,
,
, , p.119
Evgeniou Theodoros, and Colliot Olivier, for the ADNI & the AIBL, Reproducible evaluation of classification methods in Alzheimers disease: Framework and application to MRI and PET data, NeuroImage, vol.183, 2018. ,
Multilevel Modeling with Structured Penalties for Classification from Imaging Genetics data, 3rd MICCAI Workshop on Imaging Genetics, pp.230-240, 2017. ,
Multilevel Survival Analysis with Structured Penalties for Imaging Genetics data, Poster at GDR Statistiques & Santé, 2019. ,
A log-logistic survival model from multimodal data for prediction of Alzheimer's Disease. Poster at SAFJR, 2019. ,
Multilevel Modeling with Structured Penalties for Classification from Imaging Genetics data, Poster at ICM -IoN Workshop, 2017. ,
Multilevel Modeling with Structured Penalties for Classification from Imaging Genetics data. Oral presentation at MICGen, 2017. ,
, Simplified biological pathway leading to Alzheimer's Disease, p.24
, The bounding box represents the hippocampus region (reproduced from, Coronal slides of the T1-weighted MRI for normal brain, MCI brain and AD brain, 2017.
PIB-PET scan for Normal brain versus AD brain (reproduced from, p.26, 2007. ,
,
, Histogram of the number of visits at each month among MCI patients at baseline
Histogram of the conversion date T * and the censored date C, p.32 ,
estimator for the survival function for MCI patients from ADNI1 dataset ,
Aalen estimator for the cumulative hazard function (top) and estimated hazard function h with bandwidth of 6 months (bottom), p.34 ,
, Two topics of research in imaging genetics
, 2 Four categories of analyses in imaging genetics, reproduced from, p.37, 2014.
, Real vs hypothetic distribution (N is the number of subjects, N A (resp. N a ) the number of subjects with allele A (resp. a), N CN (resp. N AD ) the number of subjects who are CN (resp. have AD))
38 2.5 (left) Main PLS eigen-component of V for the phenotype features. (right) Chromosome representativeness among the set of most informative SNPs associated to the main PLS eigen-component of U, Genome wide meta-analysis results in AD with 524,993 SNPs. Manhattan plot showing the p-values obtained in the meta-analysis. The end and beginning of a chromosome is denoted by the change of colour pattern of the SNPs (black, grey and brown dots), p.43, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-00306759
45 2.11 Illustration of ? learnt using the LASSO regression (top), and the Group LASSO regression where L = 3 (bottom). The LASSO regression selects only some variables, p.48, 2012. ,
The problem of overlapping genes/pathways, p.48 ,
, , p.50
, Different type of censorships
Survival and hazard functions for exponential model, p.58 ,
59 3.4 Order graphs representing the ranking constraints. (a) No censored data and (b) with censored data. The empty circle represents a censored point. The points are arranged in the increasing value of their survival times with the lowest being at the bottom (reproduced from, p.62, 2008. ,
, Illness-death model (reproduced from, 2002.
The disease status y is predicted from imaging data x I and the parameters ? 0 (x G ), ?(x G ) (which are computed from genetic data x G ), p.73 ,
For brain region i and gene ?, W[i, ?] = max g?G ? |W[i, g]|, ? I [i] = |? I [i]| and ? G [?] = max g?G ? |? G [g]|. Only some brain regions are shown in this figure, p.79 ,
Intercept ? I and slope W in the function ?(x G ), p.80 ,
,
84 5.2 TADPOLE Challenge design. Participants are required to train a predictive model on a training dataset (D1 and/or others) and make forecasts for different datasets (D2, D3) by the submission deadline, 2018. ,
D3 is a subset of D2, which in turn is a subset of D1. Other non-ADNI data can also be used for training (reproduced from, Venn diagram of the ADNI datasets for training (D1), p.85, 2018. ,
, , vol.87
, , p.88
, Estimators for the survival, cumulative hazard, and hazard functions, p.90
, Cumulative regression functions of A(t)
, , p.92
, Coefficients learnt for the Cox PH model, with confidence intervals, p.99
100 6.3 Comparison between the survival function estimated using the Kaplan-Meier estimator and the log-logistic regression; and between the cumulative hazard estimated using the Nelson-Aalen estimator and the log-logistic regression ,
, Coefficients of the parameter vector ? of the logistic regression at fixed time t (left); Cumulative regression functions in the Aalen additive model, p.102
, AUC (middle), C-index (bottom), Assesment of the predictive value for the different models: Balanced Accuracy (top)
, Estimator of Kaplan-Meier of the survival function S(t) = P{T > t|APOE}, vol.104
,
It can be seen that ? I x I only translates the curve of S(t) (for fixed ? G x G ), whereas ? G x G changes both the shape and translation of the curve of S(t) ,
107 6.10 Estimated survival function S for the Kaplan Meier estimate and for the multilevel log-logistic model; Estimated hazard function h for the Nelson-Aalen estimated hazard and for the multilevel log-logistic model, p.109 ,
, Median survival time, estimated survival function S for the Kaplan Meier estimate and for the multilevel log-logistic model
