To control for potential fluctuations in intensity across the training sessions and the test session, we normalized

global intensity across all functional volumes by scaling each volume by the aggregate voxel mean. The design matrix included all trial types as well as the blocking variables for run epochs. We determined relative differences in the BOLD signal by using a general linear model (GLM) for event-related functional data. We created first-level designs with stimulus onset timing vectors for each frequent sequence. To isolate brain regions that are involved in chunking the frequent sequences, we included an additional covariate vector that contained the normalized φ values based on GW-572016 cost the segmentation patterns attained from community detection. Differences in brain activity due to MT were accounted for by

using MT as the modeled duration for corresponding events. MT is a direct measure of time spent on the task rather than the magnitude of a behavior, so it is logical to model this temporal measure in terms of duration. This approach leads to accurate modeling of the BOLD response in the GLM (Grinband et al., 2008). We convolved events using the canonical hemodynamic response function (HRF) and temporal derivative of the BOLD signal. Using freely available software (Steffener et al., 2010), we combined beta image pairs for each event type (HRF and temporal derivative) at the voxel level to form a magnitude image (Calhoun et al., 2004) equation(Equation 4) H=sign(Bˆ1)∗Bˆ1+Bˆ2,where H   is the combined amplitude of both the estimation of BOLD (Bˆ1) and its temporal selleck chemical derivative (Bˆ2). We performed mixed-effects group analysis using a full-factorial design, with chunking as the factor (three levels, one for each frequent sequence). We minimized detection of false positives (type II error) the by using cluster-corrected family-wise error-rate correction at p < 0.05. We evaluated results pertaining to hypothesis-driven contrasts that failed to survive this corrected threshold at uncorrected p < 0.001 with a 10-voxel cluster threshold.

The aim of this investigation was to identify which regions are involved in motor-sequence chunking based on the correlation of the BOLD response with φ. Both negative and positive correlations might be present: positive correlations indicate the regions that support the concatenation of chunks within a sequence, and negative correlations indicate the regions that support the segmentation of sequences into separable chunks. This research is supported in part by Public Health Service grant NS44393 and the Institute for Collaborative Biotechnologies through contract W911NF-09-D-0001 from the US Army Research Office, as well as the National Science Foundation (DMS-0645369). M.A.P. acknowledges research award 220020177 from the James S.