Article
Neurosciences
Christian Sandoe Musaeus, Gunhild Waldemar, Birgitte Bo Andersen, Peter Hogh, Preben Kidmose, Martin Christian Hemmsen, Mike Lind Rank, Troels Wesenberg Kjaer, Kristian Steen Frederiksen
Summary: This study investigated the feasibility of using ear-EEG for long-term EEG monitoring in patients with AD. The results showed that ear-EEG was safe to use with minor adverse events, but adjustments to the equipment may be necessary to improve patient comfort.
JOURNAL OF ALZHEIMERS DISEASE
(2022)
Review
Clinical Neurology
Shirin Arjmandi-Rad, John D. Vestergaard Nieland, Kathryn G. Goozee, Salar Vaseghi
Summary: This article reviews the effect of different AChEIs on EEG patterns in patients with AD. The results show that AChEIs can decrease beta, theta, and delta frequency bands, but conflicting results were found for alpha band.
NEUROLOGICAL SCIENCES
(2023)
Article
Engineering, Biomedical
Kai Li, Jiang Wang, Shanshan Li, Haitao Yu, Lin Zhu, Jing Liu, Lingyun Wu
Summary: The study explores the extraction of characteristics of Alzheimer's disease through the decoding of latent factors from EEG data, identifying significant differences in latent factors in specific frequency bands. A novel fuzzy classifier is proposed for recognition and classification, showing improved performance compared to linear classifiers.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2021)
Article
Clinical Neurology
Apoorva Bharthur Sanjay, Alice Patania, Xiaoran Yan, Diana Svaldi, Tugce Duran, Niraj Shah, Sara Nemes, Eric Chen, Liana G. Apostolova
Summary: This study characterized gene-expression patterns in the early stages of Alzheimer's disease (AD) and identified critical mRNA measures and gene clusters associated with AD pathogenesis. The findings suggest potential therapeutic targets for the treatment of AD.
ALZHEIMERS & DEMENTIA
(2022)
Article
Clinical Neurology
Yuan Hou, Jessica Z. K. Caldwell, Justin D. Lathia, James B. Leverenz, Andrew A. Pieper, Jeffrey Cummings, Feixiong Cheng
Summary: This study investigated the cellular metabolism and immune responses in Alzheimer's disease (AD) patients, particularly focusing on sex differences. The results revealed sex-specific metabolites, genes, and pathways associated with AD diagnosis and progression. Female AD patients showed distinct immune metabolite profiles and decreased communication between excitatory neurons and microglia, providing new insights into the molecular basis of female predominance in AD.
ALZHEIMERS & DEMENTIA
(2023)
Article
Multidisciplinary Sciences
Gloriia Novikova, Manav Kapoor, T. C. W. Julia, Edsel M. Abud, Anastasia G. Efthymiou, Steven X. Chen, Haoxiang Cheng, John F. Fullard, Jaroslav Bendl, Yiyuan Liu, Panos Roussos, Johan Lm Bjorkegren, Yunlong Liu, Wayne W. Poon, Ke Hao, Edoardo Marcora, Alison M. Goate
Summary: This study integrates Alzheimer's disease (AD) GWAS data with myeloid cell genomics, and reports that myeloid active enhancers are most burdened by AD risk alleles. The authors also nominate candidate causal regulatory elements, variants and genes that likely modulate the risk for AD.
