Sensorimotor control: computing the immediate future from the delayed present.

BACKGROUND: The predictive nature of the primate sensorimotor systems, for example the smooth pursuit system and their ability to compensate for long delays have been proven by many physiological experiments. However, few theoretical models have tried to explain these facts comprehensively. Here, we propose a sensorimotor learning and control model that can be used to (1) predict the dynamics of variable time delays and current and future sensory states from delayed sensory information; (2) learn new sensorimotor realities; and (3) control a motor system in real time. RESULTS: This paper proposed a new time-delay estimation method and developed a computational model for a predictive control solution of a sensorimotor control system under time delay. Simulation experiments are used to demonstrate how the proposed model can explain a sensorimotor system's ability to compensate for delays during online learning and control. To further illustrate the benefits of the proposed time-delay estimation method and predictive control in sensorimotor systems a simulation of the horizontal Vestibulo-Ocular Reflex (hVOR) system is presented. Without the proposed time-delay estimation and prediction, the hVOR can be unstable and could be affected by high frequency oscillations. These oscillations are reminiscent of a fast correction mechanism, e.g., a saccade to compensate for the hVOR delays. Comparing results of the proposed model with those in literature, it is clear that the hVOR system with impaired time-delay estimation or impaired sensory state predictor can mimic certain outcomes of sensorimotor diseases. Even more, if the control of hVOR is augmented with the proposed time-delay estimator and the predictor for eye position relative to the head, then hVOR control system can be stabilized. CONCLUSIONS: Three claims with varying degrees of experimental support are proposed in this paper. Firstly, the brain or any sensorimotor system has time-delay estimation circuits for the various sensorimotor control systems. Secondly, the brain continuously estimates current/future sensory states from the previously sensed states. Thirdly, the brain uses predicted sensory states to perform optimal motor control.

A practical guideline for intracranial volume estimation in patients with Alzheimer's disease.

BACKGROUND: Intracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure. METHODS: Two groups of 22 subjects are considered, including adult controls (AC) and patients with Alzheimer Disease (AD). Reference measurements were calculated for each subject by manually tracing intracranial cavity by the means of visual inspection. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (Freesurfer, FSL, and SPM) were examined in their ability to automatically estimate ICV across the groups. RESULTS: Analysis of the results supported the significant effect of estimation method, gender, cognitive condition of the subject and the interaction among method and cognitive condition factors in the measured ICV. Results on sub-sampling studies with a 95% confidence showed that in order to keep the accuracy of the interleaved slice sampling protocol above 99%, the sampling period cannot exceed 20 millimeters for AC and 15 millimeters for AD. Freesurfer showed promising estimates for both adult groups. However SPM showed more consistency in its ICV estimation over the different phases of the study. CONCLUSIONS: This study emphasized the importance in selecting the appropriate protocol, the choice of the sampling period in the manual estimation of ICV and selection of suitable software for the automated estimation of ICV. The current study serves as an initial framework for establishing an appropriate protocol in both manual and automatic ICV estimations with different subject populations.

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks.

BACKGROUND: The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. METHODS: A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. RESULTS: The study results show the existence of a statistically significant difference (p < 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. CONCLUSIONS: The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed.

The effects of pediatric epilepsy on a language connectome.

This study introduces a new approach for assessing the effects of pediatric epilepsy on a language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI. An auditory word definition decision task paradigm was used to activate the language network for 29 patients and 30 controls. Evaluations illustrated that pediatric epilepsy is associated with a network efficiency reduction. Patients showed a propensity to inefficiently use the whole brain network to perform the language task; whereas, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was performed. The analysis revealed substantial global network feature differences between the patients and controls for the extent of activation network. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency toward randomness. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. We finally showed that a clustering scheme was able to fairly separate the subjects into their respective patient or control groups. The clustering was initiated using local and global nodal measurements. Compared to the intensity of activation network, the extent of activation network clustering demonstrated better precision. This ascertained that the network differences presented by the networks were associated with pediatric epilepsy.

Can Deep Learning Hit a Moving Target? A Scoping Review of Its Role to Study Neurological Disorders in Children.

