Autism

Early Brain Over-Growth Is Indicative of Autism as Predicted

Another brain imaging study confirms predictions of the imprinted brain theory.

Posted Feb 16, 2017

We have known for 20 years that autism spectrum disorder (ASD) tends to go with increased brain volume in adolescents and adults, and research has also found evidence of it in early childhood. A study published today examined MRI data from a set of individuals at high familial risk for ASD compared to others at low risk and a control group at no risk at 6,12 and 24 months of age. The results described below were obtained from a subset of 106 high-risk and 42 low-risk infants.

The researchers first examined group differences in the trajectories of brain growth rate. The growth rate of the total brain volume did not differ between groups from 6 to 12 months of age. However, the high risk ASD group showed a significantly increased total brain volume growth rate in the second year compared to both the low risk and control groups. 

In addition, the high risk ASD group showed a significantly increased surface area growth rate from 6 to 12 months of age compared to both the control and low risk groups, with the most robust increases observed in the left/right middle occipital gyrus, right cuneus and right lingual gyrus area (see figure below). No group differences were observed in cortical thickness, but a significant correlation between surface area growth rate of 6-12 months and enlargement in total brain volume at 24 months of age in all subjects was observed. Brain volume overgrowth was also found to be linked to the emergence and severity of autistic social deficits.

Nature Vol 542, 16 FEBRUARY 2017.

Cortical regions that show significant expansion in surface area from 6 to 12 months in HR-ASD. A map of significant group differences in surface area from 6 to 12 months. Exploratory analyses were conducted with a surface map containing 78 regions of interest. The coloured areas show the group effect for the high risk (HR-ASD) versus low risk (LR) subjects. Compared to the LR group, the HR-ASD group had significant expansion in the cortical surface area in the left/right middle occipital gyrus and right cuneus (1), right lingual gyrus (2), and to a lesser extent in the left inferior temporal gyrus (3), and middle frontal gyrus (4) (HR-ASD, n = 34; LR, n = 84).

Source: Nature Vol 542, 16 FEBRUARY 2017.

Our data suggest that very early, post-natal hyperexpansion of cortical surface areas may have an important role in the development of autism. The rate of cortical surface area expansion from 6 to 12 months was significantly increased in individuals diagnosed with autism at 24 months, and was linked to subsequent brain overgrowth, which, in turn, was linked to the emergence of social deficits. This suggests a sequence whereby hyperexpansion of the cortical surface area is an early event in a cascade leading to brain overgrowth and emerging autistic deficits. In infants diagnosed with autism at 24 months, surface area hyperexpansion in the first year was observed in cortical areas linked to the processing of sensory information (for example, the left middle occipital cortex), consistent with regions previously reported to show the earliest increase in surface area growth rate in typically developing infants and with reports showing early sensory differences in infants who will later develop ASD.

A deep-learning algorithm that primarily uses surface area information from magnetic resonance imaging of the brain of 6- to 12-month-old individuals predicted the diagnosis of autism in individual high-risk children at 24 months (see figure below). These findings demonstrate that early brain changes occur during the period in which autistic behaviours are first emerging.

Nature Vol 542, 16 Feb. 2017.
Visualization of cortical regions with surface area measurements among the top 40 features contributing to the reduction in deep learning dimensionality.The cortical regions with surface area measurements that were among the top 40 features obtained from the nonlinear deep learning approach are visualized. The top 10 deep learning features observed include: surface area at 6 months in the right and left superior frontal gyrus, post-central gyrus, and inferior parietal gyri, and intracranial volume at 6 months. These features produced by the deep learning approach are highly consistent with those observed using an alternative approach (linear sparse learning).
Source: Nature Vol 542, 16 Feb. 2017.

Clearly, this finding is exactly what the imprinted brain theory predicts, given that it sees ASD as the result of the over-expression of paternally-active, growth-enhancing imprinted genes. Indeed, the theory also predicts the exact opposite findings where psychotic spectrum disorder (PSD) is concerned, and as I pointed out in a previous post, another recent brain imaging study strikingly confirmed this in relation to white versus grey matter volumes in the brain.

The authors of the study published today go on to note that their finding has clear implications for early diagnosis of ASD. Indeed, it beautifully fits the PlacentASD Test described in an earlier post, which revealed that over-growth in certain placental cells is also indicative of risk of ASD. And of course, the same goes for PSD: according to the imprinted brain theory, if over-growth of the brain or placenta is found in ASD in childhood, corresponding under-growth should be associated with a psychotic illness in adolescence or adulthood (also suggesting a PlacentPSD Test for the future).

For the time being, the long interval between birth and the relatively late age of onset of PSD presents a serious obstacle to testing this prediction, but sooner or later someone is bound to investigate it, and if the finding were as predicted here, the imprinted brain theory and its diametric model of mental illness would have found one of their most decisive demonstrations.

(With thanks to Lenis Badcock for bringing this to my attention.)