, , p.111
, ADNI1 Dataset: baseline survival function and hazard function, p.118
, 29 1.3 Descriptive statistics for variables measured at study entry of ADNI1 participants who are AD or CN at baseline, ? means that this feature is computed on a subset of ADNI1
, Descriptive statistics for variables measured at study entry of ADNI1 participants with mild cognitive impairment (MCI), ? means that this feature is computed on a subset of ADNI1
Classification results for different modalities and methods, p.78 ,
84 5.2 Descriptive statistics of D1, D2, D3 datasets (reproduced from https:// tadpole.grand-challenge.org/Results/) ,
Descriptive statistics of D4 dataset (219 subjects, reproduced from https: //tadpole.grand-challenge.org/Results/), p.92 ,
, Ranking based on mAUC
,
, Number of positif labels and total number of labels throughout time, p.99
100 6.3 Coefficients for the Cox model and log-logistic model, p.108 ,
,
, Results for different modalities and methods (mean value across the test folds ± standard deviation)
A Model for Nonparametric Regression Analysis of Counting Processes, Mathematical Statistics and Probability Theory, pp.1-25, 1980. ,
Recognition of Alzheimer's disease and Mild Cognitive Impairment with multimodal image-derived biomarkers and Multiple Kernel Learning, Neurocomputing, vol.220, pp.98-110, 2017. ,
EasyMKL: a scalable multiple kernel learning algorithm, Neurocomputing, vol.169, pp.215-224, 2015. ,
Cognitive variabilityA marker for incident MCI and AD: An analysis for the, 2016. ,
, Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, vol.4, pp.47-55
Probabilistic Modeling of Imaging, Genetics and Diagnosis, IEEE Transactions on Medical Imaging, vol.35, issue.7, pp.1765-1779, 2016. ,
Gradient-based algorithms with applications to signal-recovery problems, 2009. ,
Incidence of Dementia over Three Decades in the Framingham Heart Study, New England Journal of Medicine, vol.375, issue.1, pp.92-94, 2016. ,
Survival prediction from clinico-genomic models -a comparative study, BMC Bioinformatics, vol.10, issue.1, p.413, 2009. ,
Estimation of timedependent area under the ROC curve for long-term risk prediction, Statistics in Medicine, vol.25, issue.20, pp.3474-3486, 2006. ,
A Bayesian predictive model for imaging genetics with application to schizophrenia, The Annals of Applied Statistics, vol.10, issue.3, pp.1547-1571, 2016. ,
, , 2015.
, Domain Transfer Learning for MCI Conversion Prediction, IEEE transactions on biomedical engineering, vol.62, issue.7, pp.1805-1817
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification, Neurobiology of Aging, vol.32, issue.12, pp.2322-2341, 2011. ,
, , 2014.
, A novel structure-aware sparse learning algorithm for brain imaging genetics. Medical image computing and computer-assisted intervention: MICCAI, International Conference on Medical Image Computing and Computer-Assisted Intervention, vol.17, pp.329-336
Amnestic MCI or prodromal Alzheimer's disease?, The Lancet Neurology, vol.3, issue.4, pp.246-248, 2004. ,
Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria, The Lancet. Neurology, vol.6, issue.8, pp.734-746, 2007. ,
Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria, The Lancet. Neurology, vol.13, issue.6, pp.614-629, 2014. ,
Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria, Alzheimer's & Dementia: The Journal of the Alzheimer's Association, vol.12, issue.3, pp.292-323, 2016. ,
Classification and basic pathology of Alzheimer disease, Acta Neuropathologica, vol.118, issue.1, pp.5-36, 2009. ,
Assessment and comparison of prognostic classification schemes for survival data, Statistics in Medicine, vol.18, pp.2529-2545, 1999. ,
Multiple Kernel Learning Algorithms, Journal of Machine Learning Research, vol.12, pp.2211-2268, 2011. ,
Perspective on future role of biological markers in clinical therapy trials of Alzheimer's disease: a long-range point of view beyond 2020, Biochemical Pharmacology, vol.88, issue.4, pp.426-449, 2014. ,
Statistical Learning with Sparsity -The Lasso and Generalizations, Monographs on Statistics and Applied Probability, vol.143, 2015. ,
Group Lasso with Overlap and Graph Lasso, Proceedings of the 26 th International Conference on Machine Learning, 2009. ,
Alzheimer's disease due to an intronic presenilin-1 (PSEN1 intron 4) mutationA clinicopathological study, Brain, vol.123, issue.5, pp.894-907, 2000. ,
A penalized likelihood approach for an illness-death model with interval-censored data: application to age-specific incidence of dementia, Biostatistics, vol.3, issue.3, pp.433-443, 2002. ,
URL : https://hal.archives-ouvertes.fr/inserm-00182448
lp-Norm Multiple Kernel Learning, Journal of Machine Learning Research, vol.12, pp.953-997, 2011. ,
Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression, and Alzheimers Disease Neuroimaging Initiative, vol.6, p.115, 2012. ,
Discovery and Replication of Gene Influences on Brain Structure Using LASSO Regression, and Alzheimers Disease Neuroimaging Initiative, vol.6, p.115, 2012. ,
Introduction to Statistical Genetics and Background in Molecular Genetics, The Fundamentals of Modern Statistical Genetics, pp.1-13, 2011. ,
, , 2013.
