INTRODUCTION
Down syndrome (DS) is the most common genetic cause of intellectual disability (ID) occurring at a rate of ~1/700 live births (Parker et al., Reference Parker, Mai, Canfield, Rickard, Wang, Meyer and Kirby2010). Although ID is the most widely recognized feature of the DS cognitive–behavioral phenotype (Gibson, Reference Gibson1978), research has identified a profile of relative strengths and weaknesses that distinguishes children with DS both from younger typically developing (TD) peers matched on developmental level and same age peers with other forms of ID (for a review, see Fidler, Most, & Philofsky, Reference Fidler, Most and Philofsky2009). Specifically, those with DS demonstrate deficits in different aspects of language functioning that exceed overall cognitive impairments (Martin, Klusek, Estigarribia, & Roberts, Reference Martin, Klusek, Estigarribia and Roberts2009). Furthermore, challenges with explicit memory are prominent and are in excess of global impairments (Jarrold, Baddeley, & Phillips, Reference Jarrold, Baddeley and Phillips2007). Motor difficulties are noted from early on and persist beyond the first years of life at more severe levels than within other ID syndromes (Spanò et al., Reference Spanò, Mercuri, Randò, Pantò, Gagliano, Henderson and Guzetta1999; Vicari, Reference Vicari2006; Volman, Visser, & Lensvelt-Mulders, Reference Volman, Visser and Lensvelt-Mulders2007).
Despite advances in our understanding of the DS cognitive–behavioral phenotype in childhood, relatively little neuroimaging research exists for this critical period. Indeed, there are approximately twice as many neuroimaging investigations of adults with DS than childrenFootnote 1 , likely due to a focus on Alzheimer’s disease (AD) neuropathology in adults with DS and challenges inherent to scanning children. This dearth of knowledge on brain development in a syndrome that can be identified in utero is surprising. Whereas research focused on neuroanatomy of adult DS is crucial, it must be recognized that neuroanatomical abnormalities present in adulthood are the result of a lifetime of development. A greater understanding of the brain in DS through a developmental perspective will not only illuminate important neurological principles in pediatric DS, but may elucidate underpinnings of adult phenotypes, particularly the heightened risk for precocious AD (Zigman & Lott, Reference Zigman and Lott2007). Thus, the current study sought to provide a review of the pediatric structural neuroimaging literature in DS within the context of typical brain development and the DS cognitive–behavioral phenotype. Before summarizing this literature, we describe the methods used in our review.
REVIEW METHODS
A comprehensive liturature review was completed using the electronic database Web of Science. One author completed the initial search using search terms of topic: “Down syndrome” or “Trisomy 21” and topic: “brain”, “MRI”, “magnetic resonance imaging”, “neuroim*”, “neuroan*”, or “morpho*”, resulting in 2678 unique articles. Two additonal authors reviewed all resulting abstracts and extracted relevant papers. Additionally, reference sections of identified articles were reviewed to locate additional articles and recently published manuscripts were identified via ResearchGate.
Structural neuroimaging studies were selected that (a) compared a DS group to a TD control group (although studies could include other comparison groups) and (b) had a DS group with a mean age of ≤30 years. This upper age limit was imposed in attempt to only include imaging studies completed before the development of AD neuropathology, as nearly all individuals with DS present with AD neuropathology by age 40 (Zigman & Lott, Reference Zigman and Lott2007). This conservative upper age limit illuminated a surprising dearth of literature in early adulthood, as no DS structural neuroimaging studies were identified with mean ages between 17 and 30 years.
Using these criteria, 14 studies published between 1990 and 2017 informed our review. These studies are summarized in Tables 1 and 2. To synthesize findings across studies, we identified a subset of these studies to gather effect size data to examine the magnitude of volumetric differences for the DS group relative to TD peers using the following criteria: (1) the study reported volumetric findings for one or more of the following: (a) total brain volume (TBV), (b) total, gray matter (GM), or white matter (WM) volumes, or (c) cortical lobar volumes; and (2) means and standard deviations were reported so that effect sizes could be calculated using Cohen’s d (Cohen, Reference Cohen1988). Using the established criteria, 6 of the 14 studies contributed data and 46 effect sizes were calculated. Contributing studies are noted in Table 1 and findings are summarized in Figures 1 and 2. More details about the figures are found in the Cerebral Cortex and DS Brain Imaging Findings in a Developmental Context sections. Also included in the latter section are published data from Lee et al. (Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016) and data from the the NIH Study of Normal Brain Development. These data are presented in Figure 3 and were obtained with appropriate institutional review boards approvals from their respective institutions.
