In this longitudinal MRI study of very early brain volume development in individuals with ASD we observed generalized cerebral cortical enlargement in children with ASD at both 2 and 4 – 5 years of age. Rate of cerebral cortical growth across multiple brain regions and tissue compartments, in children with ASD, parallels that seen in controls, indicating that there is no increased rate of cerebral cortical growth during this age interval. Our findings provide evidence that increased brain volume at age 2, largely due to increased cerebral cortical volume, results from an increased rate of brain growth occurring prior to two years of age. Together with previously reported findings from a longitudinal study of head circumference (11
), and a recent longitudinal MRI study of early brain volume development (12
), these data provide further evidence that brain overgrowth in autism occurs in the early post-natal period, before 2 years of age. In the cross-sectional analysis of two year olds (11
), we previously reported that children with autism had significantly enlarged gray and white matter volumes compared to the DD subgroup, but only white matter volumes were enlarged compared to the TYP subgroup. The longitudinal analyses reveal increased white and gray matter volume in autism versus both the TYP and DD control subgroups. Given the fact that no differences in our data are seen between autistic and control subjects at either age point in this longitudinal study, we feel confident in our conclusion that volume increases are evident in this sample of autistic subjects. Of course, the small size of the control subgroups compels us to be most certain about our findings with respect to the total sample of controls.
The findings from the present study point to increased cerebral cortical surface area and not increased cortical thickness as the underlying factor in the increased cerebral cortical gray matter volume observed in very young children with ASD. Emerging literature on cortical maturation in older males with ASD has found evidence for decreased cortical thickness in adolescence (25
), so it may be that a period of cortical thinning occurs in ASD after childhood. It is unclear at this point whether increased white matter results in enlarged gray matter and/or SA, or if instead a common etiology causes both increased white matter and SA. As we have learned in our study of the MAOA gene (28
), where we find MAOA effects on both white and gray matter volumes, but not with the serotonin transporter, the biological mechanisms underlying cortical growth are complex. . Increased surface area results from an increase in the number and/or size of cerebral cortical gyri. Several studies suggest that such gyral abnormalities may be present in individuals with ASD. Nordahl et al. (29
) observed ‘cortical folding abnormalities’ in autism, while Lenroot et al. (personal communication) has reported an increase in surface area in 4–5 year olds with ASD. Kates et al. (30
) noted abnormal ‘gyrification’ in monozygotic twins discordant for autism. And, Raznahan et al. (31
) reported that adults with ASD differ from controls in the relationship between a key genotype for determining regional cortical volume (Brain Derived Neurotrophic Factor, or BDNF val66met) and cortical volume and surface area (but not cortical thickness). Petropoulos et al. (32
) reported prolonged T2 relaxation for cortical gray matter in a large sample of 2–4 year olds with ASD compared to typically-developing controls. Our findings and the observation by Petropoulos et al. both suggest that abnormal early development of gray matter is associated with ASD.
Human studies have suggested several candidate genes that may play a role in the increased cerebral cortical volume in ASD (28
). The likely importance of epistasis in brain overgrowth in ASD is underscored by a mouse study of deletions in the serotonin transporter and PTEN genes showing an interactive effect, increasing both brain volume and autistic-like behaviors in mice (34
). Family studies have revealed that both cortical surface area and cortical thickness are highly heritable but unrelated genetically, suggesting distinct genetic architecture underlying these phenomena (35
). The finding of surface area but not cortical thickness differences provides a narrower phenotypic target for future studies exploring the genetic basis of autism; as distinct neurobiological mechanisms are thought to underlie these two determinants of cortical volume (36
Surface area is thought to be determined by division of progenitor cells in the embryological periventricular area (with increased progenitor cells occurring in association with increased cortical surface area); whereas cortical thickness is thought to reflect variation in dendritic development (arborization and pruning) in gray matter (38
) or myelination (40
). Molecular studies in mice have demonstrated the role of β-catenin in regulating cerebral cortical size (and resultant increases in cortical surface area but not thickness) by controlling the generation of neural precurors (37
). Glycogen Synthetase Kinase-3 (GSK) was recently shown to cause massive hyper-proliferaton of neural progenitor cells in mice resulting in large brains with increased convolutions. GSK interacts with the Phosphatidyl Inositol-3 (PI3) kinase pathway, implicated in several neurodevelopmental disorders (e.g., Fragile X Syndrome and tuberous sclerosis) that are characterized by having autistic behavior (41
). GSK also interacts with the receptor tyrosine kinase (RTK) signaling system, which has been linked to idiopathic autism (43
). These various pathways for brain overgrowth clearly point to areas that need further study in autism.
