Clustering Brain Activation to Study social Cognition

Over the last 20 years, thousands of studies have tried to capture social abilities, and came up with many ingenious ways to measure their basis in the human brain. In a recent study, we performed a meta-analysis of some of these results, including data from 4207 participants. The initial picture emerging from our review suggested quite some heterogeneity, and so we decided to explore a data-driven approach for sorting it out. Specifically, we wanted to see if findings can be clustered into different patterns of brain activation.

To implement the idea of a cluster analysis, we first had to decide how brain activation maps are compared and grouped together. We wanted to capture the pattern of activity across the whole brain (rather for a specific brain region of interest) when characterizing the similarity between maps. In many recent works, this is determined based on multi-voxel pattern similarity, which is commonly calculated as Pearson image correlation. However, several questions arose at this point for us. First, should image similarity be determined for brain activation maps that have been statistically thresholded, and thus only contain the most reliable information about brain activation? Second, should we use a parametric or a non-parametric measure for image correlation, that is, Pearson's or Spearman's correlation coefficients?

To find the answers to these questions, we determined how well different approaches can distinguish between meta-analysis maps derived from studies belonging to the same task group (and therefore sampling very similar cognitive states), versus maps from studies from different task groups. Good discrimination performance is evident in the case of high image correlation between the former maps (same task group), and low correlation between the latter. 

Concretely, we picked two different task groups from our meta-analysis, and divided each into two random halves. We then ran separate meta-analyses for these 4 sub-samples, and calculated pairwise correlations between sub-samples drawn from either the same or from different task groups. We repeated this procedure 100 times with new random-halves (i.e. running 400 new meta-analyses), and compared correlation results for different parameterizations and thresholdings. In particular, we compared three types of maps:
 

• Unthresholded Hedges' g maps (representing the effect size of brain activation)
• Unthresholded SDM z maps (weighed effect sizes accounting for inter-study heterogeneity)
• Thresholded SDM z maps (where non-statistically significant voxels had null value) 

Moreover, we compared two correlation methods (i) pearson and (ii) spearman correlation. The results are shown in Figure 1, where correlation values are z-transformed. As you can see, the largest differences in correlation strength between same and different group data is found for unthresholded Hedges' g maps. We could also corroborate this observation by running a repeated measures ANOVA on the z-transformed correlation coefficients.

 

 

Clustering brain activation maps into common networks

Having found a well suited measure for whole brain image similarity (Hedges' g maps), we determined image dissimilarity (1-r) between a range of meta-analyses for different social tasks, as shown in Figure 2A. Based on this image dissimilarity matrix, it was easy to then carry out a cluster analysis. We decided to use hierarchical agglomerative clustering, and Figure 2B shows the initial dendrogram illustrating task-by-task and cluster-by-cluster relations.

 

Based on the information shown in the dendrogram, we selected an optimal clustering based on two features, which are shown in Figure 3. First, our desired model should ensure a good separation of brain activation patterns between clusters. The left Y-axis in Figure 3 (red) gives the relative difference in cophenetic distances when moving from one model to the next. A relatively high difference in cophenetic distances indicates that introducing new subclusters results in a better separation of brain activity patterns and thus good clustering. Second, the components of a good model should be sufficiently abstract to generalize across concrete instances, that is, multiple task groups, rather than picking up variance related to one outlier task group. The plot in Figure 3 shows on the right Y-axis and in blue the density of task separation, reflecting whether separating clusters into subclusters maintains a balanced number of task groups in each subcluster. A relatively low density of task separation indicates good clustering. Metrics in Figure 3 suggest that a good clustering shows up on two levels: On the one hand, the best overall clustering performance was reached when the data were divided into a three-cluster solution. However, further local peaks in performance were found when the data were split into eight and eleven clusters.

 

The whole structure of our proposed multi-level model is shown below in Figure 4. We carried out pooled meta-analyses, i.e., one individual meta-analysis for each cluster, where all its task groups were joined together. The colors show how the 3-cluster solution relates to both higher- and lower-level clusterings (blue – cognitive cluster, green – intermediate cluster, red – affective cluster). At the lowest level of the dendrogram, we provide for each cluster some exemplary stimulus and task categorizations.

Implications

The higher-level, three-cluster solution provides a solid foundation of evidence for the assumption that different social cognition tasks share certain processes, and therefore brain activity. However, the concurrent existence of an additional lower level of clustering (essentially by task) highlights the question of the appropriate level of concreteness and detail in neurocognitive accounts of social cognition. We think these results speak in favor of modeling social cognition as a multilevel construct, similar to models from other psychological fields such as intelligence research. A well-known example for this is the concept of “general intelligence”. Usually, it is based upon a latent variable of a model, and reflects a broader, more abstract neurocognitive function. It is assumed that this function is played out differently when solving various problems. Similarly, our results found a high-level clustering that points out two overarching networks, which mediate more sensory-affective versus more abstract and decoupled representations of others’ mental states.

What we conclude from our study is that although previous theories of social abilities were good at explaining how specific sub-sets of skills are implemented in the brain, they did not consider how different components relate to each other. Our results present a way to make this possible, which is capturing social cognition in terms of a multilevel model.

 

References

Schurz, M., Radua, J., Tholen, M. G., Maliske, L., Margulies, D. S., Mars, R. B., ... & Kanske, P. (2021). Toward a hierarchical model of social cognition: A neuroimaging meta-analysis and integrative review of empathy and theory of mind. Psychological Bulletin, 147(3), 293-327.

 

DOI: https://www.doi.org/10.48763/000001