Fig. 1 Effect size estimates (Cohen’s d) of DS–control group differences for lobar volumes (total, GM, and WM) from published research studies reporting raw findings plotted as a function of age. N.B., negative effect size estimates indicate smaller volume in DS group. Line represents average volume effect size with upper and lower limits.
Fig. 2 GM volume for each lobe plotted by age and effect size. N.B., negative effect size denotes smaller volume in the DS group.
Fig. 3 Frontal GM–age relations in DS relative to TD controls. Panel A shows a hypothetical DS frontal GM curve (gray line) created from effect size estimates from three published studies of youth with DS with mean ages of 5, 9, and 15 (Carter, Capone, & Kaufmann, Reference Carter, Capone and Kaufmann2008; Kates, Folley, Lanham, Capone, & Kaufmann, Reference Kates, Folley, Lanham, Capone and Kaufmann2002; Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016). These effect size estimates were used as a measure of degree of deviance from typical development by “working backwards” from Cohen’s d (e.g., for the study with mean age of 5: DShypothetical volume age 5=Mvol TD age 5 – (Cohen’s dDS age 5 * SDTD age 5). To plot these relative to frontal GM volume across childhood for TD peers, frontal GM data from the NIH Normative Brain Development Study were used as a comparison group. Effect sizes from the three DS studies were used to approximate standard deviations of difference between youth with DS and TD youth of a similar age (based on the mean age of the DS group in the published study). These three points are plotted as a function of the mean age of the DS group in the respective study. In addition, data are plotted for frontal GM as a function of age for TD youth (black) ages 5 to 15 from the NIH sample. Panel B shows frontal GM–age relations examined cross-sectionally for DS and TD control groups from Lee et al. (Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016). The data plotted here are from a subsample of the participants, ages 5–15, from the DS (gray) and TD (black) groups.
Table 1 Study descriptions and major volumetric findings organized by age period
Note. 
indicates significant reduction in DS; 
indicates significant increase in DS; n.s. indicates non-significant findings; a indicates finding once adjusted; *indicates additional comparison groups included; HC=hippocampus; CB=cerebellum; d=days. Boldface indicates studies represented in Figure 1.
Boldface italics
indicates studies included in Figure 1 and 2. # indicates other findings reported in Table 2.
Table 2 Other notable findings
Note. indicates reduction in DS; indicates increase in DS; n.s. indicates non-significant findings; VBM=voxel-based morphometry. aFindings once adjusted
BRAIN DEVELOPMENT IN DS AND TYPICAL DEVELOPMENT
Over the past 30 years, knowledge about the developing brain studied in vivo has grown substantially thanks to the advent of non-invasive magnetic resonance imaging (MRI). Unlike older imaging methodologies (e.g., positron emission tomography) involving radiation exposure, MRI is a safe method to obtain detailed images of the brain in children (Bunge & Kahn, Reference Bunge and Kahn2009). Whereas current imaging methods [e.g., voxel- and surface-based morphometry, diffusion tensor imaging (DTI)] allow the quantification of very localized regions of the brain at the level of the individual voxel or vertex using metrics such as volume, cortical thickness and surface area, gyrification, and fractional anisotropy (FA), the vast majority of DS neuroimaging studies provide gross descriptions of the brain. With the exception of a handful of recent studies, existing descriptions of the developing brain are limited to quantification of TBV [as well as total gray matter (GM) and white matter (WM)], lobar GM and WM, and measurements of targeted structures, such as the hippocampus, amygdala, basal ganglia, and cerebellum.
Given the state of DS pediatric neuroimaging research, we organized our review by first describing global deviations in TBV as well as GM and WM. We then focus on cortical anatomy and specific subcortical structures by embedding findings within the context of what is known about the DS cognitive–behavioral phenotype. In particular, we focus on structures relevant to four prominent features of the phenotype: intellectual functioning, language skills, explicit memory, and motor development. We close by discussing DS neuroimaging findings within the context of dynamic age-related changes known to occur in typical development and hypothesize about differing cortical developmental trajectories within pediatric DS.
TBV
Typical brain development begins in utero producing an incredibly plastic brain prime for development and specialization. TBV reaches 95% of adult size by approximately age 6. However, dynamic changes in TBV continue through early adulthood (Giedd et al., Reference Giedd, Stockman, Weddle, Liverpool, Alexander-Bloch, Wallace and Lenroot2010). Although TBV is a gross measure of neuroanatomy, positive associations between TBV and intelligence have been repeatedly found in studies of children and adults (for a review, see McDaniel, Reference McDaniel2005). Consistent with this research, cogent support exists for reduced TBV in pediatric DS, as all six investigations of TBV (M ages: 5–15 years) identified reductions.
