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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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13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Differential mGluR5 expression in response to the same stress causes individually adapted hippocampal network activity

Highlights: • Individuals show different changes in mGluR5 expression in response to the same stress. • Repetitive restraint stress causes theta EEG activity in the hippocampus. • Hippocampal mGluR5 expression determines the theta EEG response to repetitive restraint stress. Individual differences in stress vulnerability and resilience have been observed even within a single cohort of inbred rats or mice. Stress phenotypes are typically quantified as changes in the behavior of experimental animals, which is the outcome of altered electrical activity of the brain network. Although mGluR5 is associated with individual vulnerability to stress and can act as a sensitive biomarker of stress adaptation, our understanding of mGluR5-dependent modifications to neural network activities in vivo remains limited. Here, we examined individual rats for changes in hippocampal mGluR5 expression induced by restraint stress and found that these changes cause accompanying changes in hippocampal electroencephalography (EEG) activity. We found six days of restraint stress caused variable changes in hippocampal mGluR5 expression, ranging from 20.9% to 210.7% of the control group. The low mGluR5 protein group (LE) showed increased methylation of the mGluR5 CpG island, reduced mGluR5 mRNA levels, and unaltered basal EEG theta spectral power between stress day 1 and 6. In contrast, the high mGluR5 protein group (HE) showed reduced methylation of CpG sites, increased mGluR5 mRNA expression, and reduced basal theta spectral power on stress day 6. We also found that injection of lentiviruses expressing mGluR5-specific shRNAs into the hippocampus rescued this reduction in baseline theta power in HE rats. These data suggest a causal relationship between individual differences in the changes in hippocampal mGluR5 expression induced by repetitive restraint stress and the accompanying changes in ensemble neural activity in the hippocampus.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors

Multivariate autoregressive (MVAR) model estimation enables assessment of causal interactions in brain networks. However, accurately estimating MVAR models for high-dimensional electrophysiological recordings is challenging due to the extensive data requirements. Hence, the applicability of MVAR models for study of brain behavior over hundreds of recording sites has been very limited. Prior work has focused on different strategies for selecting a subset of important MVAR coefficients in the model to reduce the data requirements of conventional least-squares estimation algorithms. Here we propose incorporating prior information, such as resting state functional connectivity derived from functional magnetic resonance imaging, into MVAR model estimation using a weighted group least absolute shrinkage and selection operator (LASSO) regularization strategy. The proposed approach is shown to reduce data requirements by a factor of two relative to the recently proposed group LASSO method of Endemann et al (Neuroimage 254:119057, 2022) while resulting in models that are both more parsimonious and more accurate. The effectiveness of the method is demonstrated using simulation studies of physiologically realistic MVAR models derived from intracranial electroencephalography (iEEG) data. The robustness of the approach to deviations between the conditions under which the prior information and iEEG data is obtained is illustrated using models from data collected in different sleep stages. This approach allows accurate effective connectivity analyses over short time scales, facilitating investigations of causal interactions in the brain underlying perception and cognition during rapid transitions in behavioral state.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology

he Brain Imaging Data Structure (BIDS) is a community-driven specification for organizing neuroscience data and metadata with the aim to make datasets more transparent, reusable, and reproducible. Intracranial electroencephalography (iEEG) data offer a unique combination of high spatial and temporal resolution measurements of the living human brain. To improve internal (re)use and external sharing of these unique data, we present a specification for storing and sharing iEEG data: iEEG-BIDS.

Science & Technology - Other Topics↗
DOE OSTI2022

Time-series forecasting using manifold learning, radial basis function interpolation, and geometric harmonics

We address a three-tier numerical framework based on nonlinear manifold learning for the forecasting of high-dimensional time series, relaxing the “curse of dimensionality” related to the training phase of surrogate/machine learning models. At the first step, we embed the high-dimensional time series into a reduced low-dimensional space using nonlinear manifold learning (local linear embedding and parsimonious diffusion maps). Then, we construct reduced-order surrogate models on the manifold (here, for our illustrations, we used multivariate autoregressive and Gaussian process regression models) to forecast the embedded dynamics. Finally, we solve the pre-image problem, thus lifting the embedded time series back to the original high-dimensional space using radial basis function interpolation and geometric harmonics. The proposed numerical data-driven scheme can also be applied as a reduced-order model procedure for the numerical solution/propagation of the (transient) dynamics of partial differential equations (PDEs). In conclusion, we assess the performance of the proposed scheme via three different families of problems: (a) the forecasting of synthetic time series generated by three simplistic linear and weakly nonlinear stochastic models resembling electroencephalography signals, (b) the prediction/propagation of the solution profiles of a linear parabolic PDE and the Brusselator model (a set of two nonlinear parabolic PDEs), and (c) the forecasting of a real-world data set containing daily time series of ten key foreign exchange rates spanning the time period 3 September 2001–29 October 2020.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Efficacy of Midazolam and Ketamine Combination Therapy over Midazolam Monotherapy against Soman Exposure in Carboxylesterase Knockout Mice