,
European Alzheimer's Disease Initiative (EADI), Genetic and Environmental Risk in Alzheimer's Disease, Alzheimer's Disease Genetic Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology ,
,
,
,
Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease, Nature Genetics, vol.45, issue.12, pp.1452-1458, 2013. ,
Intervalcensored time-to-event and competing risk with death: is the illness-death model more accurate than the Cox model?, International Journal of Epidemiology, vol.42, issue.4, pp.1177-1186, 2013. ,
A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-toevent data, 2017. ,
A review of multivariate analyses in imaging genetics, Frontiers in Neuroinformatics, vol.8, 2014. ,
Prediction of Mild Cognitive Impairment Conversion Using a Combination of Independent Component Analysis and the Cox Model, Frontiers in Human Neuroscience, vol.11, p.33, 2017. ,
Partial least squares modelling for imaginggenetics in Alzheimer's disease: Plausibility and generalization, IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp.838-841, 2016. ,
Apolipoprotein E4: A causative factor and therapeutic target in neuropathology, including Alzheimers disease, Proceedings of the National Academy of Sciences, vol.103, issue.15, pp.5641-5643, 2006. ,
, TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease, 2018.
Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease, Neurology, vol.34, issue.7, pp.939-944, 1984. ,
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, Alzheimer's & Dementia: The Journal of the Alzheimer's Association, vol.7, issue.3, pp.263-269, 2011. ,
The group lasso for logistic regression, pp.53-71, 2008. ,
Model selection and estimation in regression with grouped variables, J. R. Statist. Soc. B, vol.68, pp.49-67, 2006. ,
, Visual Assessment Versus Quantitative Assessment of 11c-PIB PET and 18f-FDG PET for Detection of Alzheimer's Disease, 2007.
Structured Sparse Kernel Learning for Imaging Genetics Based Alzheimers Disease Diagnosis, MICCAI 2016, number 9901 in Lecture Notes in Computer Science, pp.70-78, 2016. ,
Overrepresentation of Glutamate Signaling in Alzheimer's Disease: Network-Based Pathway Enrichment Using Meta-Analysis of Genome-Wide Association Studies, PloS one, vol.9, p.95413, 2014. ,
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages, NeuroImage, vol.155, pp.530-548, 2017. ,
Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data, 2019. ,
, page 109490V. International Society for Optics and Photonics, vol.10949
Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data, NeuroImage, vol.183, pp.504-521, 2018. ,
Incidence of Dementia over Three Decades in the Framingham Heart Study, New England Journal of Medicine, vol.374, issue.6, pp.523-532, 2016. ,
Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates, Neurosurgery, and Psychiatry, vol.55, issue.10, pp.967-972, 1992. ,
Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression, NeuroImage, vol.63, issue.3, pp.1681-1694, 2012. ,
, Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps. Statistical applications in genetics and molecular biology, vol.11, p.7, 2012.
, Random Forests on Distance Matrices for Imaging Genetics Studies, 2008.
, Advances in Neural Information Processing Systems, vol.20, pp.1209-1216
Super Learner, 2007. ,
Cross-validated Cox regression on microarray gene expression data, pp.3201-3216, 2006. ,
Dynamic Prediction in Clinical Survival Analysis, volume Monographs on Statistics and Applied Probability 123, 2012. ,
Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort, Bioinformatics, vol.28, issue.2, pp.229-237, 2012. ,
Multiple Imputation for Estimating the Risk of Developing Dementia and Its Impact on Survival, Biometrical journal. Biometrische Zeitschrift, vol.52, issue.5, pp.616-627, 2010. ,
Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors, Shawe, 2011. ,
, Advances in Neural Information Processing Systems, vol.24, pp.1845-1853