Results 81 to 90 of about 2,890,246 (202)

Structural subnetwork evolution across the life-span: rich-club, feeder, seeder

, 2018
The impact of developmental and aging processes on brain connectivity and the connectome has been widely studied. Network theoretical measures and certain topological principles are computed from the entire brain, however there is a need to separate and ...
A Zalesky, AW Chung, D Batalle, D Batalle, DJ Watts, DS Bassett, DS Grayson, E Bullmore, E Bullmore, G Ball, G Collin, JA Brown, KB Nooner, L Bonilha, M Cao, M Daianu, M Ingalhalikar, M Rubinov, MN Moussa, MP Heuvel van den, MP Heuvel Van Den, O Sporns, O Sporns, O Sporns, O Sporns, O Tymofiyeva, P Hagmann, RC Craddock, S Mori, S Ray, T Opsahl, T Zhao   +31 more
core   +1 more source

Brain Age Assessment of Patients with Major Depressive Disorder Based on Convolutional Neural Network

Chinese Journal of Magnetic Resonance
Brain age has become an important analysis object in the diagnosis and mechanism research of neurodegenerative diseases. There is no consistent conclusion on whether major depression increases the brain age of patients, and few studies in this direction ...
ZHANG Haowei, WANG Yuncheng, LIU Ying
doaj   +1 more source

Aging and brain plasticity

Aging, 2018
sponsorship: This work was supported by the Research Fund KU Leuven (C16/15/070) and the Research Foundation Flanders grant (G089818N) and Excellence of Science grant (EOS 30446199, MEMODYN) awarded to SPS. LP is funded by a postdoctoral fellowship from the Research Fund KU Leuven (PDM/18/180) (Research Fund KU Leuven|C16/15/070, Research Fund KU ...
Pauwels, Lisa, Chalavi, Sima, Swinnen, Stephan   +2 more
openaire   +3 more sources

Early Childhood Education in Buffalo, New York [PDF]

, 2008
The National Association for the Education of Young Children (NAEYC) defines early childhood education as the learning experience of a child from birth to age eight.
Connelly, Caitlin M
core   +1 more source

A multimodal ensemble stacking model improves brain age prediction and reveals associations with schizophrenia symptoms

Frontiers in Psychiatry
IntroductionBrain age prediction using neuroimaging and machine learning has emerged as a promising approach to assess brain health and detect deviations associated with neurological and psychiatric disorders. The difference between chronological age and
Junhyeok Lee, Seo Yeong Kim, Hye Won Park, Juhyuk Han, Sung Woo Joo, Jungsun Lee, Won Hee Lee, Won Hee Lee   +7 more
doaj   +1 more source

Differing Effects of Alcohol Use on Epigenetic and Brain Age in Adult Children of Parents with Alcohol Use Disorder

Brain Sciences
Background: It is known that being the adult child of a parent with an alcohol use disorder (ACoA) can confer a wide variety of increased health and psychological risks, including higher rates of anxiety, depression, and post-traumatic stress disorder ...
Jamie L. Scholl, Kami Pearson, Kelene A. Fercho, Austin J. Van Asselt, Noah A. Kallsen, Erik. A. Ehli, Kari N. Potter, Kathleen A. Brown-Rice, Gina L. Forster, Lee A. Baugh   +9 more
doaj   +1 more source

Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso

Frontiers in Aging Neuroscience
IntroductionThe neural dynamics underlying cognition and behavior change greatly during the lifespan of brain development and aging. EEG is a promising modality due to its high temporal resolution in capturing neural oscillations.
Shiang Hu, Xue Xiang, Xiaolong Huang, Yan Lu, Xulai Zhang, Dezhong Yao, Pedro A. Valdes-Sosa   +6 more
doaj   +1 more source

Association between BrainAGE and Alzheimer's disease biomarkers

Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
INTRODUCTION The brain age gap estimation (BrainAGE) method uses a machine learning model to generate an age estimate from structural magnetic resonance imaging (MRI) scans. The goal was to study the association of brain age with Alzheimer's disease (AD)
Yousaf Abughofah, Rachael Deardorff, Aaron Vosmeier, Savannah Hottle, Jeffrey L. Dage, Desarae Dempsey, Liana G. Apostolova, Jared Brosch, David Clark, Martin Farlow, Tatiana Foroud, Sujuan Gao, Sophia Wang, Henrik Zetterberg, Kaj Blennow, Andrew J. Saykin, Shannon L. Risacher   +16 more
doaj   +1 more source

Analysis of Brain Age Gap across Subject Cohorts and Prediction Model Architectures

Biomedicines
Background: Brain age prediction from brain MRI scans and the resulting brain age gap (BAG)—the difference between predicted brain age and chronological age—is a general biomarker for a variety of neurological, psychiatric, and other diseases or ...
Lara Dular, Žiga Špiclin, for the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing   +3 more
doaj   +1 more source

Age-related changes in blood-brain barrier integrity in C57BL/6J mice [PDF]

, 2013
The blood-brain barrier (BBB) is formed by the endothelial cells of the brain microvasculature, which control the molecular traffic between the blood and brain to maintain the neural ...
Romero, I. A., Saffrey, M. J., Wang, C.
core