TBV is often controled for when examining differences in structures. However, this is not a universal practice, as some account for other volumetric differences (e.g., cerebral GM volume) and others do not make any adjustments. In the sections that follow, we note whether findings are reported as unadjusted, raw values or adjusted for TBV.
GM and WM
Although TBV is an important measure linked to individual differences in intellectual ability in the typical population, mechanisms by which TBV changes over development are more nuanced. That is, TBV is a global measure further segmented into total GM and WM. GM, comprised of cell bodies and synaptic connections (Stiles & Jernigan, Reference Stiles and Jernigan2010), makes up the cerebral cortex and areas deep in the brain (e.g., hippocampus, amygdala, hypothalamus, thalamus, basal ganglia) and in the cerebellum. The development of GM over the first 2 decades of life follows an inverted U-shaped trajectory, in which it increases during childhood and decreases into adolescence (Giedd et al., Reference Giedd, Stockman, Weddle, Liverpool, Alexander-Bloch, Wallace and Lenroot2010; Sowell et al., Reference Sowell, Peterson, Thompson, Welcome, Henkenius and Toga2003). Much of this reduction is thought to be driven by synaptic pruning (Huttenlocher, Reference Huttenlocher1979) which permits strengthening of relevant connections and elimination of redundant, non-essential ones (Webb, Monk, & Nelson, Reference Webb, Monk and Nelson2001).
WM is composed of myelinated axons and glial cells found beneath the cortical ribbon (Stiles & Jernigan, Reference Stiles and Jernigan2010). It forms vast webs which enhance fluency and communication between brain regions (Giedd, Reference Giedd2008). Unlike GM, WM largely increases over childhood and an age-related WM decline begins in middle adulthood (Gunning‐Dixon, Brickman, Cheng, and Alexopoulos, Reference Gunning‐Dixon, Brickman, Cheng and Alexopoulos2009). As such, deviations in GM and WM across the course of development in a disorder like DS must be interpreted within the context of what is known about the developmental trajectories for these tissue types.
Within DS, GM and WM reductions are described in all studies of school age children and adolecents (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016; Menghini, Costanzo, & Vicari, Reference Menghini, Costanzo and Vicari2011; Pinter, Eliez, Schmitt, Capone, & Reiss, Reference Pinter, Eliez, Schmitt, Capone and Reiss2001). However, a recent study by Gunbey et al. (Reference Gunbey, Bilgici, Aslan, Has, Ogur, Alhan and Lutfi2017) in which toddlers (M age ~2.5) were studied (the youngest group studied to date) reported that GM was non-significantly larger in children with DS relative to TD peers. They also reported a non-significant WM decrease in this group.
Thus, both GM and WM volumes are reported to be smaller by the time youth with DS are of school age. However, more nuanced descriptions in youth with typical development indicate that these tissue types do not mature uniformly across the brain (Gogtay et al., Reference Gogtay, Giedd, Lusk, Hayashi, Greenstein, Vaituzis and Thompson2004; Krogsrud et al., Reference Krogsrud, Fjell, Tamnes, Grydeland, Mork, Due-Tønnessen and Johansen-Berg2016); rather, different regions reach maturation during different developmental periods. In a landmark study, Gogtay and colleagues (Reference Gogtay, Giedd, Lusk, Hayashi, Greenstein, Vaituzis and Thompson2004) found that cortical structures that are phylogenetically older and support sensory and motor functioning tend to mature first, whereas those that support higher-order cognition have a protracted developmental trajectory and mature the latest, well into adolescence.
The regional specificity of GM maturation suggests that a review of the developing brain in DS should provide descriptions of regionally specific findings. Further support for this comes from research indicating higher-level cognitive abilities are supported by specialized neural hubs embedded in distributed networks across the brain (van den Heuvel & Sporns, Reference van den Heuvel and Sporns2013). Thus, understanding deviations in specific brain structures (rather than deviations in TBV, GM, and WM) is crucial to understanding neural underpinnings of atypical cognitive development in DS.
