Neuromarkers of social behavior: paving the way to electrophysiological endophenotypes of autism

Emmanuelle Tognoli*, J. A. Scott Kelso*


A striking feature of Autistic Spectrum Disorders is their heterogeneous semiology. This poses challenges at every step of disease management: diagnosis and treatment, etiology, pathogeny and genetics. Such complexity has motivated the search for endophenotypes in the hope that an adequate sub-classification of autismís expression will aid understanding.  Electrophysiological markers may play a significant role in this effort. As direct manifestations of neuronal activity, such neuromarkers provide information both about local components (brain areas) and their interaction (networks). Our purpose is to lay out a framework for the study of the brain as a complex, dynamical and coordinated system within which disorders such as autism may be interpreted and understood. Our approach is composed of three steps: (1) identification of meaningful brain components; (2) study of their coordination dynamics and (3) modeling of their functional organization. The first step consists of identifying neuromarkers corresponding to relevant behavioral states. Studies by our team in healthy adults have revealed several EEG neuromarkers in the 10Hz band that reflect different functional processes of the brain. One of them, the phi complex, is particularly relevant to autism because it predicts successful or failed information exchange between people, a primitive aspect of social behavior. Other neuromarkers may expose important variables such as the degree of attention a person devotes to a social interaction. Knowing the magnitude of an EEG neuromarker during a given task, however, falls short of explaining brain deficits: its distribution over time also matters. The second step in our framework focuses on brain coordination dynamics. Using the excellent temporal resolution of EEG, we show how it is possible to connect brain and behavior levels by identifying spatiotemporal patterns of brain activity in conjunction with behavioral events. Patterns that distinguish two groups (e.g. autistic patients and controls) are further analyzed: some of these are shown to arise from the independent activity of local areas and others from coordinated activity among multiple brain areas. Recent reports describe altered large-scale anatomical and functional organization in the autistic brain. We emphasize that continuous EEG analysis is crucial for the meaningful study of functional connectivity. In a third step, we stress the importance of integrating the neuromarkers in a neurobehavioral model. This step is paramount because a great deal of discriminant brain activity may arise from behavioral biases and may not reflect core deficits. Thus, all neuromarkers may not contribute directly to understanding the biological basis of autism. However, when properly inserted in a functional model, they will point toward the core factors of autistic spectrum disorders. [PPT].