r Spatial Heterogeneity in Language Lateralization r symmetrical. This was relevant to the laterality maps (LM) where we directly compared left and right hemisphere activation; see below. The normalized (symmetrical) func- tional images were then spatially smoothed with a 6 mm full width half maximum isotropic Gaussian kernel. First level Analyses For each individual subject, we carried out a ﬁxed-effect analysis on all preprocessed functional volumes of that subject, using the general linear model at each voxel. Each stimulus onset was modeled as an event in condition-spe- ciﬁc ‘‘stick-functions’’ with a duration of 4.32 sec per trial and a stimulus onset interval of 4.5 sec. This event related analysis, in the context of a block design, is more sensitive [Mechelli et al., 2003] and also allows us to exclude trials with incorrect responses. The resulting stimulus functions were convolved with a canonical hemodynamic response function which provided regressors for the linear model. Time-series from each voxel were high-pass ﬁltered (1/128 Hz cut-off) to remove low-frequency noise and signal drift. The appropriate summary or contrast images were then generated in all subjects for the contrast of interest ‘‘semantic matching on words versus perceptual matching on unfamiliar greek letters.’’ Global Laterality Index To express the relative difference in the involvement of the two hemispheres during semantic matching at the global level [Desmond et al., 1995], we computed the later- ality index (LI) for each subject. LI values were computed here on the statistical SPM{t} maps using Nagata et al.’s approach [Nagata et al., 2001] that is independent of the statistical threshold and assessed over the whole hemi- sphere after excluding the cerebellum and mesial part of the brain (i.e., around the inter-hemispheric ﬁssure). In brief, this procedure calculates the number of left and right hemisphere voxels activated for language relative to baseline, at a range of different statistical thresholds. Non- linear regression of the shape of the curve, describing the relationship between the number of voxels and the statisti- cal threshold, provides a constant term that is used to compute a normalized difference between left and right hemisphere activity [Nagata et al., 2001]. A positive LI (towards þ1) indicates left hemisphere dominance, whereas a negative LI (towards 1) indicates right hemi- sphere dominance. Voxel Based Laterality Maps To express regional differences in the involvement of the two hemispheres, we generated maps of the laterality difference at each voxel for each subject [Baciu et al., 2005; Cousin et al., 2007; Josse et al., 2008; Liegeois et al., 2002; Salmond et al., 2000] as follows: (i) contrast images from the ﬁrst-level analysis (i.e., the symmetrical images) were copied and each copy was ﬂipped along the inter-hemi- spheric ﬁssure (i.e., x-axis mirror images), and (ii) the resulting ﬂipped image was then subtracted from its origi- nal (unﬂipped) version to create a map of language lateral- ity. Voxel based LM therefore code the relative difference between the contrast ‘‘semantic word matching versus per- ceptual matching on unfamiliar greek letters’’ at every voxel and at its homologue in the other hemisphere. Thus, LM images code the interaction [Liegeois et al., 2002] between task (semantic versus perceptual matching) and hemisphere (left versus right) at each voxel. Importantly, LM are symmetrical with respect to the inter-hemispheric ﬁssure, and can be described by the relationship LM( x,y,z) ¼ LM(x,y,z). Voxel Based Second Level Group Analyses We conducted three different voxel based second-level analyses on either original (unﬂipped) contrast images or voxel based LM: 1. One sample t-test over the original contrast images to reveal the most consistently activated voxels for semantic word matching across our 82 subjects. 2. One sample t-test over the voxel based LM to reveal the voxels with the most consistently lateralized acti- vation for semantic word matching across our 82 sub- jects (as previously illustrated in eight epileptic children [Liegeois et al., 2002]). 3. The same analysis as (1) repeated with global LI val- ues included as a covariate of interest. This analysis will show where variability in individual LI values is predicted by signal changes in left and right voxels. The anatomical location of our effects was inferred from neuroanatomical knowledge after examining the location of the effect in MNI space and reference to the Duvernoy atlas [Duvernoy, 1991] when necessary. Second Level Clustering and Regional Dissociations The aim here was to characterize any regional dissocia- tion within the LM, across subjects. Our rationale was to search for regions where an increase in laterality in some voxels was associated with a decrease in laterality in other voxels. Prior to correlating laterality measures across regions, we needed to reduce the search space. To do this, data from all voxels were summarized into 50 compact clusters using the second-level fuzzy clustering [Seghier and Price, 2009] on all voxels of the LM showing greater left than right hemisphere activation (at P < 0.05 uncorrected) for the comparison of ‘‘semantic word matching versus r 1605 r