Toward Precision Psychiatry: Statistical Platform for the Personalized Characterization of Natural Behaviors

Irregularity of instantaneous gamma frequency in the motor control network characterize visuomotor and proprioceptive information processing

Objective.The study aims to characterize movements with different sensory goals, by contrasting the neural activity involved in processing proprioceptive and visuo-motor information. To accomplish this, we have developed a new methodology that utilizes the irregularity of the instantaneous gamma frequency parameter for characterization.Approach.In this study, eight essential tremor patients undergoing an awake deep brain stimulation implantation surgery repetitively touched the clinician's finger (forward visually-guided/FV movement) and then one's own chin (backward proprioceptively-guided/BP movement). Neural electrocorticographic recordings from the motor (M1), somatosensory (S1), and posterior parietal cortex (PPC) were obtained and band-pass filtered in the gamma range (30-80 Hz). The irregularity of the inter-event intervals (IEI; inverse of instantaneous gamma frequency) were examined as: (1) auto-information of the IEI time series and (2) correlation between the amplitude and its proceeding IEI. We further explored the network connectivity after segmenting the FV and BP movements by periods of accelerating and decelerating forces, and applying the IEI parameter to transfer entropy methods.Main results.Conceptualizing that the irregularity in IEI reflects active new information processing, we found the highest irregularity in M1 during BP movement, highest in PPC during FV movement, and the lowest during rest at all sites. Also, connectivity was the strongest from S1 to M1 and from S1 to PPC during FV movement with accelerating force and weakest during rest.Significance. We introduce a novel methodology that utilize the instantaneous gamma frequency (i.e. IEI) parameter in characterizing goal-oriented movements with different sensory goals, and demonstrate its use to inform the directional connectivity within the motor cortical network. This method successfully characterizes different movement types, while providing interpretations to the sensory-motor integration processes.

Oculomotor randomness is higher in autistic children and increases with the severity of symptoms

A variety of studies have suggested that at least some children with autism spectrum disorder (ASD) view the world differently. Differences in gaze patterns as measured by eye tracking have been demonstrated during visual exploration of images and natural viewing of movies with social content. Here we analyzed the temporal randomness of saccades and blinks during natural viewing of movies, inspired by a recent measure of "randomness" applied to micro-movements of the hand and head in ASD (Torres et al., 2013; Torres & Denisova, 2016). We analyzed a large eye-tracking dataset of 189 ASD and 41 typically developing (TD) children (1-11 years old) who watched three movie clips with social content, each repeated twice. We found that oculomotor measures of randomness, obtained from gamma parameters of inter-saccade intervals (ISI) and blink duration distributions, were significantly higher in the ASD group compared with the TD group and were correlated with the ADOS comparison score, reflecting increased "randomness" in more severe cases. Moreover, these measures of randomness decreased with age, as well as with higher cognitive scores in both groups and were consistent across repeated viewing of each movie clip. Highly "random" eye movements in ASD children could be associated with high "neural variability" or noise, poor sensory-motor control, or weak engagement with the movies. These findings could contribute to the future development of oculomotor biomarkers as part of an integrative diagnostic tool for ASD.

Motor Signatures in Digitized Cognitive and Memory Tests Enhances Characterization of Parkinson's Disease

Although interest in using wearable sensors to characterize movement disorders is growing, there is a lack of methodology for developing clinically interpretable biomarkers. Such digital biomarkers would provide a more objective diagnosis, capturing finer degrees of motor deficits, while retaining the information of traditional clinical tests. We aim at digitizing traditional tests of cognitive and memory performance to derive motor biometrics of pen-strokes and voice, thereby complementing clinical tests with objective criteria, while enhancing the overall characterization of Parkinson's disease (PD). 35 participants including patients with PD, healthy young and age-matched controls performed a series of drawing and memory tasks, while their pen movement and voice were digitized. We examined the moment-to-moment variability of time series reflecting the pen speed and voice amplitude. The stochastic signatures of the fluctuations in pen drawing speed and voice amplitude of patients with PD show a higher signal-to-noise ratio compared to those of neurotypical controls. It appears that contact motions of the pen strokes on a tablet evoke sensory feedback for more immediate and predictable control in PD, while voice amplitude loses its neurotypical richness. We offer new standardized data types and analytics to discover the hidden motor aspects within the cognitive and memory clinical assays.

Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders

The advent of open access to genomic data offers new opportunities to revisit old clinical debates while approaching them from a different angle. We examine anew the question of whether psychiatric and neurological disorders are different from each other by assessing the pool of genes associated with disorders that are understood as psychiatric or as neurological. We do so in the context of transcriptome data tracked as human embryonic stem cells differentiate and become neurons. Building upon probabilistic layers of increasing complexity, we describe the dynamics and stochastic trajectories of the full transcriptome and the embedded genes associated with psychiatric and/or neurological disorders. From marginal distributions of a gene's expression across hundreds of cells, to joint interactions taken globally to determine degree of pairwise dependency, to networks derived from probabilistic graphs along maximal spanning trees, we have discovered two fundamentally different classes of genes underlying these disorders and differentiating them. One class of genes boasts higher variability in expression and lower dependencies (High Expression Variability-HEV genes); the other has lower variability and higher dependencies (Low Expression Variability-LEV genes). They give rise to different network architectures and different transitional states. HEV genes have large hubs and a fragile topology, whereas LEV genes show more distributed code during the maturation toward neuronal state. LEV genes boost differentiation between psychiatric and neurological disorders also at the level of tissue across the brain, spinal cord, and glands. These genes, with their low variability and asynchronous ON/OFF states that have been treated as gross data and excluded from traditional analyses, are helping us settle this old argument at more than one level of inquiry.

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

Precision Autism: Genomic Stratification of Disorders Making Up the Broad Spectrum May Demystify Its "Epidemic Rates"

In the last decade, Autism has broadened and often shifted its diagnostics criteria, allowing several neuropsychiatric and neurological disorders of known etiology. This has resulted in a highly heterogeneous spectrum with apparent exponential rates in prevalence. I ask if it is possible to leverage existing genetic information about those disorders making up Autism today and use it to stratify this spectrum. To that end, I combine genes linked to Autism in the SFARI database and genomic information from the DisGeNET portal on 25 diseases, inclusive of non-neurological ones. I use the GTEx data on genes’ expression on 54 human tissues and ask if there are overlapping genes across those associated to these diseases and those from SFARI-Autism. I find a compact set of genes across all brain-disorders which express highly in tissues fundamental for somatic-sensory-motor function, self-regulation, memory, and cognition. Then, I offer a new stratification that provides a distance-based orderly clustering into possible Autism subtypes, amenable to design personalized targeted therapies within the framework of Precision Medicine. I conclude that viewing Autism through this physiological (Precision) lens, rather than viewing it exclusively from a psychological behavioral construct, may make it a more manageable condition and dispel the Autism epidemic myth.

Optimal time lags from causal prediction model help stratify and forecast nervous system pathology

Traditional clinical approaches diagnose disorders of the nervous system using standardized observational criteria. Although aiming for homogeneity of symptoms, this method often results in highly heterogeneous disorders. A standing question thus is how to automatically stratify a given random cohort of the population, such that treatment can be better tailored to each cluster's symptoms, and severity of any given group forecasted to provide neuroprotective therapies. In this work we introduce new methods to automatically stratify a random cohort of the population composed of healthy controls of different ages and patients with different disorders of the nervous systems. Using a simple walking task and measuring micro-fluctuations in their biorhythmic motions, we combine non-linear causal network connectivity analyses in the temporal and frequency domains with stochastic mapping. The methods define a new type of internal motor timings. These are amenable to create personalized clinical interventions tailored to self-emerging clusters signaling fundamentally different types of gait pathologies. We frame our results using the principle of reafference and operationalize them using causal prediction, thus renovating the theory of internal models for the study of neuromotor control.

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

The study of pain requires a balance between subjective methods that rely on self-reports and complementary objective biometrics that ascertain physical signals associated with subjective accounts. There are at present no objective scales that enable the personalized assessment of pain, as most work involving electrophysiology rely on summary statistics from a priori theoretical population assumptions. Along these lines, recent work has provided evidence of differences in pain sensations between participants with Sensory Over Responsivity (SOR) and controls. While these analyses are useful to understand pain across groups, there remains a need to quantify individual differences more precisely in a personalized manner. Here we offer new methods to characterize pain using the moment-by-moment standardized fluctuations in EEG brain activity centrally reflecting the person’s experiencing temperature-based stimulation at the periphery. This type of gross data is often disregarded as noise, yet here we show its utility to characterize the lingering sensation of discomfort raising to the level of pain, individually, for each participant. We show fundamental differences between the SOR group in relation to controls and provide an objective account of pain congruent with the subjective self-reported data. This offers the potential to build a standardized scale useful to profile pain levels in a personalized manner across the general population.

