and various growth plots presented in Figure 3.3, what are the limits of what can be visualized? This is critical in understanding how neuroimaging studies contribute to social brain development, because despite how impressive an image of the brain may be, brain images only portray macroscopic development. What is represented in one cubic millimeter of an MR or CT image, has been described by Insel and Landis (2013, p. 565) as follows: “Imaging with the highest spatial resolution, currently a voxel of about 1‐cubic mm isotropic, has been estimated to contain 80,000 neurons and 4.5 million synapses.” Since there are 2–3, and in some regions, 10 times the number of glial cells per neuron, the total number of neural cells (neurons + glial cells) can be in the hundreds of thousands just within a single cubic millimeter of a scan image. Micheva et al. (2020, p. 253) provide additional insight as to this micro environment: “Brains can be viewed as vast ensembles of highly diverse and dynamic synapses that shape and store information as it travels through the networks of neurons that generate and interconnect those synapses. There are more than 100 trillion synapses per human neocortex, and each synapse is itself a highly complex entity, comprising thousands of diverse and cooperative signal‐transduction proteins.” In a rodent histological study, Motta et al. (2019) demonstrated that within a 10 cubic micron sample of mouse cortical tissue, 34,221 axons could be histologically identified and counted. Accordingly, as the reader reviews neuroimaging statements in this chapter, only inferences about the cellular level of function can be made. As such, even a subtle neuroimaging finding may have major implications depending on the ROI.
Figure 3.4 White matter maturation from birth through 36 months of age
(Reproduced with permission from Pujol et al., 2006). Reproduced with permission from Wolters Kluwer.
Social Brain Networks Determined from Lesion Analysis Studies
The earliest human neuropsychological studies that focused on social behavior and brain function, all prior to the advent of contemporary neuroimaging, began mostly with adults (see Bigler, 2017). This era also included surgical treatment in the form of frontal lobotomies for neuropsychiatric disorder (Ackerly, 1950), where there could be pre‐ and postsurgical studies of a patient’s social behavior. Throughout the 20th century there were extensive animal investigations that developed experimental models of animal social behavior and studied the effects of brain lesions (Grossman, 1967). As neurosurgical treatment for traumatic brain injury (TBI) improved and survival rates increased, over the mid‐part of the 20th century, changes in social behavior following TBI also contributed significantly to the literature about social brain development (Bigler et al., 2013). It was from these studies that the consensus arose for the importance of frontal and temporal lobe regions of the brain along with the limbic system as key players in regulating social behavior, including how these brain regions modulate social anxiety in children at risk for temperamental dyscontrol (Auday & Pérez‐Edgar, 2019).
What was learned from adult lesion studies began to be applied to children and the developing brain, facilitated with the advent of contemporary neuroimaging in the 1970s (Bakker, 1984). With neuroimaging firmly in place as a clinical investigative tool to study acquired brain injury in infants and children, along with in vivo neuroimaging examinations of the infant or child brain with some type of developmental disorder or disease, the inferences from adult studies about critical brain areas of the frontal, temporal, and limbic regions for social functioning were confirmed in children (Cattelani et al., 1998; Eslinger & Biddle, 2000; Jacobs & Anderson, 2002; Janusz et al., 2002). Summarizing these lesion‐localization studies in children, Figure 3.5 from Yeates et al. (2007) and Figure 3.6 from Adolphs (2003) highlight major candidate brain regions assumed to participate in the development of social behavior. Table 3.1 summarizes these key brain regions and their presumed role related to cognition and behavior. In Figure 3.6, Adolphs emphasizes the feedback relations between self‐regulation and reappraisal in regulation of the social brain.
Implied in the identification of candidate brain regions that contribute to social behavior, as shown in Figures 3.5 and 3.6 and Table 3.1 is that these brain regions were intimately interconnected, emphasizing the importance of myelination and WM integrity. Optimal functioning and integration of these regions likely underlies prosocial, normative development. But how do these regions and networks come on‐line and how can that be demonstrated and investigated in the developing child in relation to social behavior? Diffusion tensor MRI was introduced in Figure 3.2 which included an illustration of network development in the maturing brain. In the last decade, dramatic improvements in how to study and identify brain networks has been established, especially in terms of the mathematical features of “graph theory” applied to social neuroscience (Bassett & Bullmore, 2017).
Figure 3.5 Candidate “social brain” regions
(Reproduced with permission from Yeates et al., 2007). Reproduced with permission from the American Psychological Association.
Figure 3.6 Critical regions and reciprocal relations of the social brain
(Reproduced with permission from Adolphs, 2003). Reproduced with permission from Nature Publishing.
Myelinated axons are the centerpiece of neural networks that provide the interlinking of brain regions. In graph theory, a connector (pathway) is referred to as an “edge,” which connects different regions through nodes and hubs. The most efficient connections, if fully functional, probably involve the fewest and shortest pathways (edges). What is shown in Figure 3.2 is a whole‐brain schematic using DTI‐derived myelinated axons aggregated into a network. The lines in Figure 3.2c represent pathways, the size of the line and different shading reflecting different aspects of a functioning network and their strength, possibly related to importance. As more networks come online with maturation, the spherical representation of a hub or node importance, is reflected by its increased size (Figure 3.2c).
Kennedy and Adolphs (2012) use these and other methods to derive a cognitive neuroscience framework for these key brain regions and networks that participate in self‐regulation and monitoring of social behavior. Their model, presented in Figure 3.7, is based on just four networks – amygdala, mentalizing, empathy, and mirror (see Tables 3.1 and
