Multivariate decoding of brain images using ordinal regression
Orla Doyle,John Ashburner,Fernando Zelaya,Stephen C. R. Williams,Mitul A. Mehta,Andre F. Marquand +5 more
TLDR
The results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection, and propose a novel, alternative multivariate approach that overcomes limitations.About:
This article is published in NeuroImage.The article was published on 2013-11-01 and is currently open access. It has received 32 citations till now. The article focuses on the topics: Ordinal regression & Ordinal data.read more
Citations
More filters
Journal ArticleDOI
A Review of Feature Reduction Techniques in Neuroimaging
TL;DR: Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability and in this review, feature reduction techniques used with machine learning in neuroimaging studies are discussed.
Journal ArticleDOI
Prediction of brain age suggests accelerated atrophy after traumatic brain injury
TL;DR: This work defines individuals' differences in chronological and predicted structural "brain age," and test whether TBI produces progressive atrophy and how this relates to cognitive function, and then applies this model to TBI patients.
Journal ArticleDOI
A Spatiotemporal Profile of In Vivo Cerebral Blood Flow Changes Following Intranasal Oxytocin in Humans
Yannis Paloyelis,Orla Doyle,Fernando Zelaya,Stefanos Maltezos,Steven Williams,Aikaterini Fotopoulou,Matthew A. Howard +6 more
TL;DR: This study provides the first visualization and quantification of IN-OT-induced changes in rCBF in the living human brain unaffected by cognitive, affective, or social manipulations.
Journal ArticleDOI
Predictive modelling using neuroimaging data in the presence of confounds.
TL;DR: It is found that none of the methods for dealing with confounding gives more accurate predictions than a baseline model which ignores confounding, although including the confound as a predictor gives models that are less accurate than the baseline model.
Proceedings ArticleDOI
Stochastic theory of minimal realization
James Clary,Kwang Y. Lee +1 more
TL;DR: It is shown that the coefficient matrices of the stochastic system representation constitute a solution to the minimal realization problem for the deterministic system with given impulse response matrix.
References
More filters
Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Journal ArticleDOI
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Book
Gaussian Processes for Machine Learning
TL;DR: The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.