Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework

[1]  J. Whitwell,et al.  Alzheimer's disease neuroimaging , 2018, Current opinion in neurology.

[2]  K. Hao,et al.  A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease , 2017, Nature Neuroscience.

[3]  Yan Liu,et al.  Detecting Statistical Interactions from Neural Network Weights , 2017, ICLR.

[4]  Ankur Taly,et al.  Axiomatic Attribution for Deep Networks , 2017, ICML.

[5]  Simona Maria Brambati,et al.  Altered Gray Matter Structural Covariance Networks in Early Stages of Alzheimer's Disease. , 2016, Cerebral cortex.

[6]  Marco Tulio Ribeiro,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, HLT-NAACL Demos.

[7]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[8]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  M. Gill,et al.  Common polygenic variation enhances risk prediction for Alzheimer's disease. , 2015, Brain : a journal of neurology.

[10]  Hongtu Zhu,et al.  Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data. , 2015, Journal of Alzheimer's disease : JAD.

[11]  Michael W. Weiner,et al.  2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception , 2015, Alzheimer's & Dementia.

[12]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[13]  Matthew L Senjem,et al.  Age, Sex, and APOE ε4 Effects on Memory, Brain Structure, and β-Amyloid Across the Adult Life Span. , 2015, JAMA neurology.

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

[15]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[16]  Dinggang Shen,et al.  Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction , 2014, Front. Aging Neurosci..

[17]  C. Jack,et al.  Age-specific population frequencies of cerebral β-amyloidosis and neurodegeneration among people with normal cognitive function aged 50–89 years: a cross-sectional study , 2014, The Lancet Neurology.

[18]  Perry G. Ridge,et al.  Population-based Analysis of Alzheimer’s Disease Risk Alleles Implicates Genetic Interactions , 2014, Biological Psychiatry.

[19]  Norbert Schuff,et al.  Locally linear embedding (LLE) for MRI based Alzheimer's disease classification , 2013, NeuroImage.

[20]  J. Trojanowski,et al.  Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers , 2013, NeuroImage: Clinical.

[21]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[22]  Nick C Fox,et al.  Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease , 2013, Nature Genetics.

[23]  Francis J McMahon,et al.  In vivo radioligand binding to translocator protein correlates with severity of Alzheimer's disease. , 2013, Brain : a journal of neurology.

[24]  A. Simmons,et al.  Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment , 2013, Psychiatry Research: Neuroimaging.

[25]  M. Jorge Cardoso,et al.  Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment☆ , 2013, NeuroImage: Clinical.

[26]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[27]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[28]  P. Scheltens,et al.  Atrophy of medial temporal lobes on MRI in “probable” Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates , 2012, Journal of Neurology, Neurosurgery & Psychiatry.

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

[30]  Mark E. Schmidt,et al.  The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception , 2012, Alzheimer's & Dementia.

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

[32]  J. Marchini,et al.  Genotype Imputation with Thousands of Genomes , 2011, G3: Genes | Genomes | Genetics.

[33]  D. Rueckert,et al.  Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease , 2011, PloS one.

[34]  Hojjat Adeli,et al.  Probabilistic neural networks for diagnosis of Alzheimer's disease using conventional and wavelet coherence , 2011, Journal of Neuroscience Methods.

[35]  C. Jack,et al.  Alzheimer's Disease Neuroimaging Initiative (ADNI) , 2010, Neurology.

[36]  Karin Bammann,et al.  Neural networks for modeling gene-gene interactions in association studies , 2009, BMC Genetics.

[37]  S. Resnick,et al.  Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD index. , 2009, Brain : a journal of neurology.

[38]  Li Shen,et al.  Baseline MRI Predictors of Conversion from MCI to Probable AD in the ADNI Cohort , 2009, Current Alzheimer research.

[39]  P. Donnelly,et al.  A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.

[40]  M. Weiner,et al.  Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease* , 2009, Brain : a journal of neurology.

[41]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[42]  Christian Büchel,et al.  Contributions of occipital, parietal and parahippocampal cortex to encoding of object-location associations , 2005, Neuropsychologia.

[43]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[44]  F. Collette,et al.  Alzheimer' Disease as a Disconnection Syndrome? , 2003, Neuropsychology Review.

[45]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[46]  A. D. Roses,et al.  Association of apolipoprotein E allele €4 with late-onset familial and sporadic Alzheimer’s disease , 2006 .

[47]  P. Scheltens,et al.  Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. , 1992, Journal of neurology, neurosurgery, and psychiatry.

[48]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[49]  D. Sharp,et al.  The role of the posterior cingulate cortex in cognition and disease. , 2014, Brain : a journal of neurology.

[50]  B. Ripley Pattern Recognition and Neural Networks , 1996 .

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