Characterization of neurodegenerative diseases with tree ensemble methods: the case of Alzheimer's disease

[1]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[2]  Corinne E. Fischer,et al.  Tablet-Based Functional MRI of the Trail Making Test: Effect of Tablet Interaction Mode , 2017, Front. Hum. Neurosci..

[3]  Robert K. Brayton.  On the asymptotic behavior of the number of trials necessary to complete a set with random selection , 1963 .

[4]  Daniel Rueckert,et al.  Random forest-based similarity measures for multi-modal classification of Alzheimer's disease , 2013, NeuroImage.

[5]  Pierre Geurts,et al.  Random Forests Based Group Importance Scores and Their Statistical Interpretation: Application for Alzheimer's Disease , 2018, Front. Neurosci..

[6]  Computer aided diagnosis system based on random forests for the prognosis of Alzheimer's disease , 2017 .

[7]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[8]  J. Morris.  The Clinical Dementia Rating (CDR) , 1993, Neurology.

[9]  Marco Ferrante,et al.  A note on the coupon - collector's problem with multiple arrivals and the random sampling , 2012, 1209.2667.

[10]  Clifford R. Jack,et al.  Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies , 2008, NeuroImage.

[11]  Olivier Debeir,et al.  Limiting the Number of Trees in Random Forests , 2001, Multiple Classifier Systems.

[12]  Pierre Geurts,et al.  Exploiting tree-based variable importances to selectively identify relevant variables , 2008, FSDM.

[13]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[14]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[15]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[16]  Michael I. Jordan,et al.  Multiple kernel learning, conic duality, and the SMO algorithm , 2004, ICML.

[17]  Witold R. Rudnicki,et al.  The All Relevant Feature Selection using Random Forest , 2011, ArXiv.

[18]  W. Markesbery,et al.  Hippocampal volume as an index of Alzheimer neuropathology: Findings from the Nun Study , 2002, Neurology.

[19]  A. Rey.  L'examen psychologique dans les cas d'encéphalopathie traumatique. (Les problems.). , 1941 .

[20]  O. J. Dunn.  Multiple Comparisons among Means , 1961 .

[21]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Janaina Mourão Miranda,et al.  PRoNTo: Pattern Recognition for Neuroimaging Toolbox , 2013, Neuroinformatics.

[23]  Christos Davatzikos,et al.  A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages , 2017, NeuroImage.

[24]  E. Kaplan,et al.  The assessment of aphasia and related disorders , 1972 .

[25]  Pierre Geurts,et al.  Pattern Extraction for Time Series Classification , 2001, PKDD.

[26]  Jean-Philippe Vert,et al.  Group lasso with overlap and graph lasso , 2009, ICML '09.

[27]  R. Reitan.  Validity of the Trail Making Test as an Indicator of Organic Brain Damage , 1958 .

[28]  T. Tombaugh,et al.  The Mini‐Mental State Examination: A Comprehensive Review , 1992, Journal of the American Geriatrics Society.

[29]  Vassilis G. Papanicolaou,et al.  The Coupon Collector's Problem Revisited: Asymptotics of the Variance , 2012, Advances in Applied Probability.

[30]  Chao Chen,et al.  Using Random Forest to Learn Imbalanced Data , 2004 .

[31]  Yongchao Ge.  Resampling-based Multiple Testing for Microarray Data Analysis , 2003 .

[32]  Yvan Saeys,et al.  Statistical interpretation of machine learning-based feature importance scores for biomarker discovery , 2012, Bioinform..

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

[34]  J Mazziotta,et al.  A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[35]  Stephen C. Strother,et al.  Support vector machines for temporal classification of block design fMRI data , 2005, NeuroImage.

[36]  Nick C Fox,et al.  The clinical use of structural MRI in Alzheimer disease , 2010, Nature Reviews Neurology.

[37]  S. Kiebel,et al.  An Introduction to Random Field Theory , 2003 .

[38]  Daoqiang Zhang,et al.  Multimodal classification of Alzheimer's disease and mild cognitive impairment , 2011, NeuroImage.

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

[40]  Pierre Dupont,et al.  Inferring statistically significant features from random forests , 2015, Neurocomputing.

[41]  R. Petersen,et al.  Mild Cognitive Impairment: An Overview , 2008, CNS Spectrums.

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

[43]  George C. Runger,et al.  Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination , 2009, J. Mach. Learn. Res..

[44]  L. Holst.  On Birthday, Collectors', Occupancy and Other Classical Urn Problems , 1986 .

[45]  Vincent Botta,et al.  A walk into random forests: adaptation and application to Genome-Wide Association Studies , 2013 .

[46]  Luca Baldassarre,et al.  Localizing and Comparing Weight Maps Generated from Linear Kernel Machine Learning Models , 2013, 2013 International Workshop on Pattern Recognition in Neuroimaging.

[47]  Nick C Fox,et al.  Automatic classification of MR scans in Alzheimer's disease. , 2008, Brain : a journal of neurology.