Neurological disorders dramatically impact patients of any age population, their families, and societies. Pediatrics are among vulnerable age populations who differently experience the devastating consequences of neurological conditions, such as attention-deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD), cerebral palsy, concussion, and epilepsy. System-level understanding of these neurological disorders, particularly from the brain networks' dynamic perspective, has led to the significant trend of recent scientific investigations. While a dramatic maturation in the network science application domain is evident, leading to a better understanding of neurological disorders, such rapid utilization for studying pediatric neurological disorders falls behind that of the adult population. Aside from the specific technological needs and constraints in studying neurological disorders in children, the concept of development introduces uncertainty and further complexity topping the existing neurologically driven processes caused by disorders. To unravel these complexities, indebted to the availability of high-dimensional data and computing capabilities, approaches based on machine learning have rapidly emerged a new trend to understand pathways better, accurately diagnose, and better manage the disorders. Deep learning has recently gained an ever-increasing role in the era of health and medical investigations. Thanks to its relatively more minor dependency on feature exploration and engineering, deep learning may overcome the challenges mentioned earlier in studying neurological disorders in children. The current scoping review aims to explore challenges concerning pediatric brain development studies under the constraints of neurological disorders and offer an insight into the potential role of deep learning methodology on such a task with varying and uncertain nature. Along with pinpointing recent advancements, possible research directions are highlighted where deep learning approaches can assist in computationally targeting neurological disorder-related processes and translating them into windows of opportunities for interventions in diagnosis, treatment, and management of neurological disorders in children.

Preclinical Western Blot in the Era of Digital Transformation and Reproducible Research, an Eastern Perspective.

The current research is an interdisciplinary endeavor to develop a necessary tool in preclinical protein studies of diseases or disorders through western blotting. In the era of digital transformation and open access principles, an interactive cloud-based database called East-West Blot ( https://rancs-lab.shinyapps.io/WesternBlots ) is designed and developed. The online interactive subject-specific database built on the R shiny platform facilitates a systematic literature search on the specific subject matter, here set to western blot studies of protein regulation in the preclinical model of TBI. The tool summarizes the existing publicly available knowledge through a data visualization technique and easy access to the critical data elements and links to the study itself. The application compiled a relational database of PubMed-indexed western blot studies labeled under HHS public access, reporting downstream protein regulations presented by fluid percussion injury model of traumatic brain injury. The promises of the developed tool include progressing toward implementing the principles of 3Rs (replacement, reduction, and refinement) for humane experiments, cultivating the prerequisites of reproducible research in terms of reporting characteristics, paving the ways for a more collaborative experimental design in basic science, and rendering an up-to-date and summarized perspective of current publicly available knowledge.

Bridging the gap: Mechanisms of plasticity and repair after pediatric TBI.

Traumatic brain injury is the leading cause of death and disability in the United States, and may be associated with long lasting impairments into adulthood. The multitude of ongoing neurobiological processes that occur during brain maturation confer both considerable vulnerability to TBI but may also provide adaptability and potential for recovery. This review will examine and synthesize our current understanding of developmental neurobiology in the context of pediatric TBI. Delineating this biology will facilitate more targeted initial care, mechanism-based therapeutic interventions and better long-term prognostication and follow-up.

Quantification of Biological Responses as Predictors of Cognitive Outcome after Developmental TBI.

Successful translational studies within the field of Traumatic Brain Injury (TBI) are concerned with determining reliable markers of injury outcome at chronic time points. Determination of injury severity following Fluid Percussion Injury (FPI) has long been limited to the measured atmospheric pressure associated with the delivered pulse. Duration of unresponsiveness to toe pinch (unconsciousness) was next introduced as an extra marker of injury severity. The current study is an effort to assess the utilization of acute injury-induced biological responses (duration of toe pinch unresponsiveness, percent body weight change, quantification of brain edema, and apnea duration) to predict cognitive performance at a subacute time point following developmental brain injury. Cognitive performance, when measured at a subacute phase, after developmental FPI was negatively correlated with the following variables, duration of toe pinch unresponsiveness, percent weight change, and quantified level of brain edema. These finding suggest the potential utilization of reliable severity assessment of injury-induced biological responses in determining outcome measures at subacute time points.

