hand it demonstrated the success of ECT. CONCLUSIONS In this case, ECT was a beneficial treatment option for depressive symptoms in FTD. Also, 18F-FDG PET/CT should be discussed as a valuable tool in differentiating depression and dementia and as an indicator of treatment response.
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Affiliation(s)
- Sebastian Schröder
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Lena Bönig
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Phileas Johannes Proskynitopoulos
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Eva Janke
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Johannes Heck
- Institute for Clinical Pharmacology, Hannover Medical School, Hannover, Germany
| | - Nima Mahmoudi
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Adrian Groh
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Georg Berding
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Felix Wedegärtner
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Stephanie Deest-Gaubatz
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Hannah Benedictine Maier
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Stefan Bleich
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Helge Frieling
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Martin Schulze Westhoff
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.
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Jiao B, Li R, Zhou H, Qing K, Liu H, Pan H, Lei Y, Fu W, Wang X, Xiao X, Liu X, Yang Q, Liao X, Zhou Y, Fang L, Dong Y, Yang Y, Jiang H, Huang S, Shen L. Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer's disease using EEG technology. Alzheimers Res Ther 2023; 15:32. [PMID: 36765411 PMCID: PMC9912534 DOI: 10.1186/s13195-023-01181-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/06/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Electroencephalogram (EEG) has emerged as a non-invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer's disease (AD). However, the effectiveness of EEG in the precise diagnosis and assessment of AD and its preclinical stage, amnestic mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD. METHODS A total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting-state EEG recordings for a three-level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of models by combining participants' EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for assessing the disease progression and individual's cognitive function. RESULTS The identified EEG biomarkers achieved over 70% accuracy in the three-level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD-linked neurodegeneration on EEG metrics were localized at parieto-occipital regions. In the cross-validation predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction. CONCLUSIONS Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.
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Affiliation(s)
- Bin Jiao
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China ,grid.216417.70000 0001 0379 7164National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China ,grid.216417.70000 0001 0379 7164Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China ,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China ,grid.216417.70000 0001 0379 7164Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Rihui Li
- grid.168010.e0000000419368956Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA USA ,Brainup Institute of Science and Technology, Chongqing, China
| | - Hui Zhou
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Kunqiang Qing
- Brainup Institute of Science and Technology, Chongqing, China
| | - Hui Liu
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Hefu Pan
- Brainup Institute of Science and Technology, Chongqing, China
| | - Yanqin Lei
- Brainup Institute of Science and Technology, Chongqing, China
| | - Wenjin Fu
- Brainup Institute of Science and Technology, Chongqing, China
| | - Xiaoan Wang
- Brainup Institute of Science and Technology, Chongqing, China
| | - Xuewen Xiao
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xixi Liu
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Qijie Yang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Xinxin Liao
- grid.216417.70000 0001 0379 7164Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Yafang Zhou
- grid.216417.70000 0001 0379 7164Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, China
| | - Liangjuan Fang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yanbin Dong
- Brainup Institute of Science and Technology, Chongqing, China
| | - Yuanhao Yang
- grid.1003.20000 0000 9320 7537Mater Research Institute, The University of Queensland, Woolloongabba, Queensland 4102 Australia
| | - Haiyan Jiang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Sha Huang
- grid.216417.70000 0001 0379 7164Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China. .,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China. .,Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China. .,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China. .,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China. .,Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China.
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Rossini PM, Miraglia F, Vecchio F. Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis. Alzheimers Dement 2022; 18:2699-2706. [PMID: 35388959 PMCID: PMC10083993 DOI: 10.1002/alz.12645] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/12/2022] [Accepted: 02/03/2022] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline-including Alzheimer's disease (AD) dementia-does not develop in few days; disease mechanisms act progressively for several years before clinical evidence. METHODS A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to-or, more accurately, is already in a prodromal form of-AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods. RESULTS Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers. DISCUSSION On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments.
