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 - Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K. (Brain connectivity, 2018)
Keywords: Accounting; Activation analysis; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Brain Mapping - methods; Caffeine; Cluster Analysis; Correlation analysis; Data Interpretation, Statistical; Data processing; Datasets; Female; Functional magnetic resonance imaging; Humans; Linear Models; Magnetic Resonance Imaging - methods; Male; Methods; Neuroimaging; Neurology; NMR; Noise; Nuclear magnetic resonance; Physiology; Principal Component Analysis; Principal components analysis; Rehabilitation; Spatial distribution; Stochastic models; Time Factors; Time series; Visual Perception - physiology; Young Adult; Atlanta Georgia; Georgia; United States--US; Pennsylvania; first-level analysis; temporal autocorrelation; cluster-based family-wise error rate calculation; Gaussian random noise fields; general linear model residuals; spatial autocorrelation function; fMRI parametric methods; Monte Carlo simulation; principal component analysis; independent and identically distributed