The Autonomic Nervous System Differentiates between Levels of Motor Intent and End Effector

While attempting to bridge motor control and cognitive science, the nascent field of embodied cognition has primarily addressed intended, goal-oriented actions. Less explored, however, have been unintended motions. Such movements tend to occur largely beneath awareness, while contributing to the spontaneous control of redundant degrees of freedom across the body in motion. We posit that the consequences of such unintended actions implicitly contribute to our autonomous sense of action ownership and agency. We question whether biorhythmic activities from these motions are separable from those which intentionally occur. Here we find that fluctuations in the biorhythmic activities of the nervous systems can unambiguously differentiate across levels of intent. More important yet, this differentiation is remarkable when we examine the fluctuations in biorhythmic activity from the autonomic nervous systems. We find that when the action is intended, the heart signal leads the body kinematics signals; but when the action segment spontaneously occurs without instructions, the heart signal lags the bodily kinematics signals. We conclude that the autonomic nervous system can differentiate levels of intent. Our results are discussed while considering their potential translational value.

Age-Dependent Statistical Changes of Involuntary Head Motion Signatures Across Autism and Controls of the ABIDE Repository

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.

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

Aging with Autism Departs Greatly from Typical Aging

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.

Hidden Aspects of the Research ADOS Are Bound to Affect Autism Science

The research-grade Autism Diagnostic Observational Schedule (ADOS) is a broadly used instrument that informs and steers much of the science of autism. Despite its broad use, little is known about the empirical variability inherently present in the scores of the ADOS scale or their appropriateness to define change and its rate, to repeatedly use this test to characterize neurodevelopmental trajectories. Here we examine the empirical distributions of research-grade ADOS scores from 1324 records in a cross-section of the population comprising participants with autism between five and 65 years of age. We find that these empirical distributions violate the theoretical requirements of normality and homogeneous variance, essential for independence between bias and sensitivity. Further, we assess a subset of 52 typical controls versus those with autism and find a lack of proper elements to characterize neurodevelopmental trajectories in a coping nervous system changing at nonuniform, nonlinear rates. Repeating the assessments over four visits in a subset of the participants with autism for whom verbal criteria retained the same appropriate ADOS modules over the time span of the four visits reveals that switching the clinician changes the cutoff scores and consequently influences the diagnosis, despite maintaining fidelity in the same test's modules, room conditions, and tasks' fluidity per visit. Given the changes in probability distribution shape and dispersion of these ADOS scores, the lack of appropriate metric spaces to define similarity measures to characterize change and the impact that these elements have on sensitivity-bias codependencies and on longitudinal tracking of autism, we invite a discussion on readjusting the use of this test for scientific purposes.

Multi-Scale Coordination of Distinctive Movement Patterns During Embodied Interaction Between Adults With High-Functioning Autism and Neurotypicals

Autism Spectrum Disorder (ASD) can be understood as a social interaction disorder. This requires researchers to take a “second-person” stance and to use experimental setups based on bidirectional interactions. The present work offers a quantitative description of movement patterns exhibited during computer-mediated real-time sensorimotor interaction in 10 dyads of adult participants, each consisting of one control individual (CTRL) and one individual with high-functioning autism (HFA). We applied time-series analyses to their movements and found two main results. First, multi-scale coordination between participants was present. Second, despite this dyadic alignment and our previous finding that individuals with HFA can be equally sensitive to the other’s presence, individuals’ movements differed in style: in contrast to CTRLs, HFA participants appeared less inclined to sustain mutual interaction and instead explored the virtual environment more generally. This finding is consistent with social motivation deficit accounts of ASD, as well as with hypersensitivity-motivated avoidance of overstimulation. Our research demonstrates the utility of time series analyses for the second-person stance and complements previous work focused on non-dynamical and performance-based variables.