Craniectomy Effects on Resting State Functional Connectivity and Cognitive Performance in Immature Rats.

Experimental models have been proven to be valuable tools to understand downstream cellular mechanisms of Traumatic Brain Injury (TBI). The models allow for reduction of confounding variables and tighter control of varying parameters. It has been recently reported that craniectomy induces pro-inflammatory responses, which therefore needs to be properly addressed given the fact that craniectomy is often considered a control procedure for experimental TBI models. The current study aims to determine whether a craniectomy induces alterations in Resting State Network (RSN) in a developmental rodent model. Functional Magnetic Resonance Imaging (fMRI) data-driven RSN show clusters of peak differences (left caudate putamen, somatosensory cortex, amygdala and piriform cortex) between craniectomy and control group, four days post-craniectomy. In addition, the Novel Object Recognition (NOR) task revealed impaired working memory in the craniectomy group. This evidence supports craniectomy-induced neurological changes which need to be carefully addressed, considering the frequent use of craniectomy as a control procedure for experimental models of TBI.

A Novel Modular Headmount Design for non-invasive Scalp EEG Recordings in Awake Animal Models.

We have designed and developed a novel, noninvasive modular headmount to be used for awake animal scalp electroencephalography (EEG). The design is based on a developing rat that will accommodate rapid head growth. Desired characteristics include non-invasiveness, adjustable quantity and positioning, light weight, and tolerability by the animal. Axial Dependent Modular Electrode Mount (ADMEM), as designed here, addresses the aforementioned constraints by using light-weight and adjustable materials. The initial prototype of ADMEM has been tested in vivo with rat pups, using the open field test to assess for stress and anxiety at two post-installation time-points: one day after ADMEM installation (acute time-point) and four days after ADMEM installation (sub-acute time-point). There was no significant difference in normal developmental weight gain between Control and ADMEM rat groups. Although no significant difference was found in the level of anxiety between groups at the acute time-point, the ADMEM group spent significantly less time in the center of the open field test, suggesting higher anxiety. The test also showed no difference in the measured traveled distances between Control and ADMEM groups on either time-points.

A Comparative Study of Automatic Approaches for Preclinical MRI-based Brain Segmentation in the Developing Rat.

Accurate pre-clinical study reporting requires validated processing tools to increase data reproducibility within and between laboratories. Segmentation of rodent brain from non-brain tissue is an important first step in preclinical imaging pipelines for which well validated tools are still under development. The current study aims to clarify the best approach to automatic brain extraction for studies in the immature rat. Skull stripping modules from AFNI, PCNN-3D, and RATS software packages were assessed for their ability to accurately segment brain from non-brain by comparison to manual segmentation. Comparison was performed using Dice coefficient of similarity. Results showed that the RATS package outperformed the others by including a lower percentage of false positive, non-brain voxels in the brain mask. However, AFNI resulted in a lower percentage of false negative voxels. Although the automatic approaches for brain segmentation significantly facilitate the data stream process, the current study findings suggest that the task of rodent brain segmentation from T2 weighted MRI needs to be accompanied by a supervised quality control step when developmental brain imaging studies were targeted.

A Dual-Mode Magnetic-Acoustic System for Monitoring Fluid Intake Behavior in Animals.

We have developed an unobtrusive magnetic-acoustic fluid intake monitoring (MAFIM) system using a conventional stainless-steel roller-ball nipple to measure licking and drinking behavior in animals. Movements of a small permanent magnetic tracer attached to stainless-steel roller balls that operate as a tongue-actuated valve are sensed by a pair of three-axial magnetometers, and transformed into a time-series indicating the status of the ball (up or down), using a Gaussian mixture model based data-driven classifier. The sounds produced by the rise and fall of the roller balls are also recorded and classified to substantiate the magnetic data by an independent modality for a more robust solution. The operation of the magnetic and acoustic sensors is controlled by an embedded system, communicating via Universal Serial Bus (USB) with a custom-designed user interface, running on a PC. The MAFIM system has been tested in vivo with minipigs, accurately measuring various drinking parameters and licking patterns without constraints imposed by current lick monitoring systems, such as nipple access, animal-nipple contact, animal training, and complex parameter settings.

