Article
Biochemistry & Molecular Biology
Ying Xue, Xiangqun Xie
Summary: This study explores the potential impact of metformin on the development of severe dementia in individuals with AD and T2DM. The findings suggest that metformin usage is not significantly associated with a decreased risk of severe dementia, regardless of APOE epsilon 4 carrier status.
Article
Clinical Neurology
Juraj Secnik, Hong Xu, Emilia Schwertner, Niklas Hammar, Michael Alvarsson, Bengt Winblad, Maria Eriksdotter, Sara Garcia-Ptacek, Dorota Religa
Summary: The study found that the use of metformin and DPP-4i in patients with diabetes and dementia was associated with a slower decline in MMSE scores, while insulin and sulfonylureas were associated with larger decrements in MMSE scores. Further research on the cognitive effects of metformin and incretin-based medications is needed.
ALZHEIMERS RESEARCH & THERAPY
(2021)
Article
Neurosciences
Taegyun Jeong, Ukeob Park, Seung Wan Kang
Summary: This study introduces a novel QEEG feature image that integrates spatial and spectral information into a single image, improving the training of deep learning algorithms with EEG data. The classification accuracy for Alzheimer's disease dementia and non-Alzheimer's disease dementia data reached 97.4%.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Geriatrics & Gerontology
Mandy Roheger, Hong Xu, Minh Tuan Hoang, Maria Eriksdotter, Sara Garcia-Ptacek
Summary: This study presents a reliable and simple method for converting MMSE scores to MoCA scores (and vice versa) in patients with different types of dementia. By establishing a conversion table, the scores of the two assessment tools can be easily compared, facilitating research and clinical practice.
JOURNAL OF THE AMERICAN MEDICAL DIRECTORS ASSOCIATION
(2022)
Article
Geriatrics & Gerontology
Jael S. Fasnacht, Alexandra S. Wueest, Manfred Berres, Alessandra E. Thomann, Sabine Krumm, Klemens Gutbrod, Luzius A. Steiner, Nicolai Goettel, Andreas U. Monsch
Summary: This study aimed to define corresponding scores for the Mini-Mental Status Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The results showed that MoCA scores were consistently lower than MMSE scores. A conversion table was provided to convert MoCA scores into MMSE scores and vice versa. This allows for direct comparison of cognitive test scores and observation of changes over the course of disease in patients with neurocognitive disorders.
JOURNAL OF THE AMERICAN GERIATRICS SOCIETY
(2022)
Article
Neurosciences
Letteria Tomasello, Leonardo Carlucci, Angelina Lagana, Santi Galletta, Chiara Valeria Marinelli, Massimo Raffaele, Pierluigi Zoccolotti
Summary: This study examined the effectiveness of EEG resting state and neuropsychological performances in distinguishing between different types of dementia or mild cognitive impairment (MCI) in comparison to control subjects. The results showed that patients with Alzheimer's disease (AD) and Fronto-Temporal Dementia (FTD) exhibited greater theta activation in posterior areas compared to MCI and control groups. AD patients also had higher delta band activity in temporal-occipital areas compared to controls and MCI patients. The joint use of cognitive and neurophysiological data can provide converging evidence for monitoring cognitive skills in individuals at risk.
Article
Neurosciences
Jordi A. Matias-Guiu, Vanesa Pytel, Laura Hernandez-Lorenzo, Nikil Patel, Katie A. Peterson, Jorge Matias-Guiu, Peter Garrard, Fernando Cuetos
Summary: The study adapted and validated the Spanish version of MLSE for PPA diagnosis, showing excellent internal consistency and discriminative properties. MLSE performed well in distinguishing PPA patients from healthy controls and differentiating between clinical variants.
JOURNAL OF ALZHEIMERS DISEASE
(2021)
Article
Geriatrics & Gerontology
Xiaowei Zheng, Bozhi Wang, Hao Liu, Wencan Wu, Jiamin Sun, Wei Fang, Rundong Jiang, Yajie Hu, Cheng Jin, Xin Wei, Steve Shyh-Ching Chen
Summary: This study recommended the integration of EEG features of spectrum, complexity, and synchronization for aiding the diagnosis of AD. Three supervised machine learning classification algorithms were compared, and they achieved high accuracy in classifying AD and normal subjects based on processed EEG signal features.
