In pursuit of neurophenotypes: The consequences of having autism and a big brain

Abstract

A consensus has emerged that despite common core features, autism spectrum disorder (ASD) has multiple etiologies and various genetic and biological characteristics. The fact that there are likely to be subtypes of ASD has complicated attempts to develop effective therapies. The UC Davis MIND Institute Autism Phenome Project is a longitudinal, multidisciplinary analysis of children with autism and age-matched typically developing controls; nearly 400 families are participating in this study. The overarching goal is to gather sufficient biological, medical, and behavioral data to allow definition of clinically meaningful subtypes of ASD. One reasonable hypothesis is that different subtypes of autism will demonstrate different patterns of altered brain organization or development i.e., different neurophenotypes. In this Commentary, we discuss one neurophenotype that is defined by megalencephaly, or having brain size that is large and disproportionate to body size. We have found that 15% of the boys with autism demonstrate this neurophenotype, though it is far less common in girls. We review behavioral and medical characteristics of the large-brained group of boys with autism in comparison to those with typically sized brains. While brain size in typically developing individuals is positively correlated with cognitive function, the children with autism and larger brains have more severe disabilities and poorer prognosis. This research indicates that phenotyping in autism, like genotyping, requires a very substantial cohort of subjects. Moreover, since brain and behavior relationships may emerge at different times during development, this effort highlights the need for longitudinal analyses to carry out meaningful phenotyping.

Purkinje cell number-correlated cerebrocerebellar circuit anomaly in the valproate model of autism

“…Our structural MRI findings in this model may contradict the widely known "early brain overgrowth" phenotyping efforts in ASD [35][36][37] , although increases in brain volume in high-risk infants may co-occur with excessive extra-axial cerebrospinal fluid (CSF) 5 . Nonetheless, several studies have suggested divergent age effects on brain morphology changes in ASD 36 , with a more rapid age-related cortical thinning in temporal and parietal regions compared to controls 38 .…”

Paternal age contribution to brain white matter aberrations in autism spectrum disorder

“…Lastly, the current cross‐sectional study in adults with ASD cannot infer causality of the relation between advancing PA and white matter development. Nonetheless, it is invaluable to future studies conducting such investigation in a younger age group …”

Big data approaches to decomposing heterogeneity across the autism spectrum

“…For example, in the 'genetics first' approach, an investigator may be interested in identifying how different highimpact genetic causes for autism may be similar or different at a phenotypic or cognitive level of analysis [88][89][90][91] . In another example, an investigator may compare autism subtypes at the level of neural systems or structural brain features (e.g., with or without early brain enlargement), and then ask the question of whether such a stratification provides a meaningful indicator of differentiation at a clinical level 14 . Both top-down and bottom-up approaches can be useful, depending on the particular research question, and each can highlight different aspects of important heterogeneity in autism.…”

“…This idea relates to what others have discussed regarding autism as an umbrella label referring to many different kinds of 'autisms' 9 and regarding how the scientific community should abandon attempts to continue characterizing all of autism under a single theory 10 . Research has begun along these new directions but is highly fractionated because heterogeneity is discussed across multiple levels of analysis, from genetics 11 , neural systems [12][13][14] , cognition 15 , behavior and development [16][17][18] , and clinical topics (e.g., response to treatment, outcome 19,20 ). Approaches differ in how heterogeneity is decomposed, from utilizing theoretical a priori known stratifiers 21,22 or dimensions, to completely data-driven approaches 12,[23][24][25] .…”