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The EM algorithm returns the probability density function p of k Gaussian mixture components, with P(x)=��i=1k?��ini(x;��,��), where ni(x; ��, ��) represents the kth Gaussian probability density function with mean �� and standard deviation Navitoclax supplier ��. The number of components k was determined by means of cross-validation, which estimates the log-likelihood for different component solutions by performing a simple dataset splitting, where a randomly selected half of the data is use to fit the model, and half to test. Conventionally, a likelihood ratio test is performed to compare the goodness of fit of 2 (or more) models with different model parameters. Here, we simply chose the model with the fewest number of n Gaussian components that provided a considerable increase in log-likelihood relative to the n + 1 component model. Depending on the number of components and their respective length cut-offs, streamlines were separated into short- and long-distance tract classes based on their proximity to the surface-based label. Statistical Comparison Vertex-wise between-group Differences in lGI and SA Exploratory vertex-based statistical analysis of lGI and SA measures was conducted Bcl-2 inhibitor using the SurfStat Toolbox (www.math.mcgill.ca/keith/surfstat/) for MATLAB. To improve the ability to detect population changes, the lGI and SA maps were smoothed with a 5-mm full-width at half-maximum surface-based Gaussian kernel. Parameter estimates for vertex-wise lGI and Bumetanide SA estimates (Yi) were estimated by regression of a general linear model (GLM) at each vertex i with diagnostic group, and center as categorical fixed-effects factor, and age and FSIQ as continuous covariates: Yi=��0+��1Group+��2Center+��3Age+��4FSIQ+?i, where ?i is the residual error. Between-group differences were estimated from the coefficient ��1 normalized by the corresponding standard error. Corrections for multiple comparisons across the whole brain were performed using ��random field theory�� (RFT)-based cluster analysis for nonisotropic images using a P