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Carrarini C, Nardulli C, Titti L, Iodice F, Miraglia F, Vecchio F, Rossini PM. Neuropsychological and electrophysiological measurements for diagnosis and prediction of dementia: a review on Machine Learning approach. Ageing Res Rev 2024; 100:102417. [PMID: 39002643 DOI: 10.1016/j.arr.2024.102417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/29/2024] [Accepted: 07/07/2024] [Indexed: 07/15/2024]
Abstract
INTRODUCTION Emerging and advanced technologies in the field of Artificial Intelligence (AI) represent promising methods to predict and diagnose neurodegenerative diseases, such as dementia. By using multimodal approaches, Machine Learning (ML) seems to provide a better understanding of the pathological mechanisms underlying the onset of dementia. The purpose of this review was to discuss the current ML application in the field of neuropsychology and electrophysiology, exploring its results in both prediction and diagnosis for different forms of dementia, such as Alzheimer's disease (AD), Vascular Dementia (VaD), Dementia with Lewy bodies (DLB), and Frontotemporal Dementia (FTD). METHODS Main ML-based papers focusing on neuropsychological assessments and electroencephalogram (EEG) studies were analyzed for each type of dementia. RESULTS An accuracy ranging between 70 % and 90 % or even more was observed in all neurophysiological and electrophysiological results trained by ML. Among all forms of dementia, the most significant findings were observed for AD. Relevant results were mostly related to diagnosis rather than prediction, because of the lack of longitudinal studies with appropriate follow-up duration. However, it remains unclear which ML algorithm performs better in diagnosing or predicting dementia. CONCLUSIONS Neuropsychological and electrophysiological measurements, together with ML analysis, may be considered as reliable instruments for early detection of dementia.
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Affiliation(s)
- Claudia Carrarini
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Neuroscience, Catholic University of Sacred Heart, Largo Agostino Gemelli 8, Rome 00168, Italy
| | - Cristina Nardulli
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Laura Titti
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Francesco Iodice
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy
| | - Francesca Miraglia
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Theoretical and Applied Sciences, eCampus University, via Isimbardi 10, Novedrate 22060, Italy
| | - Fabrizio Vecchio
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy; Department of Theoretical and Applied Sciences, eCampus University, via Isimbardi 10, Novedrate 22060, Italy
| | - Paolo Maria Rossini
- Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele, via della Pisana 235, Rome 00163, Italy.
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Han X, Wang Y, Chen Y, Qiu Y, Gu X, Dai Y, Xu Q, Sun Y, Zhou Y. Predicting white-matter hyperintensity progression and cognitive decline in patients with cerebral small-vessel disease: a magnetic resonance-based habitat analysis. Quant Imaging Med Surg 2024; 14:6621-6634. [PMID: 39281120 PMCID: PMC11400666 DOI: 10.21037/qims-24-238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 07/18/2024] [Indexed: 09/18/2024]
Abstract
Background White-matter hyperintensity (WMH) is the key magnetic resonance imaging (MRI) marker of cerebral small-vessel disease (CSVD). This study aimed to investigate whether habitat analysis based on physiologic MRI parameters can predict the progression of WMH and cognitive decline in CSVD. Methods Diffusion- and perfusion-weighted imaging data were obtained from 69 patients with CSVD at baseline and at 1-year of follow-up. The white-matter region was classified into constant WMH, growing WMH, shrinking WMH, and normal-appearing white matter (NAWM) according to the T2-fluid-attenuated inversion recovery (FLAIR) sequences images at the baseline and follow-up. We employed k-means clustering on a voxel-wise basis to delineate WMH habitats, integrating multiple diffusion metrics and cerebral blood flow (CBF) values derived from perfusion data. The WMH at the baseline and the predicted WMH from the habitat analysis were used as regions of avoidance (ROAs). The decreased rate of global efficiency for the whole brain structural connectivity was calculated after removal of the ROA. The association between the decreased rate of global efficiency and Montreal Cognitive Assessment (MoCA) and mini-mental state examination (MMSE) scores was evaluated using Pearson correlation coefficients. Results We found that the physiologic MRI habitats with lower fractional anisotropy and CBF values and higher mean diffusivity, axial diffusivity, and radial diffusivity values overlapped considerably with the new WMH (growing WMH of baseline) after a 1-year follow-up; the accuracy of distinguishing growing WMH from NAWM was 88.9%±12.7% at baseline. Similar results were also found for the prediction of shrinking WMH. Moreover, after the removal of the predicted WMH, a decreased rate of global efficiency had a significantly negative correlation with the MoCA and MMSE scores at follow-up. Conclusions This study revealed that a habitat analysis combining perfusion with diffusion parameters could predict the progression of WMH and related cognitive decline in patients with CSVD.
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Affiliation(s)
- Xu Han
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Wang
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuewei Chen
- Department of Neurology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yage Qiu
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiyao Gu
- Department of Anesthesiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yongming Dai
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Qun Xu
- Department of Neurology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yawen Sun
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Zhou
- Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Sun Y, Hu W, Hu Y, Qiu Y, Chen Y, Xu Q, Wei H, Dai Y, Zhou Y. Exploring cognitive related microstructural alterations in normal appearing white matter and deep grey matter for small vessel disease: A quantitative susceptibility mapping study. Neuroimage 2024; 298:120790. [PMID: 39147292 DOI: 10.1016/j.neuroimage.2024.120790] [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: 07/31/2024] [Accepted: 08/12/2024] [Indexed: 08/17/2024] Open
Abstract
Brain microstructural alterations possibly occur in the normal-appearing white matter (NAWM) and grey matter of small vessel disease (SVD) patients, and may contribute to cognitive impairment. The aim of this study was to explore cognitive related microstructural alterations in white matter and deep grey matter nuclei in SVD patients using magnetic resonance (MR) quantitative susceptibility mapping (QSM). 170 SVD patients, including 103 vascular mild cognitive impairment (VaMCI) and 67 no cognitive impairment (NCI), and 21 healthy control (HC) subjects were included, all underwent a whole-brain QSM scanning. Using a white matter and a deep grey matter atlas, subregion-based QSM analysis was conducted to identify and characterize microstructural alterations occurring within white matter and subcortical nuclei. Significantly different susceptibility values were revealed in NAWM and in several specific white matter tracts including anterior limb of internal capsule, corticospinal tract, medial lemniscus, middle frontal blade, superior corona radiata and tapetum among VaMCI, NCI and HC groups. However, no difference was found in white matter hyperintensities between VaMCI and NCI. A trend toward higher susceptibility in the caudate nucleus and globus pallidus of VaMCI patients compared to HC, indicating elevated iron deposition in these areas. Interestingly, some of these QSM parameters were closely correlated with both global and specific cognitive function scores, controlling age, gender and education level. Our study suggested that QSM may serve as a useful imaging tool for monitoring cognitive related microstructural alterations in brain. This is especially meaningful for white matter which previously lacks of attention.
