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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. Furthermore, we observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Scalable method for micro-CT analysis enables large scale quantitative characterization of brain lesions and implants

Anatomic evaluation is an important aspect of many studies in neuroscience; however, it often lacks information about the three-dimensional structure of the brain. Micro-CT imaging provides an excellent, nondestructive, method for the evaluation of brain structure, but current applications to neurophysiological or lesion studies require removal of the skull as well as hazardous chemicals, dehydration, or embedding, limiting their scalability and utility. Here we present a protocol using eosin in combination with bone decalcification to enhance contrast in the tissue and then employ monochromatic and propagation phase-contrast micro-CT imaging to enable the imaging of brain structure with the preservation of the surrounding skull. Instead of relying on descriptive, time-consuming, or subjective methods, we develop simple quantitative analyses to map the locations of recording electrodes and to characterize the presence and extent of hippocampal brain lesions.

3-D reconstruction↗
DOE OSTI2024

Tractometry of the Human Connectome Project: resources and insights

The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2002

Surface-based atlases of cerebellar cortex in the human, macaque, and mouse

This study describes surface reconstructions and associated flat maps that represent the highly convoluted shape of cerebellar cortex in three species: human, macaque, and mouse. The reconstructions were based on high-resolution structural MRI data obtained from other laboratories. The surface areas determined for the fiducial reconstructions are about 600 cm(2) for the human, 60 cm(2) for the macaque, and 0.8 cm(2) for the mouse. As expected from the ribbon-like pattern of cerebellar folding, the cerebellar flat maps are elongated along the axis parallel to the midline. However, the degree of elongation varies markedly across species. The macaque flat map is many times longer than its mean width, whereas the mouse flat map is only slightly elongated and the human map is intermediate in its aspect ratio. These cerebellar atlases, along with associated software for visualization and for mapping experimental data onto the atlas, are freely available to the neuroscience community (see http:/brainmap.wustl.edu).

NASA Discipline Neuroscience↗
NASA NTRS2001

'The surface management system' (SuMS) database: a surface-based database to aid cortical surface reconstruction, visualization and analysis

Surface reconstructions of the cerebral cortex are increasingly widely used in the analysis and visualization of cortical structure, function and connectivity. From a neuroinformatics perspective, dealing with surface-related data poses a number of challenges. These include the multiplicity of configurations in which surfaces are routinely viewed (e.g. inflated maps, spheres and flat maps), plus the diversity of experimental data that can be represented on any given surface. To address these challenges, we have developed a surface management system (SuMS) that allows automated storage and retrieval of complex surface-related datasets. SuMS provides a systematic framework for the classification, storage and retrieval of many types of surface-related data and associated volume data. Within this classification framework, it serves as a version-control system capable of handling large numbers of surface and volume datasets. With built-in database management system support, SuMS provides rapid search and retrieval capabilities across all the datasets, while also incorporating multiple security levels to regulate access. SuMS is implemented in Java and can be accessed via a Web interface (WebSuMS) or using downloaded client software. Thus, SuMS is well positioned to act as a multiplatform, multi-user 'surface request broker' for the neuroscience community.

NASA Discipline Neuroscience↗
NASA NTRS1997

Changes in gravity influence rat postnatal motor system development: from simulation to space flight

Our research examines the role of the environment in postnatal nervous system development. Recently we have been studying the effects of changes in gravity on the motor system of rats from postnatal day (P) 2 to 31 using kinematic analysis of swimming, walking, and righting reflexes. Using the tail suspension model of weightlessness we identified sensitive and critical periods of motor system development corresponding to the time during which a motor skill is first achieved. Motor performance in suspended animals was marked by slow swimming, walking, and air-righting, all of which were characterized by hindlimb extension. (Walton et al, Neurosci. 52,763,1992). The critical periods identified in these studies contributed to determining the age of animals for a small payload, NIH.R3. This 9-day mission (STS-72) included 2 litters at P5, P7, or P15 at launch. The P7-16 and P15-24 groups were studied post-flight. On the landing day (R+0) surface righting, swimming and walking were slower in flight compared to control animals. Differences were more marked in the younger animals and the hindlimbs were more affected than the forelimbs with marked, prolonged extension of, at least, the ankle joint angle. Readaptation to 1G was slower in the P7-16 group with righting reflexes adapting first, walking last. We have shown that gravity is an important factor in postnatal nervous system development and that its affect depends on the age of the animal, duration of the perturbation, and the motor function studied.