NATURE COMMUNICATIONS
(2021)
Article
Biochemistry & Molecular Biology
Natalia Vilor-Tejedor, Diego Garrido-Martin, Blanca Rodriguez-Fernandez, Sander Lamballais, Roderic Guigo, Juan Domingo Gispert
Summary: Imaging genetic studies aim to investigate how genetic information influences brain structure and function by analyzing the correlation and association between genetic variants and brain measurements. While univariate approaches have been successful, the development and application of multivariate methods become crucial when dealing with multiple brain phenotypes and genetic data.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2021)
Article
Neurosciences
Davide Cappon, Rachel Fox, Tim den Boer, Wanting Yu, Nicole LaGanke, Gabriele Cattaneo, Ruben Perellon-Alfonso, David Bartres-Faz, Brad Manor, Alvaro Pascual-Leone
Summary: This study investigates the feasibility and safety of a home-based transcranial alternating current stimulation (tACS) protocol for older adults with Alzheimer's disease (AD) to improve memory. The results show that memory improvement was observed in the participants, but there was no change in overall cognitive function. These preliminary findings provide evidence for further research on the feasibility and efficacy of home-based tACS intervention.
FRONTIERS IN HUMAN NEUROSCIENCE
(2023)
Article
Neurosciences
Bahar Guntekin, Furkan Erdal, Burcu Bolukbas, Lutfu Hanoglu, Gorsev Yener, Rumeysa Duygun
Summary: Alzheimer's disease is an important brain disease associated with aging. This study found differences in the frequency and power values of resting-state EEG gamma oscillations between AD patients and healthy elderly and young subjects.
COGNITIVE NEURODYNAMICS
(2023)
Article
Clinical Neurology
Jinhyeong E. Bae, Paige J. Logan, Dominic Acri, Apoorva Bharthur, Kwangsik J. Nho, Andrew L. Saykin, Shannon Risacher, Kelly J. Nudelman, Angelina Polsinelli, Valentin Pentchev, Jungsu B. Kim, Dustin G. Hammers, Liana Apostolova
Summary: This study employed a novel simulative deep learning model to analyze genetic data related to Alzheimer's disease (AD) and identified the top 35 single nucleotide polymorphisms (SNPs) on chromosome 19 that are most associated with AD risk. Two SNPs (APOC1 and ERCC1/CD3EAP) were found to have significant influence on AD risk. These findings contribute to the development of individualized preventive precision medicine strategies.
ALZHEIMERS & DEMENTIA
(2023)
Article
Geriatrics & Gerontology
Florinda Ferreri, Andrea Guerra, Luca Vollero, David Ponzo, Sara Maeaetta, Mervi Koenoenen, Fabrizio Vecchio, Patrizio Pasqualetti, Francesca Miraglia, Ilaria Simonelli, Maurizio Corbetta, Paolo Maria Rossini
Summary: The study revealed reduced excitability in the sensorimotor cortex and disrupted EEG synchronization in aMCI patients, with alterations in intertrial coherence in different frequency bands serving as potential predictors of cognitive status in aMCI. Importantly, differences in beta and gamma coherence were observed between those who clinically converted to AD and those who remained cognitively stable, suggesting these changes may serve as neurophysiological biomarkers for AD.
FRONTIERS IN AGING NEUROSCIENCE
(2021)
Article
Engineering, Biomedical
Dominik Klepl, Fei He, Min Wu, Daniel J. Blackburn, Ptolemaios Sarrigiannis
Summary: Alzheimer's disease (AD) is the most common form of dementia worldwide. Functional connectivity (FC) graph-based biomarkers have shown to be effective for automated diagnosis of AD using electroencephalography (EEG). Graph neural networks (GNN) have been used to classify EEG brain graphs, but it is unclear which method should be used to estimate the brain graph. This study compares the performance of different FC measures and shows that GNN models outperform other baseline models. Furthermore, using FC measures to estimate brain graphs improves the performance of GNN compared to using a fixed graph based on spatial distance between EEG sensors. However, no FC measure consistently performs better than others.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Review
Neurosciences
Michael Hen Forbord Fischer, Ivan Chrilles Zibrandtsen, Peter Hogh, Christian Sandoe Musaeus
Summary: This article systematically reviewed the literature on changes in electroencephalography (EEG) magnitude-squared coherence (MSCOH) in patients with Alzheimer's disease (AD), and found that alpha coherence was significantly reduced in AD patients, which may serve as a diagnostic marker for AD. However, further research is needed to validate the diagnostic utility of MSCOH.