Gait Pattern and Motor Performance During Discrete Gait Perturbation in Children With Autism Spectrum Disorders

Quantitative evaluation of gait has been considered a useful tool with which to identify subtle signs of motor system peculiarities in autism spectrum disorder (ASD). However, there is a paucity of studies reporting gait data in ASD as well as investigating learning processes of locomotor activity. Novel advanced technologies that couple treadmills with virtual reality environments and motion capture systems allows the evaluation of gait patterns on multiple steps and the effects of induced gait perturbations, as well as the ability to manipulate visual and proprioceptive feedbacks. This study aims at describing the gait pattern and motor performance during discrete gait perturbation of drug-naïve, school-aged children with ASD compared to typically developing (TD) peers matched by gender and age. Gait analysis was carried out in an immersive virtual environment using a 3-D motion analysis system with a dual-belt, instrumented treadmill. After 6 min of walking, 20 steps were recorded as baseline. Then, each participant was exposed to 20 trials with a discrete gait perturbation applying a split-belt acceleration to the dominant side at toe-off. Single steps around perturbations were inspected. Finally, 20 steps were recorded during a post-perturbation session. At baseline, children with ASD had reduced ankle flexion moment, greater hip flexion at the initial contact, and greater pelvic anteversion. After the discrete gait perturbation, variations of peak of knee extension significantly differed between groups and correlated with the severity of autistic core symptoms. Throughout perturbation trials, more than 60% of parameters showed reliable adaptation with a decay rate comparable between groups. Overall, these findings depicted gait peculiarities in children with ASD, including both kinetic and kinematic features; a motor adaptation comparable to their TD peers, even though with an atypical pattern; and a motor adaptation rate comparable to TD children but involving different aspects of locomotion. The platform showed its usability with children with ASD and its reliability in the definition of paradigms for the study of motor learning while doing complex tasks, such as gait. The additional possibility to accurately manipulate visual and proprioceptive feedback will allow researchers to systematically investigate motor system features in people with ASD.

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

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.

Autism Research: An Objective Quantitative Review of Progress and Focus Between 1994 and 2015

The nosology and epidemiology of Autism has undergone transformation following consolidation of once disparate disorders under the umbrella diagnostic, autism spectrum disorders. Despite this re-conceptualization, research initiatives, including the NIMH's Research Domain Criteria and Precision Medicine, highlight the need to bridge psychiatric and psychological classification methodologies with biomedical techniques. Combining traditional bibliometric co-word techniques, with tenets of graph theory and network analysis, this article provides an objective thematic review of research between 1994 and 2015 to consider evolution and focus. Results illustrate growth in Autism research since 2006, with nascent focus on physiology. However, modularity and citation analytics demonstrate dominance of subjective psychological or psychiatric constructs, which may impede progress in the identification and stratification of biomarkers as endorsed by new research initiatives.

Characterization of Sensory-Motor Behavior Under Cognitive Load Using a New Statistical Platform for Studies of Embodied Cognition

The field of enacted/embodied cognition has emerged as a contemporary attempt to connect the mind and body in the study of cognition. However, there has been a paucity of methods that enable a multi-layered approach tapping into different levels of functionality within the nervous systems (e.g., continuously capturing in tandem multi-modal biophysical signals in naturalistic settings). The present study introduces a new theoretical and statistical framework to characterize the influences of cognitive demands on biophysical rhythmic signals harnessed from deliberate, spontaneous and autonomic activities. In this study, nine participants performed a basic pointing task to communicate a decision while they were exposed to different levels of cognitive load. Within these decision-making contexts, we examined the moment-by-moment fluctuations in the peak amplitude and timing of the biophysical time series data (e.g., continuous waveforms extracted from hand kinematics and heart signals). These spike-trains data offered high statistical power for personalized empirical statistical estimation and were well-characterized by a Gamma process. Our approach enabled the identification of different empirically estimated families of probability distributions to facilitate inference regarding the continuous physiological phenomena underlying cognitively driven decision-making. We found that the same pointing task revealed shifts in the probability distribution functions (PDFs) of the hand kinematic signals under study and were accompanied by shifts in the signatures of the heart inter-beat-interval timings. Within the time scale of an experimental session, marked changes in skewness and dispersion of the distributions were tracked on the Gamma parameter plane with 95% confidence. The results suggest that traditional theoretical assumptions of stationarity and normality in biophysical data from the nervous systems are incongruent with the true statistical nature of empirical data. This work offers a unifying platform for personalized statistical inference that goes far beyond those used in conventional studies, often assuming a “one size fits all model” on data drawn from discrete events such as mouse clicks, and observations that leave out continuously co-occurring spontaneous activity taking place largely beneath awareness.

