Related references
Note: Only part of the references are listed.Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group
Laura K. M. Han et al.
MOLECULAR PSYCHIATRY (2021)
Accurate brain age prediction with lightweight deep neural networks
Han Peng et al.
MEDICAL IMAGE ANALYSIS (2021)
Predicting brain age with complex networks: From adolescence to adulthood
Loredana Bellantuono et al.
NEUROIMAGE (2021)
Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019
Angela Lombardi et al.
FRONTIERS IN PSYCHIATRY (2021)
Post-hoc explanation of black-box classifiers using confident itemsets
Milad Moradi et al.
EXPERT SYSTEMS WITH APPLICATIONS (2021)
Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging
Xinyang Feng et al.
NEUROBIOLOGY OF AGING (2020)
From a deep learning model back to the brain-Identifying regional predictors and their relation to aging
Gidon Levakov et al.
HUMAN BRAIN MAPPING (2020)
Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups
Premika S. W. Boedhoe et al.
AMERICAN JOURNAL OF PSYCHIATRY (2020)
Predicting intelligence from brain gray matter volume
Kirsten Hilger et al.
BRAIN STRUCTURE & FUNCTION (2020)
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta et al.
INFORMATION FUSION (2020)
Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction
Angela Lombardi et al.
BRAIN SCIENCES (2020)
Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019
Pedro F. Da Costa et al.
FRONTIERS IN PSYCHIATRY (2020)
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
Baptiste Couvy-Duchesne et al.
FRONTIERS IN PSYCHIATRY (2020)
Association between Structural Connectivity and Generalized Cognitive Spectrum in Alzheimer's Disease
Angela Lombardi et al.
BRAIN SCIENCES (2020)
An Explainable Machine Learning Framework for Intrusion Detection Systems
Maonan Wang et al.
IEEE ACCESS (2020)
Explainable AI in Fintech Risk Management
Niklas Bussmann et al.
FRONTIERS IN ARTIFICIAL INTELLIGENCE (2020)
Brain age and other bodily 'ages': implications for neuropsychiatry
James H. Cole et al.
MOLECULAR PSYCHIATRY (2019)
A Survey of Methods for Explaining Black Box Models
Riccardo Guidotti et al.
ACM COMPUTING SURVEYS (2019)
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller
ARTIFICIAL INTELLIGENCE (2019)
Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age
Nicola Amoroso et al.
FRONTIERS IN AGING NEUROSCIENCE (2019)
Causability and explainability of artificial intelligence in medicine
Andreas Holzinger et al.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2019)
Charting shared developmental trajectories of cortical thickness and structural connectivity in childhood and adolescence
Gareth Ball et al.
HUMAN BRAIN MAPPING (2019)
A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop
Curtis P. Langlotz et al.
RADIOLOGY (2019)
Morphological Brain Age Prediction using Multi-View Brain Networks Derived from Cortical Morphology in Healthy and Disordered Participants
Joshua Corps et al.
SCIENTIFIC REPORTS (2019)
Machine Learning Interpretability: A Survey on Methods and Metrics
Diogo Carvalho et al.
ELECTRONICS (2019)
Gray Matter Age Prediction as a Biomarker for Risk of Dementia
Johnny Wang et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
Fabian Eitel et al.
NEUROIMAGE-CLINICAL (2019)
Brain age prediction: Cortical and subcortical shape covariation in the developing human brain
Yihong Zhao et al.
NEUROIMAGE (2019)
Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation
Jean-Marc Fellous et al.
FRONTIERS IN NEUROSCIENCE (2019)
Cortical and Subcortical Brain Morphometry Differences Between Patients With Autism Spectrum Disorder and Healthy Individuals Across the Lifespan: Results From the ENIGMA ASD Working Group
Daan van Rooij et al.
AMERICAN JOURNAL OF PSYCHIATRY (2018)
A Shared Vision for Machine Learning in Neuroscience
Mai-Anh T. Vu et al.
JOURNAL OF NEUROSCIENCE (2018)
Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge
Nicola Amoroso et al.
JOURNAL OF NEUROSCIENCE METHODS (2018)
Trajectories of imaging markers in brain aging: the Rotterdam Study
Elisabeth J. Vinke et al.
