represents an indirect measure of brain development. However, this is only informative for first few years of life and provides no information about age‐mediated dynamic changes within the brain, only implied information about its overall size. HC findings in terms of social development are important in terms of certain perinatal disorders such as prematurity and birth injury as well as genetic conditions and nutritional deficiencies, but only as a coarse indicator of brain development. Importantly though, the expansion rate of HC from birth to 3 years of age has been shown to be a modest predictor related to intellectual development (Flensborg‐Madsen et al., 2020), which has implications related to health care and nutritional and socioeconomic status, all of which play a role in the development of social behavior.
Historically, human developmental neuroscience research was entirely dependent on postmortem examination (Ernhart, 1991; Eskenazi et al., 1988; Towbin, 1978) and animal studies (Rakic, 1978). These early postmortem investigations often examined specific regions of interest (ROI) involving certain brain structures, often reporting size, type of cells, and cellular configurations, but such procedures were extremely time‐consuming, requiring meticulous dissection and effort (Blinkov & Glezer, 1968). Of course, since this was all postmortem, none of this could be related to social‐emotional functioning in the living child, unless an antemortem, anecdotal record had some information about social behavior. Despite these pre‐neuroimaging limitations, it was established that there were a minimum of four key features of brain growth that related to its size and assumed importance for social development: (1) myelination increased throughout childhood and adolescence, (2) changes in cellular density within GM occurred during development, which actually included apoptosis (see Figure 3.1) and pruning, (3) synaptic complexity increased along with neural connectivity, and (4) the development of integrated neural networks (Davison & Dobbing, 1966; Herschkowitz & Rossi, 1971).
The ability to study these four areas and their relevance to social brain development, all changed with the introduction of computed tomography (CT) in the early 1970s, followed by magnetic resonance (MR) a few years later. These neuroimaging technologies permitted in vivo assessment of brain structure, providing the first direct visualization and quantitative metrics to investigate brain development. The initial problem was that CT involved radiation exposure, so not a brain imaging method possible for normative, and especially longitudinal studies of children, brain, and social development. Nonetheless, CT rapidly became instrumental in identifying various aspects of brain pathology in pediatric neurological and neuropsychiatric disease and acquired injuries, which in turn, permitted the study of the developing brain in the living child who had a change in social behavior (Bigler et al., 2013; Yeates et al., 2007). With CT, the first in vivo studies emerged showing how acquired lesions, especially from trauma, altered social‐emotional functioning in children (Bigler, 1999). Now with contemporary neuroimaging methods this approach to studying damage to the social brain network has become commonplace as reviewed by Ryan et al. (2021).
MR technologies do not expose the child to radiation, but there were a host of technical issues that had to be overcome, especially for quantitative analyses of the brain (see Bigler, 2017). Courchesne et al. (2000) and Pfefferbaum et al. (1994), both demonstrated how developmental brain changes could be analyzed in healthy individuals using MRI. The MR image, as shown in Figure 3.2, is actually derived from detecting a radiofrequency (RF) signal sensitive to the movement of water molecules, which differs between GM, where neuronal cell bodies and synapses are located, WM, where myelinated axons are located, and cerebrospinal fluid (CSF) spaces within the brain. Taking advantage of WM, GM, and CSF signal intensity differences permits the direct calculation of various ROIs in terms of area, volume, thickness, and other metrics (Bigler, 2017).
Using this quantitative approach with MRI scans, both Courchesne et al. (2000) and Pfefferbaum et al. (1994) calculated total intracranial volume (TICV) and plotted that along with total brain volume (TBV), WM and GM volume. Growth curves of both TICV and TBV mirror one another, with correlations that exceed 0.90 (see Bigler, 2021) through ~ 5 years of age, the HC, TICV, and TBV measures remain highly intercorrelated.
As shown in Figure 3.1, within GM, once a peak is reached there is a period of apoptosis (cell death) and cellular pruning. Theoretically, the initial excess of neural cells reflects a buffer against potential traumatic birth injury as well as dealing with inherent subtle errors making some cells expendable (Towbin, 1978). As cells compete for functional connectivity but do not become established or have an error in synaptic function, biologically these cells may not be essential. This competition from inclusion/exclusion in the neural developmental matrix that forms the brain, has been viewed as a type of a Darwinian competition for neural cell survival (Szilagyi et al., 2016). The apoptosis may come about because of critical periods where either input or output connections fail to sufficiently occur and the cell dies or is “pruned” back (Moreno et al., 2015).
As shown in Figure 3.3, while the neuronal count that makes up GM stabilizes, the robust changes in WM (i.e., myelination) continue to dominate brain development. This in no way diminishes what occurs in GM, because that is where synaptogenesis, dendritic arborization changes associated with learning and neural transmission occurs, but myelination does emphasize neural connectivity and speed of neural transmission. Therefore, developmentally, synaptic transmission rates depend on the healthiest rates of myelination and their association with critical GM regions. Graphically, displayed in Figure 3.4 shows the progression of the WM signal intensity reflecting rapid increases of WM myelination in MRI studies done from birth through 3 years of age.
The side view (sagittal) MRI taken at different stages of infant development, as shown in Figure 3.4, also aligns with standardized infant development scales designed to assess early motor, sensory, and language development of the individual infant compared to a normative sample. At this early stage of development, the degree of myelination highly correlates with basic motor, sensory, and language acquisition. Of course, being able to move about and interact as well as sense the environment along with communication are the foundational abilities for social interaction. Plotting the changes of this MRI‐derived myelination coefficient shows how motor and somatosensory regions (see upper panels) of the posterior frontal lobe and anterior parietal, respectively come online as the first to show increased myelination after birth, followed by the superior temporal gyrus (lower panels), which houses auditory cortex. These areas of increased myelination map distinctly to the primary motor, somatosensory, and auditory processing networks in the developing brain. What emerge next are the connective WM tracts from auditory cortex in the temporal lobe with motor control centers in the frontal lobe for speech production. Not shown in this particular sagittal plane is visual cortex and the optic tracts, which also develop rapidly supporting the visual sensory system as well.
Figure 3.3 Schematic illustrations depicting brain gray and white matter changes with maturation. Brain gray matter volume increases at an accelerated rate from birth to around age 8, seems to peak for several years and then because of the pruning process gradually reduces over puberty, with continued decline related to age‐mediated cellular apoptosis. In contrast, brain white matter volume displays increase from infancy throughout early adulthood.
(The graphs are based on a compilation of information extracted and adapted from Courchesne et al., 2000 and Pfferebaum et al., 1994.)
While actual images of the brain are elegantly shown in Figures 3.2 and
