Outcome Measures of Deliberate and Spontaneous Motions

Kalampratsidou, Vilelmini; Torres, Elizabeth B.

doi:10.1145/2948910.2948930

Cited by 9 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the pervasive noisy and random somatic motor micro-movements signal in ASD across sex and ages (Torres et al, 2013b ), severity (Torres and Denisova, 2016 ) and levels of motor control (voluntary, Torres et al, 2013a ; automatic, Torres et al, 2016c ), autonomic (Torres and Lande, 2015 ; Kalampratsidou and Torres, 2016 ; Torres et al, 2016b ), the present work aimed to investigate if involuntary micro-movements of head motion recorded within the scanner had a statistically different rate of noise accumulation in ASD females in relation to TD control females. Further, this was examined in light of male-specific ASD-TD differential patterns to consider the impact of gender.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Signatures of Involuntary Head Micro-movements Can Be Used to Classify Females of ABIDE into Different Subtypes of Neurodevelopmental Disorders

Torres

Mistry

Caballero

et al. 2017

Front. Integr. Neurosci.

Self Cite

View full text Add to dashboard Cite

Background: The approximate 5:1 male to female ratio in clinical detection of Autism Spectrum Disorder (ASD) prevents research from characterizing the female phenotype. Current open access repositories [such as those in the Autism Brain Imaging Data Exchange (ABIDE I-II)] contain large numbers of females to help begin providing a new characterization of females on the autistic spectrum. Here we introduce new methods to integrate data in a scale-free manner from continuous biophysical rhythms of the nervous systems and discrete (ordinal) observational scores.Methods: New data-types derived from image-based involuntary head motions and personalized statistical platform were combined with a data-driven approach to unveil sub-groups within the female cohort. Further, to help refine the clinical DSM-based ASD vs. Asperger's Syndrome (AS) criteria, distributional analyses of ordinal score data from Autism Diagnostic Observation Schedule (ADOS)-based criteria were used on both the female and male phenotypes.Results: Separate clusters were automatically uncovered in the female cohort corresponding to differential levels of severity. Specifically, the AS-subgroup emerged as the most severely affected with an excess level of noise and randomness in the involuntary head micro-movements. Extending the methods to characterize males of ABIDE revealed ASD-males to be more affected than AS-males. A thorough study of ADOS-2 and ADOS-G scores provided confounding results regarding the ASD vs. AS male comparison, whereby the ADOS-2 rendered the AS-phenotype worse off than the ASD-phenotype, while ADOS-G flipped the results. Females with AS scored higher on severity than ASD-females in all ADOS test versions and their scores provided evidence for significantly higher severity than males. However, the statistical landscapes underlying female and male scores appeared disparate. As such, further interpretation of the ADOS data seems problematic, rather suggesting the critical need to develop an entirely new metric to measure social behavior in females.Conclusions: According to the outcome of objective, data-driven analyses and subjective clinical observation, these results support the proposition that the female phenotype is different. Consequently the “social behavioral male ruler” will continue to mask the female autistic phenotype. It is our proposition that new observational behavioral tests ought to contain normative scales, be statistically sound and combined with objective data-driven approaches to better characterize the females across the human lifespan.

show abstract

Section: Introductionmentioning

confidence: 99%

Stochastic Signatures of Involuntary Head Micro-movements Can Be Used to Classify Females of ABIDE into Different Subtypes of Neurodevelopmental Disorders

Torres

Mistry

Caballero

et al. 2017

Front. Integr. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is a facet of complex human movements that transpires largely beneath awareness, as the person engages in voluntary movements. We have been able to capture and model such hidden segments of behavior in sports and the performing arts, and characterize their stochastic signatures as the athlete (or the performing artist [ 27 ]) trains and adapts to new routines [ 5 , 28 ]. These latent movement segments, which we have coined consequential to voluntary segments, have the fundamental feature that they change the geometry of the hand motion trajectory under different dynamic manipulations involving changes in speed and body mass distributions [ 5 , 11 , 12 , 13 , 14 ].…”