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Affiliation(s)
- Yawen Sun
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wentao Hu
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ying Hu
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yage Qiu
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuewei Chen
- Department of Neurology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Renji-UNSW CHeBA Neurocognitive Center, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qun Xu
- Department of Neurology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Renji-UNSW CHeBA Neurocognitive Center, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Department of Health Manage Center, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Hongjiang Wei
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yongming Dai
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
| | - Yan Zhou
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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He C, Hu X, Wang M, Yin X, Zhan M, Li Y, Sun L, Du Y, Chen Z, Wang H, Shao H. Frontiers and hotspots evolution in mild cognitive impairment: a bibliometric analysis of from 2013 to 2023. Front Neurosci 2024; 18:1352129. [PMID: 39221008 PMCID: PMC11361971 DOI: 10.3389/fnins.2024.1352129] [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: 12/23/2023] [Accepted: 06/07/2024] [Indexed: 09/04/2024] Open
Abstract
Background Mild cognitive impairment is a heterogeneous syndrome. The heterogeneity of the syndrome and the absence of consensus limited the advancement of MCI. The purpose of our research is to create a visual framework of the last decade, highlight the hotspots of current research, and forecast the most fruitful avenues for future MCI research. Methods We collected all the MCI-related literature published between 1 January 2013, and 24 April 2023, on the "Web of Science." The visual graph was created by the CiteSpace and VOSviewer. The current research hotspots and future research directions are summarized through the analysis of keywords and co-cited literature. Results There are 6,075 articles were included in the final analysis. The number of publications shows an upward trend, especially after 2018. The United States and the University of California System are the most prolific countries and institutions, respectively. Petersen is the author who ranks first in terms of publication volume and influence. Journal of Alzheimer's Disease was the most productive journal. "neuroimaging," "fluid markers," and "predictors" are the focus of current research, and "machine learning," "electroencephalogram," "deep learning," and "blood biomarkers" are potential research directions in the future. Conclusion The cognition of MCI has been continuously evolved and renewed by multiple countries' joint efforts in the past decade. Hotspots for current research are on diagnostic biomarkers, such as fluid markers, neuroimaging, and so on. Future hotspots might be focused on the best prognostic and diagnostic models generated by machine learning and large-scale screening tools such as EEG and blood biomarkers.
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Affiliation(s)
- Chunying He
- Department of Neurology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
- Graduate School, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaohua Hu
- Department of Neurology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
- Graduate School, China Academy of Chinese Medical Sciences, Beijing, China
| | - Muren Wang
- Department of Neurology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
- Graduate School, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xiaolan Yin
- Department of Gastroenterology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
| | - Min Zhan
- Department of Neurology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
| | - Yutong Li
- Department of Neurology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
- Graduate School, Beijing University of Traditional Chinese Medicine, Beijing, China
| | - Linjuan Sun
- Department of Neurology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
| | - Yida Du
- Department of Neurology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
- Graduate School, Beijing University of Traditional Chinese Medicine, Beijing, China
| | - Zhiyan Chen
- Department of Neurology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
- Graduate School, Beijing University of Traditional Chinese Medicine, Beijing, China
| | - Huan Wang
- Department of Neurology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
- Graduate School, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haibin Shao
- Department of Neurology, China Academy of Chinese Medical Sciences Xiyuan Hospital, Beijing, China
- Graduate School, China Academy of Chinese Medical Sciences, Beijing, China
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Singh SG, Das D, Barman U, Saikia MJ. Early Alzheimer's Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment. Diagnostics (Basel) 2024; 14:1759. [PMID: 39202248 PMCID: PMC11353639 DOI: 10.3390/diagnostics14161759] [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: 07/04/2024] [Revised: 07/22/2024] [Accepted: 08/09/2024] [Indexed: 09/03/2024] Open
Abstract
Alzheimer's disease is a weakening neurodegenerative condition with profound cognitive implications, making early and accurate detection crucial for effective treatment. In recent years, machine learning, particularly deep learning, has shown significant promise in detecting mild cognitive impairment to Alzheimer's disease conversion. This review synthesizes research on machine learning approaches for predicting conversion from mild cognitive impairment to Alzheimer's disease dementia using magnetic resonance imaging, positron emission tomography, and other biomarkers. Various techniques used in literature such as machine learning, deep learning, and transfer learning were examined in this study. Additionally, data modalities and feature extraction methods analyzed by different researchers are discussed. This review provides a comprehensive overview of the current state of research in Alzheimer's disease detection and highlights future research directions.
