A Biomarker Characterizing Neurodevelopment with applications in Autism.

Despite great advances in neuroscience and genetic studies, our understanding of neurodevelopmental disorders is still quite limited. An important reason is not having objective psychiatric clinical tests. Here we propose a quantitative neurodevelopment assessment by studying natural movement outputs. Movement is central to behaviors: It involves complex coordination, temporal alterations, and precise dynamic controls. We carefully analyzed the continuous movement output data, collected with high definition electromagnetic sensors at millisecond time scales. We unraveled new metrics containing striking physiological information that was unseen neither by using traditional motion assessments nor by naked eye observations. Our putative biomarker leads to precise individualized classifications. It illustrates clear differences between Autism Spectrum Disorder (ASD) subjects from mature typical developing (TD) individuals. It provides an ASD complementary quantitative classification, which closely agrees with the clinicaly assessed functioning levels in the spectrum. It also illustrates TD potential age-related neurodevelopmental trajectories. Applying our movement biomarker to the parents of the ASD individuals studied in the cohort also shows a novel potential familial signature ASD tie. This paper proposes a putative behavioral biomarker to characterize the level of neurodevelopment with high predicting power, as illustrated in ASD subjects as an example.

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

There is a critical need for new analytics to personalize behavioral data analysis across different fields, including kinesiology, sports science, and behavioral neuroscience. Specifically, to better translate and integrate basic research into patient care, we need to radically transform the methods by which we describe and interpret movement data. Here, we show that hidden in the "noise," smoothed out by averaging movement kinematics data, lies a wealth of information that selectively differentiates neurological and mental disorders such as Parkinson's disease, deafferentation, autism spectrum disorders, and schizophrenia from typically developing and typically aging controls. In this report, we quantify the continuous forward-and-back pointing movements of participants from a large heterogeneous cohort comprising typical and pathological cases. We empirically estimate the statistical parameters of the probability distributions for each individual in the cohort and report the parameter ranges for each clinical group after characterization of healthy developing and aging groups. We coin this newly proposed platform for individualized behavioral analyses "precision phenotyping" to distinguish it from the type of observational-behavioral phenotyping prevalent in clinical studies or from the "one-size-fits-all" model in basic movement science. We further propose the use of this platform as a unifying statistical framework to characterize brain disorders of known etiology in relation to idiopathic neurological disorders with similar phenotypic manifestations.

Cognitive load results in motor overflow in essential tremor.

SK is an 84-year-old woman diagnosed with essential tremor (ET) but no cognitive deficits. In this experiment, we tested the effects of mental rotation (a form of additional cognitive load) during reaching behavior (with the right hand) on the tremor profile of the non-moving left hand. We observed a marked increase in tremor and its variability, as well as the "freezing" of the movement pattern as effects of the cognitive load. These findings imply cognitive-motor overlaps in patients with ET, raising the possibility that the deficits reflect the loss of a common pool of neural resources, despite the heterogeneity of the symptoms of the disorder.

Strategies to develop putative biomarkers to characterize the female phenotype with autism spectrum disorders.

Current observational inventories used to diagnose autism spectrum disorders (ASD) apply similar criteria to females and males alike, despite developmental differences between the sexes. Recent work investigating the chronology of diagnosis in ASD has raised the concern that females run the risk of receiving a delayed diagnosis, potentially missing a window of opportunity for early intervention. Here, we retake this issue in the context of the objective measurements of natural behaviors that involve decision-making processes. Within this context, we quantified movement variability in typically developing (TD) individuals and those diagnosed with ASD across different ages. We extracted the latencies of the decision movements and velocity-dependent parameters as the hand movements unfolded for two movement segments within the reach: movements intended toward the target and withdrawing movements that spontaneously, without instruction, occurred incidentally. The stochastic signatures of the movement decision latencies and the percent of time to maximum speed differed between males and females with ASD. This feature was also observed in the empirically estimated probability distributions of the maximum speed values, independent of limb size. Females with ASD showed different dispersion than males with ASD. The distinctions found for females with ASD were better appreciated compared with those of TD females. In light of these results, behavioral assessment of autistic traits in females should be performed relative to TD females to increase the chance of detection.

Autism: the micro-movement perspective.

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.

A unified and quantitative network model for spatial attention in area V4.

Electrophysiological experiments in visual area V4 have shown that spatial attention induces a number of neural activity modulations. Depending on the stimulus characteristics, neuronal firing rates either increase or decrease. At the network level, the oscillatory activity in the gamma frequency range (30-70Hz) is enhanced by attention. Recently, pyramidal neurons and interneurons have been surmised to respond differently, but have been shown to have both a high firing variability. These results raise the question of the nature of the modulatory attentional input to V4 and of the network mechanisms that lead to the emergence of these different modulations. Here, we propose a biophysical network model of V4. We first reproduce the neural activity observed in response to different stimulus configurations. We found that different forms of the attentional input are possible, and that this fact could explain the observed multiplicity of modulations when stimulus contrast is varied. Our model offers a unified and quantitative picture, from which the cognitive roles played by these attentional modulations can be investigated.

Chapter 24: Computational modeling of self-organized spindle formation.