In the sections that follow, we refine our focus to specific structures thought to be important neural hubs for different aspects of the DS cognitive–behavioral phenotype. We first discuss the neuroanatomy of cortical structures relevant to two aspects of the DS cognitive–behavioral phenotype: intelligence and language. Next, subcortical structures central to explicit memory and motor functioning are discussed, the hippocampus and cerebellum, respectively. As our review focuses on structures relevant to the DS cognitive–behavioral phenotype, we do not summarize research on other structures that may be of interest to some readers in the text (e.g., amygdala, basal ganglia, brainstem, corpus callosum). However, we have summarized key findings of all studies in Tables 1 and 2 in an attempt to be comprehensive yet remain focused in the text.
Cerebral Cortex
The cerebral cortex comprises the outermost layer of GM and is divided into lobes. Research on the the neural correlates of both intellectual and language functioning suggests that subregions of each lobe contribute to higher-level cognitive processes. One prominent theory of the neural correlates of intellectual functioning, the Parieto-Frontal Integration Theory (P-FIT), identifies a distributed network of primarily cortical structures thought to underlie intellectual functioning (Jung & Haier, Reference Jung and Haier2007), including the dorsolateral prefrontal cortex, anterior cingulate, superior parietal lobule, and the supramarginal and angular gyri. In addition, lateral and inferior portions of the temporal lobes as well as portions of the visual association cortices in the occipital lobes are implicated.
Analagous to studies of intelligence, neural correlates of language functioning are supported by complex networks containing key activity hubs (Friederici & Gierhan, Reference Friederici and Gierhan2013). Two such hubs are located in the temporal and inferior frontal lobes, in regions that include Wernicke’s and Broca’s areas, respectively. However, the neural correlates of langauge are certainly more widespread than originally thought, and current theories also implicate stuctures at the temporo-parietal junction (e.g., angular and supramarginal gyri) and occipito-temporal structures (e.g., fusiform gyrus) (Friederici & Gierhan, Reference Friederici and Gierhan2013; Hickok & Poeppel, Reference Hickok and Poeppel2004). Given the distributed nature of intellectual and language functioning, we first review investigations of lobar volumes followed by voxel- and surface-based morphometric and DTI studies.
As the greatest number of studies examined the cortical lobes in DS and several studies include small samples which may have resulted in insufficient power to detect statistically significant findings, effect sizes for raw lobar volumes (as well as TBV) were calculated using Cohen’s d (Cohen, Reference Cohen1988) to synthesize findings across studies. These were plotted as a function of the mean age of the DS group and are presented in Figure 1. The figure includes effect sizes for four (Carter, Capone, & Kaufmann, Reference Carter, Capone and Kaufmann2008; Kates, Folley, Lanham, Capone, & Kaufmann, Reference Kates, Folley, Lanham, Capone and Kaufmann2002; Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016; Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001) of the five studies that described lobar volumes in DS.Footnote 2
Illustrated in Figure 1, raw frontal lobar volumes are substantially smaller in DS (all Cohen’s d values m1 or less; Carter et al., Reference Carter, Capone and Kaufmann2008; Kates et al., Reference Kates, Folley, Lanham, Capone and Kaufmann2002; Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016; Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001). However, the statistical significance varies across studies: some studies report statistically significant decreases in frontal volumes (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016; Smigielska-Kuzia et al., Reference Smigielska-Kuzia, Bockowski, Sobaniec, Sendrowski, Olchowik, Cholewa and Lebkowska2011) and some do not (Kates et al., Reference Kates, Folley, Lanham, Capone and Kaufmann2002; Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001). When adjusting for TBV, statistically smaller frontal WM volumes are reported (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016). In contrast, non-significant findings are noted for adjusted frontal GM (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016) and total frontal volume (Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001).
Medium to large effect sizes are observed in most studies of the parietal lobes (although the statistical significance varies). Specifically, raw parietal WM is noted as significantly smaller, whereas group differences are not found for parietal GM (Kates et al., Reference Kates, Folley, Lanham, Capone and Kaufmann2002; Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016). However, unlike the frontal findings, when adjustments are made for TBV, parietal GM is significantly larger (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016; Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001). When adjusted parietal WM volumes are considered, findings are mixed. One study reported significantly larger volumes (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016) and another did not (Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001).
For the temporal lobes, fairly consistent findings are reported for unadjusted volumes (medium to large effect sizes across studies). Specifically, total temporal (Kates et al., Reference Kates, Folley, Lanham, Capone and Kaufmann2002; Smigielska-Kuzia et al., Reference Smigielska-Kuzia, Bockowski, Sobaniec, Sendrowski, Olchowik, Cholewa and Lebkowska2011) and temporal GM and WM volumes (Kates et al., Reference Kates, Folley, Lanham, Capone and Kaufmann2002; Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016) are smaller in those with DS. However, this reduction does not survive TBV-correction. Once adjusted, one study reported no significant differences in total temporal and temporal GM (Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001) and others reported significant increases in temporal GM (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016) and WM (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016; Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001).