JOURNAL OF ALZHEIMERS DISEASE
(2023)
Article
Clinical Neurology
Emma M. Coomans, Jori Tomassen, Rik Ossenkoppele, Sandeep S. Golla, Marijke den Hollander, Lyduine E. Collij, Emma Weltings, Sophie M. van der Landen, Emma E. Wolters, Albert D. Windhorst, Frederik Barkhof, Eco J. C. de Geus, Philip Scheltens, Pieter Jelle Visser, Bart N. M. van Berckel, Anouk den Braber
Summary: Coomans et al. found substantial similarities in tau load and spatial distribution among identical twins, indicating a significant role of genetic factors in tau pathology. However, differences between twin pairs suggest the influence of environmental factors in tau accumulation. This study provides insights into factors associated with tau pathology and may be important for preventive strategies against Alzheimer's disease.
Article
Clinical Neurology
Wei-Ming Su, Xiao-Jing Gu, Meng Dou, Qing-Qing Duan, Zheng Jiang, Kang-Fu Yin, Wei-Chen Cai, Bei Cao, Yi Wang, Yong-Ping Chen
Summary: A study identified three novel potential druggable genes, EPHX2, SERPINB1, and SIGLEC11, for Alzheimer's disease (AD) treatment using Mendelian randomisation (MR). These genes were found to have significant pathological effects in both blood and brain tissues. Further analysis revealed no potential side effects of treatments targeting these genes. This study provides important genetic evidence for prioritising AD drug development.
JOURNAL OF NEUROLOGY NEUROSURGERY AND PSYCHIATRY
(2023)
Article
Oncology
Jose Garcia-Pelaez, Rita Barbosa-Matos, Silvana Lobo, Alexandre Dias, Luzia Garrido, Sergio Castedo, Sonia Sousa, Hugo Pinheiro, Liliana Sousa, Rita Monteiro, Joaquin J. Maqueda, Susana Fernandes, Fatima Carneiro, Nadia Pinto, Carolina Lemos, Carla Pinto, Manuel R. Teixeira, Stefan Aretz, Svetlana Bajalica-Lagercrantz, Judith Balmana, Ana Blatnik, Patrick R. Benusiglio, Maud Blanluet, Vincent Bours, Hilde Brems, Joan Brunet, Daniele Calistri, Gabriel Capella, Sergio Carrera, Chrystelle Colas, Karin Dahan, Robin de Putter, Camille Desseignes, Elena Dominguez-Garrido, Conceicao Egas, D. Gareth Evans, Damien Feret, Eleanor Fewings, Rebecca C. Fitzgerald, Florence Coulet, Maria Garcia-Barcina, Maurizio Genuardi, Lisa Golmard, Karl Hackmann, Helen Hanson, Elke Holinski-Feder, Robert Huneburg, Mateja Krajc, Kristina Lagerstedt-Robinson, Conxi Lazaro, Marjolijn J. L. Ligtenberg, Cristina Martinez-Bouzas, Sonia Merino, Genevieve Michils, Srdjan Novakovic, Ana Patino-Garcia, Guglielmina Nadia Ranzani, Evelin Schrock, Ines Silva, Catarina Silveira, Jose L. Soto, Isabel Spier, Verena Steinke-Lange, Gianluca Tedaldi, Maria-Isabel Tejada, Emma R. Woodward, Marc Tischkowitz, Nicoline Hoogerbrugge, Carla Oliveira
Summary: This study analyzed families carrying rare CDH1 variants, comparing the cancer spectrum in carriers of pathogenic or likely pathogenic variants (PV/LPV) or missense variants of unknown significance, and evaluated the performance of expanded criteria for CDH1 testing. The results showed that PV/LPV carriers were positively associated with lobular breast cancer, diffuse gastric cancer, and gastric cancer, while missense variants of unknown significance did not show this positive association.
