Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease

[1]  T. SHALLICE,et al.  Learning and Memory , 1970, Nature.

[2]  David G. Stork,et al.  Pattern Classification , 1973 .

[3]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[4]  A. Atkinson Subset Selection in Regression , 1992 .

[5]  P. S. St George-Hyslop,et al.  Prediction of probable Alzheimer's disease in memory-impaired patients , 1996, Neurology.

[6]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[7]  Jagath C. Rajapakse,et al.  Statistical approach to segmentation of single-channel cerebral MR images , 1997, IEEE Transactions on Medical Imaging.

[8]  Marti J. Anderson,et al.  Permutation Tests for Linear Models , 2001 .

[9]  Karl J. Friston,et al.  A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains , 2001, NeuroImage.

[10]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[11]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .

[12]  Geoffrey J McLachlan,et al.  Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  J. Kulisevsky,et al.  Rey verbal learning test is a useful tool for differential diagnosis in the preclinical phase of Alzheimer's disease: comparison with mild cognitive impairment and normal aging , 2003, International journal of geriatric psychiatry.

[14]  Michael E. Tipping,et al.  Fast Marginal Likelihood Maximisation for Sparse Bayesian Models , 2003 .

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  Alan C. Evans,et al.  Fast and robust parameter estimation for statistical partial volume models in brain MRI , 2004, NeuroImage.

[17]  Hans C. van Houwelingen,et al.  The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, New York, 2001. No. of pages: xvi+533. ISBN 0‐387‐95284‐5 , 2004 .

[18]  Meritxell Bach Cuadra,et al.  Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images , 2005, IEEE Transactions on Medical Imaging.

[19]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[20]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[21]  James G Scott,et al.  Test performance and classification statistics for the Rey Auditory Verbal Learning Test in selected clinical samples. , 2006, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[22]  Francisco Melo,et al.  StAR: a simple tool for the statistical comparison of ROC curves , 2008, BMC Bioinformatics.

[23]  L. Malloy-Diniz,et al.  The Rey Auditory-Verbal Learning Test: applicability for the Brazilian elderly population. , 2007, Revista brasileira de psiquiatria.

[24]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[25]  H. Benali,et al.  Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI , 2009, Neuroradiology.

[26]  Benito P Damasceno,et al.  Learning, retrieval, and recognition are compromised in aMCI and mild AD: Are distinct episodic memory processes mediated by the same anatomical structures? , 2009, Journal of the International Neuropsychological Society.

[27]  A. Ravishankar Rao,et al.  Prediction and interpretation of distributed neural activity with sparse models , 2009, NeuroImage.

[28]  C. Gaser,et al.  Partial Volume Segmentation with Adaptive Maximum A Posteriori (MAP) Approach , 2009, NeuroImage.

[29]  Nick C Fox,et al.  Revising the definition of Alzheimer's disease: a new lexicon , 2010, The Lancet Neurology.

[30]  Clifford R. Jack,et al.  Predicting Clinical Scores from Magnetic Resonance Scans in Alzheimer's Disease , 2010, NeuroImage.

[31]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[32]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[33]  Stefan Klöppel,et al.  Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.

[34]  A. Poreh Rey Auditory Verbal Learning Test , 2010 .

[35]  A. C. Hamdan,et al.  The Rey Auditory Verbal Learning Test: normative data for the Brazilian population and analysis of the influence of demographic variables , 2010 .

[36]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[37]  C. Jack,et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade , 2010, The Lancet Neurology.

[38]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[39]  M. Corbetta,et al.  Episodic Memory Retrieval, Parietal Cortex, and the Default Mode Network: Functional and Topographic Analyses , 2011, The Journal of Neuroscience.

[40]  L. Squire,et al.  The cognitive neuroscience of human memory since H.M. , 2011, Annual review of neuroscience.

[41]  Hernando Ombao,et al.  Penalized least squares regression methods and applications to neuroimaging , 2011, NeuroImage.

[42]  M. Albert,et al.  Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease , 2011, Alzheimer's & Dementia.

[43]  Shannon L. Risacher,et al.  Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance , 2011, 2011 International Conference on Computer Vision.

[44]  Karen F Berman,et al.  The neurobiology of Alzheimer disease defined by neuroimaging. , 2012, Current opinion in neurology.