NEUROBIOLOGY OF AGING (2018)
Harmonization of cortical thickness measurements across scanners and sites
Jean-Philippe Fortin et al.
NEUROIMAGE (2018)
Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation
Runa Bhaumik et al.
NEUROINFORMATICS (2018)
Identification of autism spectrum disorder using deep learning and the ABIDE dataset
Anibal Solon Heinsfeld et al.
NEUROIMAGE-CLINICAL (2018)
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi et al.
IEEE ACCESS (2018)
Development of the Cerebral Cortex across Adolescence: A Multisample Study of Inter-Related Longitudinal Changes in Cortical Volume, Surface Area, and Thickness
Christian K. Tamnes et al.
JOURNAL OF NEUROSCIENCE (2017)
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Alexandre Abraham et al.
NEUROIMAGE (2017)
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
James H. Cole et al.
NEUROIMAGE (2017)
Quantifying cortical development in typically developing toddlers and young children, 1-6 years of age
Justin Remer et al.
NEUROIMAGE (2017)
Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers
James H. Cole et al.
TRENDS IN NEUROSCIENCES (2017)
Are Developmental Trajectories of Cortical Folding Comparable Between Cross-sectional Datasets of Fetuses and Preterm Newborns?
Julien Lefevre et al.
CEREBRAL CORTEX (2016)
Prediction of Brain Age Suggests Accelerated Atrophy after Traumatic Brain Injury
James H. Cole et al.
ANNALS OF NEUROLOGY (2015)
Development and aging of cortical thickness correspond to genetic organization patterns
Anders M. Fjell et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2015)
The Preprocessed Connectomes Project Quality Assessment Protocol - a resource for measuring the quality of MRI data.
Shehzad Zarrar et al.
Frontiers in Neuroscience (2015)
Imaging Patterns of Brain Development and their Relationship to Cognition
Guray Erus et al.
CEREBRAL CORTEX (2015)
Longitudinal changes in cortical thickness in autism and typical development
Brandon A. Zielinski et al.
BRAIN (2014)
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism
A. Di Martino et al.
MOLECULAR PSYCHIATRY (2014)
Spatial distribution and longitudinal development of deep cortical sulcal landmarks in infants
Yu Meng et al.
NEUROIMAGE (2014)
Accelerated Brain Aging in Schizophrenia and Beyond: A Neuroanatomical Marker of Psychiatric Disorders
Nikolaos Koutsouleris et al.
SCHIZOPHRENIA BULLETIN (2014)
BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease
Christian Gaser et al.
PLOS ONE (2013)
The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives
Craddock Cameron et al.
Frontiers in Neuroinformatics (2013)
Age-Dependent Changes in the Cerebrospinal Fluid Proteome by Slow Off-Rate Modified Aptamer Array
Geoffrey S. Baird et al.
AMERICAN JOURNAL OF PATHOLOGY (2012)
Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer's Disease
Katja Franke et al.
GEROPSYCH-THE JOURNAL OF GERONTOPSYCHOLOGY AND GERIATRIC PSYCHIATRY (2012)
Age-Related Changes in the Thickness of Cortical Zones in Humans
Scott M. McGinnis et al.
BRAIN TOPOGRAPHY (2011)
Brain Maturation in Adolescence and Young Adulthood: Regional Age-Related Changes in Cortical Thickness and White Matter Volume and Microstructure
Christian K. Tamnes et al.
CEREBRAL CORTEX (2010)
Characterization of cortical thickness and ventricular width in normal aging: A morphometric study at 3 Tesla
Christoph Preul et al.
JOURNAL OF MAGNETIC RESONANCE IMAGING (2006)
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest
Rahul S. Desikan et al.
NEUROIMAGE (2006)
Measurement of signal-to-noise and contrast-to-noise in the fBIRN multicenter imaging study
Vincent A. Magnotta et al.
JOURNAL OF DIGITAL IMAGING (2006)
Intellectual ability and cortical development in children and adolescents
P Shaw et al.
NATURE (2006)
Mapping IQ and gray matter density in healthy young people
S Frangou et al.
NEUROIMAGE (2004)
Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain
B Fischl et al.
NEURON (2002)
Share Paper