FRONTIERS IN AGING NEUROSCIENCE
(2023)
Article
Medicine, General & Internal
Andreas Miltiadous, Katerina D. Tzimourta, Nikolaos Giannakeas, Markos G. Tsipouras, Theodora Afrantou, Panagiotis Ioannidis, Alexandros T. Tzallas
Summary: Dementia is characterized by progressive loss of cognitive and emotional abilities. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are two common types. Analysis of EEG signals can provide an accurate biomarker for detecting changes in neuronal and cognitive dynamics associated with dementia. Different machine-learning techniques were compared for classifying processed EEG signals of AD and FTD, with decision trees achieving 78.5% accuracy for AD detection and random forests achieving 86.3% accuracy for FTD detection. Cross-validation methods were also evaluated for performance in this classification problem.
Article
Computer Science, Interdisciplinary Applications
Hezhe Qiao, Lin Chen, Fan Zhu
Summary: The study introduces a ranking convolutional neural network (rankCNN) method to predict MMSE through multi-classification, which outperforms other state-of-the-art methods on both ADNI-1 and ADNI-2 datasets. The proposed model achieved RMSE of 2.238 and 2.434 at baseline on the two datasets, demonstrating high prediction accuracy.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Review
Computer Science, Artificial Intelligence
Katerina D. Tzimourta, Vasileios Christou, Alexandros T. Tzallas, Nikolaos Giannakeas, Loukas G. Astrakas, Pantelis Angelidis, Dimitrios Tsalikakis, Markos G. Tsipouras
Summary: Alzheimer's Disease (AD) is a neurodegenerative disorder that can be diagnosed through various clinical procedures, with recent focus on analyzing electrophysiological dynamics. Most studies reviewed in this paper concentrate on AD detection and the correlation of quantitative EEG features with AD progression, often utilizing Support Vector Machines.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Multidisciplinary Sciences
Soo Hyun Cho, Sookyoung Woo, Changsoo Kim, Hee Jin Kim, Hyemin Jang, Byeong C. Kim, Si Eun Kim, Seung Joo Kim, Jun Pyo Kim, Young Hee Jung, Samuel Lockhart, Rik Ossenkoppele, Susan Landau, Duk L. Na, Michael Weiner, Seonwoo Kim, Sang Won Seo
Summary: This study aimed to construct a disease course model from preclinical AD to AD dementia, finding that ADAS-cog 13 scores decreased most rapidly in women APOE epsilon 4 carriers and most slowly in men APOE epsilon 4 non-carriers. The results suggest that both sex and APOE epsilon 4 status have an impact on the progression of AD.
SCIENTIFIC REPORTS
(2021)
Article
Neurosciences
Tom Ala, Danah Bakir, Srishti Goel, Nida Feller, Albert Botchway, Cindy Womack
Summary: This study aims to improve the ability to differentiate between AD and DLB by studying MMSE scores and including other dementia patients for perspective. The results suggest that the equation P minus M can effectively distinguish between AD and DLB, and it can also differentiate AD from Parkinson's disease dementia.
JOURNAL OF ALZHEIMERS DISEASE
(2022)
Article
Clinical Neurology
Jakub P. Hlavka, Jeffrey C. Yu, Darius N. Lakdawalla
Summary: This study presents a crosswalk between MMSE and TICS, providing a comparable cognitive measurement tool for adults aged 65 and older.
ALZHEIMERS & DEMENTIA
(2022)
Article
Neurosciences
Xiaoli Pan, Xiaoqin Cheng, Jie Zhang, Yingfeng Xia, Chunjiu Zhong, Guoqiang Fei
Summary: The five-minute cognitive test (FCT) is a novel and reliable method for detecting cognitive impairment at an early stage. This study analyzed the impact of sociodemographic and health-related factors on FCT performance and investigated its consistency. The results showed that FCT scores were influenced by age, education attainment, dwelling condition, and Body Mass Index. Additionally, FCT scores correlated significantly with specific neuropsychological tests.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Clinical Neurology
Valentina Papadopoulou, Electra Chatzidimitriou, Eleni Konstantinopoulou, Dimitrios Parissis, Panagiotis Ioannidis
Summary: The case report describes a 59-year-old woman diagnosed with logopenic variant of primary progressive aphasia (lvPPA), who developed new visual artistic talent after the onset of language deficits. Similar cases have been reported in patients with semantic and non-fluent/agrammatic variants of primary progressive aphasia, but this is the first related case with lvPPA. Neurological and neuropsychological assessments were conducted to establish the diagnosis, and a reassessment was performed two years later. Neuroimaging data showed decreased blood flow in the left hemisphere, and neuropsychological deficits were predominantly in language production. The report also provides a description of the patient's portraits and suggests possible neural mechanisms associated with this ability.