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Affiliation(s)
- Soraisam Gobinkumar Singh
- Faculty of Computer Technology, Assam down town University, Guwahati 781026, Assam, India; (S.G.S.); (U.B.)
| | - Dulumani Das
- Faculty of Computer Technology, Assam down town University, Guwahati 781026, Assam, India; (S.G.S.); (U.B.)
| | - Utpal Barman
- Faculty of Computer Technology, Assam down town University, Guwahati 781026, Assam, India; (S.G.S.); (U.B.)
| | - Manob Jyoti Saikia
- Biomedical Sensors and Systems Lab, University of North Florida, Jacksonville, FL 32224, USA
- Department of Electrical Engineering, University of North Florida, Jacksonville, FL 32224, USA
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Javaid H, Nouman M, Cheaha D, Kumarnsit E, Chatpun S. Complexity measures reveal age-dependent changes in electroencephalogram during working memory task. Behav Brain Res 2024; 470:115070. [PMID: 38806100 DOI: 10.1016/j.bbr.2024.115070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 05/09/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
The alterations in electroencephalogram (EEG) signals are the complex outputs of functional factors, such as normal physiological aging, pathological process, which results in further cognitive decline. It is not clear that when brain aging initiates, but elderly people are vulnerable to be incipient of neurodegenerative diseases such as Alzheimer's disease. The EEG signals were recorded from 20 healthy middle age and 20 healthy elderly subjects while performing a working memory task. Higuchi's fractal dimension (HFD), Katz's fractal dimension (KFD), sample entropy and three Hjorth parameters were extracted to analyse the complexity of EEG signals. Four machine learning classifiers, multilayer perceptron (MLP), support vector machine (SVM), K-nearest neighbour (KNN), and logistic model tree (LMT) were employed to distinguish the EEG signals of middle age and elderly age groups. HFD, KFD and Hjorth complexity were found significantly correlated with age. MLP achieved the highest overall accuracy of 93.75%. For posterior region, the maximum accuracy of 92.50% was achieved using MLP. Since fractal dimension associated with the complexity of EEG signals, HFD, KFD and Hjorth complexity demonstrated the decreased complexity from middle age to elderly groups. The complexity features appear to be more appropriate indicators of monitoring EEG signal complexity in healthy aging.
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Affiliation(s)
- Hamad Javaid
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand; Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, Exeter, Ex4 4QG, United Kingdom
| | - Muhammad Nouman
- Sirindhorn School of Prosthetics and Orthotics, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Dania Cheaha
- Biology program, Division of Biological Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand; Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand
| | - Ekkasit Kumarnsit
- Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand; Physiology Program, Division of Health and Applied Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand
| | - Surapong Chatpun
- Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand; Biosignal Research Centre for Health, Prince of Songkla University, Hat Yai, Songkla 90112, Thailand; Institute of Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand.
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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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De Paolis ML, Paoletti I, Zaccone C, Capone F, D'Amelio M, Krashia P. Transcranial alternating current stimulation (tACS) at gamma frequency: an up-and-coming tool to modify the progression of Alzheimer's Disease. Transl Neurodegener 2024; 13:33. [PMID: 38926897 PMCID: PMC11210106 DOI: 10.1186/s40035-024-00423-y] [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: 01/08/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Abstract
The last decades have witnessed huge efforts devoted to deciphering the pathological mechanisms underlying Alzheimer's Disease (AD) and to testing new drugs, with the recent FDA approval of two anti-amyloid monoclonal antibodies for AD treatment. Beyond these drug-based experimentations, a number of pre-clinical and clinical trials are exploring the benefits of alternative treatments, such as non-invasive stimulation techniques on AD neuropathology and symptoms. Among the different non-invasive brain stimulation approaches, transcranial alternating current stimulation (tACS) is gaining particular attention due to its ability to externally control gamma oscillations. Here, we outline the current knowledge concerning the clinical efficacy, safety, ease-of-use and cost-effectiveness of tACS on early and advanced AD, applied specifically at 40 Hz frequency, and also summarise pre-clinical results on validated models of AD and ongoing patient-centred trials.
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Affiliation(s)
- Maria Luisa De Paolis
- Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy
| | - Ilaria Paoletti
- Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy
| | - Claudio Zaccone
- Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy
| | - Fioravante Capone
- Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200 - 00128, Rome, Italy
| | - Marcello D'Amelio
- Department of Medicine and Surgery, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy.
- Department of Experimental Neurosciences, IRCCS Santa Lucia Foundation, Via del Fosso Di Fiorano, 64 - 00143, Rome, Italy.
| | - Paraskevi Krashia
- Department of Experimental Neurosciences, IRCCS Santa Lucia Foundation, Via del Fosso Di Fiorano, 64 - 00143, Rome, Italy
- Department of Sciences and Technologies for Sustainable Development and One Health, Università Campus Bio-Medico Di Roma, Via Alvaro del Portillo, 21 - 00128, Rome, Italy
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Ai Y, Zhou C, Wang M, Yang C, Zhou S, Dong X, Ye N, Li Y, Wang L, Ren H, Gao X, Xu M, Hu H, Wang Y. Higher remnant cholesterol is associated with an increased risk of amnestic mild cognitive impairment: a community-based cross-sectional study. Front Aging Neurosci 2024; 16:1332767. [PMID: 38410746 PMCID: PMC10894954 DOI: 10.3389/fnagi.2024.1332767] [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: 11/03/2023] [Accepted: 01/29/2024] [Indexed: 02/28/2024] Open
Abstract
Background and aims Amnestic mild cognitive impairment (aMCI) is the most common subtype of MCI, which carries a significantly high risk of transitioning to Alzheimer's disease. Recently, increasing attention has been given to remnant cholesterol (RC), a non-traditional and previously overlooked risk factor. The aim of this study was to explore the association between plasma RC levels and aMCI. Methods Data were obtained from Brain Health Cognitive Management Team in Wuhan (https://hbtcm.66nao.com/admin/). A total of 1,007 community-dwelling elders were recruited for this project. Based on ten tools including general demographic data, cognitive screening and some exclusion scales, these participants were divided into the aMCI (n = 401) and normal cognitive groups (n = 606). Physical examinations were conducted on all participants, with clinical indicators such as blood pressure, blood sugar, and blood lipids collected. Results The aMCI group had significantly higher RC levels compared to the normal cognitive group (0.64 ± 0.431 vs. 0.52 ± 0.447 mmol/L, p < 0.05). Binary logistics regression revealed that occupation (P<0.001, OR = 0.533, 95%CI: 0.423-0.673) and RC (p = 0.014, OR = 1.477, 95% CI:1.081-2.018) were associated factors for aMCI. Partial correlation analysis, after controlling for occupation, showed a significant negative correlation between RC levels and MoCA scores (r = 0.059, p = 0.046), as well as Naming scores (r = 0.070, p = 0.026). ROC curve analysis demonstrated that RC levels had an independent predictive efficacy in predicting aMCI (AUC = 0.580, 95%CI: 0.544 ~ 0.615, P < 0.001). Conclusion Higher RC levels were identified as an independent indicator for aMCI, particularly in the naming cognitive domain among older individuals. Further longitudinal studies are necessary to validate the predictive efficacy of RC.