[48]  Andrea Bergmann,et al.  Statistical Parametric Mapping The Analysis Of Functional Brain Images , 2016 .

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

[50]  Daoqiang Zhang,et al.  Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease , 2012, NeuroImage.

[51]  Wolfgang Stadje,et al.  THE COLLECTOR'S PROBLEM WITH GROUP DRAWINGS , 1990 .

[52]  Gilles Louppe,et al.  Understanding variable importances in forests of randomized trees , 2013, NIPS.

[53]  Juan José Rodríguez Diez,et al.  Random Subspace Ensembles for fMRI Classification , 2010, IEEE Transactions on Medical Imaging.

[54]  M. Mega,et al.  The Neuropsychiatric Inventory , 1994, Neurology.

[55]  Dimitri Van De Ville,et al.  Brain decoding of fMRI connectivity graphs using decision tree ensembles , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[56]  José Augusto Baranauskas,et al.  How Many Trees in a Random Forest? , 2012, MLDM.

[57]  Syed Muhammad Anwar,et al.  Deep Learning in Medical Image Analysis , 2017 .

[58]  Jerry L Prince,et al.  Medical Imaging Signals and Systems , 2005 .

[59]  Jean-Baptiste Poline,et al.  A supervised clustering approach for extracting predictive information from brain activation images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[60]  Shuiwang Ji,et al.  SLEP: Sparse Learning with Efficient Projections , 2011 .

[61]  Polina Golland,et al.  Detecting stable distributed patterns of brain activation using Gini contrast , 2011, NeuroImage.

[62]  O Bosnes,et al.  Wechsler Memory Scale-Revised (WMS-R)anvendt på barn i Norge. , 2000 .

[63]  Ramon Casanova,et al.  High Dimensional Classification of Structural MRI Alzheimer’s Disease Data Based on Large Scale Regularization , 2011, Front. Neuroinform..

[64]  Dinggang Shen,et al.  Deep Learning-Based Feature Representation for AD/MCI Classification , 2013, MICCAI.

[65]  Ender Konukoglu,et al.  Relevant feature set estimation with a knock-out strategy and random forests , 2015, NeuroImage.

[66]  T. Kurosaki,et al.  Measurement of functional activities in older adults in the community. , 1982, Journal of gerontology.

[67]  A. Convit,et al.  Hippocampal formation glucose metabolism and volume losses in MCI and AD , 2001, Neurobiology of Aging.

[68]  E. Reiman,et al.  Multicenter Standardized 18F-FDG PET Diagnosis of Mild Cognitive Impairment, Alzheimer's Disease, and Other Dementias , 2008, Journal of Nuclear Medicine.

[69]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[70]  S. Ross,et al.  THE COUPON-COLLECTOR'S PROBLEM REVISITED , 2003 .

[71]  J. Ashburner,et al.  Nonlinear spatial normalization using basis functions , 1999, Human brain mapping.

[72]  K. Davis,et al.  A new rating scale for Alzheimer's disease. , 1984, The American journal of psychiatry.

[73]  J. Baron,et al.  FDG-PET measurement is more accurate than neuropsychological assessments to predict global cognitive deterioration in patients with mild cognitive impairment , 2005, Neurocase.

[74]  N. Bohnen,et al.  Effectiveness and Safety of 18F-FDG PET in the Evaluation of Dementia: A Review of the Recent Literature , 2012, The Journal of Nuclear Medicine.

[75]  Satoshi Minoshima,et al.  Posterior cingulate cortex in Alzheimer's disease , 1994, The Lancet.

[76]  R. Scheaffer,et al.  Mathematical Statistics with Applications. , 1992 .

[77]  J. Baron,et al.  Mild cognitive impairment , 2003, Neurology.

[78]  Janaina Mourão Miranda,et al.  Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models , 2018, Neuroinformatics.

[79]  Vince D. Calhoun,et al.  Deep learning for neuroimaging: a validation study , 2013, Front. Neurosci..

[80]  Yvan Saeys,et al.  Robust Feature Selection Using Ensemble Feature Selection Techniques , 2008, ECML/PKDD.

[81]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[82]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[83]  C. Phillips,et al.  Combining PET Images and Neuropsychological Test Data for Automatic Diagnosis of Alzheimer's Disease , 2014, PloS one.

[84]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[85]  John G. Csernansky,et al.  Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults , 2007, Journal of Cognitive Neuroscience.

[86]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[87]  A. Chincarini,et al.  Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer's disease from healthy controls. A European Alzheimer's Disease Consortium (EADC) study , 2014, NeuroImage: Clinical.

[88]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[89]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[90]  Ramesh Nallapati,et al.  A Comparative Study of Methods for Transductive Transfer Learning , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[91]  Daniel Rueckert,et al.  Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease , 2012, NeuroImage.

[92]  Rachel L. Mistur,et al.  FDG-PET changes in brain glucose metabolism from normal cognition to pathologically verified Alzheimer’s disease , 2009, European Journal of Nuclear Medicine and Molecular Imaging.

