Autism: the micro-movement perspective

Frontiers in Integrative Neuroscience, 2013

The current assessment of behaviors in the inventories to diagnose autism spectrum disorders (ASD) focus on observation and discrete categorizations. Behaviors require movements, yet measurements of physical movements are seldom included. Their inclusion however, could provide an objective characterization of behavior to help unveil interactions between the peripheral and the central nervous systems (CNSs). Such interactions are critical for the development and maintenance of spontaneous autonomy, self-regulation, and voluntary control. At present, current approaches cannot deal with the heterogeneous, dynamic and stochastic nature of development. Accordingly, they leave no avenues for real time or longitudinal assessments of change in a coping system continuously adapting and developing compensatory mechanisms. We offer a new unifying statistical framework to reveal re-afferent kinesthetic features of the individual with ASD. The new methodology is based on the non-stationary stochastic patterns of minute fluctuations (micro-movements) inherent to our natural actions. Such patterns of behavioral variability provide re-entrant sensory feedback contributing to the autonomous regulation and coordination of the motor output. From an early age, this feedback supports centrally driven volitional control and fluid, flexible transitions between intentional and spontaneous behaviors. We show that in ASD there is a disruption in the maturation of this form of proprioception. Despite this disturbance, each individual has unique adaptive compensatory capabilities that we can unveil and exploit to evoke faster and more accurate decisions. Measuring the kinesthetic re-afference in tandem with stimuli variations we can detect changes in their micro-movements indicative of a more predictive and reliable kinesthetic percept. Our methods address the heterogeneity of ASD with a personalized approach grounded in the inherent sensory-motor abilities that the individual has already developed.

Frontiers in integrative neuroscience

The current assessment of behaviors in the inventories to diagnose autism spectrum disorders (ASD) focus on observation and discrete categorizations. Behaviors require movements, yet measurements of physical movements are seldom included. Their inclusion however, could provide an objective characterization of behavior to help unveil interactions between the peripheral and the central nervous systems. Such interactions are critical for the development and maintenance of spontaneous autonomy, self-regulation and voluntary control. At present, current approaches cannot deal with the heterogeneous, dynamic and stochastic nature of development. Accordingly, they leave no avenues for real-time or longitudinal assessments of change in a coping system continuously adapting and developing compensatory mechanisms. We offer a new unifying statistical framework to reveal re-afferent kinesthetic features of the individual with ASD. The new methodology is based on the non-stationary stochastic patterns of minute fluctuations (micro-movements) inherent to our natural actions. Such patterns of behavioral variability provide re-entrant sensory feedback contributing to the autonomous regulation and coordination of the motor output. From an early age, this feedback supports centrally driven volitional control and fluid, flexible transitions between intentional and spontaneous behaviors. We show that in ASD there is a disruption in the maturation of this form of proprioception. Despite this disturbance, each individual has unique adaptive compensatory capabilities that we can unveil and exploit to evoke faster and more accurate decisions. Measuring the kinesthetic re-afference in tandem with stimuli variations we can detect changes in their micro-movements indicative of a more predictive and reliable kinesthetic percept. Our methods address the heterogeneity of ASD with a personalized approach grounded in the inherent sensory-motor abilities that the individual has already developed.

Movement variability has emerged as a critical research component in the field of neural motor control. This chapter explains why movement variability can be seen as such a rich resource for studying neural development and ASD. This cannot be done without a framework for understanding the relationship between neural control, movement and movement sensing. Thus in the process of explaining why we should study movements several analytical and empirical aspects of motor-sensed variability from self-generated actions are recast, as are their putative role in the development of motor-sensory-sensed maps of external stimuli present in social settings. Overall, the Chapter offers a new lens for the research and treatments of neurodevelopmental disorders on a spectrum. In particular, disorders such as Autism Spectrum Disorders (ASD) giving rise to different social manifestations are discussed within the renovated unifying framework of kinesthetic reafference using the new micro-movements data type paired with new accompanying analytics. 2

2013

Autism can be conceived as an adaptive biological response to an early unexpected developmental change. Under such conceptualization one could think of emerging biological compensatory mechanisms with unique manifestations in each individual.