Magnetic implants in the tongue for assistive technologies: Tests of migration; oromotor function; and tissue response in miniature pigs.

OBJECTIVE: Uncertain biological consequences of titanium-magnet (Ti-mag) tongue implants constrain application of the Tongue Drive System (TDS), a brain-tongue-computer interface for individuals with severe physical impairment. Here we describe oromotor function and tongue tissue response following Ti-Mag implantation and explantation in the miniature pig, an animal model with a tongue similar in size to humans. DESIGN: A 1.8×6.2mm Ti-mag tracer was implanted into the anterior tongue in five Yucatan minipigs. X-rays were taken immediately and >six days after implantation to evaluate tracer migration. In three minipigs, the tracer was explanted >16days after implantation. Twenty-five days post-explantation, tongue tissue was harvested and processed for histological and immunohistochemical (IHC) markers of healing. In two minipigs tissue markers of healing were evaluated post-mortem following >12days implantation. Drink cycle rate (DCR) was characterized to determine the impact of procedures on oromotor function. RESULTS: Neither implantation (N=5) nor explantation (N=3) changed DCR. X-rays revealed minimal tracer migration (N=4, 0-4mm). By histology and IHC a robust capsule was present two weeks post-implantation with limited fibrosis. Explantation produced localized fibrosis and limited muscle remodeling. CONCLUSIONS: These findings suggest the safety of Ti-mag anterior tongue implants for assistive technologies in humans.

Scalp EEG brain functional connectivity networks in pediatric epilepsy.

This study establishes a new data-driven approach to brain functional connectivity networks using scalp EEG recordings for classifying pediatric subjects with epilepsy from pediatric controls. Graph theory is explored on the functional connectivity networks of individuals where three different sets of topological features were defined and extracted for a thorough assessment of the two groups. The rater's opinion on the diagnosis could also be taken into consideration when deploying the general linear model (GLM) for feature selection in order to optimize classification. Results demonstrate the existence of statistically significant (p<0.05) changes in the functional connectivity of patients with epilepsy compared to those of control subjects. Furthermore, clustering results demonstrate the ability to discriminate pediatric epilepsy patients from control subjects with an initial accuracy of 87.5%, prior to initiating the feature selection process and without taking into consideration the clinical rater's opinion. Otherwise, leave-one-out cross validation (LOOCV) showed a significant increase in the classification accuracy to 96.87% in epilepsy diagnosis.

Estimating Intracranial Volume in Brain Research: An Evaluation of Methods.

Intracranial volume (ICV) is a standard measure often used in morphometric analyses to correct for head size in brain studies. Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation across different subject groups in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and type of software most suitable for use in estimating the ICV measure. Four groups of 53 subjects are considered, including adult controls (AC, adults with Alzheimer's disease (AD), pediatric controls (PC) and group of pediatric epilepsy subjects (PE). Reference measurements were calculated for each subject by manually tracing intracranial cavity without sub-sampling. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (FreeSurfer Ver. 5.3.0, FSL Ver. 5.0, SPM8 and SPM12) were examined in their ability to automatically estimate ICV across the groups. Results on sub-sampling studies with a 95 % confidence showed that in order to keep the accuracy of the inter-leaved slice sampling protocol above 99 %, sampling period cannot exceed 20 mm for AC, 25 mm for PC, 15 mm for AD and 17 mm for the PE groups. The study assumes a priori knowledge about the population under study into the automated ICV estimation. Tuning of the parameters in FSL and the use of proper atlas in SPM showed significant reduction in the systematic bias and the error in ICV estimation via these automated tools. SPM12 with the use of pediatric template is found to be a more suitable candidate for PE group. SPM12 and FSL subjected to tuning are the more appropriate tools for the PC group. The random error is minimized for FS in AD group and SPM8 showed less systematic bias. Across the AC group, both SPM12 and FS performed well but SPM12 reported lesser amount of systematic bias.