The Oscillopathic Nature of Language Deficits in Autism: From Genes to Language Evolution

Abstract

Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders involving a number of deficits to linguistic cognition. The gap between genetics and the pathophysiology of ASD remains open, in particular regarding its distinctive linguistic profile. The goal of this article is to attempt to bridge this gap, focusing on how the autistic brain processes language, particularly through the perspective of brain rhythms. Due to the phenomenon of pleiotropy, which may take some decades to overcome, we believe that studies of brain rhythms, which are not faced with problems of this scale, may constitute a more tractable route to interpreting language deficits in ASD and eventually other neurocognitive disorders. Building on recent attempts to link neural oscillations to certain computational primitives of language, we show that interpreting language deficits in ASD as oscillopathic traits is a potentially fruitful way to construct successful endophenotypes of this condition. Additionally, we will show that candidate genes for ASD are overrepresented among the genes that played a role in the evolution of language. These genes include (and are related to) genes involved in brain rhythmicity. We hope that the type of steps taken here will additionally lead to a better understanding of the comorbidity, heterogeneity, and variability of ASD, and may help achieve a better treatment of the affected populations.

Keywords: RUNX2; autism; biolinguistics; evo-devo; language evolution; neural oscillations.

Figure 1

A schematic view of language representing the systems and interfaces of interest.

Figure 2

Summary of the present cognome-dynome model of linguistic computation and the observed differences in autism spectrum disorders (ASD); rSTG denotes right superior temporal gyrus, lIFG denotes left inferior frontal gyrus (see Cornew et al., ; Buard et al., ; Berman et al., ; Jochaut et al., ; Kikuchi et al., ; Murphy, 2015a).

Figure 3

Several putative gene networks seemingly account for the emergence of language-readiness in our species, including one clustered around AUTS2 and other strong ASD-candidates. As noted in the main text, two other sets of genes (centered on RUNX2 and FOXP2/ROBO, respectively) also encompass some candidates for ASD (based on Boeckx and Benítez-Burraco, ,; Benítez-Burraco and Boeckx, 2015a).

Figure 4

A multilevel approach to language deficits in ASD from an oscillatory perspective. Understanding language problems in ASD demands a systems biology approach that seeks to unravel the nature and links between all biological factors involved. The figure shows one possible line of research focused on brain oscillations, although many others will need to be explored in the future to gain a comprehensive view of this complex issue. As noted in the main text, among the candidates for ASD we find several genes that have changed during recent human evolution and that are believed to be important for the emergence of language. One of them is DLX1, known to control aspects of skull and brain development. As discussed in Boeckx and Benítez-Burraco (2014a), DLX1 is expressed in neocortical GABAergic neurons and it regulates thalamic differentiation and interconnection with the cortex. As the String 9.1 network shows, DLX1 interacts with other core candidates for globularization, like RUNX2 and DLX2. RUNX2, DLX1, and DLX2 are key components of the GAD67 regulatory network, which is important for the normal development of GABAergic neurons within the hippocampus. As noted in the text, disturbances in GABAergic mediator system may contribute to the altered γ activity detected in the hippocampus of ASD children, which may impact on syntactic operations in ASD language. The expression pattern in the forebrain of transcription factors like DLX1 (here exemplified with an in situ hybridization of the Dlx1 in E10.5 mouse embryo) has changed over the course of our history. This may explain some of the changes that reshaped our species-specific program for the generation of neocortical local circuit neurons and, ultimately, the changes in GABAergic input to several brain areas (including the hippocampus). In turn, this may have contributed to the retuning of brain oscillations that brought about modern cognitive functions like language (at the top of the figure), although the exact role played by these basic cognitive operations in language processing is still unknown. This, then, is a composite figure elaborated by the authors. The dynomic-cognomic aspects of γ-oscillations (on the top of the figure) are from Bosman et al. (2014). The micrography of the single hippocampal CA1 pyramidal neuron is from http://basulab.us/research/goals. The schematic view of the hippocampal GABAergic neurons (below) is from Feduccia et al. (2012). The scheme of the two distinct mechanisms targeting GAD67 to vesicular pathways and presynaptic clusters is from Kanaani et al. (2010). The String 9.1 network is from Boeckx and Benítez-Burraco (2014a). The schematic view of the structure of the DLX1 protein has been taken from www.uscnk.com. The in situ hybridization of Dlx1 in E10.5 mouse embryo is from Panganiban and Rubenstein (2002). The schematic representation of a transcriptionally inactive promoter is from Grayson and Guidotti (2013). Finally, the large scheme on the right of the picture shows the effect of DNA methyltransferase overexpression on GABAergic neurons and is also from Grayson and Guidotti (2013) (of interest is that GABAergic promoter downregulation is observed in some cognitive disorders like schizophrenia, resulting in increased levels of some DNA methyltransferases like DNMT1 and 3A, and reduced GAD67, RELN and a variety of interneuron markers).