Statistical Platform for Individualized Behavioral Analyses Using Biophysical Micro-Movement Spikes

Wearable biosensors, such as those embedded in smart phones, can provide data to assess neuro-motor control in mobile settings, at homes, schools, workplaces and clinics. However, because most machine learning algorithms currently used to analyze such data require several steps that depend on human heuristics, the analyses become computationally expensive and rather subjective. Further, there is no standardized scale or set of tasks amenable to take advantage of such technology in ways that permit broad dissemination and reproducibility of results. Indeed, there is a critical need for fully objective automated analytical methods that easily handle the deluge of data these sensors output, while providing standardized scales amenable to apply across large sections of the population, to help promote personalized-mobile medicine. Here we use an open-access data set from Kaggle.com to illustrate the use of a new statistical platform and standardized data types applied to smart phone accelerometer and gyroscope data from 30 participants, performing six different activities. We report full distinction without confusion of the activities from the Kaggle set using a single parameter (linear acceleration or angular speed). We further extend the use of our platform to characterize data from commercially available smart shoes, using gait patterns within a set of experiments that probe nervous systems functioning and levels of motor control.

Characterization of Noise Signatures of Involuntary Head Motion in the Autism Brain Imaging Data Exchange Repository

The variability inherently present in biophysical data is partly contributed by disparate sampling resolutions across instrumentations. This poses a potential problem for statistical inference using pooled data in open access repositories. Such repositories combine data collected from multiple research sites using variable sampling resolutions. One example is the Autism Brain Imaging Data Exchange repository containing thousands of imaging and demographic records from participants in the spectrum of autism and age-matched neurotypical controls. Further, statistical analyses of groups from different diagnoses and demographics may be challenging, owing to the disparate number of participants across different clinical subgroups. In this paper, we examine the noise signatures of head motion data extracted from resting state fMRI data harnessed under different sampling resolutions. We characterize the quality of the noise in the variability of the raw linear and angular speeds for different clinical phenotypes in relation to age-matched controls. Further, we use bootstrapping methods to ensure compatible group sizes for statistical comparison and report the ranges of physical involuntary head excursions of these groups. We conclude that different sampling rates do affect the quality of noise in the variability of head motion data and, consequently, the type of random process appropriate to characterize the time series data. Further, given a qualitative range of noise, from pink to brown noise, it is possible to characterize different clinical subtypes and distinguish them in relation to ranges of neurotypical controls. These results may be of relevance to the pre-processing stages of the pipeline of analyses of resting state fMRI data, whereby head motion enters the criteria to clean imaging data from motion artifacts.

Objective measurement of head movement differences in children with and without autism spectrum disorder

Background

Deficits in motor movement in children with autism spectrum disorder (ASD) have typically been characterized qualitatively by human observers. Although clinicians have noted the importance of atypical head positioning (e.g. social peering and repetitive head banging) when diagnosing children with ASD, a quantitative understanding of head movement in ASD is lacking. Here, we conduct a quantitative comparison of head movement dynamics in children with and without ASD using automated, person-independent computer-vision based head tracking (Zface). Because children with ASD often exhibit preferential attention to nonsocial versus social stimuli, we investigated whether children with and without ASD differed in their head movement dynamics depending on stimulus sociality.

Methods

The current study examined differences in head movement dynamics in children with (n = 21) and without ASD (n = 21). Children were video-recorded while watching a 16-min video of social and nonsocial stimuli. Three dimensions of rigid head movement—pitch (head nods), yaw (head turns), and roll (lateral head inclinations)—were tracked using Zface. The root mean square of pitch, yaw, and roll was calculated to index the magnitude of head angular displacement (quantity of head movement) and angular velocity (speed).

Results

Compared with children without ASD, children with ASD exhibited greater yaw displacement, indicating greater head turning, and greater velocity of yaw and roll, indicating faster head turning and inclination. Follow-up analyses indicated that differences in head movement dynamics were specific to the social rather than the nonsocial stimulus condition.