NEUROLOGICAL SCIENCES
(2023)
Review
Biology
Irini Vilou, Aikaterini Varka, Dimitrios Parisis, Theodora Afrantou, Panagiotis Ioannidis
Summary: This review paper analyzed various protocols of EEG neurofeedback in memory rehabilitation in patients with dementia, multiple sclerosis, strokes, and traumatic brain injury. The results showed the effectiveness of the Epsilon EEG-GNF method in improving at least one cognitive domain, regardless of the number of sessions or the type of protocol applied. Future research should address methodological weaknesses, long-term effects, and ethical issues.
Article
Computer Science, Information Systems
Ioannis G. Tsoulos, Alexandros Tzallas, Evangelos Karvounis, Dimitrios Tsalikakis
Summary: An innovative method of finding the global minimum of multidimensional functions is presented by generating an approximation of the objective function using real samples and using a machine learning model to construct the approach. The approach is improved by using found local minima as samples for the training set of the machine learning model. The proposed technique shows extremely promising results when compared to modern global minimization techniques.
Article
Clinical Neurology
Myrto Koutsonida, Fotios Koskeridis, Georgios Markozannes, Afroditi Kanellopoulou, Abdou Mousas, Evangelos Ntotsikas, Panagiotis Ioannidis, Eleni Aretouli, Konstantinos K. Tsilidis
Summary: This study aimed to explore the association between metabolic syndrome and cognitive function in middle-aged individuals. The results showed that metabolic syndrome was associated with lower performance in attention and memory, possibly due to elevated fasting glucose and abdominal obesity. This study highlights the importance of addressing cognitive decline and dementia risk in middle-aged individuals.
NEUROLOGICAL SCIENCES
(2023)
Article
Computer Science, Information Systems
Aristidis G. Anagnostakis, Charilaos Naxakis, Nikolaos Giannakeas, Markos G. Tsipouras, Alexandros T. Tzallas, Euripidis Glavas
Summary: This study investigates the process of building scalable consensus policies in the IoT ecosystems using a microblockchain framework. A set of validity rules are defined and a competitive game among nodes is conducted to study the dynamic behavior. The Proof of Existence is utilized as a universal proofing case. The findings demonstrate that finite-capacity atoms can support verifiable validity and scalable consensus in the evolutionary IoT ecosystems, even under conditions of high diversity, trivial capacity, and eventual consistency.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Information Systems
Georgios Prapas, Kosmas Glavas, Katerina D. D. Tzimourta, Alexandros T. T. Tzallas, Markos G. G. Tsipouras
Summary: This paper presents a 3D non-invasive BCI game that utilizes a Muse 2 EEG headband to capture EEG data and the OpenViBE platform for signal processing and classification into three different mental states. The game is designed to evaluate user adaptation and improvement in the BCI environment after training. Using the Multi-Layer Perceptron (MLP) algorithm, the classification accuracy reached 96.94%. A total of 33 subjects participated in the experiment and successfully controlled an avatar to collect coins using mental commands. Online metrics used for this BCI system include average game score, average number of clusters, and average user improvement.
Article
Computer Science, Information Systems
Andreas Miltiadous, Katerina D. Tzimourta, Theodora Afrantou, Panagiotis Ioannidis, Nikolaos Grigoriadis, Dimitrios G. Tsalikakis, Pantelis Angelidis, Markos G. Tsipouras, Euripidis Glavas, Nikolaos Giannakeas, Alexandros T. Tzallas
Summary: This article presents a detailed description of a resting-state EEG dataset for the diagnosis of neurodegenerative diseases. The dataset includes EEG recordings of individuals with Alzheimer's disease, frontotemporal dementia, and healthy controls. Rigorous quality control measures were applied during data collection to ensure accuracy and consistency. The dataset can be reused for studies on brain activity and connectivity alterations in these conditions, as well as for the development of new diagnostic and treatment approaches.
Article
Computer Science, Artificial Intelligence
Vasileios Christou, Ioannis Tsoulos, Vasileios Loupas, Alexandros T. Tzallas, Christos Gogos, Petros S. Karvelis, Nikolaos Antoniadis, Evripidis Glavas, Nikolaos Giannakeas
Summary: A significant goal of modern universities is to provide high-quality education and reduce failure rates. This article proposes a grammatical evolution-based method for predicting students' future grades and study duration using past course data. The experiments showed that the proposed method achieved the lowest mean square error in regression problems and the highest accuracy in classification problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Infectious Diseases
Ioannis G. Violaris, Theodoros Lampros, Konstantinos Kalafatakis, Georgios Ntritsos, Konstantinos Kostikas, Nikolaos Giannakeas, Markos Tsipouras, Evripidis Glavas, Dimitrios Tsalikakis, Alexandros Tzallas
Summary: The COVID-19 pandemic caused unprecedented events globally, with European countries initially taking individual approaches before organizing coordinated vaccination campaigns. The inability of the immune system and the emergence of different variants affected the viral epidemic outbreak. Two versions of a mathematical model were developed to capture various factors. The model showed that small initial numbers of exposed individuals could threaten a large percentage of the population, leading to a political dilemma in most countries.