"Autism: The Movement Sensing Perspective" by Torres & Whyatt (eds.), 2017

Autism has been defined as a disorder of social cognition, interaction and communication where ritualistic, repetitive behaviors are commonly observed. But how should we understand the behavioral and cognitive differences that have been the main focus of so much autism research? Can high-level cognitive processes and behaviors be identified as the core issues people with autism face, or do these characteristics perhaps often rather reflect individual attempts to cope with underlying physiological issues? Much research presented in this volume will point to the latter possibility, i.e. that people on the autism spectrum cope with issues at much lower physiological levels pertaining not only to Central Nervous Systems (CNS) function, but also to peripheral and autonomic systems (PNS, ANS) (Torres, Brincker, et al. 2013). The question that we pursue in this chapter is what might be fruitful ways of gaining objective measures of the large-scale systemic and heterogeneous effects of early atypical neurodevelopment; how to track their evolution over time and how to identify critical changes along the continuum of human development and aging. We suggest that the study of movement variability—very broadly conceived as including all minute fluctuations in bodily rhythms and their rates of change over time (coined micro-movements (Figure 1A-B) (Torres, Brincker, et al. 2013))—offers a uniquely valuable and entirely objectively quantifiable lens to better assess, understand and track not only autism but cognitive development and degeneration in general. This chapter presents the rationale firstly behind this focus on micro-movements and secondly behind the choice of specific kinds of data collection and statistical metrics as tools of analysis (Figure 1C). In brief the proposal is that the micro-movements (defined in Part I – Chapter 1), obtained using various time scales applied to different physiological data-types (some examples in Figure 1), contain information about layered influences and temporal adaptations, transformations and integrations across anatomically semi-independent subsystems that crosstalk and interact. Further, the notion of sensorimotor re-afference is used to highlight the fact that these layered micro-motions are sensed and that this sensory feedback plays a crucial role in the generation and control of movements in the first place. In other words, the measurements of various motoric and rhythmic variations provide an access point not only to the “motor systems”, but also access to much broader central and peripheral sensorimotor and regulatory systems. Lastly, we posit that this new lens can also be used to capture influences from systems of multiple entry points or collaborative control and regulation, such as those that emerge during dyadic social interactions.

The human body is in constant motion, from every breath that we take, to every visibly purposeful action that we perform. Remaining completely still on command is a major achievement as involuntary fluctuations in our motions are difficult to keep under control. Here we examine the noise-to-signal ratio of micro-movements present in time-series of head motions extracted from resting-state functional magnetic resonance imaging scans in 1048 participants. These included individuals with autism spectrum disorders (ASD) and healthy-controls in shared data from the Autism Brain Imaging Data Exchange (ABIDE) and the Attention-Deficit Hyperactivity Disorder (ADHD-200) databases. We find excess noise and randomness in the ASD cases, suggesting an uncertain motor-feedback signal. A power-law emerged describing an orderly relation between the dispersion and shape of the probability distribution functions best describing the stochastic properties under consideration with respect to intelligence quotient (IQ-scores). In ASD, deleterious patterns of noise are consistently exacerbated with the presence of secondary (comorbid) neuropsychiatric diagnoses, lower verbal and performance intelligence, and autism severity. Importantly, such patterns in ASD are present whether or not the participant takes psychotropic medication. These data unambiguously establish specific noise-to-signal levels of head micro-movements as a biologically informed core feature of ASD. Humans are naturally variable in thought, behaviour and action across the spectrum of health and illness. However, individual variability cannot be examined precisely using conventional statistical approaches that de-emphasize individual differences by, for example, assuming normality and homogeneity of the data—a stumbling block for progress in neurodevelopmental research including phenotypically and genetically heterogeneous Autism Spectrum Disorders (ASD). Implementing the recent initiative of Precision Medicine 1 , for example, would require a conceptually novel, individualized statistical framework that would facilitate linkage between different layers of information across the knowledge network (Fig. 1A). Here we focus on characterizing the spontaneous physiological signals that underlie all individuals' involuntary movements using an approach that harnesses the heretofore wayward individual variability in order to discover core biological signatures of the human nervous system indicative of its state of " health " or " illness ". In typically developing individuals, a certain degree of variation exists in natural movements across multiple levels of conscious and unconscious awareness and control (Fig. 1B) 2. Minute fluctuations in motor performance inevitably occur across different contexts, whether we intentionally move or whether the movements take place spontaneously and largely beneath awareness (Fig. 1C). Excess or deficits in involuntary motor variations relative to normative scales is undesirable, and has been found in the context of goal directed reaches 3 , decision making 4 and gait patterns 5 across various clinical populations with pathologies of the nervous system, including ASD 3,6–10. Importantly, subtle fluctuations in the movement signal generate and carry new signals in a returning afferent stream: a form of re-entrant sensory feedback from the PNS to the CNS 11 , putatively conveying sensory feedback linked to self-generated movements. Consistent with theories of internal models for action (IMA) 12 , this form of (peripheral) returning signal would inform the CNS of the moment-by-moment accumulation of sensory evidence to help predict with a degree of certainty the sensory consequences of impending decisions and

