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5 over 10000 iterations (top N features at which peak accuracy was achieved) or 50 iterations (for plots at each number of included features). Uncorrected p-values were reported, and unless otherwise stated, p-values were also corrected at p?selleck inhibitor For plots, 95% Confidence Intervals (95% CI) of the accuracy score were calculated using the normal approximation interval of the binomial distribution: p?��?Zc*��[p(1-p)/n], where p?=?TP?+?TN/(TP?+?FP?+?TN?+?FP), Zc?=?97.5 percentile of a standard normal distribution, and n?=?sample size. This formula was used as it is the simplest and most commonly used to approximate confidence intervals for proportions in a statistical population, LMTK2 and because there was adequate sample size and proportions were not extremely close to 0 or 1 (Newcombe, 1998). For SVM learning and classification we used the Spider v1.71 Matlab toolbox (http://people.kyb.tuebingen.mpg.de/spider/) using all default parameters (i.e. linear kernel SVM, regularization parameter C?=?1). We attempted SVM learning using a radial basis function kernel and sigma?=?2 as suggested previously (Dosenbach et al., 2010), but in general performance results were no better than a linear kernel SVM. Thus all analysis used default parameters. Graphical neuro-anatomical connectivity maps were displayed using Caret v5.61 software (http://brainvis.wustl.edu/wiki/index.php/Caret:About). For assessing the significance of the differences between decoding results (i.e. whole-brain FC as features vs. subcortical FC) we used the Accurate Confidence Intervals MATLAB toolbox for assessing whether the parameter p (probability of correct prediction) from two independent binomial distributions was significantly different (http://www.mathworks.com/matlabcentral/fileexchange/3031-accurate-confidence-intervals). Briefly, these methods search for confidence intervals using an integration of the Bayesian posterior with diffuse priors NLG919 chemical structure to measure the confidence level of the difference between two proportions ( Ross, 2003). We used the code prop?diff(x1,n1,x2,n2,delta), (available from the above website) returning Pr(p1???p2?��?��), where x1, n1, x2, n2, are number of correct responses and total predictions in two distributions being compared, and delta (zero in our case) is the null hypothesis difference between the probabilities. The average response rate in the color discrimination task was 98% (stdev?=?4.6%), mean accuracy was 97% (stdev?=?3.5%), and mean reaction time was 0.65?s (stdev?=?0.12), indicating that subjects performed the task as instructed. In the task used to determine d�� scores (see Materials and methods), twelve subjects reported that no masked fearful face had been presented). In the remaining subjects, mean observed d�� score was 0.13, std?=?0.35, and the max was 0.71 (~?65% accuracy).