Article
Clinical Neurology
Natalie L. Voets, Puneet Plaha, Oiwi Parker Jones, Pieter Pretorius, Andreas Bartsch
Summary: Functional magnetic resonance imaging (fMRI) is valuable in neurosurgical planning, particularly in predicting motor risks beyond anatomical localization. Accelerated resting fMRI acquisitions showed higher success rates compared to standard acquisitions.
CLINICAL NEURORADIOLOGY
(2021)
Article
Agronomy
Yuanyuan Liu, Tongzhao Wang, Rong Su, Can Hu, Fei Chen, Junhu Cheng
Summary: Customers are increasingly focusing on the sensory and physicochemical properties of food, leading to the use of near infrared hyperspectral technology to evaluate the quality parameters of Korla fragrant pears. By combining IRIV and LS-SVM, models were constructed to accurately assess the a*, firmness, and SSC of the fruit. The study demonstrated that a combination of spectral preprocessing methods, MSC and S-G, was the most effective in evaluating the quality parameters of the pears.
Article
Engineering, Aerospace
Seyyed Reza Ghaffari-Razin, Amir Reza Moradi, Navid Hooshangi
Summary: A new method for spatio-temporal modeling of ionosphere total electron content (TEC) using least squares support vector machine (LS-SVM) is proposed. The method reduces computational complexity, improves convergence speed and accuracy, and shows better performance in seasonal error analysis.
ADVANCES IN SPACE RESEARCH
(2022)
Article
Neurosciences
Wei Zhao, Huanjie Li, Yuxing Hao, Guoqiang Hu, Yunge Zhang, Blaise de B. Frederick, Fengyu Cong
Summary: The study introduces an fMRI data reduction strategy based on an adapted NPE algorithm, which outperforms principal component analysis in efficient data reduction and enhancing group-level information, ensuring greater stability and reliability in studying brain functionality and connectivity. It provides a unique method for selecting components based on an adjacency graph of eigenvectors and generates more reliable and reproducible brain networks, showing sensitivity to task-evoked activation in task fMRI and suitability for fast fMRI and large datasets.
HUMAN BRAIN MAPPING
(2022)
Article
Neurosciences
Jungtak Park, Karolina Janacsek, Dezso Nemeth, Hyeon-Ae Jeon
Summary: This study used functional magnetic resonance imaging to investigate the whole-brain connectivity involved in statistical learning. The results showed that the activation strength in the superior frontal gyrus and other brain areas were related to statistical learning performance. The activations of the superior frontal network were most correlated with statistical learning performances. The functional connectivity between the superior frontal gyrus and brain regions involved in salience, language, and dorsal attention networks decreased during statistical learning.
Article
Spectroscopy
Dongyan Zhang, Yi Yang, Gao Chen, Xi Tian, Zheli Wang, Shuxiang Fan, Zhenghua Xin
Summary: The study applied Vis/NIR spectroscopy to evaluate the soluble solids content (SSC) of tomatoes and developed a method for predicting SSC effectively. By measuring tomato samples at different maturity stages and using spectral data from different wavelength ranges, the study established the best prediction model through preprocessing and model building steps.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Article
Clinical Neurology
Ian Daly
Summary: This study proposes a new method to reduce physiological artifacts in EEG recordings during joint EEG-fMRI sessions by combining independent component analysis and fMRI-based head movement estimation. The method significantly decreases the influence of physiological artifacts and outperforms other state-of-the-art methods in removing these artifacts.
CLINICAL NEUROPHYSIOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Carlos Ruiz, Carlos M. Alaiz, Jose R. Dorronsoro
Summary: Multi-Task Learning aims to leverage the joint learning of a problem from both an overall and task-specific perspective. The proposed convex formulation for MTL typically outperforms the alternative optimal convex combination and the use of common or task-specific models alone.
Article
Automation & Control Systems
Di Song, Qianyi Wu, Mohammed Kamruzzaman
Summary: Informative spectral bands were selected from NIR spectroscopy to determine the proximate compositions in meat. Calibration models developed using these bands showed better prediction accuracy compared to individual bands and full spectral range, especially when non-linear calibration was used. The results suggest the effectiveness of common informative bands for predicting multiple quality attributes in meat.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Neurosciences
Shile Qi, Rogers F. Silva, Daoqiang Zhang, Sergey M. Plis, Robyn Miller, Victor M. Vergara, Rongtao Jiang, Dongmei Zhi, Jing Sui, Vince D. Calhoun
Summary: This study introduces a novel three-way parallel group independent component analysis (pGICA) fusion method that effectively incorporates temporal information in multimodal data fusion, demonstrating high accuracy and comparability in estimating cross-modality links. Experimental results suggest the potential of this method in investigating brain disorders.
HUMAN BRAIN MAPPING
(2022)
Article
Environmental Sciences
Yanhui Li, Jiangjun Yao, Pengcheng Nie, Xuping Feng, Jizan Liu
Summary: The study found that using the LS-SVM model combined with terahertz spectral data to predict the degree of microplastic pollution in soil has a good effect, but seems to only be useful for local regions.