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Affiliation(s)
- Yating Ai
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
- Hubei Shizhen Laboratory, Hubei University of Chinese Medicine, Wuhan, China
- Engineering Research Center of TCM Protection Technology and New Product Development for the Elderly Brain Health, Ministry of Education, Hubei University of Chinese Medicine, Wuhan, China
| | - Chunyi Zhou
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Ming Wang
- Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, China
| | - Chongming Yang
- Research Support Center, Brigham Young University, Provo, UT, United States
| | - Shi Zhou
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Xinxiu Dong
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Niansi Ye
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Yucan Li
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Ling Wang
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Hairong Ren
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Xiaolian Gao
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Man Xu
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
| | - Hui Hu
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
- Hubei Shizhen Laboratory, Hubei University of Chinese Medicine, Wuhan, China
- Engineering Research Center of TCM Protection Technology and New Product Development for the Elderly Brain Health, Ministry of Education, Hubei University of Chinese Medicine, Wuhan, China
| | - Yuncui Wang
- School of Nursing, Hubei University of Chinese Medicine, Wuhan, China
- Hubei Shizhen Laboratory, Hubei University of Chinese Medicine, Wuhan, China
- Engineering Research Center of TCM Protection Technology and New Product Development for the Elderly Brain Health, Ministry of Education, Hubei University of Chinese Medicine, Wuhan, China
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Yin Y, Tam HL, Quint J, Chen M, Ding R, Zhang X. Epidemiology of Dementia in China in 2010-2020: A Systematic Review and Meta-Analysis. Healthcare (Basel) 2024; 12:334. [PMID: 38338219 PMCID: PMC10855047 DOI: 10.3390/healthcare12030334] [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: 12/12/2023] [Revised: 01/19/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Dementia has become one of the leading causes of death across the world. AIMS The aim of this study was to investigate the incidence, prevalence, and mortality of dementia in China between 2010 and 2020, and to investigate any geographical, age, and sex differences in the prevalence and incidence of dementia. METHODS Five databases were searched. The Joanna Briggs Institute (JBI) critical appraisal tool was used to assess the quality of the included studies. A random-effects meta-analysis was performed to estimate the pooled prevalence of dementia. Subgroup analysis was based on the type of dementia. The incidence and mortality of dementia were synthesized qualitatively. RESULTS A total of 19 studies were included. The meta-analysis showed that the prevalence of dementia was 6% (95%CI 5%, 8%), the prevalence of Alzheimer's disease (AD) was 5% (95%CI 4%, 6%), and the prevalence of vascular dementia (VaD) was 1% (95%CI 0%, 2%). The subgroup analysis showed that the prevalence rates of dementia in rural (6%, 95%CI 4%, 8%) and urban areas were similar (6%, 95%CI 4%, 8%). Deaths due to dementia increased over time. CONCLUSION The prevalence, incidence, and mortality of dementia increased with age and over time. Applying consistent criteria to the diagnosis of cognitive impairment and dementia is necessary to help with disease monitoring. Promoting dementia knowledge and awareness at the community level is necessary.
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Affiliation(s)
- Yueheng Yin
- School of Nursing, Nanjing Medical University, Nanjing 210029, China;
| | - Hon Lon Tam
- The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong 999077, China;
| | - Jennifer Quint
- School of Public Health, National Heart and Lung Institute, Imperial College London, London W12 7RQ, UK; (J.Q.); (R.D.)
| | - Mengyun Chen
- School of Nursing, Lanzhou University, Lanzhou 730000, China;
| | - Rong Ding
- School of Public Health, National Heart and Lung Institute, Imperial College London, London W12 7RQ, UK; (J.Q.); (R.D.)
| | - Xiubin Zhang
- School of Public Health, National Heart and Lung Institute, Imperial College London, London W12 7RQ, UK; (J.Q.); (R.D.)
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11
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Haraldsen IH, Hatlestad-Hall C, Marra C, Renvall H, Maestú F, Acosta-Hernández J, Alfonsin S, Andersson V, Anand A, Ayllón V, Babic A, Belhadi A, Birck C, Bruña R, Caraglia N, Carrarini C, Christensen E, Cicchetti A, Daugbjerg S, Di Bidino R, Diaz-Ponce A, Drews A, Giuffrè GM, Georges J, Gil-Gregorio P, Gove D, Govers TM, Hallock H, Hietanen M, Holmen L, Hotta J, Kaski S, Khadka R, Kinnunen AS, Koivisto AM, Kulashekhar S, Larsen D, Liljeström M, Lind PG, Marcos Dolado A, Marshall S, Merz S, Miraglia F, Montonen J, Mäntynen V, Øksengård AR, Olazarán J, Paajanen T, Peña JM, Peña L, Peniche DL, Perez AS, Radwan M, Ramírez-Toraño F, Rodríguez-Pedrero A, Saarinen T, Salas-Carrillo M, Salmelin R, Sousa S, Suyuthi A, Toft M, Toharia P, Tveitstøl T, Tveter M, Upreti R, Vermeulen RJ, Vecchio F, Yazidi A, Rossini PM. Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol. Front Neurorobot 2024; 17:1289406. [PMID: 38250599 PMCID: PMC10796757 DOI: 10.3389/fnbot.2023.1289406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/12/2023] [Indexed: 01/23/2024] Open
Abstract
More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.
