Review
Cell Biology
Yi-Jun Ge, Wei Xu, Ya-Nan Ou, Yi Qu, Ya-Hui Ma, Yu-Yuan Huang, Xue-Ning Shen, Shi-Dong Chen, Lan Tan, Qian-Hua Zhao, Jin-Tai Yu
Summary: The study systematically evaluated retinal imaging and electrophysiological markers in patients with Alzheimer's disease, mild cognitive impairment, and preclinical AD. It found that these retinal biomarkers hold great potential for the diagnosis, prognosis, and risk assessment of AD and MCI. Further development of these biomarkers, especially in diagnostic test accuracy and longitudinal studies, is warranted in the future.
AGEING RESEARCH REVIEWS
(2021)
Article
Clinical Neurology
Emrah Duezel, Gabriel Ziegler, David Berron, Anne Maass, Hartmut Schuetze, Arturo Cardenas-Blanco, Wenzel Glanz, Coraline Metzger, Laura Dobisch, Martin Reuter, Annika Spottke, Frederic Brosseron, Klaus Fliessbach, Michael T. Heneka, Christoph Laske, Oliver Peters, Josef Priller, Eike Jakob Spruth, Alfredo Ramirez, Oliver Speck, Anja Schneider, Stefan Teipel, Ingo Kilimann, Wiltfang Jens, Bjoern-Hendrik Schott, Lukas Preis, Daria Gref, Franziska Maier, Matthias H. Munk, Nina Roy, Tomasso Ballarini, Renat Yakupov, John Dylan Haynes, Peter Dechent, Klaus Scheffler, Michael Wagner, Frank Jessen
Summary: This study investigated whether the impact of tau-pathology on memory performance and hippocampal/medial temporal memory function depends on the presence of amyloid pathology. The results showed a linear relationship between amyloid pathology, tau pathology, hippocampal dysfunction, and memory impairment.
Article
Biochemistry & Molecular Biology
Wenjing Li, Yinhua Zhou, Zhaofan Luo, Rixin Tang, Yuxuan Sun, Qiangsheng He, Bin Xia, Kuiqing Lu, Qinghua Hou, Jinqiu Yuan
Summary: This study aims to construct a lipid score system to predict the risk of progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). The results showed that the lipid score system based on serum lipidomics can accurately predict the progression risk from MCI to AD.
Article
Clinical Neurology
Egle Audronyte, Vaiva Sutnikiene, Gyte Pakulaite-Kazliene, Gintaras Kaubrys
Summary: This study investigated olfactory memory and its relationship with verbal memory and other clinical features in patients with early-stage Alzheimer's disease (AD). The results showed that olfactory memory was significantly impaired in patients with AD compared to individuals with mild cognitive impairment due to AD and cognitively normal older participants. Furthermore, the duration of AD symptoms was a strong predictor of olfactory recognition memory scores.
FRONTIERS IN NEUROLOGY
(2023)
Article
Biochemistry & Molecular Biology
Chieh-Hsin Lin, Hsien-Yuan Lane
Summary: This study longitudinally monitored plasma GSH levels and cognitive function in MCI patients, showing a significant decline over a 2-year period, while these levels remained relatively stable in healthy individuals. Both baseline GSH levels and changes in GSH levels were found to significantly influence cognitive decline in MCI patients, suggesting that blood GSH concentration may be a potential biomarker for monitoring cognitive changes in MCI.
Article
Clinical Neurology
Bin Jiao, Rihui Li, Hui Zhou, Kunqiang Qing, Hui Liu, Hefu Pan, Yanqin Lei, Wenjin Fu, Xiaoan Wang, Xuewen Xiao, Xixi Liu, Qijie Yang, Xinxin Liao, Yafang Zhou, Liangjuan Fang, Yanbin Dong, Yuanhao Yang, Haiyan Jiang, Sha Huang, Lu Shen
Summary: This study aimed to identify effective EEG biomarkers for distinguishing early-stage AD patients and monitoring disease progression. The results showed that EEG biomarkers can be used for the diagnosis and evaluation of MCI and AD.