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Affiliation(s)
- Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
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Yener G, Öz D. Innovations in Neurophysiology and Their Use in Neuropsychiatry. Noro Psikiyatr Ars 2022; 59:S67-S74. [PMID: 36578987 PMCID: PMC9767126 DOI: 10.29399/npa.28234] [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: 06/04/2022] [Accepted: 07/31/2022] [Indexed: 12/31/2022] Open
Abstract
Many structural and functional tests are used to explore the nature of neurodevelopmental and neurodegenerative diseases. Cognitive involvement has become more and more remarkable in many neurological and psychiatric diseases. This condition evoked a paradigm shift, and today disorders are addressed from a neuroscientific perspective, including silent symptoms. The spatial resolution of structural studies is lacking and is combined with the unique temporal resolution of EEG methods. In our current clinical practice, EEG does not have definitive diagnostic value in psychiatric disorders, but it helps to make a correct diagnosis by excluding other neurological diseases. However, the use of EEG for research purposes is promising in both groups. In this review; there is up-to-date information on the use of electrophysiological examinations in neurological diseases, especially Alzheimer's disease, Parkinson's disease, Frontotemporal dementia, and psychiatric disorders such as schizophrenia, mood disorders, attention deficit and hyperactivity disorder, and obsessive-compulsive disorder, to define the point we have reached in our journey to understand these disorders.
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Affiliation(s)
- Görsev Yener
- İzmir University of Economics, Faculty of Medicine, Department of Neurology, İzmir, Turkey,İzmir Biomedicine and Genom Center, İzmir, Turkey,Dokuz Eylül University Brain Dynamics Multidisciplinary Research Center, İzmir, Turkey
| | - Didem Öz
- Dokuz Eylül University Brain Dynamics Multidisciplinary Research Center, İzmir, Turkey,Dokuz Eylül University Hospital, Department of Neurology, İzmir, Turkey,Dokuz Eylül University, Medical Science Faculty, Neuroscience Department, İzmir, Turkey,Global Brain Health Institute, San Francisco, USA,Correspondence Address: Didem Öz, Dokuz Eylül Üniversitesi, Tıp Fakültesi, Nöroloji Anabilim Dalı, 15 Temmuz Sağlık ve Sanat Yerleşkesi, İnciraltı 35340, İzmir, Turkey • E-mail:
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10
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Monteverdi A, Palesi F, Costa A, Vitali P, Pichiecchio A, Cotta Ramusino M, Bernini S, Jirsa V, Gandini Wheeler-Kingshott CAM, D’Angelo E. Subject-specific features of excitation/inhibition profiles in neurodegenerative diseases. Front Aging Neurosci 2022; 14:868342. [PMID: 35992607 PMCID: PMC9391060 DOI: 10.3389/fnagi.2022.868342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/05/2022] [Indexed: 11/26/2022] Open
Abstract
Brain pathologies are characterized by microscopic changes in neurons and synapses that reverberate into large scale networks altering brain dynamics and functional states. An important yet unresolved issue concerns the impact of patients' excitation/inhibition profiles on neurodegenerative diseases including Alzheimer's Disease, Frontotemporal Dementia, and Amyotrophic Lateral Sclerosis. In this work, we used The Virtual Brain (TVB) simulation platform to simulate brain dynamics in healthy and neurodegenerative conditions and to extract information about the excitatory/inhibitory balance in single subjects. The brain structural and functional connectomes were extracted from 3T-MRI (Magnetic Resonance Imaging) scans and TVB nodes were represented by a Wong-Wang neural mass model endowing an explicit representation of the excitatory/inhibitory balance. Simulations were performed including both cerebral and cerebellar nodes and their structural connections to explore cerebellar impact on brain dynamics generation. The potential for clinical translation of TVB derived biophysical parameters was assessed by exploring their association with patients' cognitive performance and testing their discriminative power between clinical conditions. Our results showed that TVB biophysical parameters differed between clinical phenotypes, predicting higher global coupling and inhibition in Alzheimer's Disease and stronger N-methyl-D-aspartate (NMDA) receptor-dependent excitation in Amyotrophic Lateral Sclerosis. These physio-pathological parameters allowed us to perform an advanced analysis of patients' conditions. In backward regressions, TVB-derived parameters significantly contributed to explain the variation of neuropsychological scores and, in discriminant analysis, the combination of TVB parameters and neuropsychological scores significantly improved the discriminative power between clinical conditions. Moreover, cluster analysis provided a unique description of the excitatory/inhibitory balance in individual patients. Importantly, the integration of cerebro-cerebellar loops in simulations improved TVB predictive power, i.e., the correlation between experimental and simulated functional connectivity in all pathological conditions supporting the cerebellar role in brain function disrupted by neurodegeneration. Overall, TVB simulations reveal differences in the excitatory/inhibitory balance of individual patients that, combined with cognitive assessment, can promote the personalized diagnosis and therapy of neurodegenerative diseases.
