Results 61 to 70 of about 2,890,246 (202)

SAGEFusionNet: An Auxiliary Supervised Graph Neural Network for Brain Age Prediction as a Neurodegenerative Biomarker

Brain Sciences
Background: The ability of Graph Neural Networks (GNNs) to analyse brain structural patterns in various kinds of neurodegenerative diseases, including Parkinson’s disease (PD), has drawn a lot of interest recently. One emerging technique in this field is
Suraj Kumar, Suman Hazarika, Cota Navin Gupta   +2 more
doaj   +1 more source

Age Related Changes in Cerebrovascular Reactivity and Its Relationship to Global Brain Structure [PDF]

, 2015
ACKNOWLEDGMENTS This study was funded by Alzheimer’s Research UK (ARUK) and the Aberdeen Biomedical Imaging Centre, University of Aberdeen. GDW, ADM and CS are part of the SINASPE collaboration (Scottish Imaging Network - A Platform for Scientific ...
Ahearn, Trevor S., Cameron, George G., Murray, Alison D., Schwarzbauer, Christian, Waiter, Gordon D.   +4 more
core   +1 more source

Effects of Flight on Gene Expression and Aging in the Honey Bee Brain and Flight Muscle [PDF]

, 2012
Honey bees move through a series of in-hive tasks (e.g., “nursing”) to outside tasks (e.g., “foraging”) that are coincident with physiological changes and higher levels of metabolic activity.
Ammons, Andrew, Benito, Azucena A., Elekonich, Michelle M., Mancinelli, Georgina E., Margotta, Joseph, Roberts, Stephen P.   +5 more
core   +5 more sources

Epilepsy is associated with the accelerated aging of brain activity in sleep

Frontiers in Physiology
ObjectiveAlthough seizures are the cardinal feature, epilepsy is associated with other forms of brain dysfunction including impaired cognition, abnormal sleep, and increased risk of developing dementia.
Peter N. Hadar, Mike Westmeijer, Mike Westmeijer, Haoqi Sun, Erik-Jan Meulenbrugge, Jin Jing, Luis Paixao, Ryan A. Tesh, Madalena Da Silva Cardoso, Pierrick Arnal, Rhoda Au, Chol Shin, Chol Shin, Soriul Kim, Robert J. Thomas, Sydney S. Cash, M. Brandon Westover   +16 more
doaj   +1 more source

The role of EGFR mutation as a prognostic factor in survival after diagnosis of brain metastasis in non-small cell lung cancer: A systematic review and meta-analysis [PDF]

, 2019
Background The brain is a common site for metastasis in non-small-cell lung cancer (NSCLC). This study was designed to evaluate the relationship between the mutational of the epidermal growth factor receptor (EGFR) and overall survival (OS) in NSCLC ...
Han, Yunan, Li, Wen-Ya, Liu, Xing-Yu, Miao, Zhi-Feng, Song, Yong-Xi, Wang, Zhen-Ning, Wu, Jian-Hua, Xu, Hao, Xu, Hui-Mian, Xu, Ying-Ying, Yin, Song-Cheng, Zhao, Ting-Ting   +11 more
core   +2 more sources

Inflammation and Brain Aging

Annals of the Russian academy of medical sciences, 2015
The review covers current concepts on cell and molecular mechanisms of neuroinflammation and aging with the special focus on the regulation of cytokine-producing activity of astroglial cells and intercellular communication. The review reflects that a key component of the aging phenomenon as a result of ineffective implementation of anti-inflammatory ...
A B, Salmina, Yu K, Komleva, N V, Kuvacheva, O L, Lopatina, E A, Pozhilenkova, Ya V, Gorina, E L, Gasymly, Yu A, Panina, A V, Morgun, N A, Malinovskaya   +9 more
openaire   +3 more sources

A predictive model of Parkinsonian brain aging based on brain imaging features

Frontiers in Neurology
IntroductionThis study explores the use of imaging to evaluate brain aging to establish a model for predicting brain age in patients with Parkinson’s disease.MethodsStructural brain MRI data from 345 healthy individuals were obtained from the IXI ...
Xiaoyan Zhou, Xiaoyan Zhou, Xiaoyan Zhou, Haoyong Zhu, Xiaoming Wang, Xiaoming Wang, Qing Gao   +6 more
doaj   +1 more source

Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 Challenge

Frontiers in Psychiatry, 2020
We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images.
Baptiste Couvy-Duchesne, Baptiste Couvy-Duchesne, Baptiste Couvy-Duchesne, Baptiste Couvy-Duchesne, Baptiste Couvy-Duchesne, Baptiste Couvy-Duchesne, Johann Faouzi, Johann Faouzi, Johann Faouzi, Johann Faouzi, Johann Faouzi, Benoît Martin, Benoît Martin, Benoît Martin, Benoît Martin, Benoît Martin, Elina Thibeau–Sutre, Elina Thibeau–Sutre, Elina Thibeau–Sutre, Elina Thibeau–Sutre, Elina Thibeau–Sutre, Adam Wild, Adam Wild, Adam Wild, Adam Wild, Adam Wild, Manon Ansart, Manon Ansart, Manon Ansart, Manon Ansart, Manon Ansart, Stanley Durrleman, Stanley Durrleman, Stanley Durrleman, Stanley Durrleman, Stanley Durrleman, Didier Dormont, Didier Dormont, Didier Dormont, Didier Dormont, Didier Dormont, Didier Dormont, Ninon Burgos, Ninon Burgos, Ninon Burgos, Ninon Burgos, Ninon Burgos, Olivier Colliot, Olivier Colliot, Olivier Colliot, Olivier Colliot, Olivier Colliot   +51 more
doaj   +1 more source

Brain Weight and Life-Span in Primate Species [PDF]

, 1993
In haplorhine primates (tarsiers, monkeys, apes, and humans), there is a significant correlation between brain weight and maximum life-span when the effect of body size is removed.
Allman, John, Hakeem, Atiya, McLaughlin, Todd   +2 more
core  

Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants [PDF]

, 2019
Brain development and aging are dynamic processes that unfold over years on multiple levels in both healthy and disordered individuals. Recent studies have revealed a disparity between the chronological brain age and the ‘data-driven’ brain age using ...
Corps, Joshua, Rekik, Islem
core   +2 more sources