[45]  Rui Huang,et al.  A meta-analysis of voxel-based morphometry studies of white matter volume alterations in Alzheimer's disease , 2012, Neuroscience & Biobehavioral Reviews.

[46]  Emiliano Macaluso,et al.  Functional anatomy of temporal organisation and domain-specificity of episodic memory retrieval , 2012, Neuropsychologia.

[47]  Heikki Huttunen,et al.  MEG Mind Reading : Strategies for Feature Selection , 2012 .

[48]  Susanne Graef,et al.  Using the Rey Auditory Verbal Learning Test (RAVLT) to Differentiate Alzheimer's Dementia and Behavioural Variant Fronto-Temporal Dementia , 2012, The Clinical neuropsychologist.

[49]  Benson Mwangi,et al.  A Review of Feature Reduction Techniques in Neuroimaging , 2013, Neuroinformatics.

[50]  D. Shen,et al.  Prediction of Alzheimer's Disease and Mild Cognitive Impairment Using Cortical Morphological Patterns Chong-yaw Wee, Pew-thian Yap, and Dinggang Shen; for the Alzheimer's Disease Neuroimaging Initiative , 2022 .

[51]  Richard S. J. Frackowiak,et al.  How early can we predict Alzheimer's disease using computational anatomy? , 2013, Neurobiology of Aging.

[52]  Stefan Klöppel,et al.  BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease , 2013, PloS one.

[53]  Vladimir Fonov,et al.  Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning , 2013, NeuroImage.

[54]  Jonathan E. Taylor,et al.  Interpretable whole-brain prediction analysis with GraphNet , 2013, NeuroImage.

[55]  Stefan Haufe,et al.  On the interpretation of weight vectors of linear models in multivariate neuroimaging , 2014, NeuroImage.

[56]  Jussi Tohka,et al.  Semi-supervised learning in MCI-to-ad conversion prediction — When is unlabeled data useful? , 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.

[57]  F. Bermúdez-Rattoni The forgotten insular cortex: Its role on recognition memory formation , 2014, Neurobiology of Learning and Memory.

[58]  Terry E. Goldberg,et al.  Extension and refinement of the predictive value of different classes of markers in ADNI: Four-year follow-up data , 2014, Alzheimer's & Dementia.

[59]  G. Arbanas Diagnostic and Statistical Manual of Mental Disorders (DSM-5) , 2015 .

[60]  Michèle Allard,et al.  Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis , 2015, Human brain mapping.

[61]  R. Buchert,et al.  Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers∗ , 2015, Alzheimer's & dementia.

[62]  Heikki Huttunen,et al.  Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia , 2016, Neuroinformatics.

[63]  Vladimir Fonov,et al.  Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge , 2015, NeuroImage.

[64]  M. Gilardi,et al.  Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach , 2015, Front. Neurosci..

[65]  Alan C. Evans,et al.  Prediction of brain maturity based on cortical thickness at different spatial resolutions , 2015, NeuroImage.

[66]  N. Schuff,et al.  Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection , 2015, Lancet Neurology.

[67]  J. Kim,et al.  Episodic memory in aspects of large-scale brain networks , 2015, Front. Hum. Neurosci..

[68]  Heikki Huttunen,et al.  Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects , 2015, NeuroImage.

[69]  P. Coupé,et al.  Structural imaging biomarkers of Alzheimer's disease: predicting disease progression , 2015, Neurobiology of Aging.

[70]  H. Demirel,et al.  Feature-ranking-based Alzheimer's disease classification from structural MRI. , 2016, Magnetic resonance imaging.

[71]  P. Battista,et al.  Frontiers for the Early Diagnosis of AD by Means of MRI Brain Imaging and Support Vector Machines. , 2016, Current Alzheimer research.

[72]  H. Huttunen,et al.  Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia , 2016, Neuroinformatics.

[73]  Fabio Sambataro,et al.  Accurate Prediction of Conversion to Alzheimer's Disease using Imaging, Genetic, and Neuropsychological Biomarkers. , 2015, Journal of Alzheimer's disease : JAD.

[74]  Alan C. Evans,et al.  Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data , 2016, NeuroImage.