Frontiers in Integrative Neuroscience

The DSM-5 definition of autism spectrum disorders includes sensory issues and part of the sensory information that the brain continuously receives comes from kinesthetic reafference, in the form of self-generated motions, including those that the nervous systems produce at rest. Some of the movements that we self-generate are deliberate, while some occur spontaneously, consequentially following those that we can control. Yet, some motions occur involuntarily, largely beneath our awareness. We do not know much about involuntary motions across development, but these motions typically manifest during resting state in fMRI studies. Here we ask in a large data set from the Autism Brain Imaging Exchange repository, whether the stochastic signatures of variability in the involuntary motions of the head typically shift with age. We further ask if those motions registered from individuals with autism show a significant departure from the normative data as we examine different age groups selected at random from cross-sections of the population. We find significant shifts in statistical features of typical levels of involuntary head motions for different age groups. Further, we find that in autism these changes also manifest in non-uniform ways, and that they significantly differ from their age-matched groups. The results suggest that the levels of random involuntary motor noise are elevated in autism across age groups. This calls for the use of different age-appropriate statistical models in research that involves dynamically changing signals self-generated by the nervous systems.

Neurocase, 2012

We provide objective metrics of sequential movements and study a young adolescent with Autism Spectrum Disorders (ASD) in relation to novice typical controls (TC) as they learned to perform beginners' martial–arts routines. We studied segments staged to hit an opponent simultaneously performed with supplemental segments. In TC instructed changes in speed had profound differential effects on the intended vs. supplemental segments that were absent in the ASD case. Moreover, the frequency-distribution of velocity and acceleration maxima in TC was well fitted by a Gamma distribution but in the ASD case the fit was exponential yielding uncannily precise motions with atypically low-range of spatio-temporal variability.

2014

Autism Spectrum Disorder (ASD) affects more than 1% of school age children (Developmental and Investigators, 2014). ASD is characterized by impaired social interactions and repetitive behaviors. The causes are unknown, but genetic studies explaining the etiology of some cases could provide clues for pharmacological treatments and give a better scientific understanding of the phenomenology behind this disorder. We know that identical twins show close to 90% concordance of ASD (Rosenberg et al., 2009), suggesting possible genetic links. Unfortunately, the lack of an objective methodology to automatically classify subtypes of ASD and the reliance on observational qualitative methods for clinical diagnosis give rise to a highly heterogeneous disorder and prevent classifying the disorder according to genetic links. Recently we introduced a new Statistical Platform for Individualized Behavioral Analyses (SPIBA) (Torres and Jose, 2012) that unambiguously distinguished ASD from controls according to the signatures of motor output variability from peripheral limb movements (Torres et al., 2013). This variability arises from many sources of synaptic noise across the nervous system (Faisal et al., 2008), including noise from sensory and motor nerves at the periphery. SPIBA introduced new statistical metrics to track the evolution of noise-to-signal ratios in the continuous flow of peripheral limb movements performed with different levels of intent. Our previous results were based on the velocity-dependent variability contributed by the speed maxima across trials of natural movements. Here, however, we analyze the full speed trajectories as a function of time. We find that the trajectories are not smooth but have millisecond range peaks that we term "peripheral-Spikes" (p-Spikes). We analyzed spatial-temporal signatures of the p-Spikes in 17 individuals with ASD and their parents. The synchronicity and periodicity of the p-Spikes yielded a finer refinement for automatic classification of ASD subtypes on a statistical-parameter plane according to different cognitive levels. A nearest-neighbor interval probability distribution analysis of the p-Spikes clustered ASD subtypes in different locations of the plane. Inclusion of the parents' statistical signatures on the plane revealed that 13 out of 18 parents clustered together with their affected child, suggesting a potential genetic link reflected in the clustering. We conjecture that our velocity dependent p-Spike data paired with the SPIBA indexes may provide a biomarker for objective and automatic classification of ASD subtypes which may be used in ASD-genetic related studies. :

Scientific Reports

Failure to develop prospective motor control has been proposed to be a core phenotypic marker of autism spectrum disorders (ASD). However, whether genuine differences in prospective motor control permit discriminating between ASD and non-ASD profiles over and above individual differences in motor output remains unclear. Here, we combined high precision measures of hand movement kinematics and rigorous machine learning analyses to determine the true power of prospective movement data to differentiate children with autism and typically developing children. Our results show that while movement is unique to each individual, variations in the kinematic patterning of sequential grasping movements genuinely differentiate children with autism from typically developing children. These findings provide quantitative evidence for a prospective motor control impairment in autism and indicate the potential to draw inferences about autism on the basis of movement kinematics.