ALZHEIMERS RESEARCH & THERAPY
(2023)
Article
Geriatrics & Gerontology
Kym McNicholas, Maxime Francois, Jian-Wei Liu, James D. Doecke, Jane Hecker, Jeff Faunt, John Maddison, Sally Johns, Tara L. Pukala, Robert A. Rush, Wayne R. Leifert
Summary: This study identified biomarkers in saliva that can be used for early detection of cognitive impairment and Alzheimer's disease. The findings suggest that combinations of specific proteins can effectively distinguish patients with cognitive impairment and Alzheimer's disease from cognitively normal individuals.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Geriatrics & Gerontology
Yotam Lavy, Tzvi Dwolatzky, Zeev Kaplan, Jonathan Guez, Doron Todder
Summary: The study found that using an EEG-based neurofeedback system can significantly improve memory performance in patients with mild cognitive impairment (MCI), with this improvement lasting for at least one month.
FRONTIERS IN AGING NEUROSCIENCE
(2021)
Article
Geriatrics & Gerontology
Aladdin H. Shadyab, Linda K. McEvoy, Steve Horvath, Eric A. Whitsel, Stephen R. Rapp, Mark A. Espeland, Susan M. Resnick, JoAnn E. Manson, Jiu-Chiuan Chen, Brian H. Chen, Wenjun Li, Kathleen M. Hayden, Wei Bao, Cynthia D. J. Kusters, Andrea Z. LaCroix
Summary: The study examined the association between epigenetic age acceleration and cognitive impairment, finding that intrinsic AgeAccel was not significantly associated with cognitive impairment overall, but was associated with impairment among women who developed coronary heart disease.
JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES
(2022)
Article
Neurosciences
Riccardo Sacripante, Nicola Girtler, Elisa Doglione, Flavio Nobili, Sergio Della Sala
Summary: This study aimed to investigate if the forgetting rates of individuals with mild cognitive impairment due to Alzheimer's disease (MCI-AD) differ from age-matched healthy controls (HC) using a prose paradigm. The results showed that compared to HC, MCI-AD individuals exhibited poorer encoding at immediate recall and steeper forgetting up to 1 hour in prose memory. However, there were no differences in forgetting rates between groups from 1 hour to 24 hours.
JOURNAL OF ALZHEIMERS DISEASE
(2023)
Article
Clinical Neurology
Salvatore Mazzeo, Assunta Ingannato, Giulia Giacomucci, Alberto Manganelli, Valentina Moschini, Juri Balestrini, Arianna Cavaliere, Carmen Morinelli, Giulia Galdo, Filippo Emiliani, Diletta Piazzesi, Chiara Crucitti, Daniele Frigerio, Cristina Polito, Valentina Berti, Silvia Bagnoli, Sonia Padiglioni, Sandro Sorbi, Benedetta Nacmias, Valentina Bessi
Summary: Plasma neurofilament light chain (NfL) can accurately predict Alzheimer's disease and the progression of cognitive decline, serving as an important non-invasive tool for early diagnosis.
EUROPEAN JOURNAL OF NEUROLOGY
(2023)
Review
Psychology, Multidisciplinary
Alexandra Wolf, Kornkanok Tripanpitak, Satoshi Umeda, Mihoko Otake-Matsuura
Summary: Mild cognitive impairment (MCI) is a transitional zone between normal cognition and dementia, and has become a novel topic in clinical research. Early detection is crucial but logistically challenging, and technological advancements in cognitive scoring methodologies are needed. Non-invasive eye-tracking-based paradigms may contribute to early AD detection, but further longitudinal investigations are necessary for clinical applications.
FRONTIERS IN PSYCHOLOGY
(2023)
Review
Behavioral Sciences
Yi Qu, Ya-Hui Ma, Yu-Yuan Huang, Ya-Nan Ou, Xue-Ning Shen, Shi-Dong Chen, Qiang Dong, Lan Tan, Jin-Tai Yu
Summary: Blood-based biomarkers of AD pathology show significant changes between AD, aMCI, and control groups, indicating their strong validity in identifying AD and aMCI, and providing a new prospect for early diagnosis and monitoring of AD.
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS
(2021)
Article
Behavioral Sciences
Davide Quaranta, Naike Caraglia, Federica L'Abbate, Guido Maria Giuffre, Valeria Guglielmi, Giovanna Masone Iacobucci, Paolo Maria Rossini, Paolo Calabresi, Camillo Marra
Summary: Early impairment of semantic memory could predict the progression to Alzheimer's disease before the onset of memory disorders, and the discrepancy between phonological and semantic verbal fluency tests could be able to detect this impairment in advance.
