Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease

[1]  Aixia Guo,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2014 .

[2]  J. Trojanowski,et al.  Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification , 2011, Neurobiology of Aging.

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

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Vikas Singh,et al.  Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population , 2011, NeuroImage.

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

[7]  Olivier Salvado,et al.  Increasing Power to Predict Mild Cognitive Impairment Conversion to Alzheimer's Disease Using Hippocampal Atrophy Rate and Statistical Shape Models , 2010, MICCAI.

[8]  Amity E. Green,et al.  3D PIB and CSF biomarker associations with hippocampal atrophy in ADNI subjects , 2010, Neurobiology of Aging.

[9]  Cindee M. Madison,et al.  Comparing predictors of conversion and decline in mild cognitive impairment , 2010, Neurology.

[10]  A. Dale,et al.  Multi-modal imaging predicts memory performance in normal aging and cognitive decline , 2010, Neurobiology of Aging.

[11]  Ying Wang,et al.  High-dimensional Pattern Regression Using Machine Learning: from Medical Images to Continuous Clinical Variables However, Support Vector Regression Has Some Disadvantages That Become Especially , 2022 .

[12]  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.

[13]  Dinggang Shen,et al.  Joint estimation of multiple clinical variables of neurological diseases from imaging patterns , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

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

[15]  A. Dale,et al.  CSF Biomarkers in Prediction of Cerebral and Clinical Change in Mild Cognitive Impairment and Alzheimer's Disease , 2010, The Journal of Neuroscience.

[16]  A. Dale,et al.  Combining MR Imaging, Positron-Emission Tomography, and CSF Biomarkers in the Diagnosis and Prognosis of Alzheimer Disease , 2010, American Journal of Neuroradiology.

[17]  Eric P. Xing,et al.  Heterogeneous multitask learning with joint sparsity constraints , 2009, NIPS.

[18]  D. Louis Collins,et al.  Relating one-year cognitive change in mild cognitive impairment to baseline MRI features , 2009, NeuroImage.

[19]  C. Jack,et al.  MRI and CSF biomarkers in normal, MCI, and AD subjects , 2009, Neurology.

[20]  W. M. van der Flier,et al.  CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. , 2009, JAMA.

[21]  A. Dale,et al.  Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. , 2009, Radiology.

[22]  R. Petersen,et al.  Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects , 2009, Annals of neurology.

[23]  Christos Davatzikos,et al.  Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI , 2009, NeuroImage.

[24]  Katharina Morik,et al.  Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I , 2008 .

[25]  Murat Dundar,et al.  An Improved Multi-task Learning Approach with Applications in Medical Diagnosis , 2008, ECML/PKDD.

[26]  Z. Khachaturian Alzheimer's & Dementia: The Journal of the Alzheimer's Association , 2008, Alzheimer's & Dementia.

[27]  Xiaoying Wu,et al.  Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study , 2008, NeuroImage.

[28]  Massimiliano Pontil,et al.  Convex multi-task feature learning , 2008, Machine Learning.

[29]  M. J. Leon,et al.  Longitudinal CSF isoprostane and MRI atrophy in the progression to AD , 2007, Journal of Neurology.

[30]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[31]  C. DeCarli,et al.  FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer's disease. , 2007, Brain : a journal of neurology.

[32]  W. M. van der Flier,et al.  Longitudinal changes of CSF biomarkers in memory clinic patients , 2007, Neurology.

[33]  Matthias J. Müller,et al.  FDG-PET and CSF phospho-tau for prediction of cognitive decline in mild cognitive impairment , 2007, Psychiatry Research: Neuroimaging.

[34]  P. Scheltens,et al.  CSF biomarkers and medial temporal lobe atrophy predict dementia in mild cognitive impairment , 2007, Neurobiology of Aging.

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

[36]  N. Schuff,et al.  Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia. , 2006, Brain : a journal of neurology.

[37]  G. Frisoni,et al.  Medial temporal atrophy but not memory deficit predicts progression to dementia in patients with mild cognitive impairment , 2006, Journal of Neurology, Neurosurgery & Psychiatry.

[38]  Michael I. Jordan,et al.  Multi-task feature selection , 2006 .

[39]  D. Louis Collins,et al.  Predicting Clinical Variable from MRI Features: Application to MMSE in MCI , 2005, MICCAI.

[40]  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.

[41]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[42]  Nick C Fox,et al.  Imaging cerebral atrophy: normal ageing to Alzheimer's disease , 2004, The Lancet.

[43]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[44]  Dinggang Shen,et al.  HAMMER: hierarchical attribute matching mechanism for elastic registration , 2002, IEEE Transactions on Medical Imaging.

[45]  J. Baron,et al.  Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment , 2002, Neuroreport.

[46]  P. Scheltens,et al.  Medial temporal lobe atrophy predicts Alzheimer's disease in patients with minor cognitive impairment , 2002, Journal of neurology, neurosurgery, and psychiatry.

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

[48]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[49]  J. Price,et al.  Mild cognitive impairment represents early-stage Alzheimer disease. , 2001, Archives of neurology.

[50]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[51]  A. Convit,et al.  Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimer’s disease☆ , 2000, Neurobiology of Aging.

[52]  C. Jack,et al.  Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment , 1999, Neurology.

[53]  Alan C. Evans,et al.  3D Anatomical Atlas of the Human Brain , 1998, NeuroImage.

[54]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

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

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

[57]  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.

[58]  J. Meigs,et al.  WHO Technical Report , 1954, The Yale Journal of Biology and Medicine.