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Affiliation(s)
- Anita Monteverdi
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Alfredo Costa
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Paolo Vitali
- Department of Radiology, IRCCS Policlinico San Donato, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Advanced Imaging and Radiomic Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Matteo Cotta Ramusino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy
| | - Sara Bernini
- Dementia Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, INSERM, INS, Aix-Marseille University, Marseille, France
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, University College London (UCL) Queen Square Institute of Neurology, London, United Kingdom
| | - Egidio D’Angelo
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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11
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Rocha P, Dagnino PC, O’Sullivan R, Soria-Frisch A, Paúl C. BRAINCODE for Cognitive Impairment Diagnosis in Older Adults: Designing a Case-Control Pilot Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19095768. [PMID: 35565162 PMCID: PMC9105735 DOI: 10.3390/ijerph19095768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 11/16/2022]
Abstract
An early, extensive, accurate, and cost-effective clinical diagnosis of neurocognitive disorders will have advantages for older people and their families, but also for the health and care systems sustainability and performance. BRAINCODE is a technology that assesses cognitive impairment in older people, differentiating normal from pathologic brain condition, based in an EEG biomarkers evaluation. This paper will address BRAINCODE's pilot design, which intends to validate its efficacy, to provide guidelines for future studies and to allow its integration on the SHAPES platform. It is expected that BRAINCODE confirms a regular clinical diagnosis and neuropsychologic tests to discriminate 'normal' from pathologic cognitive decline and differentiates mild cognitive impairment from dementia in older adults with/without subjective cognitive complains.
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Affiliation(s)
- Pedro Rocha
- Departamento de Ciências do Comportamento, ICBAS—Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Rua Jorge de Viterbo Ferreira, 228, 4050-313 Porto, Portugal;
- CINTESIS—Centro de Investigação em Tecnologias e Serviços de Saúde, R. Dr. Plácido da Costa, 4200-450 Porto, Portugal
- Correspondence:
| | - Paulina Clara Dagnino
- Starlab Barcelona SL, Neuroscience Business Unit, Avda. Tibidabo 47 bis, 08035 Barcelona, Spain; (P.C.D.); (A.S.-F.)
| | - Ronan O’Sullivan
- Centre for Gerontology and Rehabilitation, School of Medicine, University College Cork, College Road, T12 K8AF Cork, Ireland;
| | - Aureli Soria-Frisch
- Starlab Barcelona SL, Neuroscience Business Unit, Avda. Tibidabo 47 bis, 08035 Barcelona, Spain; (P.C.D.); (A.S.-F.)
| | - Constança Paúl
- Departamento de Ciências do Comportamento, ICBAS—Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Rua Jorge de Viterbo Ferreira, 228, 4050-313 Porto, Portugal;
- CINTESIS—Centro de Investigação em Tecnologias e Serviços de Saúde, R. Dr. Plácido da Costa, 4200-450 Porto, Portugal
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12
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Ouchani M, Gharibzadeh S, Jamshidi M, Amini M. A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5425569. [PMID: 34746303 PMCID: PMC8566072 DOI: 10.1155/2021/5425569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/20/2021] [Accepted: 10/18/2021] [Indexed: 01/27/2023]
Abstract
This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.
