The neural representation of body part concepts

Abstract

Neuropsychological and neuroimaging studies provide evidence for a degree of category-related organization of conceptual knowledge in the brain. Some of this evidence indicates that body part concepts are distinctly represented from other categories; yet, the neural correlates and mechanisms underlying these dissociations are unclear. We expand on the limited prior data by measuring functional magnetic resonance imaging responses induced by body part words and performing a series of analyses investigating the cortical representation of this semantic category. Across voxel-level contrasts, pattern classification, representational similarity analysis, and vertex-wise encoding analyses, we find converging evidence that the posterior middle temporal gyrus, the supramarginal gyrus, and the ventral premotor cortex in the left hemisphere play important roles in the preferential representation of this category compared to other concrete objects.

Keywords: body representation; categories; functional MRI; representational similarity analysis; semantics.

Fig. 1

Mass univariate and multivoxel pattern classifier analyses. Top: the top portion of the figure shows where blood-oxygen-level-dependent signal was greater for body part concepts than concepts from other categories based on standard mass univariate contrasts. All individual contrasts are significant at FDR corrected P < 0.01 using a 1-tailed Wilcoxon signed-rank test. The conjunction map shows where body parts produced greater activation in all comparisons. Colors in the individual comparisons represent average difference in beta-values. Bottom: the lower portion shows average classification accuracies for held out data in cross-validated searchlight SVM category classification. Classes were balanced, and 50% indicates chance accuracy. Shown in the center is the difference in average accuracy when one of the categories was body parts compared with when body parts are not a target category. Abbreviations are identified in the caption for Table 2.

Fig. 2

ICC and RSA comparisons. ICC and RSA maps were generated for each of 4 categories of nouns. Results for the body part category are shown in the top row, and contrasts between body part and other categories are shown in subsequent rows. The bottom row shows conjunction maps of the categorical comparisons. Colors in the ICC conjunction map represent the difference between the body part ICC value and the highest upper limit of the confidence interval for any of the other 3 categories. The RSA conjunction maps were generated by testing the mean of paired differences against zero using a Wilcoxon signed-rank test. The conjunction maps show vertices where body part RSA values were larger than for any other category using an FDR corrected P < 0.01 threshold. The conjunction map color shows the mean paired difference between body part RSA values and whichever other category had the highest RSA value for that vertex.

Fig. 3

Vertex–wise encoding results. Shown on the left is the difference between the average of all body part concepts and average of all other concrete concepts. On the right is the result of a vertex–wise encoding model trained on 150 concrete concepts to predict beta values for the 50 body part concepts. Shown is the average difference between the average of the 50 predicted values and the 150 observed values along with vertices that were significantly larger for body parts at FDR corrected P < 0.01 using a 1-tailed Wilcoxon signed-rank test.

Fig. 4

a) Select HCP parcels are labeled. The coloring corresponds to that used in the original report by Glasser et al. (2016). b) Body part word > other category univariate results overlaid with HCP parcel boundaries. c) Body part pictures > other category during a working memory task, univariate results from the HCP dataset. d) Overlap of the maps generated by body part words relative to other categories in the current study and body part images relative to other categories in the HCP dataset. The current results are displayed in Cohen’s D units, the same units as the HCP data, and thresholded so that only values greater than 0.3 are displayed for both maps. e) Resting-state fMRI connectivity map using seed-based correlation, with a seed placed in left PHT applied to data from 1003 participants in the HCP S1200 release.