Conclusions

Head movement dynamics (displacement and velocity) were greater in children with ASD than in children without ASD, providing a quantitative foundation for previous clinical reports. Head movement differences were evident in lateral (yaw and roll) but not vertical (pitch) movement and were specific to a social rather than nonsocial condition. When presented with social stimuli, children with ASD had higher levels of head movement and moved their heads more quickly than children without ASD. Children with ASD may use head movement to modulate their perception of social scenes.

Electronic supplementary material

The online version of this article (10.1186/s13229-018-0198-4) contains supplementary material, which is available to authorized users.

Motor Abnormalities: From Neurodevelopmental to Neurodegenerative Through "Functional" (Neuro)Psychiatric Disorders

Background

Motor abnormalities (MAs) of severe mental disorders have been traditionally neglected both in clinical practice and research, although they are an increasing focus of attention because of their clinical and neurobiological relevance. For historical reasons, most of the literature on MAs has been focused to a great extent on schizophrenia, and as a consequence their prevalence and featural properties in other psychiatric or neuropsychiatric disorders are poorly known. In this article, we evaluated the extent to which catatonic, extrapyramidal and neurological soft signs, and their associated clinical features, are present transdiagnostically.

Methods

We examined motor-related features in neurodevelopmental (schizophrenia, obsessive compulsive disorder, autism spectrum disorders), "functional" (nonschizophrenic nonaffective psychoses, mood disorders) and neurodegenerative (Alzheimer's disease) disorders. Examination of the literature revealed that there have been very few comparisons of motor-related features across diagnoses and we had to rely mainly in disorder-specific studies to compare it transdiagnostically.

Results

One or more motor domains had a substantial prevalence in all the diagnoses examined. In "functional" disorders, MAs, and particularly catatonic signs, appear to be markers of episode severity; in chronic disorders, although with different degree of strength or evidence, all motor domains are indicators of both disorder severity and poor outcome; lastly, in Alzheimer's disease they are also indicators of disorder progression.

Conclusions

MAs appear to represent a true transdiagnostic domain putatively sharing neurobiological mechanisms of neurodevelopmental, functional or neurodegenerative origin.

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

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.

Cortical interactions during the resolution of information processing demands in autism spectrum disorders

INTRODUCTION

Our flexible and adaptive interactions with the environment are guided by our individual representation of the physical world, estimated through sensation and evaluation of available information against prior knowledge. When linking sensory evidence with higher-level expectations for action, the central nervous system (CNS) in typically developing (TD) individuals relies in part on distributed and interacting cortical regions to communicate neuronal signals flexibly across the brain. Increasing evidence suggests that the balance between levels of signal and noise during information processing may be disrupted in individuals with Autism Spectrum Disorders (ASD).

METHODS

Participants with and without ASD performed a visuospatial interference task while undergoing functional Magnetic Resonance Imaging (fMRI). We empirically estimated parameters characterizing participants' latencies and their subtle fluctuations (noise accumulation) over the 16-min scan. We modeled hemodynamic activation and used seed-based analyses of neural coupling to study dysfunction in interference-specific connectivity in a subset of ASD participants who were nonparametrically matched to TD participants on age, male-to-female ratio, and magnitude of movement during the scan.

RESULTS

Stochastic patterns of response fluctuations reveal significantly higher noise-to-signal levels and a more random and noisy structure in ASD versus TD participants, and in particular ASD adults who have the greatest clinical autistic deficits. While individuals with ASD show an overall weaker modulation of interference-specific functional connectivity relative to TD individuals, in particular between the seeds of Anterior Cingulate Cortex (ACC) and Inferior Parietal Sulcus (IPS) and the rest of the brain, we found that in ASD, higher uncertainty during the task is linked to increased interference-specific coupling between bilateral anterior insula and prefrontal cortex.

CONCLUSIONS

Subtle and informative differences in the structure of experiencing information exist between ASD and TD individuals. Our findings reveal in ASD an atypical capacity to apply previously perceived information in a manner optimal for adaptive functioning, plausibly revealing suboptimal message-passing across the CNS.

