ArticleThe annals of applied statistics2015
WAVELET-DOMAIN REGRESSION AND PREDICTIVE INFERENCE IN PSYCHIATRIC NEUROIMAGING.
Article in The annals of applied statistics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Multi-task Learning with High-Dimensional Noisy Images.Journal of the American Statistical Association · 2024Article
- Identifying causal subsequent memory effects.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A Spatio-Temporal Model for Longitudinal Image-on-Image Regression.Statistics in biosciences · 2019Article
- Article
- A Bayesian Spatial Model to Predict Disease Status Using Imaging Data From Various Modalities.Frontiers in neuroscience · 2018Article
- Methods for scalar-on-function regression.International statistical review = Revue internationale de statistique · 2017Article
- Classification of ADNI PET Images via Regularized 3D Functional Data Analysis.Biostatistics & epidemiology · 2017Article
- Generalized Scalar-on-Image Regression Models via Total Variation.Journal of the American Statistical Association · 2017Article
- Cross-validation and hypothesis testing in neuroimaging: An irenic comment on the exchange between Friston and Lindquist et al.NeuroImage · 2015Article
- WAVELET-DOMAIN REGRESSION AND PREDICTIVE INFERENCE IN PSYCHIATRIC NEUROIMAGING.The annals of applied statistics · 2015Article
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5 authors.
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Abstract
An increasingly important goal of psychiatry is the use of brain imaging data to develop predictive models. Here we present two contributions to statistical methodology for this purpose. First, we propose and compare a set of wavelet-domain procedures for fitting generalized linear models with scalar responses and image predictors: sparse variants of principal component regression and of partial least squares, and the elastic net. Second, we consider assessing the contribution of image predictors over and above available scalar predictors, in particular via permutation tests and an extension of the idea of confounding to the case of functional or image predictors. Using the proposed methods, we assess whether maps of a spontaneous brain activity measure, derived from functional magnetic resonance imaging, can meaningfully predict presence or absence of attention deficit/hyperactivity disorder (ADHD). Our results shed light on the role of confounding in the surprising outcome of the recent ADHD-200 Global Competition, which challenged researchers to develop algorithms for automated image-based diagnosis of the disorder.
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