Article
Chemistry, Analytical
Hanlu Yang, Trung Vu, Qunfang Long, Vince Calhoun, Tuelay Adali
Summary: This study proposes a framework for subgroup identification of psychiatric patients using functional connectivity profiles obtained from fMRI data. The pipeline incorporates a data-driven method and constraint-based independent component analysis to identify meaningful subgroups with similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas.
Article
Chemistry, Multidisciplinary
Thi Thanh Nha Tran, Thi Dieu Thuan Tran, Thi Thu Thuy Bui
Summary: The study introduces a novel modeling routine that combines machine learning techniques and tuning algorithms to improve the performance of 3D-CoMSIA models. The developed routine successfully mitigates the problem of overfitting and demonstrates superior performance compared to linear models. It also leads to the discovery of promising antioxidant peptides.
Article
Clinical Neurology
E. Diachek, V. L. Morgan, S. M. Wilson
Summary: This study compared two adaptive language mapping paradigms with the currently recommended standard paradigms, and found that the adaptive semantic paradigm resulted in the most strongly lateralized activation maps and the greatest extent of frontal and temporal activations.
AMERICAN JOURNAL OF NEURORADIOLOGY
(2022)
Article
Environmental Sciences
Tianyi Chen, Changbao Yang, Liguo Han, Senmiao Guo
Summary: This study applies remote sensing technology and texture data for lithological classification. After comparing different methods, support vector machine is selected for further investigation. The results show that combining GF-2 data with texture data improves the accuracy of lithological classification. Additionally, combining principal component analysis and independent component analysis with texture data enhances classification accuracy.
Article
Clinical Neurology
Jaiver Macea, Miguel Bhagubai, Victoria Broux, Maarten De Vos, Wim Van Paesschen
Summary: The performance of an EEG seizure-detector algorithm was evaluated in patients with refractory epilepsy using a wearable device. The sensitivity of the device was found to be 52% in inpatients and 23% in outpatients, with high false alarm rates and low performance scores. Although well-received by patients, the device had side effects and its implementation in clinical practice is currently limited.
Article
Clinical Neurology
Anke Wouters, Lauranne Scheldeman, Hannelore Liessens, Patrick Dupont, Florent Boutitie, Bastian Cheng, Martin Ebinger, Matthias Endres, Jochen B. Fiebach, Christian Gerloff, Keith W. Muir, Norbert Nighoghossian, Salvador Pedraza, Claus Z. Simonsen, Vincent Thijs, Goetz Thomalla, Robin Lemmens
Summary: This study aimed to explore the sex-based differences in acute ischemic stroke within the WAKE-UP trial. It was found that women, compared to men, were older, had higher baseline NIHSS scores, and smoked less frequently. However, the treatment effect of alteplase on mRS scores was not modified by sex.
EUROPEAN JOURNAL OF NEUROLOGY
(2023)
Letter
Respiratory System
Paul Desbordes, Maarten De Vos, Julie Maes, Frans de Jongh, Karl Sylvester, Claus Franz Vogelmeier, Anh Tuan Dinh-Xuan, Jann Mortensen, Wim Janssens, Marko Topalovic
EUROPEAN RESPIRATORY JOURNAL
(2023)
Article
Neurosciences
Ahmed Radwan, Lisa Decraene, Patrick Dupont, Nicolas Leenaerts, Cristina Simon-Martinez, Katrijn Klingels, Els Ortibus, Hilde Feys, Stefan Sunaert, Jeroen Blommaert, Lisa Mailleux
Summary: This study explored the structural brain connectomes in children with spastic unilateral cerebral palsy (uCP) and its relationship to sensory-motor function using graph theory. The results showed a hyperconnectivity pattern in the CDGM-lesion group compared to the PWM-lesion group, with higher clustering coefficient, characteristic path length, and local efficiency. The CST-wiring pattern was found to be the strongest predictor for motor function. The findings highlight the potential of structural connectomics in understanding disease severity and brain development in children with uCP.
HUMAN BRAIN MAPPING
(2023)
Article
Engineering, Biomedical
Tim Hermans, Laura Smets, Katrien Lemmens, Anneleen Dereymaeker, Katrien Jansen, Gunnar Naulaers, Filippo Zappasodi, Sabine Van Huffel, Silvia Comani, Maarten De Vos
Summary: This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG. The proposed method outperforms existing state-of-the-art models and achieves good performance on two separate datasets. The results demonstrate the effectiveness of the semi-supervised multi-task training strategy and the relevance of artefact detection for automated EEG analysis.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Clinical Neurology
Lauranne Scheldeman, Anke Wouters, Jeroen Bertels, Patrick Dupont, Bastian Cheng, Martin Ebinger, Matthias Endres, Jochen B. Fiebach, Christian Gerloff, Keith W. Muir, Norbert Nighoghossian, Salvador Pedraza, Claus Z. Simonsen, Vincent Thijs, Goetz Thomalla, Robin Lemmens
Summary: The study aims to investigate the reversibility of diffusion-weighted imaging (DWI) lesions and its association with thrombolysis, reperfusion, and functional outcomes. It found that reversibility of DWI lesions is common in patients from the WAKE-UP trial, and it is more pronounced after thrombolysis.
