The Wiley-Blackwell Handbook of Childhood Social Development. Группа авторов
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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. Size and shape ROI distributions reflect proxy indices of brain integrity in reference to a normative sample. Since the quantitative neuroimaging, including DTI measures only assess the anatomical aspects of the network, functional activation of a ROI or network could be assessed with functional MRI (fMRI).
Table 3.2 Networks creating the functioning social brain (the letters in the table refer to entries in the references list at the end of the chapter).
| Affective Networks | Emotional scene and face processing a, e, f, h, i, m, o, s, t, z, dd |
| Reward‐related decision making c, d, e, f, h, i, m, aa, dd | |
| Cognitive emotion regulation a, c, d, e, f, g, h, i, j, k, l, m, n, p, q, r, s, t, v, w, x, y, z, bb, cc, dd, ee, ff, gg | |
| Executive Networks | Vigilant attention a, b, c, e, h, i, k, m, o, t, u, z, dd, gg |
| Cognitive action control a, b, c, e, f, g, h, i, j, k, l, m, n, q, t, v, w, x, z, bb, cc, dd, ee, ff, gg | |
| Extended multi‐demand c, e, h, i, m, s, t, v, w, z, bb, dd, ee | |
| Working memory c, h, i, m, q, s, t, z, aa, bb, dd | |
| Social Networks | Empathy a, b, d, e, f, g, h, i, k, l, m, n, r, w, x, y, z, cc, dd, ee, ff |
| Mirror neuron system a, c, e, h, i, k, l, m, t, z, cc, dd | |
| Theory of mind a, b, c, e, g, h, i, j, k, l, m, o, p, r, t, u, w, y, bb, cc, dd, ee, ff | |
| Task‐deactivation and Interacting Networks | Default mode c, e, h, i, m, n, q, s, t, u, aa, dd, ee |
| Extended socio‐affective default b, d, e, f, g, h, i, k, l, m, n, o, p, r, s, t, v, w, x, y, z, aa, bb, cc, dd, ee, ff |
A gradient difference can be detected in the fMRI signal from oxygenated to the de‐oxygenated state when a particular ROI is participating in a function (Jones et al., 2020). This is the basis for what is referred to as blood oxygen level dependent (BOLD) contrast imaging. Detection of increased BOLD fMRI signal shows regional changes in oxygen uptake that can be used to infer task participation.
The BOLD fMRI method introduced another novel neuroimaging technique to study social brain development (Kishida & Montague, 2012; Tymofiyeva et al., 2020). Returning to the brain structures identified in Figures 3.5 and 3.7 and Table 3.1, social‐developmental experiments could be designed and brain activation patterns assessed to target those structures. For example, an empirically designed social scenario could be crafted to display a visual threat simulation, all presented in the MR scanner, studying fMRI brain activation patterns. This fMRI approach permitted a more direct way to study the role of ROI brain structures like the amygdala in perceiving threatening social situations, to include how these types of neural responses are governed by maturation (Hein & Monk, 2017; McClure et al., 2007; Muhlberger et al., 2011; Noack et al., 2019; Sudre et al., 2017). This approach has even been used in large research centers with multiple scanners, where research participants engage in social discourse all‐the‐while in separate MRI machines, allowing real‐time BOLD activation to study human social interaction between individuals (Xie et al., 2020).
As well as the fMRI brain activation paradigms described above, considerable information about neural networks can actually be extracted from imaging the brain at rest (Kim & Yoon, 2018; Sato & Uono, 2019; Tompson et al., 2018, 2020; Wong et al., 2019). Using fMRI techniques in conjunction with other neuroimaging and electrophysiological measures led to the discovery of what is referred to as the “default mode network” (DMN) (Raichle, 2015). Justifiably, cognitive neuroscience in the past had been focused on activation techniques in the quest to examine regional and coordinated brain activity, as described in the previous paragraph. But what occurred with the discovery of the DMN focused brain regions that became disengaged from an activity or the state of brain activity just prior to engagement. These studies showed a network of frontal, parietal, and temporal lobe regions, all of which displayed characteristic connectivity and organization when at rest. In other words, until the brain had to respond, DMN controlled the brain’s idle, keeping the brain in a “ready” position for when activation was necessary.
While a variety of functional neuroimaging methods have been used to study the DMN, the most common have used a fMRI paradigm (Al‐Ezzi et al., 2020), especially in the study of social cognition (Schilbach et al., 2008). In fact, early in the discovery of the DMN, Schilbach et al. noted the “remarkable overlap between the brain regions typically involved in social cognitive processes and the ‘default system’. We, henceforth, suggest that the physiological ‘baseline’ of the brain is intimately linked to a psychological ‘baseline’: human beings have a predisposition for social cognition as the default mode of cognizing which is implemented in the robust pattern of intrinsic brain activity known as the ‘default system’” (2008, p. 457). The resting state fMRI quickly resulted in a new method to examine brain connectivity, referred to as resting state (rs), functional connectivity (fc) MRI or rs‐fcMRI (Nielsen et al., 2014; Parkes et al., 2020), which has become a mainstay in network theory and development related to understanding the social brain.
With these novel methods for neuroimaging‐identified networks, the emphasis shifted to how these networks interfaced with the DMN and how many separate networks related to social processing