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      Characterization of the Statistical Signatures of Micro-Movements Underlying Natural Gait Patterns in Children with Phelan McDermid Syndrome: Towards Precision-Phenotyping of Behavior in ASD

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          Abstract

          Background: There is a critical need for precision phenotyping across neurodevelopmental disorders, especially in individuals who receive a clinical diagnosis of autism spectrum disorder (ASD). Phelan-McDermid deletion syndrome (PMS) is one such example, as it has a high penetrance of ASD. At present, no biometric characterization of the behavioral phenotype within PMS exists.

          Methods: We introduce a data-type and statistical framework that permits the personalized profiling of naturalistic behaviors. Walking patterns were assessed in 30 participants (16 PMS, 3 idiopathic-ASD and 11 age- and sex-matched controls). Each individual's micro-movement signatures were recorded at 240 Hz. We empirically estimated the parameters of the continuous Gamma family of probability distributions and calculated their ranges. These estimated stochastic signatures were then mapped on the Gamma plane to obtain several statistical indexes for each child. To help visualize complex patterns across the cohort, we introduce new tools that enable the assessment of connectivity and modularity indexes across the peripheral network of rotational joints.

          Results: Typical walking signatures are absent in all children with PMS as well as in the children with idiopathic-ASD (iASD). Underlying these patterns are atypical leg rotational acceleration signatures that render participants with PMS unstable with rotations that are much faster than controls. The median values of the estimated Gamma parameters serve as a cutoff to automatically separate children with PMS 5–7 years old from adolescents with PMS 12–16 years old, the former displaying more randomness and larger noise. The fluctuations in the arm's motions during the walking also have atypical statistics that separate males from females in PMS and show higher rates of noise accumulation in idiopathic ASD (iASD) children. Despite high heterogeneity, all iASD children have excess noise, a narrow range of probability-distribution shapes across the body joints and a distinct joint network connectivity pattern. Both PMS and iASD have systemic issues with noise in micro-motions across the body with specific signatures for each child that, as a cohort, selectively deviates from controls.

          Conclusions: We provide a new methodology for precision behavioral phenotyping with the potential to use micro-movement output noise as a natural classifier of neurodevelopmental disorders of known etiology. This approach may help us better understand idiopathic neurodevelopmental disorders and personalize the assessments of natural movements in these populations.

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          Most cited references62

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          Noise in the nervous system.

          Noise--random disturbances of signals--poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.
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            Neocortical excitation/inhibition balance in information processing and social dysfunction.

            Severe behavioural deficits in psychiatric diseases such as autism and schizophrenia have been hypothesized to arise from elevations in the cellular balance of excitation and inhibition (E/I balance) within neural microcircuitry. This hypothesis could unify diverse streams of pathophysiological and genetic evidence, but has not been susceptible to direct testing. Here we design and use several novel optogenetic tools to causally investigate the cellular E/I balance hypothesis in freely moving mammals, and explore the associated circuit physiology. Elevation, but not reduction, of cellular E/I balance within the mouse medial prefrontal cortex was found to elicit a profound impairment in cellular information processing, associated with specific behavioural impairments and increased high-frequency power in the 30-80 Hz range, which have both been observed in clinical conditions in humans. Consistent with the E/I balance hypothesis, compensatory elevation of inhibitory cell excitability partially rescued social deficits caused by E/I balance elevation. These results provide support for the elevated cellular E/I balance hypothesis of severe neuropsychiatric disease-related symptoms.
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              A neuroligin-3 mutation implicated in autism increases inhibitory synaptic transmission in mice.

              Autism spectrum disorders (ASDs) are characterized by impairments in social behaviors that are sometimes coupled to specialized cognitive abilities. A small percentage of ASD patients carry mutations in genes encoding neuroligins, which are postsynaptic cell-adhesion molecules. We introduced one of these mutations into mice: the Arg451-->Cys451 (R451C) substitution in neuroligin-3. R451C mutant mice showed impaired social interactions but enhanced spatial learning abilities. Unexpectedly, these behavioral changes were accompanied by an increase in inhibitory synaptic transmission with no apparent effect on excitatory synapses. Deletion of neuroligin-3, in contrast, did not cause such changes, indicating that the R451C substitution represents a gain-of-function mutation. These data suggest that increased inhibitory synaptic transmission may contribute to human ASDs and that the R451C knockin mice may be a useful model for studying autism-related behaviors.
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                Author and article information

                Contributors
                Journal
                Front Integr Neurosci
                Front Integr Neurosci
                Front. Integr. Neurosci.
                Frontiers in Integrative Neuroscience
                Frontiers Media S.A.
                1662-5145
                27 June 2016
                2016
                : 10
                : 22
                Affiliations
                [1] 1Department of Psychology, Computer Science, Rutgers Center for Cognitive Sciences and Computational Biomedicine Imaging and Modelling Center of Computer Science, Rutgers The State University of New Jersey New Brunswick, NJ, USA
                [2] 2Graduate Program in Neuroscience, Rutgers The State University of New Jersey New Brunswick, NJ, USA
                [3] 3Department of Mathematics, Rutgers The State University of New Jersey New Brunswick, NJ, USA
                [4] 4Department of Psychology, Rutgers The State University of New Jersey New Brunswick, NJ, USA
                [5] 5Department of Computer Science, Rutgers The State University of New Jersey New Brunswick, NJ, USA
                [6] 6Psychiatry, Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai New York, NY, USA
                Author notes

                Edited by: Henry H. Yin, Duke University, USA

                Reviewed by: Xin Jin, The Salk Institute for Biological Studies, USA; Alexandra Lyndon Bey, Duke University, USA

                *Correspondence: Elizabeth B. Torres ebtorres@ 123456rci.rutgers.edu
                Article
                10.3389/fnint.2016.00022
                4921802
                27445720
                1b4f4470-6d56-4589-bec4-21a4f5a26d8f
                Copyright © 2016 Torres, Nguyen, Mistry, Whyatt, Kalampratsidou and Kolevzon.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 22 April 2016
                : 07 June 2016
                Page count
                Figures: 11, Tables: 0, Equations: 0, References: 92, Pages: 22, Words: 16105
                Funding
                Funded by: Nancy Lurie Marks Family Foundation 10.13039/100007429
                Award ID: Early Career Award
                Funded by: National Institute of Mental Health 10.13039/100000025
                Award ID: R34 MH100276-01
                Categories
                Neuroscience
                Original Research

                Neurosciences
                micro-movements,noise,pms,phelan-mcdermid syndrome,stochastic signatures,gait,gamma distribution,precision phenotyping

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