In this chapter, we provide a derivation and computational details of a biophysical model we introduced to describe the self-organized mitotic spindle formation properties in the chromosome dominated pathway studied in Xenopus meiotic extracts. The mitotic spindle is a biological structure composed of microtubules. This structure forms the scaffold on which mitosis and cytokinesis occurs. Despite the seeming mechanical simplicity of the spindle itself, its formation and the way in which it is used in mitosis and cytokinesis is complex and not fully understood. Biophysical modeling of a system as complex as mitosis requires contributions from biologists, biochemists, mathematicians, physicists, and software engineers. This chapter is written for biologists and biochemists who wish to understand how biophysical modeling can complement a program of biological experimentation. It is also written for a physicist, computer scientist, or mathematician unfamiliar with this class of biological physics model. We will describe how we built such a mathematical model and its numerical simulator to obtain results that agree with many of the results found experimentally. The components of this system are large enough to be described in terms of coarse-grained approximations. We will discuss how to properly model such systems and will suggest effective tradeoffs between reliability, simulation speed, and accuracy. At all times we have in mind the realistic biophysical properties of the system we are trying to model.

Biophysical model of self-organized spindle formation patterns without centrosomes and kinetochores.

Eukaryotic cell division and chromosome segregation depend crucially on the mitotic spindle pattern formation. The usual pathway for spindle production involves microtubule polymerization from two centrosomes. However, experiments using Xenopus extracts with micrometer-sized chromatin-coated beads found, remarkably, that spindle patterns can form in the absence of centrosomes, kinetochores, and duplicated chromosomes. Here we introduce a previously undescribed biophysical model inspired by the heuristic interpretations of the experiments that provides a quantitative explanation and constraints for this type of experiment. The model involves plus-directed (chromokinesin and Eg5) and minus-directed (cytoplasmic dynein oligomers) motors walking on microtubules and the boundary conditions caused by the chromatin-coated spheres. This model combines the effects of the plus-directed cross-linking motor Eg5 and any chromokinesin on the chromatin-covered beads, reflecting current uncertainties in the division of function between the two kinds of motors. The model can nucleate dynamically a variety of self-organized spindle patterns over a wide range of biological parameter values. Our calculations show that spindles will form over a wide range of parameter values. Some parameter values cause a monaster to form instead of a bipolar spindle. Varying the processivity and the dynein microtubule attachment and detachment rates, we find stability parameters for spindle formations. These results not only constrain the possible parameter values, but they point toward the proper division of function between Eg5 and chromokinesin in this spindle formation pathway. The model results suggest experiments that would further enhance our understanding of the basic elements needed for spindle pattern formation in this pathway.

Inhibitory synchrony as a mechanism for attentional gain modulation.

Recordings from area V4 of monkeys have revealed that when the focus of attention is on a visual stimulus within the receptive field of a cortical neuron, two distinct changes can occur: The firing rate of the neuron can change and there can be an increase in the coherence between spikes and the local field potential (LFP) in the gamma-frequency range (30-50 Hz). The hypothesis explored here is that these observed effects of attention could be a consequence of changes in the synchrony of local interneuron networks. We performed computer simulations of a Hodgkin-Huxley type neuron driven by a constant depolarizing current, I, representing visual stimulation and a modulatory inhibitory input representing the effects of attention via local interneuron networks. We observed that the neuron's firing rate and the coherence of its output spike train with the synaptic inputs was modulated by the degree of synchrony of the inhibitory inputs. When inhibitory synchrony increased, the coherence of spiking model neurons with the synaptic input increased, but the firing rate either increased or remained the same. The mean number of synchronous inhibitory inputs was a key determinant of the shape of the firing rate versus current (f-I) curves. For a large number of inhibitory inputs (approximately 50), the f-I curve saturated for large I and an increase in input synchrony resulted in a shift of sensitivity-the model neuron responded to weaker inputs I. For a small number (approximately 10), the f-I curves were non-saturating and an increase in input synchrony led to an increase in the gain of the response-the firing rate in response to the same input was multiplied by an approximately constant factor. The firing rate modulation with inhibitory synchrony was highest when the input network oscillated in the gamma frequency range. Thus, the observed changes in firing rate and coherence of neurons in the visual cortex could be controlled by top-down inputs that regulated the coherence in the activity of a local inhibitory network discharging at gamma frequencies.

Synchronization as a mechanism for attentional gain modulation.

Responses of neurons in monkey visual cortex are modulated when attention is directed into the receptive field of the neuron: the gain or sensitivity of the response is increased or the synchronization of the spikes to the local field potential (LFP) is increased. We investigated, using model simulations, whether the synchrony of inhibitory networks could link these observations. We found that, indeed, an increase in inhibitory synchrony could enhance the coherence of the model neurons with the simulated LFP, and could have different effects on the firing rate. When the firing rate vs. current (f-I) response curves saturated at high I, attention yielded a shift in sensitivity; alternatively, when the f-I curves were non-saturating, the most significant effect was on the gain of the response. This suggests that attention may act through changes in the synchrony of inhibitory networks.

Classical solutions of an electron in magnetized wedge billiards.

We have studied the classical solutions of a free electron constrained to move inside a circular wedge of angle theta(w), in the presence of a homogeneous constant magnetic field B. These billiards have broken rotational symmetry. As B and theta(w) are varied, the apex of the billiards affects the classical dynamics in an important way. We find that for billiards with angles (sqrt[5]-1)/2< or =theta(w)< or =pi/2, the dynamics exhibits a reentrant transition as the field increases. The transition is from regular-to-mixed-to-chaotic-to-mixed-to-chaotic regimes. The reentrance is connected to the appearance and disappearance of periodic orbits nucleated at the boundaries of these billiards as the field increases. There is no reentrance when theta(w)>pi/2. In the latter case as B increases the dynamics goes from quasiintegrable, to intermediate and then to chaotic whispering gallery Larmor modes.