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IEEE Journal of Biomedical and Health Informatics, ISSN 2168-2194, 6/2019, pp. 1 - 1
The study of healthy brain development helps to better understand both brain transformation and connectivity patterns, which happen during childhood to... 
Brain age prediction | Neuroimaging | Extreme learning machine
Journal Article
Journal Article
Human Brain Mapping, ISSN 1065-9471, 2017, Volume 38, Issue 7, pp. 3566 - 3578
Journal Article
NeuroImage: Clinical, ISSN 2213-1582, 11/2019, p. 102063
Journal Article
Neurobiology of Aging, ISSN 0197-4580, 02/2019, Volume 74, pp. 112 - 120
The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as “brain age (BA),” which can be... 
Sleep | EEG | Brain age | Machine learning | DEMENTIA | RISK | HEALTH | NEUROSCIENCES | GERIATRICS & GERONTOLOGY | Quantitative Biology - Quantitative Methods
Journal Article
Clinical Neurophysiology, ISSN 1388-2457, 08/2019, Volume 130, Issue 8, pp. 1311 - 1319
Under General Anesthesia (GA), age and Burst Suppression (BS) are associated with cognitive postoperative complications, yet how these parameters are related... 
Burst-suppressions | SEF95 | EEG | Brain age | General anesthesia | Propofol | Cerebral fragility | SEF | ANESTHESIA | ELECTROENCEPHALOGRAM | POSTOPERATIVE DELIRIUM | NEUROSCIENCES | CLINICAL NEUROLOGY | Phenols | Anesthesia | Central nervous system agents
Journal Article
Schizophrenia Research, ISSN 0920-9964, 06/2019, Volume 208, pp. 41 - 43
In a recent study, a neuroanatomical-based age prediction model observed neuromaturational deviance among clinical high-risk individuals who developed... 
Structural MRI | Biomarker | Clinical high risk | Risk calculator | Brain age | Psychosis prodrome | PSYCHIATRY | VALIDATION | CLINICIAN | PREDICTION | Human acts | Human behavior | Neurosciences
Journal Article
Procedia Computer Science, ISSN 1877-0509, 2019, Volume 159, pp. 981 - 989
During the last decade, deep learning and Convolutional Neural Networks (CNNs) have produced a devastating impact on computer vision, yielding exceptional... 
Medical Image Analysis | Magnetic Resonance Imaging | Convolutional Neural Networks | Brain Age Estimation
Journal Article
Magnetic Resonance Imaging, ISSN 0730-725X, 10/2019, Volume 62, pp. 70 - 77
Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that... 
Deep learning | Medical image processing | Convolutional neural networks | Brain age | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Electrical engineering | Computer science | CT imaging | Neurosciences | Neural networks | Special education | Psychophysiology | Medical imaging equipment | Biomedical engineering
Journal Article
NeuroImage, ISSN 1053-8119, 11/2019, Volume 202, p. 116149
Journal Article
BioMedical Engineering OnLine, ISSN 1475-925X, 12/2017, Volume 16, Issue 1, pp. 1 - 20
Abstract Objectives Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore... 
Brain age estimation | Alzheimer’s disease | Magnetic resonance imaging | Brain pathological age | Classification | Correlation criterion
Journal Article
Developmental Cognitive Neuroscience, ISSN 1878-9293, 04/2017, Volume 24, pp. 93 - 106
Developmental differences regarding decision making are often reported in the absence of emotional stimuli and without context, failing to explain why some... 
Emotional state | Pseudo-resting state fMRI | Multivariate | Brain age | Risky behavior | Prediction | Risky Behavior | Brain Age | Pseudo-Resting State fMRI
Journal Article