Non-NASA Center↗
NASA NTRS1998

Posture, locomotion, spatial orientation, and motion sickness as a function of space flight

This article summarizes a variety of newly published findings obtained by the Neuroscience Laboratory, Johnson Space Center, and attempts to place this work within a historical framework of previous results on posture, locomotion, motion sickness, and perceptual responses that have been observed in conjunction with space flight. In this context, we have taken the view that correct transduction and integration of signals from all sensory systems is essential to maintaining stable vision, postural and locomotor control, and eye-hand coordination as components of spatial orientation. The plasticity of the human central nervous system allows individuals to adapt to altered stimulus conditions encountered in a microgravity environment. However, until some level of adaptation is achieved, astronauts and cosmonauts often experience space motion sickness, disturbances in motion control and eye-hand coordination, unstable vision, and illusory motion of the self, the visual scene, or both. Many of the same types of disturbances encountered in space flight reappear immediately after crew members return to earth. The magnitude of these neurosensory, sensory-motor and perceptual disturbances, and the time needed to recover from them, tend to vary as a function of mission duration and the space travelers prior experience with the stimulus rearrangement of space flight. To adequately chart the development of neurosensory changes associated with space flight, we recommend development of enhanced eye movement systems and body position measurement. We also advocate the use of a human small radius centrifuge as both a research tool and as a means of providing on-orbit countermeasures that will lessen the impact of living for long periods of time with out exposure to altering gravito-inertial forces. Copyright 1998 Elsevier Science B.V.

Flight Experiment↗
NASA NTRS1998

Effect of gravity on vestibular neural development

The timing, molecular basis, and morphophysiological and behavioral consequences of the interaction between external environment and the internal genetic pool that shapes the nervous system over a lifetime remain important questions in basic neuroscientific research. Space station offers the opportunity to study this interaction over several life cycles in a variety of organisms. This short review considers past work in altered gravity, particularly on the vestibular system, as the basis for proposing future research on space station, and discusses the equipment necessary to achieve goals. It is stressed that, in keeping with the international investment being made in this research endeavor, both the questions asked and the technologies to be developed should be bold. Advantage must be taken of this unique research environment to expand the frontiers of neuroscience. Copyright 1998 Published by Elsevier Science B.V.

NASA Discipline Neuroscience↗
NASA NTRS1998

Postnatal development under conditions of simulated weightlessness and space flight

The adaptability of the developing nervous system to environmental influences and the mechanisms underlying this plasticity has recently become a subject of interest in space neuroscience. Ground studies on neonatal rats using the tail suspension model of weightlessness have shown that the force of gravity clearly influences the events underlying the postnatal development of motor function. These effects depend on the age of the animal, duration of the perturbation and the motor function studied. A nine-day flight study has shown that a dam and neonates can develop under conditions of space flight. The motor function of the flight animals after landing was consistent with that seen in the tail suspension studies, being marked by limb joint extension. However, there were expected differences due to: (1) the unloading of the vestibular system in flight, which did not occur in the ground-based experiments; (2) differences between flight and suspension durations; and (3) the inability to evaluate motor function during the flight. The next step is to conduct experiments in space with the flexibility and rigor that is now limited to ground studies: an opportunity offered by the International Space Station. Copyright 1998 Published by Elsevier Science B.V.