BRAIN AND BEHAVIOR
(2023)
Article
Neurosciences
Daniel A. Llano, Viswanath Devanarayan
Summary: The study suggests that serum lipid markers can differentiate AD from healthy controls and predict conversion from MCI to AD. Specifically, levels of PE and lyso-PE were found to be associated with faster progression from MCI to AD, indicating their potential as useful biomarkers for predicting disease conversion.
JOURNAL OF ALZHEIMERS DISEASE
(2021)
Article
Engineering, Electrical & Electronic
Kerstin Hammernik, Thomas Kustner, Burhaneddin Yaman, Zhengnan Huang, Daniel Rueckert, Florian Knoll, Mehmet Akcakaya
Summary: Physics-driven deep learning methods have revolutionized computational MRI reconstruction by improving the performance of reconstruction. This article provides an overview of recent developments in incorporating physics information into learning-based MRI reconstruction. It discusses both linear and non-linear forward models for computational MRI, classical approaches for solving these inverse problems, as well as physics-driven deep learning approaches such as physics-driven loss functions, plug-and-play methods, generative models, and unrolled networks. Challenges specific to MRI with linear and non-linear forward models are highlighted, and common issues and open challenges are also discussed.
IEEE SIGNAL PROCESSING MAGAZINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Qiang Ma, Liu Li, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary
Summary: CortexODE is a deep learning framework that uses neural ordinary differential equations (ODEs) to reconstruct cortical surfaces. By modeling the trajectories of points on the surface as ODEs and parameterizing the derivatives with a learnable deformation network, CortexODE is able to prevent self-intersections. Integrated with an automatic learning-based pipeline, CortexODE can efficiently reconstruct cortical surfaces in less than 5 seconds.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Chen Qin, Shuo Wang, Chen Chen, Wenjia Bai, Daniel Rueckert
Summary: This paper proposes a novel method for myocardial motion tracking by using a generative model based on variational autoencoder to learn biomechanically plausible deformations and embed them into a neural network-parameterized transformation model. Experimental results show that the proposed method outperforms other approaches in terms of motion tracking accuracy, volume preservation, and generalizability.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Pharmacology & Pharmacy
Hilkka Liedes, Juha Pajula, Anna-Leena Vuorinen, Francesco De Pretis, Mark van Gils, Kari Harno, Mika Lehto, Mikko Niemi, Jaakko Lahteenmaki
BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Cheng Ouyang, Chen Chen, Surui Li, Zeju Li, Chen Qin, Wenjia Bai, Daniel Rueckert
Summary: In this work, the authors investigate the problem of training a deep network that is robust to unseen domains using only data from one source domain. They propose a causality-inspired data augmentation approach to expose the model to synthesized domain-shifted training examples. The approach is validated on three cross-domain segmentation scenarios and shows consistent performance improvements compared to competitive methods.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
Summary: Graph Neural Networks (GNNs) have become the state-of-the-art for many machine learning applications, but differentially private training of GNNs has remained under-explored. In this work, we propose a framework for differentially private graph-level classification using DP-SGD, which is applicable to multi-graph datasets.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Robert Wright, Alberto Gomez, Veronika A. Zimmer, Nicolas Toussaint, Bishesh Khanal, Jacqueline Matthew, Emily Skelton, Bernhard Kainz, Daniel Rueckert, Joseph V. Hajnal, Julia A. Schnabel
Summary: This paper introduces a novel method to fuse partially imaged fetal head anatomy from multiple views into a single coherent 3D volume. The method aligns and fuses ultrasound images to improve image detail and minimize artifacts, achieving state-of-the-art performance in terms of image quality and robustness.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Engineering, Industrial
Asta Pundziene, Neringa Gerulaitiene, Sea Matilda Bez, Irene Georgescu, Christopher Mathieu, Jordi Carrabina-Bordoll, Josep Rialp-Criado, Hannu Nieminen, Alpo Varri, Susanne Boethius, Mark van Gils, Victor Gimenez-Garcia, Isabel Narbon-Perpina, Diego Prior-Jimenez, Laura Vilutiene
Summary: This study examines the impact of a social-purpose-driven ecosystem on value capture from digital health platforms. The social-purpose-driven ecosystem is characterized by seeking social impact before profits and empowering citizens for individual and collective well-being. The study finds that capturing value from digital healthcare platforms embedded in a social-purpose-driven ecosystem requires considering unique contingencies such as multilayer value creation, multipurpose complementary assets, emerging dominant design, and distributed socio-economic returns mechanisms. The study emphasizes the importance of acknowledging the contextual effect of a social-purpose-driven ecosystem and highlights the factors that can positively affect value capture from digital healthcare platforms.