We previously reported retrospective head circumference data on a large sample of children with ASD compared to local controls from birth to age 3 years that suggested increased head size in ASD has its onset around 12 months of age (11
). We hypothesized that this increased head size was the result of increased brain size and that brain overgrowth had its onset in the latter part of the first year of life. Longitudinal behavioral studies of infants at high genetic risk for ASD, who are later diagnosed with ASD at 36 months, report no difference in social behavior at 6 months of age in comparison to controls, whereas marked deficits in reciprocal social interaction are observed by 12−14 months of age (13
). These behavioral studies suggest that the onset of autistic behavior has its origins in the latter part of the first year of life. The temporal relationship between the onset of both autistic behavior and brain overgrowth at the end of the first year of life suggests a relationship between these two phenomena; and specifically that increased rate of brain growth may be linked to the onset of autistic symptoms.
It is possible that brain overgrowth directly results in the development of autistic behavior, perhaps through a physical disruption of neural circuitry. An alternative hypothesis is that brain overgrowth is a secondary response to a more proximal event that affects downstream remodeling of neuronal processes. Disruption in experience-dependent cortical refinements caused by impaired synaptic plasticity has been reported in a mouse model of Angelman Syndrome, a disorder thought to be associated with autistic behavior (45
). Similarly, disruptions in normal synaptic plasticity and experience-dependent neuronal development have been observed in a mouse model of Fragile X syndrome (46
), a disorder also associated with autism. Consistent with the idea that autism is linked to impaired experience-dependent cortical development, a recent study has observed a high number of diverse mutations known to cause defective expression of activity-driven genes, in a sample of autistic individuals (47
). Alterations in synapse development have also been proposed as a common mechanism in a number of neurodevelopmental disorders, including autism (48
A potential limitation of this study stems from our inability to measure surface area directly in very young children. As such, we were only able to obtain regional estimates of cortical thickness and an estimate of surface area, and the surface area findings should therefore be considered preliminary While mean cortical thickness in each lobar region is not necessarily indicative of uniformity of cortical thickness throughout the cerebral cortical lobes (there exists normal variation in cortical thickness, known to be increased for example in heteromodal association areas (49
)), the convergence of cortical thickness findings across the three cortical regions measured supports the validity of our findings. Software to enable local cortical thickness and surface area measurement in the developing pediatric brain is currently under development in our lab, and will be an important future step in characterizing early brain volume changes in individuals with ASD. An additional potential limitation of our study was the use of sedation with some participants (ASD, DD) and not others (TYP). However, we have no reason to believe that sedation at the time of the scan had any significant effect on cortical volume as there is no evidence in the literature to suggest a state effect that would confound our results.
Studies currently underway by our group (http://www.ibis-network.org/
) are prospectively (at 6, 12 and 24 months) examining MRI/DTI brain and behavior development in infants at high risk for ASD, further characterizing the timing of brain-behavior changes in this disorder. Given the findings in other brain disorders (e.g., Parkinson’s, Alzheimer’s, and Huntington’s Disease), where brain changes are well known to precede the cognitive and/or behavioral manifestation of symptoms (50
), observations from the present study support future research aimed at identifying early (under 2 years of age) brain markers that may increase prediction of ASD risk (e.g., maturational differences in selected DTI fiber tracts in infants with high genetic risk for ASD). Future studies should continue the strategy of longitudinal imaging to more definitively characterize the pattern of brain changes as individuals with ASD age across the lifespan.