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Robust regression for large-scale neuroimaging studies
2015
NeuroImage
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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
2013
2013 International Workshop on Pattern Recognition in Neuroimaging
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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
2011
NeuroImage
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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]
2020
bioRxiv
pre-print
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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]
2018
arXiv
pre-print
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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
2011
Medical Imaging 2011: Image Processing
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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
2012
Annals of Applied Statistics
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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
2005
NeuroImage
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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
2016
PLoS ONE
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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
2021
Molecular Psychiatry
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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
2016
Frontiers in Aging Neuroscience
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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]
2019
biorxiv/medrxiv
pre-print
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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
2005
Cerebrovascular Diseases
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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]
2019
arXiv
pre-print
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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
2016
Parkinsonism & Related Disorders
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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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