Autism, marked by language deficits and behavioral issues, is now termed Autism Spectrum Disorders (ASD) in recognition of the diversity and complexity of its etiology and neuropathology. The breadth of genetic associations, epigenetic mechanisms, and environmental factors linked to ASD thus far suggest that no single molecular explanation will suffice.
Our research attempts to address the lack of illustrative power of any single unifying molecular explanation of the affliction. Given the heterogeneity of ASD manifestations, the multiplicity of genetic associations, and the variety of suspected causative agents that may precipitate or exacerbate ASD, BSF aims to use statistical and neurocomputational methodologies to discover the molecular and genetic informational structures of autism as well as develop more reliable classification of subcategories of ASD which could potentially guide targeted, mechanism-based future therapies.
The explanation of ASD at the neurophysiological and molecular levels will likely be multifaceted and complex. In order to unite these disparate factors into a multi-causal framework for meaningful analysis, it is necessary to link phenomena at each level of analysis: environmental, genetic, cellular, and the neural network. By combining computational and topological mapping techniques with genetic, molecular, and linguistic data, we more precisely define underlying substructural patterns of ASD and provide new etiological information to build a comprehensive cognitive model of the disorder.