Region-wise stacking ensembles for estimating brain-age using MRI | Paper Citation Record

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This paper cites Brain-age prediction: A systematic comparison of machine learning workflows,.

Region-wise stacking ensembles for estimating brain-age using MRI Brain-age prediction: A systematic comparison of machine learning workflows,

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This paper cites Age-related gray matter volume changes in the brain during non-elderly adulthood,.

Region-wise stacking ensembles for estimating brain-age using MRI Age-related gray matter volume changes in the brain during non-elderly adulthood,

Reference 3

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This paper cites Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures,.

Region-wise stacking ensembles for estimating brain-age using MRI Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures,

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This paper cites Gray Matter Volume and Cognitive Performance During Normal Aging. A Voxel-Based Morphometry Study,.

Region-wise stacking ensembles for estimating brain-age using MRI Gray Matter Volume and Cognitive Performance During Normal Aging. A Voxel-Based Morphometry Study,

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This paper cites Prefrontal Gray and White Matter Volumes in 17 Healthy Aging and Alzheimer Disease,.

Region-wise stacking ensembles for estimating brain-age using MRI Prefrontal Gray and White Matter Volumes in 17 Healthy Aging and Alzheimer Disease,

Reference 6

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This paper cites Longitudinal pattern of regional brain volume change differentiates normal aging from MCI,.

Region-wise stacking ensembles for estimating brain-age using MRI Longitudinal pattern of regional brain volume change differentiates normal aging from MCI,

Reference 7

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Region-wise stacking ensembles for estimating brain-age using MRI Age-related changes in grey and white matter structure throughout adulthood,

Reference 8

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This paper cites Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index,.

Region-wise stacking ensembles for estimating brain-age using MRI Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index,

Reference 9

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This paper cites Predicting brain-age from multimodal imaging data captures cognitive impairment,.

Region-wise stacking ensembles for estimating brain-age using MRI Predicting brain-age from multimodal imaging data captures cognitive impairment,

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Region-wise stacking ensembles for estimating brain-age using MRI Ageing, neurodegeneration and brain rejuvenation,

Reference 12

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This paper cites Gray Matter Age Prediction as a Biomarker for Risk of Dementia,.

Region-wise stacking ensembles for estimating brain-age using MRI Gray Matter Age Prediction as a Biomarker for Risk of Dementia,

Reference 13

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This paper cites Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers,.

Region-wise stacking ensembles for estimating brain-age using MRI Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers,

Reference 14

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This paper cites A voxel-based morphometric study of ageing in 465 normal adult human brains.,.

Region-wise stacking ensembles for estimating brain-age using MRI A voxel-based morphometric study of ageing in 465 normal adult human brains.,

Reference 15

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This paper cites Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease,.

Region-wise stacking ensembles for estimating brain-age using MRI Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease,

Reference 16

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This paper cites What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus,.

Region-wise stacking ensembles for estimating brain-age using MRI What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus,

Reference 17

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This paper cites Pseudonymisation of neuroimages and data protection: Increasing access to data while retaining scientific utility ,.

Region-wise stacking ensembles for estimating brain-age using MRI Pseudonymisation of neuroimages and data protection: Increasing access to data while retaining scientific utility ,

Reference 18

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This paper cites Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters,.

Region-wise stacking ensembles for estimating brain-age using MRI Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters,

Reference 19

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This paper cites Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker,.

Region-wise stacking ensembles for estimating brain-age using MRI Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker,

Reference 20

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This paper cites Learning patterns of the ageing brain in MRI using deep convolutional networks,.

Region-wise stacking ensembles for estimating brain-age using MRI Learning patterns of the ageing brain in MRI using deep convolutional networks,

Reference 21

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This paper cites Deep learning-based brain age prediction in normal aging and dementia,.

Region-wise stacking ensembles for estimating brain-age using MRI Deep learning-based brain age prediction in normal aging and dementia,

Reference 22

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Region-wise stacking ensembles for estimating brain-age using MRI Brain age prediction using deep learning uncovers associated sequence variants,

Reference 24

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This paper cites Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019,.

Region-wise stacking ensembles for estimating brain-age using MRI Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019,

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This paper cites Accelerated Brain Aging in Schizophrenia and Beyond: A Neuroanatomical Marker of Psychiatric Disorders,.

