2022
Computational modeling of threat learning reveals links with anxiety and neuroanatomy in humans
Abend R, Burk D, Ruiz S, Gold A, Napoli J, Britton J, Michalska K, Shechner T, Winkler A, Leibenluft E, Pine D, Averbeck B. Computational modeling of threat learning reveals links with anxiety and neuroanatomy in humans. ELife 2022, 11: e66169. PMID: 35473766, PMCID: PMC9197395, DOI: 10.7554/elife.66169.Peer-Reviewed Original ResearchMeSH KeywordsAnxietyAnxiety DisordersComputer SimulationExtinction, PsychologicalFearFemaleHumansNeuroanatomyConceptsThreat learningAnxiety severitySafety learningNeuroanatomical substratesGreater anxiety severitySkin conductance dataThreat conditioningThreat extinctionThreat generalizationGray matter volumeSafe stimuliAssociative learningComputational mechanismsAnxiety symptomsAnxiety disordersInfluential theoriesSlower extinctionComputational modelLearningWhole brain anatomyAnxietyComputational modelingStructural imagingStimuliTask
2018
Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes
Ganjgahi H, Winkler AM, Glahn DC, Blangero J, Donohue B, Kochunov P, Nichols TE. Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes. Nature Communications 2018, 9: 3254. PMID: 30108209, PMCID: PMC6092439, DOI: 10.1038/s41467-018-05444-6.Peer-Reviewed Original ResearchMeSH KeywordsAnisotropyBrainComputer SimulationDatabases, GeneticGenome-Wide Association StudyHumansLinear ModelsModels, GeneticPhenotypeSoftware
2016
Faster permutation inference in brain imaging
Winkler AM, Ridgway GR, Douaud G, Nichols TE, Smith SM. Faster permutation inference in brain imaging. NeuroImage 2016, 141: 502-516. PMID: 27288322, PMCID: PMC5035139, DOI: 10.1016/j.neuroimage.2016.05.068.Peer-Reviewed Original ResearchConceptsGamma distributionPermutation distributionProperties of statisticsReal data exampleInexpensive computing powerLinear modelGeneralised Pareto distributionMatrix theoryTail approximationNegative binomial distributionSymmetric errorsPareto distributionDirect fittingFamily-wise error ratePermutation inferenceReference resultsComplex modelsBinomial distributionReal dataSynthetic dataExact error rateComputing powerGeneral linear modelFamilywise errorNull hypothesis
2015
Fast and powerful heritability inference for family-based neuroimaging studies
Ganjgahi H, Winkler AM, Glahn DC, Blangero J, Kochunov P, Nichols TE. Fast and powerful heritability inference for family-based neuroimaging studies. NeuroImage 2015, 115: 256-268. PMID: 25812717, PMCID: PMC4463976, DOI: 10.1016/j.neuroimage.2015.03.005.Peer-Reviewed Original ResearchConceptsAuxiliary linear modelSemi-parametric inferenceInference methodsP-value computationParametric inference methodsNovel inference methodSum of squaresMultiple testing problemLikelihood computationInference proceduresFast estimationStandard resultsComputational intensityData settingTesting problemLinear modelPermutation schemeMethodological resultsAnisotropy measuresSignificance testsComputationInferenceWald testFalse positive riskHeritability estimation
2014
Permutation inference for the general linear model
Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage 2014, 92: 381-397. PMID: 24530839, PMCID: PMC4010955, DOI: 10.1016/j.neuroimage.2014.01.060.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsBrainBrain MappingComputer SimulationData Interpretation, StatisticalHumansLinear ModelsNerve NetResearch DesignConceptsPermutation inferenceNon-standard statisticsComplex general linear modelArbitrary experimental designsLinear modelPermutation methodOnly weak assumptionsGLM parametersWeak assumptionsSymmetric distributionExact controlGeneral linear modelNuisance variablesInferenceDetailed exampleComplete algorithmAlgorithmExperimental designIndependent dataNuisance effectsUseful caseGeneric frameworkModelStatisticsGLM