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Affiliation(s)
| | | | - Camillo Marra
- Memory Clinic, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Department of Neuroscience, Catholic University of the Sacred Heart, Rome, Italy
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Fernando Maestú
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
| | | | - Soraya Alfonsin
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | | | - Abhilash Anand
- Performance and Assurance Solutions, Digital Solutions, DNV, Oslo, Norway
| | | | - Aleksandar Babic
- Healthcare Programme, Group Research and Development, DNV, Oslo, Norway
| | - Asma Belhadi
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | | | - Ricardo Bruña
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Department of Radiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Naike Caraglia
- Memory Clinic, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Claudia Carrarini
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
| | | | - Americo Cicchetti
- The Graduate School of Health Economics and Management (ALTEMS), Catholic University of the Sacred Heart, Rome, Italy
| | - Signe Daugbjerg
- The Graduate School of Health Economics and Management (ALTEMS), Catholic University of the Sacred Heart, Rome, Italy
| | - Rossella Di Bidino
- The Graduate School of Health Economics and Management (ALTEMS), Catholic University of the Sacred Heart, Rome, Italy
| | | | - Ainar Drews
- IT Department, University of Oslo, Oslo, Norway
| | - Guido Maria Giuffrè
- Memory Clinic, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Department of Neuroscience, Catholic University of the Sacred Heart, Rome, Italy
| | | | - Pedro Gil-Gregorio
- Department of Geriatric Medicine, Hospital Universitario Clínico San Carlos, Madrid, Spain
- Department of Geriatrics, Fundación para la Investigación Biomédica del Hospital Clínico San Carlos, Madrid, Spain
| | | | - Tim M. Govers
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands
| | - Harry Hallock
- Healthcare Programme, Group Research and Development, DNV, Oslo, Norway
| | - Marja Hietanen
- Division of Neuropsychology, HUS Neurocenter, Helsinki University Hospital and Helsinki University, Helsinki, Finland
| | - Lone Holmen
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Jaakko Hotta
- Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, Neurology, University of Helsinki, Helsinki, Finland
| | - Samuel Kaski
- Department of Computer Science, Helsinki Institute of Information Technology, Aalto University, Helsinki, Finland
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Rabindra Khadka
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | - Antti S. Kinnunen
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Anne M. Koivisto
- Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, Neurology, University of Helsinki, Helsinki, Finland
- Department of Neurosciences, University of Helsinki, Helsinki, Finland
- Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Shrikanth Kulashekhar
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Denis Larsen
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Pedro G. Lind
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | - Alberto Marcos Dolado
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Neurology Department, Hospital Universitario Clínico San Carlos, Madrid, Spain
| | - Serena Marshall
- Healthcare Programme, Group Research and Development, DNV, Oslo, Norway
| | - Susanne Merz
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
| | - Francesca Miraglia
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
| | - Juha Montonen
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Ville Mäntynen
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | | | - Javier Olazarán
- Neurology Service, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Teemu Paajanen
- Finnish Institute of Occupational Health, Helsinki, Finland
| | | | | | | | - Ana S. Perez
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Mohamed Radwan
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | - Federico Ramírez-Toraño
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Andrea Rodríguez-Pedrero
- Centre for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Psychology, Cognitive Psychology and Speech and Language Therapy, Universidad Complutense de Madrid, Pozuelo de Alarcón, Spain
| | - Timo Saarinen
- BioMag Laboratory, HUS Medical Imaging Centre, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Mario Salas-Carrillo
- Institute of Sanitary Investigation (IdISSC), San Carlos University Hospital, Madrid, Spain
- Memory Unit, Department of Geriatrics, Hospital Clínico San Carlos, Madrid, Spain
| | - Riitta Salmelin
- Department of Neuroscience and Biomedical Engineering, Aalto University, Helsinki, Finland
| | - Sonia Sousa
- School of Digital Technologies, Tallinn University, Tallinn, Estonia
| | - Abdillah Suyuthi
- Performance and Assurance Solutions, Digital Solutions, DNV, Oslo, Norway
| | - Mathias Toft
- Department of Neurology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pablo Toharia
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Mats Tveter
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Ramesh Upreti
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Robin J. Vermeulen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands
| | - Fabrizio Vecchio
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Como, Italy
| | - Anis Yazidi
- Department of Computer Science, OsloMet—Oslo Metropolitan University, Oslo, Norway
- NordSTAR—Nordic Center for Sustainable and Trustworthy AI Research, Oslo, Norway
| | - Paolo Maria Rossini
- Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
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12
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Geng C, Wang Z, Tang Y. Machine learning in Alzheimer's disease drug discovery and target identification. Ageing Res Rev 2024; 93:102172. [PMID: 38104638 DOI: 10.1016/j.arr.2023.102172] [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: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.
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Affiliation(s)
- Chaofan Geng
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - ZhiBin Wang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China.
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13
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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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14
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Baldimtsi E, Mouzakidis C, Karathanasi EM, Verykouki E, Hassandra M, Galanis E, Hatzigeorgiadis A, Goudas M, Zikas P, Evangelou G, Papagiannakis G, Bellis G, Kokkotis C, Tsatalas T, Giakas G, Theodorakis Y, Tsolaki M. Effects of Virtual Reality Physical and Cognitive Training Intervention On Cognitive Abilities of Elders with Mild Cognitive Impairment. J Alzheimers Dis Rep 2023; 7:1475-1490. [PMID: 38225966 PMCID: PMC10789285 DOI: 10.3233/adr-230099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/28/2023] [Indexed: 01/17/2024] Open
Abstract
Background Virtual reality (VR) technology has become increasingly used for assessment and intervention in the neuroscience field. Objective We aimed to investigate the effects of a VR Training System, named VRADA (VR Exercise App for Dementia and Alzheimer's Patients), on the cognitive functioning of older people with mild cognitive impairment (MCI). Methods In this intervention study, 122 older adults with MCI were randomly assigned to five groups (the VRADA group (n = 28), a bike group (n = 11), a physical exercise group (n = 24), a mixed group (physical and cognitive exercise) (n = 31), and a non-contact control group (n = 28). The VRADA group underwent 32 physical and cognitive training sessions, performed 2 or 3 times weekly for 12 weeks in the VR environment. All participants had detailed neuropsychological assessments before and after intervention. Results A series of linear regression models revealed that the VRADA group showed improvement or no deterioration in cognitive decline in global cognitive function (MMSE), verbal memory (Rey Auditory Verbal Learning Test and WAIS forward test), and executive functions, mental flexibility (Trail Making Test B). Conclusions This interventionstudy indicates that the VRADA system improves the cognitive function of elders with MCI.
