Statistical agnostic mapping: a framework in neuroimaging based on concentration inequalities

[1]  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.

[2]  Diego Castillo-Barnes,et al.  Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders , 2020, IEEE Journal of Biomedical and Health Informatics.

[3]  Roman Rosipal,et al.  Overview and Recent Advances in Partial Least Squares , 2005, SLSFS.

[4]  Karl J. Friston,et al.  Human Brain Function, Second Edition , 2004 .

[5]  M. Hallett.  Human Brain Function , 1998, Trends in Neurosciences.

[6]  Rainer Goebel,et al.  Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns , 2008, NeuroImage.

[7]  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.

[8]  Thomas M. Cover,et al.  Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..

[9]  Danilo Bzdok,et al.  Classical Statistics and Statistical Learning in Imaging Neuroscience , 2016, Front. Neurosci..

[10]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[11]  Colin McDiarmid,et al.  Surveys in Combinatorics, 1989: On the method of bounded differences , 1989 .

[12]  Norbert Sauer,et al.  On the Density of Families of Sets , 1972, J. Comb. Theory A.

[13]  P. Massart.  Some applications of concentration inequalities to statistics , 2000 .

[14]  Jonathan Flint,et al.  Confidence and precision increase with high statistical power , 2013, Nature Reviews Neuroscience.

[15]  John Shawe-Taylor,et al.  Correction to "SCoRS - A Method Based on Stability for Feature Selection and Mapping in Neuroimaging" , 2014, IEEE Trans. Medical Imaging.

[16]  Karl J. Friston.  Ten ironic rules for non-statistical reviewers , 2012, NeuroImage.

[17]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[18]  J. Ioannidis.  Why Most Published Research Findings Are False , 2005, PLoS medicine.

[19]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[20]  Catriona Dutreuilh,et al.  Introduction , 2019 .

[21]  Tamás Linder,et al.  Data-dependent margin-based generalization bounds for classification , 2001, J. Mach. Learn. Res..

[22]  Karl J. Friston.  Sample size and the fallacies of classical inference , 2013, NeuroImage.

[23]  J. V. Haxby,et al.  Spatial Pattern Analysis of Functional Brain Images Using Partial Least Squares , 1996, NeuroImage.

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

[25]  Jussi Tohka,et al.  Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging , 2017, Neuroinformatics.

[26]  Francisco Jesús Martínez-Murcia,et al.  A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms , 2019, Int. J. Neural Syst..

[27]  Gaël Varoquaux,et al.  Cross-validation failure: Small sample sizes lead to large error bars , 2017, NeuroImage.

[28]  S. Shelah.  A combinatorial problem; stability and order for models and theories in infinitary languages. , 1972 .

[29]  Shai Ben-David,et al.  Understanding Machine Learning: From Theory to Algorithms , 2014 .

[30]  Martin A. Lindquist,et al.  Ironing out the statistical wrinkles in “ten ironic rules” , 2013, NeuroImage.

[31]  John Suckling,et al.  On the computation of distribution-free performance bounds: Application to small sample sizes in neuroimaging , 2019, Pattern Recognit..

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

[33]  John Shawe-Taylor,et al.  SCoRS—A Method Based on Stability for Feature Selection and Mapping in Neuroimaging , 2014, IEEE Transactions on Medical Imaging.

[34]  C. Jack,et al.  NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease , 2018, Alzheimer's & Dementia.

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

[36]  J. Morris,et al.  The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging and the Alzheimer's Association workgroup , 2011 .

[37]  Philip T. Reiss,et al.  Cross-validation and hypothesis testing in neuroimaging: An irenic comment on the exchange between Friston and Lindquist et al. , 2015, NeuroImage.

[38]  H. Zaidi,et al.  Quantitative Analysis in Nuclear Medicine Imaging , 2007, Journal of Nuclear Medicine.

[39]  F Segovia,et al.  Automatic assistance to Parkinson's disease diagnosis in DaTSCAN SPECT imaging. , 2012, Medical physics.

[40]  David Haussler,et al.  Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..