New method for fMRI investigations of language: defining ROIs functionally in individual subjects

Abstract

Previous neuroimaging research has identified a number of brain regions sensitive to different aspects of linguistic processing, but precise functional characterization of these regions has proven challenging. We hypothesize that clearer functional specificity may emerge if candidate language-sensitive regions are identified functionally within each subject individually, a method that has revealed striking functional specificity in visual cortex but that has rarely been applied to neuroimaging studies of language. This method enables pooling of data from corresponding functional regions across subjects rather than from corresponding locations in stereotaxic space (which may differ functionally because of the anatomical variability across subjects). However, it is far from obvious a priori that this method will work as it requires that multiple stringent conditions be met. Specifically, candidate language-sensitive brain regions must be identifiable functionally within individual subjects in a short scan, must be replicable within subjects and have clear correspondence across subjects, and must manifest key signatures of language processing (e.g., a higher response to sentences than nonword strings, whether visual or auditory). We show here that this method does indeed work: we identify 13 candidate language-sensitive regions that meet these criteria, each present in >or=80% of subjects individually. The selectivity of these regions is stronger using our method than when standard group analyses are conducted on the same data, suggesting that the future application of this method may reveal clearer functional specificity than has been evident in prior neuroimaging research on language.

Figures

Fig. 1.

Sample activations in the left hemispheres of individual subjects for the sentences > nonwords contrast (top: sample subjects from experiment 1; bottom: sample subjects from experiment 2). Threshold: false discovery rate (FDR) 0.05.

Fig. 2.

Activations in 2 sample subjects (S02, S10) for all of the runs (left: 8 runs in S02, 7 runs in S10), only the odd-numbered runs (middle), and only the even-numbered runs (right). [Four runs using the design of the experiments presented here take ∼30–40 min; however, this is because these runs include four experimental conditions. With just 2 conditions (sentences and nonwords), which is all that is necessary for functionally defining language-sensitive regions of interest (ROIs), only 2 runs are required, which take ∼15–20 min.]

Fig. 3.

Probabilistic overlap map for subjects in experiments 1 and 2 (n = 25). Colors indicate the number of subjects showing significant activation for sentences > nonwords in each voxel (the maximum possible value of a voxel equals the number of subjects included in the map, i.e., 25).

Fig. 4.

The relationship between the size of the group-level partitions (for all 180 partitions) and the number of subjects that have a nonzero intersection with the partition (i.e., ≥1 suprathreshold voxel within the borders of the partition). In dark gray are partitions that have a nonzero intersection with ≥80% of the subjects. In light gray are partitions that have a nonzero intersection with 60–79% of the subjects.

Fig. 5.

Two sample functional ROIs [fROIs, left inferior frontal gyrus (IFG) and left middle frontal gyrus (MFG)] in 7 sample subjects. The borders of the group-level partitions are shown in blue and the subject-specific activations are shown in red.

Fig. 6.

Group-level partitions corresponding to the 13 key fROIs shown on a slice mosaic.

Fig. 7.

Group-level partitions corresponding to the 13 key fROIs projected onto the brain surface.

Fig. 8.

Responses of the 13 fROIs to the sentences and nonwords conditions in an independent subset of the data in experiments 1 and 2 (1st 2 bars), in the 1st visual run in experiment 3 (2nd 2 bars), and in the auditory runs in experiment 3 (last 2 bars). Error bars represent SE.

Fig. 9.

Correlations across the voxels in each of the 13 group-level partitions comparing odd- vs. even-numbered runs within subjects (■) and comparing odd- vs. even-numbered runs between subjects (). Error bars represent standard errors of the mean.

Fig. 10.

A comparison of the selectivity (the size of the sentences > nonwords effect) of group-constrained subject-specific (GcSS) fROIs vs. group-level fROIs (based on the random-effects group analysis of subjects in experiments 1 and 2, n = 25). Significance levels: left orbital IFG (IFGorb) < 0.05; left cerebellum < 0.01; left angular gyrus (AngG), left middle frontal (MFG), right cerebellum, left superior frontal gyrus (SFG) < 0.005; the rest of the regions <0.001. Error bars represent SE.