Aging with Autism Departs Greatly from Typical Aging

Area of Science:

• Neuroscience
• Developmental Psychology
• Biomedical Engineering

Background:

• Autism is often misconstrued as a psychiatric or childhood condition, neglecting its nature as a lifelong neurological condition with evolving, heterogeneous developmental trajectories.
• Aging with autism remains understudied, particularly concerning the sense of movement and kinesthetic-reafference from self-generated motions, including subtle involuntary movements.
• Involuntary micro-motions, often undetectable by observation, are captured in functional MRI (fMRI) data and can serve as biomarkers for neurological differences.

Purpose of the Study:

• To investigate age-related changes in involuntary motion signatures in autistic individuals compared to neurotypical controls.
• To introduce novel computational methods for analyzing age-stratified trajectories of involuntary movements in autism.
• To identify potential inflection points in aging trajectories for autistic individuals.

Main Methods:

• Utilized open-access fMRI datasets spanning ages 5-65 for both neurotypical and autistic populations.
• Developed and applied computational techniques to stratify individuals by age group based on probability distances in representational spaces.
• Analyzed cross-sectional population trajectories to compare motion signatures between autistic and neurotypical groups.

Main Results:

• Autistic individuals exhibit distinct cross-sectional population trajectories in probability space compared to neurotypical controls.
• An inflection point was observed around age 40 in autistic individuals, marking a divergence in involuntary motion signatures from age-matched neurotypical norms.
• These differences indicate a monotonically increasing divergence in involuntary motion patterns as autistic individuals age.

Conclusions:

• Autism represents a lifelong neurological condition with unique aging trajectories, particularly concerning the sense of movement.
• The identified age-related divergence in involuntary motion signatures suggests a need for dynamic diagnostic approaches that consider neurological aging.
• This research provides novel stochastic analyses for redefining basic research and understanding aging with autism.