Review
Genetics & Heredity
Luis Daniel Gonzalez-Vazquez, Miguel Arenas
Summary: SARS-CoV-2 has produced various molecular variants during its expansion in humans, leading to different transmissibility, disease severity, and resistance to treatments. Recent studies have investigated the molecular evolution of the virus and found that it evolves at a moderate rate with fluctuations over time. Recombination events between related coronaviruses were infrequent, and molecular adaptation varied among SARS-CoV-2 genes. Monitoring the molecular evolution of the virus is crucial for predicting phenotypic consequences and designing effective treatments.
Article
Biotechnology & Applied Microbiology
Daniela Felicio, Miguel Alves-Ferreira, Mariana Santos, Marlene Quintas, Alexandra M. Lopes, Carolina Lemos, Nadia Pinto, Sandra Martins
Summary: This study established a workflow to prioritize the functional relevance of non-coding SNPs as susceptibility loci in polygenic neurological disorders. By annotating the overlap of selected SNPs with regulatory elements and predicting their potential impact on gene expression, this study demonstrated the applicability of the workflow in prioritizing potentially relevant non-coding SNPs in multifactorial neurological diseases.
BRIEFINGS IN FUNCTIONAL GENOMICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Eduardo Santamaria-Vazquez, Victor Martinez-Cagigal, Diego Marcos-Martinez, Victor Rodriguez-Gonzalez, Sergio Perez-Velasco, Selene Moreno-Calderon, Roberto Hornero
Summary: This study aimed to propose a novel software ecosystem called MEDUSA to overcome the barriers in bringing neurotechnologies to the general public. MEDUSA (c) provides a complete suite of signal processing functions and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. It also facilitates the development of custom experiments and encourages community participation for the progress of these fields.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Engineering, Biomedical
Marcos Revilla-Vallejo, Carlos Gomez, Javier Gomez-Pilar, Roberto Hornero, Miguel Angel Tola-Arribas, Monica Cano, Yoshihito Shigihara, Hideyuki Hoshi, Jesus Poza
Summary: This study aims to introduce a new methodology to evaluate network robustness and apply it to assess the brain activity in Alzheimer's disease (AD) patients. By simulating attacks on functional connectivity networks and evaluating the network changes through Spearman's correlation, significant differences in network robustness were found between controls, mild cognitive impairment subjects, and AD patients in three different databases. Furthermore, the changes in network robustness were found to be associated with the progressive deterioration in brain functioning due to AD.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Sofia Antao-Sousa, Nadia Pinto, Pablo Rende, Antonio Amorim, Leonor Gusmao
Summary: This study aimed to investigate the correlation between the repetitive motif composition of short tandem repeats (STRs) and their mutational dynamics. The results showed significant differences in the occurrence of multistep mutations among markers with different repetitive motifs. These findings have important implications for population genetics, epidemiology, phylogeography, and evolutionary studies involving STR mutation models.
SCIENTIFIC REPORTS
(2023)
Article
Biology
Fernando Vaquerizo-Villar, Gonzalo C. Gutierrez-Tobal, Eva Calvo, Daniel Alvarez, Leila Kheirandish-Gozal, Felix del Campo, David Gozal, Roberto Hornero
Summary: This study developed an accurate and interpretable deep-learning model for sleep staging in children using single-channel EEG data. The results showed that a standard CNN demonstrated the highest performance for automated sleep stage detection, and the CNN-based estimation of total sleep time exhibited strong agreement in the clinical dataset. The study also used the explainable AI algorithm Grad-CAM to highlight the EEG features associated with each sleep stage.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Editorial Material
Clinical Neurology
Javier Gomez-Pilar, Gonzalo C. Gutierrez-Tobal, David Gozal, Roberto Hornero
Article
Medicine, General & Internal
Veronica Barroso-Garcia, Marta Fernandez-Poyatos, Benjamin Sahelices, Daniel Alvarez, David Gozal, Roberto Hornero, Gonzalo C. Gutierrez-Tobal
Summary: This study developed a 2D-convolutional neural network model using thoracic and abdominal movement signals to automatically estimate the severity of sleep apnea and evaluate the contribution of central respiratory events. The model achieved high accuracy and low error rates in estimating the apnea-hypopnea index, especially for central apnea events.