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Affiliation(s)
- Mahshad Ouchani
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Shahriar Gharibzadeh
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Mahdieh Jamshidi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Morteza Amini
- Shahid Beheshti University, Tehran, Iran
- Institute for Cognitive Science Studies (ICSS), Tehran, Iran
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13
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Fröhlich S, Kutz DF, Müller K, Voelcker-Rehage C. Characteristics of Resting State EEG Power in 80+-Year-Olds of Different Cognitive Status. Front Aging Neurosci 2021; 13:675689. [PMID: 34456708 PMCID: PMC8387136 DOI: 10.3389/fnagi.2021.675689] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 07/07/2021] [Indexed: 11/28/2022] Open
Abstract
Compared with healthy older adults, patients with Alzheimer's disease show decreased alpha and beta power as well as increased delta and theta power during resting state electroencephalography (rsEEG). Findings for mild cognitive impairment (MCI), a stage of increased risk of conversion to dementia, are less conclusive. Cognitive status of 213 non-demented high-agers (mean age, 82.5 years) was classified according to a neuropsychological screening and a cognitive test battery. RsEEG was measured with eyes closed and open, and absolute power in delta, theta, alpha, and beta bands were calculated for nine regions. Results indicate no rsEEG power differences between healthy individuals and those with MCI. There were also no differences present between groups in EEG reactivity, the change in power from eyes closed to eyes open, or the topographical pattern of each frequency band. Overall, EEG reactivity was preserved in 80+-year-olds without dementia, and topographical patterns were described for each frequency band. The application of rsEEG power as a marker for the early detection of dementia might be less conclusive for high-agers.
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Affiliation(s)
- Stephanie Fröhlich
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Dieter F Kutz
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Katrin Müller
- Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany.,Department of Social Science of Physical Activity and Health, Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
| | - Claudia Voelcker-Rehage
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, Faculty of Psychology and Sport Sciences, University of Münster, Münster, Germany.,Department of Sports Psychology (With Focus on Prevention and Rehabilitation), Institute of Human Movement Science and Health, Faculty of Behavioural and Social Sciences, Chemnitz University of Technology, Chemnitz, Germany
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14
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Özbek Y, Fide E, Yener GG. Resting-state EEG alpha/theta power ratio discriminates early-onset Alzheimer's disease from healthy controls. Clin Neurophysiol 2021; 132:2019-2031. [PMID: 34284236 DOI: 10.1016/j.clinph.2021.05.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 03/12/2021] [Accepted: 05/17/2021] [Indexed: 11/17/2022]
Abstract
OBJECTIVES The present study aims to compare early-onset Alzheimer's disease (EOAD) patients with healthy controls (HC), and late-onset Alzheimer's disease (LOAD) patients using resting-state delta, theta, alpha, and beta oscillations and provide a cut-off score of alpha/theta ratio to discriminate individuals with EOAD and young HC. METHODS Forty-seven individuals with EOAD, 51 individuals with LOAD, and demographically-matched 49 young and 51 older controls were included in the study. Spectral-power analysis using Fast-Fourier Transformation (FFT) is performed on resting-state electroencephalography (EEG) data. Delta, theta, alpha, and beta oscillations compared between groups and Receiver Operating Characteristic (ROC) curve analysis was conducted. RESULTS Compared to healthy controls individuals with EOAD showed an increase in slow frequency bands and a decrease in fast frequency bands. Frontal alpha/theta power ratio is the best discriminating value between EOAD and young HC with the sensitivity and specificity greater than 80% with area under the curve (AUC) 0.881. CONCLUSIONS EOAD display more widespread and severe electrophysiological abnormalities than LOAD and HC which may reflect more pronounced pathological burden and cholinergic deficits in EOAD. Additionally, the alpha/theta ratio can discriminate EOAD and young HC successfully. SIGNIFICANCE This study is the first to report that resting-state EEG power can be a promising marker for diagnostic accuracy between EOAD and healthy controls.
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Affiliation(s)
- Yağmur Özbek
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Ezgi Fide
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
| | - Görsev G Yener
- Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Izmir, Turkey; Izmir University of Economics, Faculty of Medicine, Izmir, Turkey.
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15
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Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Tsalikakis D, Tsipouras MG. Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review. Int J Neural Syst 2021; 31:2130002. [PMID: 33588710 DOI: 10.1142/s0129065721300023] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.
