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Open AccessArticle

Aging with Autism Departs Greatly from Typical Aging

1
Psychology Department Center for Biomedicine Imaging and Modelling, CS Department and Rutgers Center for Cognitive Science, Rutgers University, Camden, NJ 08854, USA
2
Sports Science Department, Miguel Hernandez University of Elche, 03202 Alicante, Spain
3
Biomathematics Department, Rutgers University, Camden, NJ 08854, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 572; https://doi.org/10.3390/s20020572
Received: 9 December 2019 / Revised: 13 January 2020 / Accepted: 14 January 2020 / Published: 20 January 2020
(This article belongs to the Special Issue Machine Learning for Biomedical Imaging and Sensing)
Autism has been largely portrayed as a psychiatric and childhood disorder. However, autism is a lifelong neurological condition that evolves over time through highly heterogeneous trajectories. These trends have not been studied in relation to normative aging trajectories, so we know very little about aging with autism. One aspect that seems to develop differently is the sense of movement, inclusive of sensory kinesthetic-reafference emerging from continuously sensed self-generated motions. These include involuntary micro-motions eluding observation, yet routinely obtainable in fMRI studies to rid images of motor artifacts. Open-access repositories offer thousands of imaging records, covering 5–65 years of age for both neurotypical and autistic individuals to ascertain the trajectories of involuntary motions. Here we introduce new computational techniques that automatically stratify different age groups in autism according to probability distance in different representational spaces. Further, we show that autistic cross-sectional population trajectories in probability space fundamentally differ from those of neurotypical controls and that after 40 years of age, there is an inflection point in autism, signaling a monotonically increasing difference away from age-matched normative involuntary motion signatures. Our work offers new age-appropriate stochastic analyses amenable to redefine basic research and provide dynamic diagnoses as the person’s nervous systems age. View Full-Text
Keywords: stochastic analyses; classification methods; probability distance stochastic analyses; classification methods; probability distance
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MDPI and ACS Style

Torres, E.B.; Caballero, C.; Mistry, S. Aging with Autism Departs Greatly from Typical Aging. Sensors 2020, 20, 572.

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