Non-NASA Center↗
NASA NTRS1998

Using factor analysis to identify neuromuscular synergies during treadmill walking

Neuroscientists are often interested in grouping variables to facilitate understanding of a particular phenomenon. Factor analysis is a powerful statistical technique that groups variables into conceptually meaningful clusters, but remains underutilized by neuroscience researchers presumably due to its complicated concepts and procedures. This paper illustrates an application of factor analysis to identify coordinated patterns of whole-body muscle activation during treadmill walking. Ten male subjects walked on a treadmill (6.4 km/h) for 20 s during which surface electromyographic (EMG) activity was obtained from the left side sternocleidomastoid, neck extensors, erector spinae, and right side biceps femoris, rectus femoris, tibialis anterior, and medial gastrocnemius. Factor analysis revealed 65% of the variance of seven muscles sampled aligned with two orthogonal factors, labeled 'transition control' and 'loading'. These two factors describe coordinated patterns of muscular activity across body segments that would not be evident by evaluating individual muscle patterns. The results show that factor analysis can be effectively used to explore relationships among muscle patterns across all body segments to increase understanding of the complex coordination necessary for smooth and efficient locomotion. We encourage neuroscientists to consider using factor analysis to identify coordinated patterns of neuromuscular activation that would be obscured using more traditional EMG analyses.

NASA Center JSC↗
DOE OSTI2023

Microelectrode Arrays Measure Blocking of Voltage-Gated Calcium Ion Channels on Supported Lipid Bilayers Derived from Primary Neurons

Drug studies targeting neuronal ion channels are crucial to understand neuronal function and develop therapies for neurological diseases. The traditional method to study neuronal ion-channel activities heavily relies on the whole-cell patch clamp as the industry standard. However, this technique is both technically challenging and labour-intensive, while involving the complexity of keeping cells alive with low throughput. Therefore, the shortcomings are limiting the efficiency of ion-channel-related neuroscience research and drug testing. Here, this work reports a new system of integrating neuron membranes with organic microelectrode arrays (OMEAs) for ion-channel-related drug studies. This work demonstrates that the supported lipid bilayers (SLBs) derived from both neuron-like (neuroblastoma) cells and primary neurons are integrated with OMEAs for the first time. The increased expression of voltage-gated calcium (CaV) ion channels on differentiated SH-SY5Y SLBs compared to non-differentiated ones is sensed electrically. Also, dose-response of the CaV ion-channel blocking effect on primary cortical neuronal SLBs from rats is monitored. The dose range causing ion channel blocking is comparable to literature. This system overcomes the major challenges from traditional methods (e.g., patch clamp) and showcases an easy-to-test, rapid, ultra-sensitive, cell-free, and high-throughput platform to monitor dose-dependent ion-channel blocking effects on native neuronal membranes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Sensory integration for neuroprostheses: from functional benefits to neural correlates

In the field of sensory neuroprostheses, one ultimate goal is for individuals to perceive artificial somatosensory information and use the prosthesis with high complexity that resembles an intact system. To this end, research has shown that stimulation elicited somatosensory information improves prosthesis perception and task performance. While studies strive to achieve sensory integration, a crucial phenomenon that entails naturalistic interaction with the environment, this topic has not been commensurately reviewed. Therefore, here we present a perspective for understanding sensory integration in neuroprostheses. First, we review the engineering aspects and functional outcomes in sensory neuroprosthesis studies. In this context, we summarize studies that have suggested sensory integration. We focus on how they have used stimulation-elicited percepts to maximize and improve the reliability of somatosensory information. Next, we review studies that have suggested multisensory integration. These works have demonstrated that congruent and simultaneous multisensory inputs provided cognitive benefits such that an individual experiences a greater sense of authority over prosthesis movements (i.e., agency) and perceives the prosthesis as part of their own (i.e., ownership). Thereafter, we present the theoretical and neuroscience framework of sensory integration. We investigate how behavioral models and neural recordings have been applied in the context of sensory integration. Sensory integration models developed from intact-limb individuals have led the way to sensory neuroprosthesis studies to demonstrate multisensory integration. Neural recordings have been used to show how multisensory inputs are processed across cortical areas. Lastly, we discuss some ongoing research and challenges in achieving and understanding sensory integration in sensory neuroprostheses. Here, resolving these challenges would help to develop future strategies to improve the sensory feedback of a neuroprosthetic system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics

Abstract We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions, created from a set of $$C^{\infty }$$ C ∞ bump functions of varying support sizes.We demonstrate the high robustness and computational efficiency by applying WENDy to estimate parameters in some common models from population biology, neuroscience, and biochemistry, including logistic growth, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark model. Software and code for reproducing the examples is available at https://github.com/MathBioCU/WENDy .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Mass Spectrometry-Based Approaches for Clinical Biomarker Discovery in Traumatic Brain Injury

Purpose of Review: Precision treatments to address the multifaceted pathophysiology of traumatic brain injury (TBI) are desperately needed, which has led to the intense study of fluid-based protein biomarkers in TBI. Mass Spectrometry (MS) is increasingly being applied to biomarker discovery and quantification in neurological disease to explore the proteome, allowing for more flexibility in biomarker discovery than commonly encountered antibody-based assays. In this narrative review, we will provide specific examples of how MS technology has advanced translational research in traumatic brain injury (TBI) focusing on clinical studies, and looking ahead to promising emerging applications of MS to the field of Neurocritical Care. Recent Findings: Proteomic biomarker discovery using MS technology in human subjects has included the full range of injury severity in TBI, though critically ill patients can offer more options to biofluids given the need for invasive monitoring. Blood, urine, cerebrospinal fluid, brain specimens, and cerebral extracellular fluid have all been sources for analysis. Emerging evidence suggests there are distinct proteomic profiles in radiographic TBI subtypes, and that biomarkers may be used to distinguish patients sustaining TBI from healthy controls. Metabolomics may offer a window into the perturbations of ongoing cerebral insults in critically ill patients after severe TBI. Summary: Emerging MS technologies may offer biomarker discovery and validation opportunities not afforded by conventional means due to its ability to handle the complexities associated with the proteome. In conclusion, while MS techniques are relatively early in development in the neurosciences space, the potential applications to TBI and neurocritical care are likely to accelerate in the coming decade.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Random field optimization

Herein we present a new modeling paradigm for optimization that we call random field optimization. Random fields are a powerful modeling abstraction that aims to capture the behavior of random variables that live on infinite-dimensional spaces (e.g., space and time) such as stochastic processes (e.g., time series, Gaussian processes, and Markov processes), random matrices, and random spatial fields. This paradigm involves sophisticated mathematical objects (e.g., stochastic differential equations and space-time kernel functions) and has been widely used in neuroscience, geoscience, physics, civil engineering, and computer graphics. Despite of this, however, random fields have seen limited use in optimization; specifically, existing optimization paradigms that involve uncertainty (e.g., stochastic programming and robust optimization) mostly focus on the use of finite random variables. This trend is rapidly changing with the advent of statistical optimization (e.g., Bayesian optimization) and multi-scale optimization (e.g., integration of molecular sciences and process engineering). Our work extends a recently-proposed abstraction for infinite-dimensional optimization problems by capturing more general uncertainty representations. Moreover, we discuss solution paradigms for this new class of problems based on finite transformations and sampling, and identify open questions and challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data

Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genomewide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.

Association study↗
DOE OSTI2025

Backpropagation-based learning with local derivative approximation and memory replay in biologically plausible neural systems

When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.

42 ENGINEERING↗
DOE OSTI2021

Graphene Electric Field Sensor Enables Single Shot Label-Free Imaging of Bioelectric Potentials

The measurement of electrical activity across systems of excitable cells underlies current progress in neuroscience, cardiac pharmacology, and neurotechnology. However, bioelectricity spans orders of magnitude in intensity, space, and time, posing substantial technological challenges. The development of methods permitting network-scale recordings with high spatial resolution remains key to studies of electrogenic cells, emergent networks, and bioelectric computation. Here, for this work, we demonstrate single-shot and label-free imaging of extracellular potentials with high resolution across a wide field-of-view. The critically coupled waveguide-amplified graphene electric field (CAGE) sensor leverages the field-sensitive optical transitions in graphene to convert electric potentials into the optical regime. As a proof-of-concept, we use the CAGE sensor to detect native electrical activity from cardiac action potentials with tens-of-microns resolution, simultaneously map the propagation of these potentials at tissue-scale, and monitor their modification by pharmacological agents. This platform is robust, scalable, and compatible with existing microscopy techniques for multimodal correlative imaging.

47 OTHER INSTRUMENTATION↗