Aging with Autism Departs Greatly from Typical Aging

Abstract

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.

Personalized Biometrics of Physical Pain Agree with Psychophysics by Participants with Sensory over Responsivity

“…The EMD (also known as the Kantarovich–Wasserstein distance [ 47 , 48 , 50 , 51 ]) is a distance metric that can quantify stochastic shifts in probability space. Previous work elaborates on the algorithm to compute this distance adapted to our biometrics [ 7 ]. The stochastic shifts in the EMD across the data set were thus examined, by obtaining the distribution of EMD values ( Figure 2 H) using Freedman–Diaconis binning rule [ 52 ] and fitting a Gamma probability distribution function (PDF) using maximum likelihood estimation with 95% confidence intervals ( Figure 2 I).…”

“…The peripheral nervous systems include an interconnected network of afferent nerve fibers carrying information from the skin to the spinal cord and onto the brain [ 1 ]. This flow of activity can be modeled as it updates the brain moment by moment, reflecting the trajectories of our bodies in motion [ 2 , 3 ] or of the fluctuations in bodily signals at rest [ 4 , 5 , 6 , 7 ], within a given environment where sensory input is processed and integrated with ongoing movements making up intended [ 8 , 9 ] or spontaneous [ 10 ] behavioral states. The afferent fibers from the periphery carry information about touch, pressure and movements sensed by the mechanoreceptors [ 11 ], while thermoreceptors and nociceptors process information about temperature and pain, respectively [ 1 , 12 ].…”

Personalized Biometrics of Physical Pain Agree with Its Psychological Perception by Participants with Sensory Over Responsivity

“…The EMD (also known as the Kantarovich-Wasserstein distance [49,50,52,53]) is a distance metric that can quantify stochastic shifts in probability space. Previous work elaborates on the algorithm to compute this distance adapted to our biometrics [7]. The stochastic shifts in the EMD across the data set were thus examined, by obtaining the distribution of EMD values ( Fig 2H) using Freedman-Diaconis binning rule [54], and fitting a Gamma probability distribution function (PDF) using maximum likelihood estimation with 95% confidence intervals ( Fig 2I).…”

“…The peripheral nervous systems include an interconnected network of afferent nerve fibers carrying information from the skin to the spinal cord and onto the brain [1]. This flow of activity can be modelled as it updates the brain moment by moment, reflecting the trajectories of our bodies in motion [2,3] or of the fluctuations in bodily signals at rest [4][5][6][7], within a given environment where sensory input is processed and integrated with ongoing movements making up intended [8,9] or spontaneous [10] behavioral states. The afferent fibers from the periphery carry information about touch, pressure and movements sensed by the mechanoreceptors [11], while thermoreceptors and nociceptors process information about temperature and pain, respectively [1,12].…”

Dental Shade Matching Method Based on Hue, Saturation, Value Color Model with Machine Learning and Fuzzy Decision

“…The techniques of big data analysis and machine learning are used widely in medicine [detecting pneumonia, (1) autism, (2) and risk of falling, (3) the classification of cancers, (4) and the analysis of cell pseudo-color images (5) ]. A method of dental image analysis is developed in this study.…”