Within occipital lobes, reductions of medium to large effect are observed in most studies. Again, the statistical significance of these findings varies: one study reported statistically smaller occipital GM and WM volumes (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016) and one study did not (Kates et al., Reference Kates, Folley, Lanham, Capone and Kaufmann2002). When adjusted volumes were considered, total occipital (Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001) and occipital WM have been reported not to differ from TD peers (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016), whereas adjusted occipital GM is reportedly larger (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016).
Complementing volumetric investigations, voxel-based morphometric investigations have reported reduced GM and WM (after adjusting for TBV) in multiple frontal regions, particularly medially (Carducci et al., Reference Carducci, Onorati, Condoluci, Di Gennaro, Quarato, Pierallini and Albertini2013). Reduced GM has been reported in inferior and medial portions of the temporal lobes (Menghini et al., Reference Menghini, Costanzo and Vicari2011). Reductions in WM have also been reported in regions of the insula and in frontal and parietal lobes (Carducci et al., Reference Carducci, Onorati, Condoluci, Di Gennaro, Quarato, Pierallini and Albertini2013). In addition, relative increases in GM have been reported in regions of the insula, frontal (Menghini et al., Reference Menghini, Costanzo and Vicari2011), temporal (Carducci et al., Reference Carducci, Onorati, Condoluci, Di Gennaro, Quarato, Pierallini and Albertini2013; Menghini et al., Reference Menghini, Costanzo and Vicari2011), and parietal (Carducci et al., Reference Carducci, Onorati, Condoluci, Di Gennaro, Quarato, Pierallini and Albertini2013) lobes.
Surface-based morphometric approaches also note atypicalities in subregions of all lobes. Reduced surface area is reported in the frontal and temporal lobes, with the largest reductions (surviving TBV-correction) in the dorsal medial frontal cortex and superior temporal gyrus (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016). In contrast, increased cortical thickness (which is thought to reflect less ‘mature’ cortex) is found throughout much of the frontal lobe, superior parietal lobe, and occipital lobe (Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016).
Lastly, findings from the only published DTI study completed with a pediatric sample (Gunbey et al., Reference Gunbey, Bilgici, Aslan, Has, Ogur, Alhan and Lutfi2017) revealed reduced FA in the fronto-temporo-occipital tracts (particularly in frontal circuits) in toddlers with DS. Interestingly, these findings are consistent with the small DTI literature in adults with DS, which also reports frontal atypicalities in WM (Fenoll et al., Reference Fenoll, Pujol, Esteba-Castillo, De Sola, Ribas-Vidal, García-Alba and Dierssen2017; Powell et al., Reference Powell, Caban-Holt, Jicha, Robertson, Davis, Gold and Head2014).
Hippocampus
The hippocampus is widely believed to be a central hub for explicit memory (Squire, Reference Squire1992), with lesions resulting in catastrophic memory impairments across development (Isaacs et al., Reference Isaacs, Vargha-Khadem, Watkins, Lucas, Mishkin and Gadian2003; Savage, Buzzetti, & Ramirez, Reference Savage, Buzzetti and Ramirez2004). Like the structures reviewed thus far, the hippocampus undergoes massive changes across childhood, with peak volume between ages 9 and 11 (Uematsu et al., Reference Uematsu, Matsui, Tanaka, Takahashi, Noguchi, Suzuki and Nishijo2012) and subsequent decreases during adolescence. Investigations of youth with DS highlight significant impairments on hippocampally mediated memory tasks (Clark, Fernandez, Sakhon, Spano, & Edgin, Reference Clark, Fernandez, Sakhon, Spano and Edgin2017; Pennington, Moon, Edgin, Stedron, & Nadel, Reference Pennington, Moon, Edgin, Stedron and Nadel2003).