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Affiliation(s)
- Eleni Baldimtsi
- Greek Association of Alzheimer’s Disease & Related Disorders, Alzheimer Hellas, Thessaloniki, Macedonia, Greece
- 1st Department of Neurology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Macedonia, Greece
| | - Christos Mouzakidis
- Greek Association of Alzheimer’s Disease & Related Disorders, Alzheimer Hellas, Thessaloniki, Macedonia, Greece
| | - Eleni Maria Karathanasi
- Greek Association of Alzheimer’s Disease & Related Disorders, Alzheimer Hellas, Thessaloniki, Macedonia, Greece
| | - Eleni Verykouki
- School of Medicine, Department of Hygiene, Social-Preventive Medicine and Medical Statistics, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Department of Agriculture, Crop Production and Rural Environment, School of Agricultural Sciences, University of Thessaly, Volos, Greece
| | - Mary Hassandra
- School of Physical Education, Sport Science and Dietetics, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Evangelos Galanis
- School of Physical Education, Sport Science and Dietetics, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Antonis Hatzigeorgiadis
- School of Physical Education, Sport Science and Dietetics, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Marios Goudas
- School of Physical Education, Sport Science and Dietetics, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Paul Zikas
- ORamaVR S.A., Science and Technology Park of Crete, Heraklion, Crete, Greece
| | - Giannis Evangelou
- ORamaVR S.A., Science and Technology Park of Crete, Heraklion, Crete, Greece
| | - George Papagiannakis
- ORamaVR S.A., Science and Technology Park of Crete, Heraklion, Crete, Greece
- Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), University of Crete, Heraklion, Crete, Greece
| | - George Bellis
- Biomechanical Solutions Engineering (BME), Karditsa, Greece
| | - Christos Kokkotis
- School of Physical Education, Sport Science and Dietetics, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
- Biomechanical Solutions Engineering (BME), Karditsa, Greece
| | - Themistoklis Tsatalas
- School of Physical Education, Sport Science and Dietetics, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Giannis Giakas
- School of Physical Education, Sport Science and Dietetics, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Yannis Theodorakis
- School of Physical Education, Sport Science and Dietetics, Department of Physical Education and Sport Science, University of Thessaly, Trikala, Greece
| | - Magda Tsolaki
- Greek Association of Alzheimer’s Disease & Related Disorders, Alzheimer Hellas, Thessaloniki, Macedonia, Greece
- Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI - AUTh), Balkan Center, Building A, Greece
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15
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Cai Y, Fan X, Zhao L, Liu W, Luo Y, Lau AYL, Au LWC, Shi L, Lam BYK, Ko H, Mok VCT. Comparing machine learning-derived MRI-based and blood-based neurodegeneration biomarkers in predicting syndromal conversion in early AD. Alzheimers Dement 2023; 19:4987-4998. [PMID: 37087687 DOI: 10.1002/alz.13083] [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: 12/21/2022] [Revised: 03/13/2023] [Accepted: 03/20/2023] [Indexed: 04/24/2023]
Abstract
INTRODUCTION We compared the machine learning-derived, MRI-based Alzheimer's disease (AD) resemblance atrophy index (AD-RAI) with plasma neurofilament light chain (NfL) level in predicting conversion of early AD among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects. METHODS We recruited participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had the following data: clinical features (age, gender, education, Montreal Cognitive Assessment [MoCA]), structural MRI, plasma biomarkers (p-tau181 , NfL), cerebrospinal fluid biomarkers (CSF) (Aβ42, p-tau181 ), and apolipoprotein E (APOE) ε4 genotype. We defined AD using CSF Aβ42 (A+) and p-tau181 (T+). We defined conversion (C+) if a subject progressed to the next syndromal stage within 4 years. RESULTS Of 589 participants, 96 (16.3%) were A+T+C+. AD-RAI performed better than plasma NfL when added on top of clinical features, plasma p-tau181 , and APOE ε4 genotype (area under the curve [AUC] = 0.832 vs. AUC = 0.650 among CU, AUC = 0.853 vs. AUC = 0.805 among MCI) in predicting A+T+C+. DISCUSSION AD-RAI outperformed plasma NfL in predicting syndromal conversion of early AD. HIGHLIGHTS AD-RAI outperformed plasma NfL in predicting syndromal conversion among early AD. AD-RAI showed better metrics than volumetric hippocampal measures in predicting syndromal conversion. Combining clinical features, plasma p-tau181 and apolipoprotein E (APOE) with AD-RAI is the best model for predicting syndromal conversion.