Section: Methodsmentioning

confidence: 99%

“…The type of empirical work that we bring to the reader’s consideration is important to begin the path of empirical parameterization of the statistical signatures (and their real-time shifts) of the types of consequential motions that complement voluntary, goal-directed behavior and that ultimately contribute to autonomous control. These different movement classes have been precisely characterized individually in recent work involving complex boxing routines [ 5 , 6 , 26 ] and routines from the performing arts [ 27 ]. Coordination patterns of synergistic modules self-emerge as the person’s body sustains complex postures up against gravity ( Figure 1 A,B).…”

Section: Introductionmentioning

confidence: 99%

Peripheral Network Connectivity Analyses for the Real-Time Tracking of Coupled Bodies in Motion

Kalampratsidou

Torres

2018

Sensors

Self Cite

View full text Add to dashboard Cite

Dyadic interactions are ubiquitous in our lives, yet they are highly challenging to study. Many subtle aspects of coupled bodily dynamics continuously unfolding during such exchanges have not been empirically parameterized. As such, we have no formal statistical methods to describe the spontaneously self-emerging coordinating synergies within each actor’s body and across the dyad. Such cohesive motion patterns self-emerge and dissolve largely beneath the awareness of the actors and the observers. Consequently, hand coding methods may miss latent aspects of the phenomena. The present paper addresses this gap and provides new methods to quantify the moment-by-moment evolution of self-emerging cohesiveness during highly complex ballet routines. We use weighted directed graphs to represent the dyads as dynamically coupled networks unfolding in real-time, with activities captured by a grid of wearable sensors distributed across the dancers’ bodies. We introduce new visualization tools, signal parameterizations, and a statistical platform that integrates connectivity metrics with stochastic analyses to automatically detect coordination patterns and self-emerging cohesive coupling as they unfold in real-time. Potential applications of these new techniques are discussed in the context of personalized medicine, basic research, and the performing arts.

show abstract

“…To be more precise, we treat the spikes in acceleration as spike trains of random amplitudes and random times. To model them, we build on previous research whereby the amplitudes and inter-spike interval times are modeled as independent and identically distributed (iid) random variables following a Gamma distribution (18, 19, 23, 24). As such, the spike trains are the input to a Gamma process and the empirically estimated Gamma parameters are dynamically tracked on various parameter spaces to uncover self-emerging patterns.…”

Section: Methodsmentioning

confidence: 99%

Neonatal Diagnostics: Toward Dynamic Growth Charts of Neuromotor Control

et al. 2016

Self Cite

View full text Add to dashboard Cite

The current rise of neurodevelopmental disorders poses a critical need to detect risk early in order to rapidly intervene. One of the tools pediatricians use to track development is the standard growth chart. The growth charts are somewhat limited in predicting possible neurodevelopmental issues. They rely on linear models and assumptions of normality for physical growth data – obscuring key statistical information about possible neurodevelopmental risk in growth data that actually has accelerated, non-linear rates-of-change and variability encompassing skewed distributions. Here, we use new analytics to profile growth data from 36 newborn babies that were tracked longitudinally for 5 months. By switching to incremental (velocity-based) growth charts and combining these dynamic changes with underlying fluctuations in motor performance – as the transition from spontaneous random noise to a systematic signal – we demonstrate a method to detect very early stunting in the development of voluntary neuromotor control and to flag risk of neurodevelopmental derail.

show abstract

Outcome Measures of Deliberate and Spontaneous Motions

Cited by 9 publications

References 5 publications

Stochastic Signatures of Involuntary Head Micro-movements Can Be Used to Classify Females of ABIDE into Different Subtypes of Neurodevelopmental Disorders

Stochastic Signatures of Involuntary Head Micro-movements Can Be Used to Classify Females of ABIDE into Different Subtypes of Neurodevelopmental Disorders

Peripheral Network Connectivity Analyses for the Real-Time Tracking of Coupled Bodies in Motion

Neonatal Diagnostics: Toward Dynamic Growth Charts of Neuromotor Control

Contact Info

Product

Resources

About