Translational approaches to the biology of Autism: false dawn or a new era?

Abstract

Discovering novel treatments for Autism Spectrum Disorders (ASD) is a challenge. Its etiology and pathology remain largely unknown, the condition shows wide clinical diversity, and case identification is still solely based on symptomatology. Hence clinical trials typically include samples of biologically and clinically heterogeneous individuals. 'Core deficits', that is, deficits common to all individuals with ASD, are thus inherently difficult to find. Nevertheless, recent reports suggest that new opportunities are emerging, which may help develop new treatments and biomarkers for the condition. Most important, several risk gene variants have now been identified that significantly contribute to ASD susceptibility, many linked to synaptic functioning, excitation-inhibition balance, and brain connectivity. Second, neuroimaging studies have advanced our understanding of the 'wider' neural systems underlying ASD; and significantly contributed to our knowledge of the complex neurobiology associated with the condition. Last, the recent development of powerful multivariate analytical techniques now enable us to use multi-modal information in order to develop complex 'biomarker systems', which may in the future be used to assist the behavioral diagnosis, aid patient stratification and predict response to treatment/intervention. The aim of this review is, therefore, to summarize some of these important new findings and highlight their potential significant translational value to the future of ASD research.

Figure 1

Synaptic functioning and brain connectivity in Autism Spectrum Disorders (ASD)—from the molecular level to the neural systems level.

Figure 2

Pattern classification using support vector machine based on structural magnetic resonance images in Autism Spectrum Disorders (ASD). The classifier is initially ‘trained' on a well-characterized sample of individuals with ASD and controls. Training results in a ‘discrimination map' indicating regions that can be used to discriminate between ASD and controls. This map can then be used to predict group membership of a new test example (for example, ASD or control).