Wavelet-domain regression and predictive inference in psychiatric neuroimaging

Reiss, Philip T.; Huo, Lan; Zhao, Yihong; Kelly, Clare; Ogden, R. Todd

doi:10.1214/15-aoas829

Cited by 31 publications

(47 citation statements)

References 85 publications

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“…Following Reiss et al (2014) and Goldsmith, Huang, and Crainiceanu (2014), we simulate 500 images based on the first 332 weighted principal components from a subsample of ADHD-200 data (ADHD-200 Consortium 2012), where weights are randomly chosen from N(0,λ j ) withλ j , j = 1, . .…”

Section: Simulation Study-2d Image Predictormentioning

confidence: 99%

Wavelet-Based Weighted LASSO and Screening Approaches in Functional Linear Regression

Zhao¹,

Chen²,

Ogden³

2015

Journal of Computational and Graphical Statistics

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One useful approach for fitting linear models with scalar outcomes and functional predictors involves transforming the functional data to wavelet domain and converting the data-fitting problem to a variable selection problem. Applying the LASSO procedure in this situation has been shown to be efficient and powerful. In this article, we explore two potential directions for improvements to this method: techniques for prescreening and methods for weighting the LASSO-type penalty. We consider several strategies for each of these directions which have never been investigated, either numerically or theoretically, in a functional linear regression context. We compare the finite-sample performance of the proposed methods through both simulations and real-data applications with both 1D signals and 2D image predictors. We also discuss asymptotic aspects. We show that applying these procedures can lead to improved estimation and prediction as well as better stability. Supplementary materials for this article are available online.

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Section: Simulation Study-2d Image Predictormentioning

confidence: 99%

Wavelet-Based Weighted LASSO and Screening Approaches in Functional Linear Regression

Zhao¹,

Chen²,

Ogden³

2015

Journal of Computational and Graphical Statistics

Self Cite

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“…The fifth one (WNET) is to perform scalar-on-image regression in the wavelet domain by naive elastic net (Zhao et al 2014). Among these six approaches, the TV, Lasso, Lasso-Haar, and Matrix-Reg methods have been implemented by Matlab and the FPCR and WNET methods have been implemented in the R packages ‘refund’ and ‘refund.wave’ (see Reiss et al 2015), respectively. For the FPCR and WNET methods, we have used the default settings of both packages.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…For example, the isotropic total variation penalty uses the Euclidean norm of the first differences of the parameter, rather than the sum of the absolute values of the first differences. There are a few papers on the use of two-dimensional or three-dimensional imaging predictors in FLM (Guillas and Lai 2010; Reiss and Ogden 2010; Zhou et al 2013; James, et al 2009; Goldsmith et al 2010; Gertheiss et al 2013; Wang et al 2014; Reiss et al 2015), but none of them consider the piecewisely smoothed function with jumps and edges and the total variation analysis. We also derive nonasymptotic error bounds on the risk for the estimated coefficient image under the total variation penalty.…”

Section: Introductionmentioning

confidence: 99%

Generalized Scalar-on-Image Regression Models via Total Variation

Wang

Zhu

2017

Journal of the American Statistical Association

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The use of imaging markers to predict clinical outcomes can have a great impact in public health. The aim of this paper is to develop a class of generalized scalar-on-image regression models via total variation (GSIRM-TV), in the sense of generalized linear models, for scalar response and imaging predictor with the presence of scalar covariates. A key novelty of GSIRM-TV is that it is assumed that the slope function (or image) of GSIRM-TV belongs to the space of bounded total variation in order to explicitly account for the piecewise smooth nature of most imaging data. We develop an efficient penalized total variation optimization to estimate the unknown slope function and other parameters. We also establish nonasymptotic error bounds on the excess risk. These bounds are explicitly specified in terms of sample size, image size, and image smoothness. Our simulations demonstrate a superior performance of GSIRM-TV against many existing approaches. We apply GSIRM-TV to the analysis of hippocampus data obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset.

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“…Their success rate segregating both groups was close to 84%. In a paper from 2015, Reiss et al [26] showed their results for the ADHD challenge in which they analyzed resting state data (ReHo and ALFF images) of patients with ADHD using wavelet analysis.…”

Section: Wavelet Analysis Applied To Adhd Patients Using Mri Resting mentioning

confidence: 99%

A minireview on the use of wavelet analyses on physiological signals to diagnose and characterize ADHD

Alonso

López

Suárez-García³

et al. 2017

IJBAS

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Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent psychological disorders in pediatric patients. The actual golden standard of ADHD diagnosis is based on conclusions derived from clinical questionnaires. Nowadays, there is no quantitative measurement performed with any imaging system (MRI, PET, EEG, etc.) that can be considered as a golden standard for this diagnosis. This issue, is highlighted by the existence of international competitions focused on the production of a technological (quantitative) solution capable of complementing ADHD diagnosis (ADHD-200 Global Competition). Wavelet analysis, on the other hand, is a flexible mathematical tool that can be used for information and data processing. Its advantage over other types of mathematical transformations is its ability to decompose a signal into two parameters (frequency and time). Based on the prevalence of ADHD and the extra functionality of wavelet tools, this review will try to answer the following question: How have wavelet analyses been used to complement diagnosis and characterization of ADHD? It will be shown that applications were not casual and limited to time-frequency decomposition, noise removal or down sampling of signals, but were pivotal for construction of learning networks, specific parameterization of signals or calculations of connectivity between brain nodes.

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Wavelet-domain regression and predictive inference in psychiatric neuroimaging

Cited by 31 publications

References 85 publications

Wavelet-Based Weighted LASSO and Screening Approaches in Functional Linear Regression

Wavelet-Based Weighted LASSO and Screening Approaches in Functional Linear Regression

Generalized Scalar-on-Image Regression Models via Total Variation

A minireview on the use of wavelet analyses on physiological signals to diagnose and characterize ADHD

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