Atrophy of the hippocampus is one of the most well-replicated neuroimaging biomarkers of AD pathology (Blanken et al., Reference Blanken, Hurtz, Zarow, Biado, Honarpisheh, Somme and Lo2017). Thus, it is surprising that there are few investigations of this structure in youth with DS. The limited research available suggests no significant differences in hippocampal volume between those with DS and TD peers in toddler years (Gunbey et al., Reference Gunbey, Bilgici, Aslan, Has, Ogur, Alhan and Lutfi2017), but as children age, those with DS are reported to have smaller hippocampal volumes when both unadjusted (Smigielska-Kuzia et al., Reference Smigielska-Kuzia, Bockowski, Sobaniec, Sendrowski, Olchowik, Cholewa and Lebkowska2011) and adjusted (Pinter, Brown, et al., Reference Pinter, Brown, Eliez, Schmitt, Capone and Reiss2001) for TBV. Additionally, reductions in hippocampal subregions are noted in voxel-based morphometric investigations (Carducci et al., Reference Carducci, Onorati, Condoluci, Di Gennaro, Quarato, Pierallini and Albertini2013; Menghini et al., Reference Menghini, Costanzo and Vicari2011).
Cerebellum
The cerebellum has long been recognized for its role in motor functioning and recent investigations implicate involvement in higher-order cognitive functioning as well (Buckner, Reference Buckner2013; Middleton & Strick, Reference Middleton and Strick2001; Ramnani, Reference Ramnani2006). Thus, we review cerebellar findings to highlight possible contributions to motor impairments in DS, but recognize that cerebellar abnormalities likely contribute to multiple aspects of the DS cognitive–behavioral phenotype. In typical development, the cerebellum follows an inverted U-shaped trajectory with peak volume met in adolescence (Tiemeier et al., Reference Tiemeier, Lenroot, Greenstein, Tran, Pierson and Giedd2010). For those with DS, support for smaller cerebellar volume has been reported in childhood after TBV adjustment (Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001) and in adolescence (Jernigan & Bellugi, Reference Jernigan and Bellugi1990; Jernigan, Bellugi, Sowell, Doherty, & Hesselink, Reference Jernigan, Bellugi, Sowell, Doherty and Hesselink1993). Specific reductions are noted in total cerebellar WM and GM in toddlerhood (Gunbey et al., Reference Gunbey, Bilgici, Aslan, Has, Ogur, Alhan and Lutfi2017) continuing through childhood (Pinter, Eliez, et al., Reference Pinter, Eliez, Schmitt, Capone and Reiss2001). Similarly, voxel-based morphometric investigations noted relative reductions (Carducci et al., Reference Carducci, Onorati, Condoluci, Di Gennaro, Quarato, Pierallini and Albertini2013; Menghini et al., Reference Menghini, Costanzo and Vicari2011) and some relative increases (Menghini et al., Reference Menghini, Costanzo and Vicari2011) in cerebellar subregions.
SUMMARY OF DS IMAGING FINDINGS
Our review of the DS pediatric neuroimaging literature revealed several key findings. First, TBV in DS is smaller overall. This finding is consistently replicated in the literature and is important to consider when interpreting other morphometric differences. Second, by childhood, GM and WM volumes are smaller relative to TD controls. Third, volumetric reductions are noted for all lobes, consistent with the distributed nature of intellectual and language functioning. However, only reductions in frontal WM lobar volumes survive adjustments for overall microcephaly. Findings from voxel- and surface-based morphometric investigations as well as the very limited DTI literature also note atypicalities in frontal anatomy in DS surviving correction for TBV. Lastly, consistent with the explicit memory and motor impairments associated with DS, volumetric reductions (in excess of TBV) in the hippocampus and cerebellum have been noted in several studies.
Our review also revealed several key gaps in knowledge that prevent the field from creating a developmentally informed model of brain development in DS. None of the 14 existing studies adopt a developmental neuroimaging approach. As the late Annette Karmiloff-Smith astutely stated, “development is not about studying children” (Karmiloff-Smith, Reference Karmiloff-Smith2012). Rather, to study the developing brain, one must be committed to examining relations between the brain and age. Ideally, this would involve longitudinal investigations consistent with the large corpus of studies on typical brain development (see Giedd et al., Reference Giedd, Stockman, Weddle, Liverpool, Alexander-Bloch, Wallace and Lenroot2010 for a review). As outlined in a study by Thomas and colleagues (Reference Thomas, Annaz, Ansari, Scerif, Jarrold and Karmiloff-Smith2009) on using developmental trajectories to understand developmental disorders, examining relations between age and a phenotype of interest in a cross-sectional analysis can begin to provide insights into the emergence of atypicalities associated with different developmental disorders. Thus, in an attempt to begin conceptualizing the brain in DS within a developmental context, we examine the existing neuroimaging findings with reference to the mean age of the participants studied in the section that follows.