Tissue engineering the mechanosensory circuit of the stretch reflex arc with human stem cells: Sensory neuron innervation of intrafusal muscle fibers

Muscle spindles are sensory organs embedded in the belly of skeletal muscles that serve as mechanoreceptors detecting static and dynamic information about muscle length and stretch. Through their connection with proprioceptive sensory neurons, sensation of axial body position and muscle movement are transmitted to the central nervous system. Impairment of this sensory circuit causes motor deficits and has been linked to a wide range of diseases. To date, no defined human-based in vitro model of the proprioceptive sensory circuit has been developed. The goal of this study was to develop a human-based in vitro muscle sensory circuit utilizing human stem cells. A serum-free medium was developed to drive the induction of intrafusal fibers from human satellite cells by actuation of a neuregulin signaling pathway. Both bag and chain intrafusal fibers were generated and subsequently validated by phase microscopy and immunocytochemistry. When co-cultured with proprioceptive sensory neurons derived from human neuroprogenitors, mechanosensory nerve terminal structural features with intrafusal fibers were demonstrated. Most importantly, patch-clamp electrophysiological analysis of the intrafusal fibers indicated repetitive firing of human intrafusal fibers, which has not been observed in human extrafusal fibers.

Motor noise is rich signal in autism research and pharmacological treatments

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.

Brain enhancer activities at the gene-poor 5p14.1 autism-associated locus

Due to the vast clinical and genetic heterogeneity, identification of causal genetic determinants for autism spectrum disorder (ASD) has proven to be complex. Whereas several dozen ‘rare’ genetic variants for ASD susceptibility have been identified, studies are still underpowered to analyse ‘common’ variants for their subtle effects. A recent application of genome-wide association studies (GWAS) to ASD indicated significant associations with the single nucleotide polymorphisms (SNPs) on chromosome 5p14.1, located in a non-coding region between cadherin10 (CDH10) and cadherin9 (CDH9). Here we apply an in vivo bacterial artificial chromosome (BAC) based enhancer-trapping strategy in mice to scan the gene desert for spatiotemporal cis-regulatory activities. Our results show that the ASD-associated interval harbors the cortical area, striatum, and cerebellum specific enhancers for a long non-coding RNA, moesin pseudogene1 antisense (MSNP1AS) during the brain developing stages. Mouse moesin protein levels are not affected by exogenously expressed human antisense RNAs in our transgenic brains, demonstrating the difficulty in modeling rather smaller effects of common variants. Our first in vivo evidence for the spatiotemporal transcription of MSNP1AS however provides a further support to connect this intergenic variant with the ASD susceptibility.

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

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.

Rethinking the Study of Volition for Clinical Use

Volition, the acquired voluntary control of our actions (at will), requires from birth to development and beyond a proper balance across multiple layers of the nervous systems. These levels range from the autonomic, to the automatic, to the voluntary control level, providing as well taxonomy with phylogenetic order of appearance in evolution. In the past few decades of movement research at the behavioral and systems levels, there has been a paucity of studies focusing on the possible contributions of involuntary movements to volitional control. Moreover, the work focusing on voluntary behavior has given us a valuable body of knowledge about constrained and highly over practiced activities while work involving unrestrained, naturalistic behaviors remains scarce. Perhaps in making theoretical assumptions about our data acquisition and analyses without properly empirically verifying, these assumptions have left us with a somewhat skewed notion of how we think the brain may be realizing the neural control of bodily motions; a notion that does not exactly correspond to the outcome of the extant empirical work assessing unrestrained movements as the nervous system acquires them and modifies skillful behaviors on demand. This chapter takes advantage of new technological advances and new analytics to invite rethinking some of the problems that we study in movement science by enforcing somewhat oversimplified assumptions on the data that we model, acquire, and analyze. I show that by relaxing our a priori assumptions of normality, linearity and stationarity in data from biophysical rhythms of the nervous systems, we would gain better insights into the motor phenomena. Further, we would shy away from a "self-fulfilling prophesy" paradigm with a tendency to a priori handcraft the outcome of our inquiry. The new lens to study natural movements and their control includes as well involuntary motions that take place largely beneath deliberate awareness. I present examples of solutions amenable to the habilitation and rehabilitation of volition in patient populations and discuss a new vision for movement science in light of making a seamless transition from volitional to intentional control of actions and thoughts.

Neonatal Diagnostics: Toward Dynamic Growth Charts of Neuromotor Control

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.