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Robust regression for large-scale neuroimaging studies

Virgile Fritsch, Benoit Da Mota, Eva Loth, Gaël Varoquaux, Tobias Banaschewski, Gareth J. Barker, Arun L.W. Bokde, Rüdiger Brühl, Brigitte Butzek, Patricia Conrod, Herta Flor, Hugh Garavan (+9 others)
2015 NeuroImage  
Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts.  ...  freedom, large-scale studies (e.g.  ...  They also thank the Centre d'Analyse et Traitement des Images (CATI) for giving access to their cluster.  ... 
doi:10.1016/j.neuroimage.2015.02.048 pmid:25731989 fatcat:jmk57vqujfas5ist34ciwe3wfi

Robust Group-Level Inference in Neuroimaging Genetic Studies

Virgile Fritsch, Benoit Da Mota, Gael Varoquaux, Vincent Frouin, Eva Loth, Jean-Baptiste Poline, Bertrand Thirion
2013 2013 International Workshop on Pattern Recognition in Neuroimaging  
In this work, we consider robust regression and its application to neuroimaging through an example gene-neuroimaging study on a large cohort of 300 subjects.  ...  We combine this approach with robust regression in an analysis method that we show is outperforming state-of-the-art neuroimaging analysis methods.  ...  Gene-neuroimaging study Figure 4 shows that robust regression always yields more significant activations than standard regression, for all number of parcels considered to reduce the data dimension.  ... 
doi:10.1109/prni.2013.15 dblp:conf/prni/FritschMVFLPT13 fatcat:rkw6c3meq5ef7b7ksgm54cij2a

Biological parametric mapping with robust and non-parametric statistics

Xue Yang, Lori Beason-Held, Susan M. Resnick, Bennett A. Landman
2011 NeuroImage  
To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model.  ...  Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power.  ...  Here we have provided two very different robust approaches for use in the neuroimaging community.  ... 
doi:10.1016/j.neuroimage.2011.04.046 pmid:21569856 pmcid:PMC3114289 fatcat:xbxmqlkovzehrpftegewa6rmb4

Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises [article]

JING SUI, Rongtao Jiang, Juan Bustillo, Vince D. Calhoun
2020 bioRxiv   pre-print
Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely-used approach to predict human behavior with neuroimaging features.  ...  In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade.  ...  Note that many studies performed prediction for more than one behavioral metric or several sub-dimensions of one cognitive scale.  ... 
doi:10.1101/2020.02.22.961136 fatcat:rtxaa5cjnvekzhkwcokmmf2toe

NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines [article]

Martin Rajchl and Nick Pawlowski and Daniel Rueckert and Paul M. Matthews and Ben Glocker
2018 arXiv   pre-print
We believe NeuroNet could be an important tool in large-scale population imaging studies and serve as a new standard in neuroscience by reducing the risk of introducing bias when choosing a specific software  ...  standard neuroimaging pipelines.  ...  In order to process neuroimaging data on such large scales, we require tools that closely reproduce outputs of well established packages in a more robust (c.f.  ... 
arXiv:1806.04224v1 fatcat:ksp56mexbbggflmqaoxf25emnq

Robust biological parametric mapping: an improved technique for multimodal brain image analysis

Xue Yang, Lori Beason-Held, Susan M. Resnick, Bennett A. Landman, David R. Haynor, Benoit M. Dawant
2011 Medical Imaging 2011: Image Processing  
To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application of the general linear model.  ...  Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power.  ...  This work described herein has not been submitted elsewhere for publication or presentation.  ... 
doi:10.1117/12.877593 pmid:21625321 pmcid:PMC3103184 dblp:conf/miip/YangBRL11 fatcat:jeix2vo7wbajfknbjrc376uiya

Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression

Yu Ryan Yue, Martin A. Lindquist, Ji Meng Loh
2012 Annals of Applied Statistics  
To address these issues, we propose a fully Bayesian nonparametric binary regression method to perform neuroimaging meta-analyses.  ...  In this work we perform a meta-analysis of neuroimaging data, consisting of locations of peak activations identified in 162 separate studies on emotion.  ...  ACKNOWLEDGEMENTS The authors thank Tor Wager for the meta-analysis data.  ... 
doi:10.1214/11-aoas523 fatcat:63t34s42hvdtnplsqzsih635se

Increased sensitivity in neuroimaging analyses using robust regression

Tor D. Wager, Matthew C. Keller, Steven C. Lacey, John Jonides
2005 NeuroImage  
We use simulations to compare several robust techniques against ordinary least squares (OLS) regression, and we apply robust regression to second-level (group brandom effectsQ) analyses in three fMRI datasets  ...  Robust regression techniques are a class of estimators that are relatively insensitive to the presence of one or more outliers in the data.  ...  Acknowledgments We would like to thank Martin Lindquist for his helpful advice. This research was supported by grant MH60655 to the University of Michigan (John Jonides, P.I.).  ... 
doi:10.1016/j.neuroimage.2005.01.011 pmid:15862210 fatcat:sudbm7usyvh7heybr4vzborvgq

Neuropsychiatric Symptom Clusters in Stroke and Transient Ischemic Attack by Cognitive Status and Stroke Subtype: Frequency and Relationships with Vascular Lesions, Brain Atrophy and Amyloid