Article
Computer Science, Artificial Intelligence
Victor Martinez-Cagigal, Eduardo Santamaria-Vazquez, Sergio Perez-Velasco, Diego Marcos-Martinez, Selene Moreno-Calderon, Roberto Hornero
Summary: This study proposes the use of non-binary p-ary m-sequences as a more pleasant alternative to traditional binary codes. It is found that all p-ary m-sequences are suitable for achieving high speed and high accuracy in c-VEP-based BCIs, and they can reduce visual fatigue as the base increases.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Clinical Neurology
Daniela Felicio, Andreia Dias, Sandra Martins, Estefania Carvalho, Alexandra M. Lopes, Nadia Pinto, Carolina Lemos, Mariana Santos, Miguel Alves-Ferreira
Summary: Migraine is a common and complex neurological disease caused by multiple gene variants. Genes associated with migraine control synaptic function and neurotransmitter release. Non-coding variants such as VAMP2_rs1150 and SNAP25_rs2327264 affect gene expression and may influence migraine susceptibility.
JOURNAL OF HEADACHE AND PAIN
(2023)
Article
Biology
Clara Garcia-Vicente, Gonzalo C. Gutierrez-Tobal, Jorge Jimenez-Garcia, Adrian Martin-Montero, David Gozal, Roberto Hornero
Summary: A novel deep-learning approach using raw electrocardiogram tracing (ECG) was proposed to simplify the diagnosis of pediatric OSA. A convolutional neural network (CNN) regression model was implemented to predict pediatric OSA and derive severity categories. The diagnostic performance of the CNN model outperformed previous algorithms relying on ECG-derived features. The proposed CNN model provides a simpler, faster, and more accessible diagnostic test for pediatric OSA.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Genetics & Heredity
Sandra Martins, Ashraf Yahia, Ines P. D. Costa, Hassab E. Siddig, Rayan Abubaker, Mahmoud Koko, Marc Corral-Juan, Antoni Matilla-Duenas, Alexis Brice, Alexandra Durr, Eric Leguern, Laura P. W. Ranum, Antonio Amorim, Liena E. O. Elsayed, Giovanni Stevanin, Jorge Sequeiros
Summary: Machado-Joseph disease (MJD/SCA3) is the most common dominant ataxia worldwide, caused by a (CAG)n expansion. This study reports the first diagnosed MJD case in Sudan, with genetic analysis revealing shared ancestry with Portuguese, Spanish, and North American families. The STR-based haplotype of the Sudanese patients is distinct, indicating a unique genetic background.
Article
Engineering, Biomedical
Jorge Jimenez-Garcia, Maria Garcia, Gonzalo C. Gutierrez-Tobal, Leila Kheirandish-Gozal, Fernando Vaquerizo-Villar, Daniel Alvarez, Felix del Campo, David Gozal, Roberto Hornero
Summary: In this study, an explainable architecture that combines convolutional and recurrent neural networks (CNN + RNN) was assessed to detect pediatric obstructive sleep apnea (OSA) and its severity. By analyzing overnight airflow (AF) and oximetry (SpO(2)) signals, the model provides an alternative diagnostic approach with high accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Victor Rodriguez-Gonzalez, Pablo Nunez, Carlos Gomez, Hideyuki Hoshi, Yoshihito Shigihara, Roberto Hornero, Jesus Poza
Summary: This study used a novel methodology called Connectivity-based Meta-Bands (CMB) to analyze individual MEG data and found that mild cognitive impairment and Alzheimer's disease could alter the neural network topology and dilute the frequency structure progressively.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