Article
Chemistry, Multidisciplinary
Ioannis G. Tsoulos, Alexandros Tzallas
Summary: This study proposes a technique that utilizes the particle swarm optimization method and grammatical evolution to significantly reduce data classification or regression errors. The technique is divided into two phases, where artificial features are constructed using grammatical evolution and controlled by the particle swarm optimization method in the first phase. In the second phase, these features are used to transform the original dataset, and any machine learning method can be applied. Experimental results show an average improvement of 30% for classification datasets and a greater improvement of 60% for data fitting datasets.
APPLIED SCIENCES-BASEL
(2023)
Review
Health Care Sciences & Services
Christos Bakirtzis, Maria Lima, Sotiria Stavropoulou De Lorenzo, Artemios Artemiadis, Paschalis Theotokis, Evangelia Kesidou, Natalia Konstantinidou, Styliani-Aggeliki Sintila, Marina-Kleopatra Boziki, Dimitrios Parissis, Panagiotis Ioannidis, Theodoros Karapanayiotides, Georgios Hadjigeorgiou, Nikolaos Grigoriadis
Summary: Secondary demyelinating diseases can result from disorders affecting neurons/axons or underlying conditions damaging the myelin sheath. In the elderly, primary demyelinating diseases are rare, but secondary causes occur frequently, requiring extensive diagnosis. Infections, osmotic disturbances, nutritional deficiencies, and malignancies contribute to CNS demyelination in the elderly, with various clinical manifestations. This review aims to help neurologists diagnose and understand secondary CNS demyelinating diseases in the elderly.
Editorial Material
Health Care Sciences & Services
Lambros Messinis, Grigorios Nasios, Panagiotis Ioannidis, Panayiotis Patrikelis
Article
Computer Science, Information Systems
Andreas Miltiadous, Emmanouil Gionanidis, Katerina D. Tzimourta, Nikolaos Giannakeas, Alexandros T. Tzallas
Summary: This paper proposes a novel approach to Alzheimer's disease (AD) EEG classification using a Dual-Input Convolution Encoder Network (DICE-net). The results show that DICE-net achieved an accuracy of 83.28% in the AD-CN problem, outperforming several baseline models and demonstrating good generalization performance. This approach has the potential to improve the accuracy of early diagnosis and contribute to the development of more effective interventions for AD.
Article
Computer Science, Hardware & Architecture
Jia Ke, Ying Wang, Mingyue Fan, Xiaojun Chen, Wenlong Zhang, Jianping Gou
Summary: This study integrates the emotional correlation analysis model and Self-organizing Map (SOM) to construct fine-grained user emotion vector based on review text and perform visual cluster analysis, which helps platform merchants quickly mine user clustering and characteristics.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Shi Qiu, Huping Ye, Xiaohan Liao, Benyue Zhang, Miao Zhang, Zimu Zeng
Summary: This paper proposes a multilevel-based algorithm for hyperspectral image interpretation, which achieves semantic segmentation through multidimensional information fusion, and introduces a context interpretation module to improve detection performance.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Jianteng Xu, Qingguo Bai, Zhiwen Li, Lili Zhao
Summary: This study constructs two optimization models for the omnichannel closed-loop supply chain by leveraging the combined power of leader-follower game and mean-variance theories. The focus is on analyzing the performance of manufacturers who distribute products through physical stores. The results show that the risk-averse attitude of the physical store has a positive impact on the overall system profitability, but if the introduced physical store belongs to another firm, total profit experiences a decline.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Jiahao Xiong, Weihua Ou, Zhonghua Liu, Jianping Gou, Wenjun Xiao, Haitao Liu
Summary: This paper proposes a novel remote photoplethysmography framework, named GraphPhys, which utilizes graph neural network to extract physiological signals and introduces Average Relative GraphConv for the task of remote physiological signal measurement. Experimental results show that the methods based on GraphPhys significantly outperform the original methods.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
Article
Computer Science, Hardware & Architecture
Zhiyao Tong, Yiyi Hu, Chi Jiang, Yin Zhang
Summary: The rise of illicit activities involving blockchain digital currencies has become a growing concern. In order to prevent illegal activities, this study combines financial risk control with machine learning to identify and predict the risks of users with poor credit. Experimental results demonstrate high performance in user financial credit analysis.
COMPUTERS & ELECTRICAL ENGINEERING
(2024)