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Affiliation(s)
- Katerina D Tzimourta
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GR50100, Greece.,Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Vasileios Christou
- Q Base R&D, Science & Technology Park of Epirus, University of Ioannina Campus, Ioannina GR45110, Greece.,Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Alexandros T Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Loukas G Astrakas
- Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Pantelis Angelidis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Dimitrios Tsalikakis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Markos G Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
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16
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Gavrilova SI, Alvarez A. Cerebrolysin in the therapy of mild cognitive impairment and dementia due to Alzheimer's disease: 30 years of clinical use. Med Res Rev 2020; 41:2775-2803. [PMID: 32808294 DOI: 10.1002/med.21722] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 08/01/2020] [Accepted: 08/05/2020] [Indexed: 12/21/2022]
Abstract
Alzheimer's disease (AD) is the most common neurocognitive disorder and a global health problem. The prevalence of AD is growing dramatically, especially in low- and middle-income countries, and will reach 131.5 million cases worldwide by 2050. Therefore, developing a disease-modifying therapy capable of delaying or even preventing the onset and progression of AD has become a world priority, and is an unmet need. The pathogenesis of AD, considered as the result of an imbalance between resilience and risk factors, begins many years before the typical clinical picture develops and involves multiple pathophysiological mechanisms. Since the pathophysiology of AD is multifactorial, it is not surprising that all attempts done to modify the disease course with drugs directed towards a single therapeutic target have been unsuccessful. Thus, combined modality therapy, using multiple drugs with a single mechanism of action or multi-target drugs, appears as the most promising strategy for both effective AD therapy and prevention. Cerebrolysin, acting as a multitarget peptidergic drug with a neurotrophic mode of action, exerts long-lasting therapeutic effects on AD that could reflect its potential utility for disease modification. Clinical trials demonstrated that Cerebrolysin is safe and efficacious in the treatment of AD, and may enhance and prolong the efficacy of cholinergic drugs, particularly in moderate to advanced AD patients. In this review, we summarize advances of therapeutic relevance in the pathogenesis and the biomarkers of AD, paying special attention to neurotrophic factors, and present results of preclinical and clinical investigations with Cerebrolysin in AD.
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Affiliation(s)
- Svetlana I Gavrilova
- Department of Geriatric Psychiatry, Cognitive Disorders and Alzheimer's Disease Unit, Mental Health Research Center, Moscow, Russia
| | - Anton Alvarez
- Department of Neuropsychiatry, Medinova Institute of Neurosciences, Clinica RehaSalud, A Coruña, Spain.,Clinical Research Department, QPS Holdings, A Coruña, Spain
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17
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McMackin R, Bede P, Pender N, Hardiman O, Nasseroleslami B. Neurophysiological markers of network dysfunction in neurodegenerative diseases. Neuroimage Clin 2019; 22:101706. [PMID: 30738372 PMCID: PMC6370863 DOI: 10.1016/j.nicl.2019.101706] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 01/28/2019] [Accepted: 01/31/2019] [Indexed: 12/11/2022]
Abstract
There is strong clinical, imaging and pathological evidence that neurodegeneration is associated with altered brain connectivity. While functional imaging (fMRI) can detect resting and activated states of metabolic activity, its use is limited by poor temporal resolution, cost and confounding vascular parameters. By contrast, electrophysiological (e.g. EEG/MEG) recordings provide direct measures of neural activity with excellent temporal resolution, and source localization methodologies can address problems of spatial resolution, permitting measurement of functional activity of brain networks with a spatial resolution similar to that of fMRI. This opens an exciting therapeutic approach focussed on pharmacological and physiological modulation of brain network activity. This review describes current neurophysiological approaches towards evaluating cortical network dysfunction in common neurodegenerative disorders. It explores how modern neurophysiologic tools can provide markers for diagnosis, prognosis, subcategorization and clinical trial outcome measures, and how modulation of brain networks can contribute to new therapeutic approaches.
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Affiliation(s)
- Roisin McMackin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, 152-160 Pearse St., Trinity College Dublin, The University of Dublin, Ireland.
| | - Peter Bede
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, 152-160 Pearse St., Trinity College Dublin, The University of Dublin, Ireland; Computational Neuroimaging Group, Trinity Biomedical Sciences Institute, 152-160 Pearse St., Trinity College Dublin, The University of Dublin, Ireland.
| | - Niall Pender
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, 152-160 Pearse St., Trinity College Dublin, The University of Dublin, Ireland; Beaumont Hospital Dublin, Department of Psychology, Beaumont Road, Beaumont, Dublin 9, Ireland.
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, 152-160 Pearse St., Trinity College Dublin, The University of Dublin, Ireland; Beaumont Hospital Dublin, Department of Neurology, Beaumont Road, Beaumont, Dublin 9, Ireland.
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, 152-160 Pearse St., Trinity College Dublin, The University of Dublin, Ireland.
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