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Affiliation(s)
- Yuan Cai
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Institute, Li Ka Shing Institute of Health Science, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Xiang Fan
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Institute, Li Ka Shing Institute of Health Science, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Lei Zhao
- BrainNow Research Institute, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Wanting Liu
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Institute, Li Ka Shing Institute of Health Science, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Yishan Luo
- BrainNow Research Institute, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Alexander Yuk Lun Lau
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Institute, Li Ka Shing Institute of Health Science, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Lisa Wing Chi Au
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Institute, Li Ka Shing Institute of Health Science, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Lin Shi
- BrainNow Research Institute, Hong Kong Science and Technology Park, Hong Kong SAR, China
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Bonnie Y K Lam
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Institute, Li Ka Shing Institute of Health Science, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Ho Ko
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Institute, Li Ka Shing Institute of Health Science, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
| | - Vincent Chung Tong Mok
- Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Institute, Li Ka Shing Institute of Health Science, Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong SAR, China
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16
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Vucic S, Stanley Chen KH, Kiernan MC, Hallett M, Benninger DH, Di Lazzaro V, Rossini PM, Benussi A, Berardelli A, Currà A, Krieg SM, Lefaucheur JP, Long Lo Y, Macdonell RA, Massimini M, Rosanova M, Picht T, Stinear CM, Paulus W, Ugawa Y, Ziemann U, Chen R. Clinical diagnostic utility of transcranial magnetic stimulation in neurological disorders. Updated report of an IFCN committee. Clin Neurophysiol 2023; 150:131-175. [PMID: 37068329 PMCID: PMC10192339 DOI: 10.1016/j.clinph.2023.03.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/28/2023] [Accepted: 03/09/2023] [Indexed: 03/31/2023]
Abstract
The review provides a comprehensive update (previous report: Chen R, Cros D, Curra A, Di Lazzaro V, Lefaucheur JP, Magistris MR, et al. The clinical diagnostic utility of transcranial magnetic stimulation: report of an IFCN committee. Clin Neurophysiol 2008;119(3):504-32) on clinical diagnostic utility of transcranial magnetic stimulation (TMS) in neurological diseases. Most TMS measures rely on stimulation of motor cortex and recording of motor evoked potentials. Paired-pulse TMS techniques, incorporating conventional amplitude-based and threshold tracking, have established clinical utility in neurodegenerative, movement, episodic (epilepsy, migraines), chronic pain and functional diseases. Cortical hyperexcitability has emerged as a diagnostic aid in amyotrophic lateral sclerosis. Single-pulse TMS measures are of utility in stroke, and myelopathy even in the absence of radiological changes. Short-latency afferent inhibition, related to central cholinergic transmission, is reduced in Alzheimer's disease. The triple stimulation technique (TST) may enhance diagnostic utility of conventional TMS measures to detect upper motor neuron involvement. The recording of motor evoked potentials can be used to perform functional mapping of the motor cortex or in preoperative assessment of eloquent brain regions before surgical resection of brain tumors. TMS exhibits utility in assessing lumbosacral/cervical nerve root function, especially in demyelinating neuropathies, and may be of utility in localizing the site of facial nerve palsies. TMS measures also have high sensitivity in detecting subclinical corticospinal lesions in multiple sclerosis. Abnormalities in central motor conduction time or TST correlate with motor impairment and disability in MS. Cerebellar stimulation may detect lesions in the cerebellum or cerebello-dentato-thalamo-motor cortical pathways. Combining TMS with electroencephalography, provides a novel method to measure parameters altered in neurological disorders, including cortical excitability, effective connectivity, and response complexity.
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Affiliation(s)
- Steve Vucic
- Brain, Nerve Research Center, The University of Sydney, Sydney, Australia.
| | - Kai-Hsiang Stanley Chen
- Department of Neurology, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
| | - Matthew C Kiernan
- Brain and Mind Centre, The University of Sydney; and Department of Neurology, Royal Prince Alfred Hospital, Australia
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health, Bethesda, Maryland, United States
| | - David H Benninger
- Department of Neurology, University Hospital of Lausanne (CHUV), Switzerland
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, University Campus Bio-Medico of Rome, Rome, Italy
| | - Paolo M Rossini
- Department of Neurosci & Neurorehab IRCCS San Raffaele-Rome, Italy
| | - Alberto Benussi
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Alfredo Berardelli
- IRCCS Neuromed, Pozzilli; Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Antonio Currà
- Department of Medico-Surgical Sciences and Biotechnologies, Alfredo Fiorini Hospital, Sapienza University of Rome, Terracina, LT, Italy
| | - Sandro M Krieg
- Department of Neurosurgery, Technical University Munich, School of Medicine, Klinikum rechts der Isar, Munich, Germany
| | - Jean-Pascal Lefaucheur
- Univ Paris Est Creteil, EA4391, ENT, Créteil, France; Clinical Neurophysiology Unit, Henri Mondor Hospital, AP-HP, Créteil, France
| | - Yew Long Lo
- Department of Neurology, National Neuroscience Institute, Singapore General Hospital, Singapore, and Duke-NUS Medical School, Singapore
| | | | - Marcello Massimini
- Dipartimento di Scienze Biomediche e Cliniche, Università degli Studi di Milano, Milan, Italy; Istituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences University of Milan, Milan, Italy
| | - Thomas Picht
- Department of Neurosurgery, Charité-Universitätsmedizin Berlin, Cluster of Excellence: "Matters of Activity. Image Space Material," Humboldt University, Berlin Simulation and Training Center (BeST), Charité-Universitätsmedizin Berlin, Germany
| | - Cathy M Stinear
- Department of Medicine Waipapa Taumata Rau, University of Auckland, Auckland, Aotearoa, New Zealand
| | - Walter Paulus
- Department of Neurology, Ludwig-Maximilians-Universität München, München, Germany
| | - Yoshikazu Ugawa
- Department of Human Neurophysiology, School of Medicine, Fukushima Medical University, Japan
| | - Ulf Ziemann
- Department of Neurology and Stroke, Eberhard Karls University of Tübingen, Hoppe-Seyler-Str. 3, 72076, Tübingen, Germany; Hertie Institute for Clinical Brain Research, Eberhard Karls University of Tübingen, Otfried-Müller-Straße 27, 72076 Tübingen, Germany
| | - Robert Chen
- Edmond J. Safra Program in Parkinson's Disease, Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital-UHN, Division of Neurology-University of Toronto, Toronto Canada
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Rossini PM, Miraglia F, Vecchio F. Commentary on Comparison of Machine Learning-based Approaches to Predict the Conversion to Alzheimer's Disease from Mild Cognitive Impairment. Neuroscience 2023; 514:141-142. [PMID: 36592944 DOI: 10.1016/j.neuroscience.2022.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/12/2022] [Accepted: 12/18/2022] [Indexed: 12/31/2022]
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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18
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Franciotti R, Nardini D, Russo M, Onofrj M, Sensi SL. Comparison of Machine Learning-based Approaches to Predict the Conversion to Alzheimer's Disease from Mild Cognitive Impairment. Neuroscience 2023; 514:143-152. [PMID: 36736612 DOI: 10.1016/j.neuroscience.2023.01.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 02/05/2023]
Abstract
In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer's Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms -when applied to different biomarkers and other conditions- is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed -with the voting procedure- to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms' performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.
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Affiliation(s)
- Raffaella Franciotti
- Department of Neuroscience, Imaging, and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Italy.
| | | | - Mirella Russo
- Department of Neuroscience, Imaging, and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Italy; Center for Advanced Studies and Technology - CAST, G. d'Annunzio University of Chieti-Pescara, Italy
| | - Marco Onofrj
- Department of Neuroscience, Imaging, and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Italy; Center for Advanced Studies and Technology - CAST, G. d'Annunzio University of Chieti-Pescara, Italy
| | - Stefano L Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Italy; Center for Advanced Studies and Technology - CAST, G. d'Annunzio University of Chieti-Pescara, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Italy.