DS BRAIN IMAGING FINDINGS IN A DEVELOPMENTAL CONTEXT
We know that bigger is not always better when it comes to the developing brain. Rather, understanding how much the brain in children with DS deviates from TD peers at what point in development may generate hypotheses about how brain trajectories differ for DS. Given the protracted developmental trajectory of the cerebral cortex, we extracted data from all studies in our review that reported on lobar volumetric findings (four studies; raw findings –i.e., uncorrected for TBV) and calculated effect size estimates for lobar GM differences in DS relative to TD controls using Cohen’s d (Cohen, Reference Cohen1988). This yielded 14 estimates. We then plotted these as a function of the mean age of the DS group in each study (Figure 2) to examine effect size–age relations for these structures. Importantly, many studies have a very wide age range requiring caution in intepretation as representing a developmental period. Also, it should be noted that while most studies use a similar method for acquiring lobar volume (i.e., Talairach-based), Lee et al. (Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016) calculate lobar volume using regional segmented volumes in native space (see Collins, Holmes, Peters, & Evans, Reference Collins, Holmes, Peters and Evans1995 for details). Thus, some caution should be exercised when comparing findings across studies.
As illustrated in Figure 2, youth with DS deviate from their TD peers across all lobes and ages examined. Indeed, the vast majority of the 14 effect sizes are described as large using Cohen’s (Reference Cohen1988) conventions (exceptions are four small-to-medium effects). Also evident is the fact that lobar GM is particularly deviant early in development and that less deviance is apparent over time. Of the lobes, frontal GM was most deviant in the oldest age group studied (M age=15 years) and was the only lobe to maintain a large effect size. Given established relationships between frontal anatomy and intelligence (Jung & Haier, Reference Jung and Haier2007), further exploration of the developmental trajectory for frontal GM within DS may help to elucidate the neurobiological underpinnings of ID in this and other groups.
To begin this investigation, we used the effect size estimates for frontal GM displayed in Figure 2 as measures of the degree of deviance observed in frontal GM relative to controls. We then plotted these relative to data from the the NIH Study of Normal Brain Development using the following methods. First, we used effect sizes from the 3 studies that reported on frontal GM to approximate standard deviations (SD) of difference between youth with DS and TD youth of a similar age from the Normative Brain Development Study (i.e., if the effect size from a given study is -1, then this indicates that the DS group mean is 1 SD below the mean of TD children from the Normative Brain Development Study of a particular age). Second, we estimated hypothetical DS volume by “working backwards” using Cohen’s d. Specifically, we subtracted the SDs of the difference from the normative mean for the age studied. For example, for youth in the study with the mean age of 5, the following equation was used to generate one point on the “curve”: DShypothetical volume age 5=M vol TD age 5 – (Cohen’s d DS age 5 * SD TD age 5). We then plotted these values for the studies of youth with DS with mean ages of 5, 9, and 15 alongside the normative data for TD youth, ages of 5–15 years, from the NIH normative sample in Figure 3a.
The hypothetical DS frontal GM ‘curve’ appears much flatter than the curve for TD peers. Because this line was created from three data points, we plotted GM volumes for youth with DS and TD controls, ages 5–15 years, as a function of age from the Lee et al. (Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016) study in Figure 3b to present our “hypothetical curve” against existing, cross-sectional data. This cross-sectional sample also reveals an inverse relationship between frontal GM and age in the TD controls; however, similar to the hypothetical curve, this relationship is not evident in DS. Thus, this preliminary investigation of age-brain relations (through examination of effect sizes relative to longitudinal TD data and cross-sectional data from Lee et al., Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016) suggests that the developmental trajectory for frontal GM in DS differs from TD youth.
The potential difference in trajectory may inform why effect sizes for frontal (and other lobar) GM volumes are smaller in adolescence (see Figure 2). Specifically, the smaller effect sizes may result from the fact that frontal GM volumes for youth with DS do not appear to follow the downward trajectory observed in typically developing youth. Whereas TD peers enter adolescence with reduced GM volume (presumably due to the pruning of redundant synapses), this robust reduction is not evident in DS. Thus, the smaller effect sizes found at older ages are likely not due to youth with DS “catching up”. Rather, they may suggest a different developmental trajectory, consistent with research suggesting atypical neural trajectories in other neurodevelopmental disorders (Shaw, Gogtay, & Rapoport, Reference Shaw, Gogtay and Rapoport2010).