Adrian Wong, Alexander Y. L. Lau, Jie Yang, Zhaolu Wang, Wenyan Liu, Bonnie Y. K. Lam, Lisa Au, Lin Shi, Defeng Wang, Winnie C. W. Chu, Yun-yun Xiong, Eugene S. K. Lo (+9 others)
2016 PLoS ONE  
Multivariable logistic regression was used to determine independent associations between demographic, clinical and neuroimaging measures of chronic brain changes (white matter changes, old infarcts, whole  ...  Frequencies of symptom clusters were largely similar between stroke subtypes.  ...  Table 2 . 2 Predictors for presence of NPI symptom clusters in multivariable logistic regression models.  ... 
doi:10.1371/journal.pone.0162846 pmid:27632159 pmcid:PMC5025073 fatcat:mjy3tcrnzzfcjbxponrdtg6uxe

A specific neural substrate predicting current and future impulsivity in young adults

J. Scott Steele, Michele Bertocci, Kristen Eckstrand, Henry W. Chase, Richelle Stiffler, Haris Aslam, Jeanette Lockovich, Genna Bebko, Mary L. Phillips
2021 Molecular Psychiatry  
Our findings are the first to associate amygdala–PFC activity and functional connectivity with impulsivity in a large, transdiagnostic sample, providing neural targets for future interventions to reduce  ...  While some studies indicate altered amygdala and prefrontal cortical (PFC) activity associated with impulsivity, it remains unclear whether these patterns of neural activity are specific to impulsivity  ...  Identified nonzero coefficients from the elastic net models were then tested for statistical significance using linear robust regression, an iteratively reweighted least squares regression that protects  ... 
doi:10.1038/s41380-021-01017-0 pmid:33495543 pmcid:PMC8589683 fatcat:itxvcthztrf7zdvkxyt5sm7v7y

Statistical Approaches for the Study of Cognitive and Brain Aging

Huaihou Chen, Bingxin Zhao, Guanqun Cao, Eric C. Proges, Andrew O'Shea, Adam J. Woods, Ronald A. Cohen
2016 Frontiers in Aging Neuroscience  
Specifically, we introduce semiparametric models for modeling age effects, graphical models for brain network analysis, and penalized regression methods for selecting the most important markers in predicting  ...  Neuroimaging studies of cognitive and brain aging often yield massive datasets that create many analytic and statistical challenges.  ...  Remark 5 Because the penalty shrinkages those regression coefficients toward to zero according to their magnitude, large differences in the original scale of those predictors can mess up the selection.  ... 
doi:10.3389/fnagi.2016.00176 pmid:27486400 pmcid:PMC4949247 fatcat:hehbjsp7h5b4bftaumhkebwree

A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank [article]

Xueyi Shen, David M Howard, Mark J Adams, Ian J Deary, Andrew M McIntosh, Heather C Whalley
2019 biorxiv/medrxiv   pre-print
of 10,674 people and a replication sample of 11,214 people from the UK Biobank Imaging Study, testing for associations with 210 behavioural and 278 neuroimaging phenotypes.  ...  This provides a timely opportunity to identify traits that are associated with polygenic risk of depression in the large and consistently phenotyped UK Biobank sample.  ...  We also thank UK Biobank team for collecting and preparing data for analyses.  ... 
doi:10.1101/617969 fatcat:vutgbftjvze67ajaohzoa6l5k4

Neuroimaging and Cardiac Correlates of Cognitive Function among Patients with Cardiac Disease

Robert H. Paul, John Gunstad, Athena Poppas, David F. Tate, Dan Foreman, Adam M. Brickman, Angela L. Jefferson, Karin Hoth, Ronald A. Cohen
2005 Cerebrovascular Diseases  
Regression analyses revealed that SH accounted for most of the variance in the initiation/perseveration scale, whereas WBV accounted for most of the variance in the attention scale.  ...  A total of 27 individuals with evidence of cardiac disease underwent neuropsychological examination, neuroimaging, and cardiac assessment.  ...  Data obtained from large epidemiological studies indicate that decreased brain volume and increased white matter hyperintensities are associated with increased risk for mild cognitive impairment [6] [  ... 
doi:10.1159/000086803 pmid:16006761 pmcid:PMC3222237 fatcat:pc7uae7bp5eolcmqtgg4ffoqfe

A Fast, Accurate Two-Step Linear Mixed Model for Genetic Analysis Applied to Repeat MRI Measurements [article]

Qifan Yang, Gennady V. Roshchupkin, Wiro J. Niessen, Sarah E. Medland, Alyssa H. Zhu, Paul M. Thompson, Neda Jahanshad
2019 arXiv   pre-print
Large-scale biobanks are being collected around the world in efforts to better understand human health and risk factors for disease.  ...  Second step provides a faster framework to obtain the effect sizes of covariates in regression model.  ...  The small asymptotic standard error of heritability (~0.02). reflects the statistical power of 2StepLMM, when applied to large-scale neuroimaging genetic datasets.  ... 
arXiv:1710.10641v4 fatcat:22zhrm2fxzhntgna6fsdi2objq

Neuroimaging and clinical predictors of fatigue in Parkinson disease

Kelvin L. Chou, Vikas Kotagal, Nicolaas I. Bohnen
2016 Parkinsonism & Related Disorders  
We explored contributions to PD fatigue using separate regression models based either on neuroimaging parameters or clinicometric scales.  ...  Methods-133 PD subjects (96M/37F) completed the Fatigue Severity Scale, Movement Disorders Society-Sponsored Revision of the Unified PD Rating Scale (MDS-UPDRS), Hoehn-Yahr staging, validated scales for  ...  Acknowledgements The authors thank Christine Minderovic, Virginia Rogers, the PET technologists, cyclotron operators, and chemists, for their assistance.  ... 
doi:10.1016/j.parkreldis.2015.11.029 pmid:26683744 pmcid:PMC4724499 fatcat:gwfdxueoenbcbbhbzelp5b2kje
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