Article
Respiratory System
Kenneth Verstraete, Iwein Gyselinck, Helene Huts, Nilakash Das, Marko Topalovic, Maarten De Vos, Wim Janssens
Summary: This study developed machine learning models to estimate and predict individual treatment effects of interventions in patients with chronic obstructive pulmonary disease (COPD). The results showed that poor lung function and blood eosinophils were the strongest predictors of individual treatment effects. The findings suggest that machine learning models can be used to guide personalized treatment decisions in COPD.
Article
Biotechnology & Applied Microbiology
Miguel Bhagubai, Kaat Vandecasteele, Lauren Swinnen, Jaiver Macea, Christos Chatzichristos, Maarten De Vos, Wim Van Paesschen
Summary: This study evaluated a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG data. The results showed that combining ECG with bte-EEG can improve the accuracy of seizure detection and reduce false alarm rates, while also saving time for both clinicians and patients.
BIOENGINEERING-BASEL
(2023)
Article
Clinical Neurology
Tim Hermans, Mohammad Khazaei, Khadijeh Raeisi, Pierpaolo Croce, Gabriella Tamburro, Anneleen Dereymaeker, Maarten De Vos, Filippo Zappasodi, Silvia Comani
Summary: This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome. The study found that MS duration decreased and occurrence increased with age in preterm neonates, and significant changes in MS topographies and transitions occurred when neonates reached 37 weeks. Additionally, the Hurst exponent of the individual MS sequence decreased with age.
Meeting Abstract
Neurosciences
Laure Sillisa, Cleo Vandegoor, Cato Vercaeren, Karel Allegaert, Annick Bogaerts, Maarten De Vos, Titia Hompes, Anne Smits, Kristel Van Calsteren, Jan Y. Verbakel, Veerle Foulon, Michael Ceulemans
NEUROTOXICOLOGY AND TERATOLOGY
(2023)
Meeting Abstract
Critical Care Medicine
M. Topalovic, M. De Vos, J. Maes, J. Kaspers, N. Stachowicz, P. Desbordes
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
(2023)
Meeting Abstract
Transplantation
Amelie Dendooven, Aristotelis Styanidis, Louis Raes, Amaryllis Van Craenenbroeck, Matthias Maeyens, Konstantinos Kotras, Maarten De Vos
NEPHROLOGY DIALYSIS TRANSPLANTATION
(2023)
Article
Linguistics
Antonietta Gabriella Liuzzi, Karen Meersmans, Gerrit Storms, Simon De Deyne, Patrick Dupont, Rik Vandenberghe
Summary: The study found that co-occurrence-based similarities calculated by predictive natural language processing models are not good at representing affective content but are powerful in their own way. The functional and neuroanatomical relationship between these two distinct ways of representing word meaning was investigated. The findings revealed a correlation between affective similarities and word embedding similarities in specific regions of the superior temporal sulcus.
NEUROBIOLOGY OF LANGUAGE
(2023)
Article
Respiratory System
Kenneth Verstraete, Nilakash Das, Iwein Gyselinck, Marko Topalovic, Thierry Troosters, James D. Crapo, Edwin K. Silverman, Barry J. Make, Elizabeth A. Regan, Robert Jensen, Maarten De Vos, Wim Janssens
Summary: The shape of MEFVC is associated with CT parameters of emphysema, small airways disease (SAD), and bronchial wall thickening (BWT) in COPD. It is a valuable predictor for emphysema and SAD in moderate-severe COPD, but not a suitable screening tool for early disease phenotypes identified by CT scan.
RESPIRATORY RESEARCH
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Oliver Y. Chen, Vu Duy Thanh, Gilbert Greub, Hengyi Cao, Xingru He, Yannick Muller, Constantinos Petrovas, Haochang Shou, Viet-Dung Nguyen, Bangdong Zhi, Laurent Perez, Jean-Louis Raisaro, Guy Nagels, Maarten de Vos, Wei He, Gottardo Palie Smart, Marcus Munafo, Giuseppe Pantaleo
Summary: This article presents a systematic approach to studying varying brain. It discusses different types of brain variability and provides examples for each. It explores classical analysis of covariance as well as advanced residual analysis methods that aim to decompose the total variance of brain or behavior data. The article also considers innate and acquired brain variability, the neural law of large numbers for big brain data, and the gut-brain axis as an important source of brain variability.
2023 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP, SSP
(2023)
Article
Neurosciences
Jose Sanchez-Bornot, Roberto C. Sotero, J. A. Scott Kelso, Ozguer Simsek, Damien Coyle
Summary: This study proposes a multi-penalized state-space model for analyzing unobserved dynamics, using a data-driven regularization method. Novel algorithms are developed to solve the model, and a cross-validation method is introduced to evaluate regularization parameters. The effectiveness of this method is validated through simulations and real data analysis, enabling a more accurate exploration of cognitive brain functions.