The data presented here from Lee et al. (Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016) are cross-sectional and represent the findings of one study, requiring caution with interpretation. However, by presenting these findings in conjunction with the hypothetical curves emerging from the literature review, it is our hope to begin to generate a conversation about atypical neurodevelopment within those with DS. With this, we provide a synopsis of the state of the current literature on the developing brain in DS and discuss future directions for research.
STATE OF THE LITERATURE: CONTRIBUTIONS, CHALLENGES, AND FUTURE DIRECTIONS
Research examining the brain in vivo in pediatric DS has provided important insights into the neural structures that likely underlie the DS cognitive–behavioral phenotype. However, this literature is still quite small and very few studies have adopted advanced neuroimaging methodologies to describe nuanced descriptions of the brain in youth with DS. This dearth of research is likely due to the many challenges inherent to successfully completing developmental neuroimaging, from methodological (e.g., child appropriate equipment and analyses) to more practical (e.g., participant anxiety, movement restrictions) limitations. However, it remains unclear why imaging within pediatric DS has not followed suit with other neurodevelopmental disorders associated with cognitive impairment (e.g., fragile X syndrome, Williams syndrome, 22q11.2 deletion syndrome), which have evolved to use modern techniques while addressing the shared challenges of imaging within these populations.
To move the field forward, a push for increased neuroimaging research, particularly focused on the first years of life and early childhood, using modern neuroimaging techniques and best practice guidelines for pediatric neuroimaging (for review, see: Raschle, Zuk, Ortiz-Mantilla, et al., Reference Raschle, Zuk, Ortiz-Mantilla, Sliva, Franceschi, Grant and Gaab2012) is needed. Given the small samples that characterize the literature on neuroimaging in DS, it is worth noting that several studies in the current literature review were conducted at the same institutions. As a result, the existing literature on this topic is likely describing a small number of individuals with DS, as there may be overlap in study participants across studies given the low incidence of DS.
Consequently, the field could benefit from the implementation of multi-site investigations to garner larger samples needed for more advanced neuroimaging analyses. Analytic methods used in such studies might be modeled off successful multi-site investigations (e.g., Alzheimer’s Disease Neuroimaging Initiative; Weiner et al., Reference Weiner, Veitch, Aisen, Beckett, Cairns, Green and Liu2013). Moreover, data collected from published studies may be pooled and examined using data harmonization methodology, consistent with meta-analytic methods used by the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium (Thompson et al., Reference Thompson, Stein, Medland, Hibar, Vasquez, Renteria and Franke2014).
In conclusion, the results of this comprehensive review suggest that the field of DS pediatric neuroimaging is very much in its infancy. Our examination of existing research revealed consistencies and discrepancies within the literature and identified a key gap in research starting in late adolescence and early adulthood. Although some findings are mixed, many findings reported as non-significant yield massive effect sizes, suggesting limitations in sample size and characterization of differences. Results from functional magnetic resonance imaging studies (a review outside the scope of this study) further support current findings that areas implicated in language and intellectual functioning are atypical in DS (Anderson et al., Reference Anderson, Nielsen, Ferguson, Burback, Cox, Dai and Korenberg2013; Jacola et al., Reference Jacola, Byars, Chalfonte-Evans, Schmithorst, Hickey, Patterson and Holland2011). Although our review focused on DS and TD peers, investigation of DS in relation to other developmental and genetic disorders can help to inform our understanding of neurological underpinnings of ID.
Lastly, and arguably most importantly, no longitudinal neuroimaging investigations of pediatric DS exist. Rather, much of what we know comes from cross-sectional studies including wide age ranges with little attention to developmental effects. This impedes our understanding of the developmental unfolding of the DS neuroanatomic and cognitive–behavioral phenotypes, limiting our ability to identify developmentally informed interventions to alter the developmental trajectory in pediatric DS and promote improved outcomes across the lifespan.
ACKNOWLEDGMENTS
Normative brain development data presented in Figure 3a were obtained from the Pediatric MRI Data Repository (version 4) created by the NIH MRI Study of Normal Brain Development. This is a multi-site, longitudinal study of TD children, from ages newborn through young adulthood, conducted by the Brain Development Cooperative Group and supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke (Contract #s N01-HD02-3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319, and -2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://www.bic.mni.mcgill.ca/nihpd/info/participating_centers.html. Data included in Figure 3b is from Lee et al. (Reference Lee, Adeyemi, Lin, Clasen, Lalonde, Condon and Giedd2016); this work was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Mental Health (NCT00001246; ProtocolID89-M-0006). We thank the children and families who made all of this research possible.
