As will be discussed in the next section, numerous networks are at play in regulating the social brain, but for demonstrable purposes the networks discussed by Kennedy and Adolphs are what are presented here.
Since most of the prior research and clinical understanding for the role of these brain regions in social behavior came from the damaged brain, it was important for investigators of age‐typical, healthy brain development to study these candidate “social brain” regions in those who did not have some acquired or developmental neuropathology. Social and neuropsychological methods through behavioral assessments had long been able to identify children with differences in social and adaptive behaviors, for example, the acquisition of reciprocal play. Like most behaviors, using behavioral testing and observational studies, the acquisition and mastery of certain social behaviors could be plotted on a spectrum – early versus late, age‐typical versus delayed, etc. The paradigm shift with neuroimaging was to design experiments with these types of differing behavioral characteristics of social behavior and then scan these children and adolescent research participants, quantitatively analyzing their brains with some of the neuroimaging techniques described in this chapter, especially in the next section. Similarly, these neuroimaging methods have been applied to the study of children with neuropsychiatric disorders, with particular emphasis on clinical syndromes involving social‐emotional functioning (Li et al., 2020). The next section discusses some of these methods and their application to studying the social brain.
Table 3.1 Candidate regions that participate in the social brain.
| Somatosensory cortices | Representation of emotional response |
| Viewing others’ actions | |
| Fusiform gyrus | Face perception |
| Superior temporal gyrus | Representation of perceived action |
| Face perception | |
| Perception of gaze direction | |
| Perception of biological motion | |
| Amygdala | Motivational evaluation |
| Self‐regulation | |
| Emotional processing | |
| Gaze discrimination | |
| Linking internal somatic states and external stimuli | |
| Ventral Striatum | Motivational evaluation |
| Self‐regulation | |
| Linking internal somatic states and external stimuli | |
| Hippocampus and temporal poles | Modulation of cognition |
| Memory for personal experiences | |
| Emotional memory retrieval | |
| Basal forebrain | Modulation of cognition |
| Cingulate cortex | Modulation of cognition |
| Error monitoring | |
| Emotion processing | |
| Theory of mind | |
| Orbitofrontal cortex | Motivational evaluation |
| Self‐regulation | |
| Theory of mind | |
| Medial frontal cortex | Theory of mind |
| Action monitoring | |
| Emotional regulation | |
| Emotional responses to socially relevant stimuli | |
| Monitoring of outcomes associated with punishment and reward | |
| Dorsolateral frontal cortex | Cognitive executive function |
| Working memory |
Figure 3.7 Network ROIs within four critical brain networks underling social cognition and brain function
(Reproduced with permission from Kennedy and Adolphs, 2012). Reproduced with permission from Elsevier.
Quantitative Neuroimaging, Network Neuroscience, and Social Brain Development
A shown in the MRIs displayed in Figure 3.2, as the brain develops, distinct boundary differentiations occur between WM, GM, and CSF (see caption in Figure 3.2). This means that each ROI and anatomical area can be identified and quantified including its shape. Accordingly, all of the regions shown in Figures 3.5 and 3.7 and discussed in Table 3.1, can be quantified and studied in comparison to other children of similar age, whether they have a neurological and/or neuropsychiatric disorder or just differ along some dimension of social behavior. Also shown in Figure 3.2, the DTI tractograms provide a method to examine axon integrity and WM connectivity. When WM pathways are extracted with DTI methods, the visualized tract as shown in Figure 3.2 is referred to as a “streamline.” A DTI streamline is comprised of tens of thousands of axons. The metrics to extract and create these images are based on the physics of water diffusion, where one of the most important metrics is fractional anisotropy (FA). Depending on the age of the child, there is a well‐established range of what may be healthy, normal development of FA, what may be delayed or even damaged. Accordingly, any given brain structure or ROI could be identified and by applying DTI metrics between that structure/ROI with others, the FA strength of WM connections can be established, along with other DTI metrics. Additionally, the size and shape of each ROI can be computed.