Region-wise stacking ensembles for estimating brain-age using MRI Accelerated Brain Aging in Schizophrenia and Beyond: A Neuroanatomical Marker of Psychiatric Disorders,

Reference 26

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This paper cites Local Brain-Age: A U-Net Model,.

Region-wise stacking ensembles for estimating brain-age using MRI Local Brain-Age: A U-Net Model,

Reference 27

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Region-wise stacking ensembles for estimating brain-age using MRI Premature brain aging in humans exposed to maternal nutrient restriction during early gestation,

Reference 28

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Region-wise stacking ensembles for estimating brain-age using MRI Brain Age in Early Stages of Bipolar Disorders or Schizophrenia,

Reference 29

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This paper cites Machine learning for brain age prediction: Introduction to methods and clinical applications,.

Region-wise stacking ensembles for estimating brain-age using MRI Machine learning for brain age prediction: Introduction to methods and clinical applications,

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Region-wise stacking ensembles for estimating brain-age using MRI Advanced brain ageing in Parkinson’s disease is related to disease duration and individual impairment,

Reference 31

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Region-wise stacking ensembles for estimating brain-age using MRI Imaging-based parcellations of the human brain,

Reference 32

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Region-wise stacking ensembles for estimating brain-age using MRI Detect and correct bias in multi-site neuroimaging datasets,

Reference 33

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Region-wise stacking ensembles for estimating brain-age using MRI A systematic comparison of VBM pipelines and their application to age prediction,

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Region-wise stacking ensembles for estimating brain-age using MRI Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review,

Reference 35

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Region-wise stacking ensembles for estimating brain-age using MRI Identification of Anonymous MRI Research Participants with Face-Recognition Software,

Reference 36

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This paper cites Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity,.

Region-wise stacking ensembles for estimating brain-age using MRI Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity,

Reference 37

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Region-wise stacking ensembles for estimating brain-age using MRI Stacked generalization,

Reference 38

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Region-wise stacking ensembles for estimating brain-age using MRI A Stacking Ensemble Learning Framework for Genomic Prediction,

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Region-wise stacking ensembles for estimating brain-age using MRI A stacking ensemble model for predicting the occurrence of carotid atherosclerosis,

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Region-wise stacking ensembles for estimating brain-age using MRI Application of stacking ensemble learning model in quantitative analysis of biomaterial activity,

Reference 41

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Region-wise stacking ensembles for estimating brain-age using MRI BrainAGE: Revisited and reframed machine learning workflow,

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This paper cites The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample,.

Region-wise stacking ensembles for estimating brain-age using MRI The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample,

Reference 43

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Region-wise stacking ensembles for estimating brain-age using MRI Every Model Learned by Gradient Descent Is Approximately a Kernel Machine

Reference 44

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This paper cites Studying variability in human brain aging in a population-based German cohort—rationale and design of 1000BRAINS,.

Region-wise stacking ensembles for estimating brain-age using MRI Studying variability in human brain aging in a population-based German cohort—rationale and design of 1000BRAINS,

Reference 45

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This paper cites Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation.,.

Region-wise stacking ensembles for estimating brain-age using MRI Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation.,

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Region-wise stacking ensembles for estimating brain-age using MRI Regularization Paths for Generalized Linear Models via Coordinate Descent,

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This paper cites Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI,.

Region-wise stacking ensembles for estimating brain-age using MRI Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI,

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This paper cites The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture,.

Region-wise stacking ensembles for estimating brain-age using MRI The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture,

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Region-wise stacking ensembles for estimating brain-age using MRI The organization of the human cerebellum estimated by intrinsic functional connectivity,

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This paper cites Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme,.

Region-wise stacking ensembles for estimating brain-age using MRI Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme,

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This paper cites Mind the gap: Performance metric evaluation in brain-age prediction,.

Region-wise stacking ensembles for estimating brain-age using MRI Mind the gap: Performance metric evaluation in brain-age prediction,

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This paper cites Early diagnosis of Alzheimer’s disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images,.

Region-wise stacking ensembles for estimating brain-age using MRI Early diagnosis of Alzheimer’s disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images,

Reference 53

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This paper cites Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets,.

Region-wise stacking ensembles for estimating brain-age using MRI Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets,

Reference 54

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