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19
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Simfukwe C, Youn YC, Kim MJ, Paik J, Han SH. CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG. Neuropsychiatr Dis Treat 2023; 19:851-863. [PMID: 37077704 PMCID: PMC10106803 DOI: 10.2147/ndt.s404528] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/05/2023] [Indexed: 04/21/2023] Open
Abstract
Purpose Electroencephalogram (EEG) signals give detailed information on the electrical brain activities occurring in the cerebral cortex. They are used to study brain-related disorders such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). Brain signals obtained using an EEG machine can be a neurophysiological biomarker for early diagnosis of dementia through quantitative EEG (qEEG) analysis. This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state (ECR). Participants and Methods The dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 AD. First, EEG signals were transformed into TF images using a Fast Fourier Transform (FFT) containing different event-rated changes of frequency sub-bands preprocessed from the EEGlab toolbox in the MATLAB R2021a environment software. The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed-forward neural network (FNN). Results The trained models', HC vs MCI, HC vs AD, and HC vs CASE (MCI + AD), performance metrics were evaluated based on the test dataset of the subjects. The accuracy, sensitivity, and specificity were evaluated: HC vs MCI was 83%, 93%, and 73%, HC vs AD was 81%, 80%, and 83%, and HC vs CASE (MCI + AD) was 88%, 80%, and 90%, respectively. Conclusion The proposed models trained with TF images and age can be used to assist clinicians as a biomarker in detecting cognitively impaired subjects at an early stage in clinical sectors.
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Affiliation(s)
- Chanda Simfukwe
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
- Correspondence: Young Chul Youn; Su-Hyun Han, Department of Neurology, Chung-Ang University Hospital, Seoul, Republic of Korea, Email ;
| | - Min-Jae Kim
- Department of Image, Chung-Ang University, Seoul, South Korea
| | - Joonki Paik
- Department of Image, Chung-Ang University, Seoul, South Korea
| | - Su-Hyun Han
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea
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van den Bos MAJ, Menon P, Vucic S. Cortical hyperexcitability and plasticity in Alzheimer's disease: developments in understanding and management. Expert Rev Neurother 2022; 22:981-993. [PMID: 36683586 DOI: 10.1080/14737175.2022.2170784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
INTRODUCTION Transcranial magnetic stimulation (TMS) is a non-invasive neurophysiological tool that provides important insights into Alzheimer's Disease (AD). A significant body of work utilizing TMS techniques has explored the pathophysiological relevance of cortical hyperexcitability and plasticity in AD and their modulation in novel therapies. AREAS COVERED This review examines the technique of TMS, the use of TMS to examine specific features of cortical excitability and the use of TMS techniques to modulate cortical function. A search was performed utilizing the PubMed database to identify key studies utilizing TMS to examine cortical hyperexcitability and plasticity in Alzheimer's dementia. We then translate this understanding to the study of Alzheimer's disease pathophysiology, examining the underlying neurophysiologic links contributing to these twin signatures, cortical hyperexcitability and abnormal plasticity, in the cortical dysfunction characterizing AD. Finally, we examine utilization of TMS excitability to guide targeted therapies and, through the use of repetitive TMS (rTMS), modulate cortical plasticity. EXPERT OPINION The examination of cortical hyperexcitability and plasticity with TMS has potential to optimize and expand the window of therapeutic interventions in AD, though remains at relatively early stage of development.
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Affiliation(s)
- Mehdi A J van den Bos
- Brain and Nerve Research Centre, Concord Repatriation General Hospital, Sydney, Australia
| | - Parvathi Menon
- Brain and Nerve Research Centre, Concord Repatriation General Hospital, Sydney, Australia
| | - Steve Vucic
- Brain and Nerve Research Centre, Concord Repatriation General Hospital, Sydney, Australia
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21
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Ferreri F, Francesca M, Fabrizio V, Manzo N, Maria C, Elda J, Rossini PM. EEG, ERPs, and EROs in patients with neurodegenerative dementing disorders: A window into the cortical neurophysiology of cognition and behavior. Int J Psychophysiol 2022; 181:85-94. [PMID: 36055410 DOI: 10.1016/j.ijpsycho.2022.08.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/20/2022] [Accepted: 08/18/2022] [Indexed: 10/31/2022]
Abstract
In the human brain, physiological aging is characterized by progressive neuronal loss, leading to disruption of synapses and to a degree of failure in neurotransmission and information flow. However, there is increasing evidence to support the notion that the aged brain has a remarkable level of resilience (i.s. ability to reorganize itself), with the aim of preserving its physiological activity. It is therefore of paramount interest to develop objective markers able to characterize the biological processes underlying brain aging in the intact human, and to distinguish them from brain degeneration associated to age-related neurological progressive diseases like Alzheimer's disease. EEG, alone and combined with transcranial magnetic stimulation (TMS-EEG), is particularly suited to this aim, due to the functional nature of the information provided, and thanks to the ease with which it can be integrated in ecological scenarios including behavioral tasks. In this review, we aimed to provide the reader with updated information about the role of modern methods of EEG and TMS-EEG analysis in the investigation of physiological brain aging and Alzheimer's disease. In particular, we focused on data about cortical connectivity obtained by using readouts such graph theory network brain organization and architecture, and transcranial evoked potentials (TEPs) during TMS-EEG. Overall, findings in the literature support an important potential contribution of such neurophysiological techniques to the understanding of the mechanisms underlying normal brain aging and the early (prodromal/pre-symptomatic) stages of dementia.
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Affiliation(s)
- Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology and Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy; Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Miraglia Francesca
- 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.
| | - Vecchio Fabrizio
- 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
| | - Nicoletta Manzo
- IRCCS San Camillo Hospital, Via Alberoni 70, 30126 Lido di Venezia, Venice, Italy
| | - Cotelli Maria
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di DioFatebenefratelli, Brescia, Italy
| | - Judica Elda
- Department of Neurorehabilitation Sciences, Casa Cura Policlinico, Milano, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
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