2024
Topological cluster statistic (TCS): Toward structural connectivity–guided fMRI cluster enhancement
L. S, Seguin C, Winkler A, Noble S, Zalesky A. Topological cluster statistic (TCS): Toward structural connectivity–guided fMRI cluster enhancement. Network Neuroscience 2024, 8: 902-925. PMID: 39355436, PMCID: PMC11424043, DOI: 10.1162/netn_a_00375.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingAbstract Functional magnetic resonance imagingBrain activityAnatomical connectivity informationMedium-sized effectsCluster-based inferenceMultimodal informationConnectivity informationAnatomical underpinningsActive inferenceNeuroimaging analysisAnatomical networksDiffusion tractographyOptimal inferenceInsufficient statistical powerWidespread activationMagnetic resonance imagingFunctional imagingConventional approachesDetect local changesInferenceBrainResonance imagingStatistical powerUsability
2019
Accelerated estimation and permutation inference for ACE modeling
Chen X, Formisano E, Blokland G, Strike L, McMahon K, de Zubicaray G, Thompson P, Wright M, Winkler A, Ge T, Nichols T. Accelerated estimation and permutation inference for ACE modeling. Human Brain Mapping 2019, 40: 3488-3507. PMID: 31037793, PMCID: PMC6680147, DOI: 10.1002/hbm.24611.Peer-Reviewed Original ResearchConceptsPermutation inferenceIterative optimizationVariance component modelComputation timeComparable biasAccelerated estimationLinear modelSquared errorCluster sizeSpatial statisticsLinear regression modelsFalse positive riskInferenceHeritability estimationEstimationComponent modelModelWealth of toolsMemory datasetsOptimizationSimple methodSmall numberStatisticsACE modelPermutations
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 ResearchConceptsPermutation 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