The Autistic Brain Over 45: The Anatomic, Functional, and Cognitive Phenotype

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Project Summary Extremely little is known about neural and cognitive changes or long-term prognosis for adults with autism spectrum disorders (ASD) older than 50 years of age, although in the US alone hundreds of thousands of people with ASD will reach this age group in the coming decades. Cross-sectional findings suggest that new cognitive, sensorimotor, and mental health problems emerge and that accelerated brain tissue loss occurs. Functional and structural brain connectivity is also affected, as shown with resting state functional MRI and diffusion MRI. This project aims to characterize mid- to late-life trajectories of brain anatomy and function in ASD, as well as cognitive and behavioral change, and identify risk factors that predict poor outcomes including suspected effects of declining GABA concentrations and excitatory:inhibitory imbalance in aging. The proposed renewal will leverage an existing cohort (Cohort 1) of 40-70 year olds with ASD and matched typical comparison (TC) participants. In addition, a new cohort (Cohort 2) of 50 individuals with ASD and 70 TC participants age 40-70 will be recruited. Both cohorts will be followed longitudinally, with data collection every 3.5-5 years. Extensive data will be acquired at each time-point, including multimodal MRI scans, measures of GABA concentration from MR spectroscopy, diagnostic, cognitive, and behavioral assessments, as well as medical and personal history information. The project will pursue 3 specific aims. Aims 1 will identify alterations (and altered rates of change) in brain anatomy, function, and connectivity and relate these to behavioral changes in 3 suspected risk domains of Motor, Executive, and Affective function. Aim 2 will test for group differences and effects of age in GABA concentrations and in network integration and segregation. Aim 3 will identify behavioral and neural risk factors of adverse (or positive) outcomes (e.g. on scales of maladaptive behavior, loss of daily living skills) using machine learning, and identify ASD subgroups based on GABA levels, differential rates of change, or differential domain-specificity.