首页> Brain connectivity> Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

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第一作者： Kaundinya,Gopinath

第一单位： 1 Department of Radiology and Imaging Sciences, Emory University , Atlanta, Georgia .

作者： Kaundinya,Gopinath ^[1] ; Venkatagiri,Krishnamurthy ^[1] ; Simon,Lacey ^[2] ; K,Sathian ^[3]

作者单位： 1 Department of Radiology and Imaging Sciences, Emory University , Atlanta, Georgia . ^[1] 2 Department of Neurology, Emory University , Atlanta, Georgia . ^[2] 2 Department of Neurology, Emory University , Atlanta, Georgia .;3 Department of Rehabilitation Medicine, Emory University , Atlanta, Georgia .;4 Department of Psychology, Emory University , Atlanta, Georgia .;5 Rehabilitation R&D Center for Visual and Neurocognitive Rehabilitation , Atlanta VAMC, Decatur, Georgia . ^[3]

关键词 Gaussian random noise fields Monte Carlo simulation cluster-based family-wise error rate calculation fMRI parametric methods first-level analysis general linear model residuals independent and identically distributed principal component analysis spatial autocorrelation function temporal autocorrelation