Objective and personalized longitudinal assessment of a pregnant patient with post severe brain trauma

Rethinking statistical learning as a continuous dynamic stochastic process, from the motor systems perspective

The brain integrates streams of sensory input and builds accurate predictions, while arriving at stable percepts under disparate time scales. This stochastic process bears different unfolding dynamics for different people, yet statistical learning (SL) currently averages out, as noise, individual fluctuations in data streams registered from the brain as the person learns. We here adopt a new analytical approach that instead of averaging out fluctuations in continuous electroencephalographic (EEG)-based data streams, takes these gross data as the important signals. Our new approach reassesses how individuals dynamically learn predictive information in stable and unstable environments. We find neural correlates for two types of learners in a visuomotor task: narrow-variance learners, who retain explicit knowledge of the regularity embedded in the stimuli. They seem to use an error-correction strategy steadily present in both stable and unstable environments. This strategy can be captured by current optimization-based computational frameworks. In contrast, broad-variance learners emerge only in the unstable environment. Local analyses of the moment-by-moment fluctuations, naïve to the overall outcome, reveal an initial period of memoryless learning, well characterized by a continuous gamma process starting out exponentially distributed whereby all future events are equally probable, with high signal (mean) to noise (variance) ratio. The empirically derived continuous Gamma process smoothly converges to predictive Gaussian signatures comparable to those observed for the error-corrective mode that is captured by current optimization-driven computational models. We coin this initially seemingly purposeless stage exploratory. Globally, we examine a posteriori the fluctuations in distributions' shapes over the empirically estimated stochastic signatures. We then confirm that the exploratory mode of those learners, free of expectation, random and memoryless, but with high signal, precedes the acquisition of the error-correction mode boasting smooth transition from exponential to symmetric distributions' shapes. This early naïve phase of the learning process has been overlooked by current models driven by expected, predictive information and error-based learning. Our work demonstrates that (statistical) learning is a highly dynamic and stochastic process, unfolding at different time scales, and evolving distinct learning strategies on demand.

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.

An assistive technology program for enabling five adolescents emerging from a minimally conscious state to engage in communication, occupation, and leisure opportunities

BACKGROUND

Post-coma patients emerging from a minimally conscious state may have extensive motor disabilities and pose serious challenges to medical centers and home settings.

OBJECTIVES

To promote academic performance and communication skills of post-coma individuals with traumatic brain injuries emerging from a minimally conscious state through an Assistive Technology setup. To evaluate its effects on the participants' positive participation. To generalize the learning process. To assess the intervention's clinical and social validity.

METHOD

Study I included five adolescents exposed to an Assistive Technology setup enabling them with targeted adaptive behaviors. Study II involved fifty external raters in a social validation assessment.

RESULTS

Data evidenced an improved performance of all the participants during the intervention, assessed through a concurrent multiple baseline design across participants. Social raters favorably scored the use of the technology.

CONCLUSION

An Assistive Technology setup may be helpful to enhance the performance and positive participation of adolescents with traumatic brain injuries emerging from a minimally conscious state.

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.

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.

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.

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.

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.

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.

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.