Article
Multidisciplinary Sciences
Emmi Antikainen, Joonas Linnosmaa, Adil Umer, Niku Oksala, Markku Eskola, Mark van Gils, Jussi Hernesniemi, Moncef Gabbouj
Summary: This study utilized deep learning techniques to predict increased risk of death in cardiovascular disease patients using electronic health records. The results showed that the XLNet model outperformed the BERT model in predicting mortality, capturing more positive cases.
SCIENTIFIC REPORTS
(2023)
Article
Clinical Neurology
Toni J. U. Niiranen, Anne-Cecile Chiollaz, Riikka S. K. Takala, Miko Voutilainen, Olli Tenovuo, Virginia F. J. Newcombe, Henna-Riikka Maanpaa, Jussi Tallus, Mehrbod Mohammadian, Iftakher Hossain, Mark van Gils, David K. Menon, Peter J. Hutchinson, Jean-Charles Sanchez, Jussi P. Posti
Summary: This study aimed to investigate the blood levels of interleukin 10 (IL-10) and heart fatty acid-binding protein (H-FABP) in patients with moderate-to-severe traumatic brain injury (TBI), and their correlations with inflammation/infection markers and cardiac injury markers. The results showed that increased IL-10 levels were associated with the inflammatory/infection status, while elevated H-FABP levels were related to cardiac injury in TBI patients.
FRONTIERS IN NEUROLOGY
(2023)
Article
Clinical Neurology
Iftakher Hossain, Mehrbod Mohammadian, Henna-Riikka Maanpaeae, Riikka S. K. Takala, Olli Tenovuo, Mark van Gils, Peter Hutchinson, David K. Menon, Virginia F. Newcombe, Jussi Tallus, Jussi Hirvonen, Timo Roine, Timo Kurki, Kaj Blennow, Henrik Zetterberg, Jussi P. Posti
Summary: This study examines the association between plasma NF-L levels at admission and white matter integrity in post-acute stage DW-MRI in mTBI patients. The results suggest that early levels of plasma NF-L may be associated with the presence of diffuse axonal injury (DAI) in mTBI patients over a period of 3 months.
Article
Computer Science, Information Systems
Pedro A. Moreno-Sanchez, Ruben Arroyo-Fernandez, Elisabeth Bravo-Esteban, Asuncion Ferri-Morales, Mark van Gils
Summary: This study analyzed data from fibromyalgia patients to assess the impact of mental health factors on fibromyalgia severity compared to pain factors. The findings suggest that mental health factors are more relevant for fibromyalgia severity.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Interdisciplinary Applications
Adam Marcus, Paul Bentley, Daniel Rueckert
Summary: The proposed study introduces a novel end-to-end multi-task transformer-based model for concurrent segmentation and age estimation of cerebral ischemic lesions. The method captures long-range dependencies using gated positional self-attention and CT-specific data augmentation, and can be effectively trained with low-data regimes in medical imaging. Experimental results demonstrate promising performance in lesion age classification, outperforming existing task-specific algorithms.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Jiazhen Pan, Manal Hamdi, Wenqi Huang, Kerstin Hammernik, Thomas Kuestner, Daniel Rueckert
Summary: This article introduces a learning-based and unrolled MCMR framework that can achieve accurate and rapid CMR reconstruction, delivering artifacts-free motion estimation and high-quality reconstruction even at imaging acceleration rates up to 20x.
MEDICAL IMAGE ANALYSIS
(2024)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Hilkka Liedes, Elina Mattila, Anita Honka, Pilvikki Absetz, Kirsikka Aittola, Suvi Manninen, Niina Lintu, Ursula Schwab, Aino-Maija Eloranta, Adil Umer, Tanja Tilles-Tirkkonen, Reija Mannikko, Ilona Ruotsalainen, Mark Van Gils, Jaana Lindstrom, Timo Lakka, Jussi Pihlajamaki, Anna-Leena Vuorinen
Summary: This study investigates the relationship between the use of the BitHabit app and changes in T2D risk factors, finding that increased use has positive impacts on diet quality.
CARING IS SHARING-EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION-PROCEEDINGS OF MIE 2023
(2023)