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

[94]  Terence P Speed,et al.  SOME STEP-DOWN PROCEDURES CONTROLLING THE FALSE DISCOVERY RATE UNDER DEPENDENCE. , 2008, Statistica Sinica.

[95]  Angelika Bayer,et al.  A First Course In Probability , 2016 .

[96]  Kaustubh Supekar,et al.  Sparse logistic regression for whole-brain classification of fMRI data , 2010, NeuroImage.

[97]  K. Lunetta,et al.  Screening large-scale association study data: exploiting interactions using random forests , 2004, BMC Genetics.

[98]  G. Alexander,et al.  Positron emission tomography in evaluation of dementia: Regional brain metabolism and long-term outcome. , 2001, JAMA.

[99]  Jean-Michel Poggi,et al.  Variable selection using random forests , 2010, Pattern Recognit. Lett..

[100]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[101]  Janaina Mourão Miranda,et al.  Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data , 2005, NeuroImage.

[102]  Seong-Whan Lee,et al.  Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis , 2014, NeuroImage.

[103]  Isabelle Guyon,et al.  An Introduction to Feature Extraction , 2006, Feature Extraction.

[104]  Pierre Geurts,et al.  Tree ensemble methods and parcelling to identify brain areas related to Alzheimer’s disease , 2017, 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

[105]  M. N. Mitruchina.  Rey Auditory-Verbal Learning Test , 1999 .

[106]  G. Frisoni,et al.  Mapping brain morphological and functional conversion patterns in amnestic MCI: a voxel-based MRI and FDG-PET study , 2009, European Journal of Nuclear Medicine and Molecular Imaging.

[107]  Andres Hoyos Idrobo,et al.  Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines , 2016, NeuroImage.

[108]  J. Hoffman,et al.  FDG PET imaging in patients with pathologically verified dementia. , 2000, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[109]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[110]  P. Bühlmann,et al.  The group lasso for logistic regression , 2008 .

[111]  R. Petersen,et al.  Alzheimer's disease and mild cognitive impairment. , 2007, Neurologic clinics.

[112]  Michael T. Goodrich,et al.  Education forum: Web Enhanced Textbooks , 1998, SIGA.

[113]  Jesper Tegnér,et al.  Consistent Feature Selection for Pattern Recognition in Polynomial Time , 2007, J. Mach. Learn. Res..

[114]  Gilles Louppe,et al.  Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies , 2014, PloS one.

[115]  N. Foster,et al.  Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease , 1997, Annals of neurology.

[116]  A. Drzezga,et al.  Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer's disease: a PET follow-up study , 2003, European Journal of Nuclear Medicine and Molecular Imaging.

[117]  Kewei Chen,et al.  Peripheral apoE isoform levels in cognitively normal APOE ε3/ε4 individuals are associated with regional gray matter volume and cerebral glucose metabolism , 2017, Alzheimer's Research & Therapy.

[118]  R. Tibshirani,et al.  A note on the group lasso and a sparse group lasso , 2010, 1001.0736.

[119]  Jean-Baptiste Poline,et al.  Which fMRI clustering gives good brain parcellations? , 2014, Front. Neurosci..

[120]  Marie Chupin,et al.  Automatic classi fi cation of patients with Alzheimer ' s disease from structural MRI : A comparison of ten methods using the ADNI database , 2010 .

[121]  Thomas Lengauer,et al.  Permutation importance: a corrected feature importance measure , 2010, Bioinform..

[122]  Achim Zeileis,et al.  Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.

[123]  K. Möllenhoff.  Novel Methods for the Detection of Functional Brain Activity using 17O MRI , 2016 .

[124]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[125]  P. Erd6s.  ON A CLASSICAL PROBLEM OF PROBABILITY THEORY b , 2001 .

[126]  Gilles Louppe,et al.  Understanding Random Forests: From Theory to Practice , 2014, 1407.7502.

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

[128]  I. Jolliffe.  Principal Component Analysis and Factor Analysis , 1986 .

[129]  A. Mechelli,et al.  Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.

[130]  R. Tibshirani,et al.  Sparsity and smoothness via the fused lasso , 2005 .

[131]  et al.,et al.  Discrimination between Alzheimer Dementia and Controls by Automated Analysis of Multicenter FDG PET , 2002, NeuroImage.

[132]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[133]  Bengt Rosen.  On the Coupon Collector's Waiting Time , 1970 .

[134]  Gilles Louppe,et al.  Random Subspace with Trees for Feature Selection Under Memory Constraints , 2018, AISTATS.

[135]  Kathryn Ziegler-Graham,et al.  Forecasting the global burden of Alzheimer’s disease , 2007, Alzheimer's & Dementia.

[136]  L. Mosconi,et al.  Brain glucose metabolism in the early and specific diagnosis of Alzheimer’s disease , 2005, European Journal of Nuclear Medicine and Molecular Imaging.

[137]  Bertrand Thirion,et al.  Multiscale Mining of fMRI Data with Hierarchical Structured Sparsity , 2012, SIAM J. Imaging Sci..