Introduction Chemical warfare nerve agents (CWNAs) inhibit acetylcholinesterase (AChE), which leads to status epilepticus (SE), spontaneous recurrent seizures (SRS) and severe neuropathology when treatment is delayed. In addition to binding to AChE, some organophosphorus (OP) CWNAs such as soman (GD) also inhibit carboxylesterase (CaE), which acts as a bioscavenger and can thus reduce the severity of the toxicity of OP agent exposure. Unlike humans, rodents have plasma CaE activity. The CaE knockout (ES1−/−) mouse specifically lacks plasma CaE and might better model human GD exposure compared to wildtype rodents. Delayed treatment of CWNA‐induced SE with midazolam leads to benzodiazepine‐refractory SE. We evaluated combination therapy of midazolam and the NMDA antagonist ketamine in male and female mice for efficacy against soman‐induced lethality, SE and epileptogenesis. Methods Mice implanted with telemetry transmitters for electroencephalography (EEG) seizure identification were exposed to 82 μg/kg GD (~4LD 50 ) and treated with an admix (ip) of atropine sulfate (4 mg/kg) and HI‐6 (50 mg/kg) 1 min after exposure, and with midazolam (3 mg/kg; ip) alone or in combination with ketamine (30 mg/kg, ip) at 40 min after seizure onset. Mice were continuously recorded to evaluate initial seizure duration and SRS and then euthanized 2 weeks after exposure for neuropathology assessment. Results Delayed treatment with midazolam resulted in poor survival, was unable to rapidly terminate behavioral or EEG seizure activity and did not prevent the development of SRS or neuronal loss following GD exposure. Combination therapy of midazolam and ketamine resulted in improved outcome, including an increase in survival from 30% to 78% in female and from 57% to 77% in males compared to midazolam monotherapy. In addition, the incidence of epileptogenesis was lower in mice treated with ketamine and midazolam dual therapy compared to midazolam monotherapy. Conclusions This study demonstrates that delayed treatment of SE with midazolam is not fully protective against the GD‐induced epileptogenesis and neuropathology, exemplifying the need for adjunct treatment to midazolam to prevent or reduce effects of GD‐induced SE. Ketamine combination with midazolam improves outcome. Support or Funding Information Research was supported by the CounterACT Program, NIH OD, and the NINDS (Grant 1R21NS103820‐01 to LA Lumley‐Lange) and by DTRA‐JSTO. Erica Kundrick, Katie Walker, and Sean O'Brien were supported in part by an appointment to the Research Participation Program for the U.S. Army Medical Research and Materiel Command administered by the Oak Ridge Institute for Science and Education through an agreement between the U.S. Department of Energy and U.S. Army Medical Research and Materiel Command. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Marrero‐Rosado, Brenda↗
DOE OSTI2019

Comparative efficacy of valnoctamide and sec ‐butylpropylacetamide ( SPD ) in terminating nerve agent–induced seizures in pediatric rats

Summary Objectives Children and adults are likely to be among the casualties in a civilian nerve agent exposure. This study evaluated the efficacy of valnoctamide (racemic‐VCD), sec ‐butylpropylacetamide (racemic‐SPD), and phenobarbital for stopping nerve agent seizures in both immature and adult rats. Methods Female and male postnatal day ( PND ) 21, 28, and 70 (adult) rats, previously implanted with electroencephalography (EEG) electrodes were exposed to seizure‐inducing doses of the nerve agents sarin or VX and EEG was recorded continuously. Five minutes after seizure onset, animals were treated with SPD , VCD, or phenobarbital. The up‐down method was used over successive animals to determine the anticonvulsant median effective dose (ED 50 ) of the drugs. Results SPD‐ED 50 values in the VX model were the following: PND 21, 53 mg/kg (male) and 48 mg/kg (female); PND 28, 108 mg/kg (male) and 43 mg/kg (female); and PND 70, 101 mg/kg (male) and 40 mg/kg (female). SPD ‐ ED 50 values in the sarin model were the following: PND 21, 44 mg/kg (male) and 28 mg/kg (female); PND 28, 79 mg/kg (male) and 34 mg/kg (female); and PND 70, 53 mg/kg (male) and 53 mg/kg (female). VCD ‐ ED 50 values in the VX model were the following: PND 21, 34 mg/kg (male) and 43 mg/kg (female); PND 28, 165 mg/kg (male) and 59 mg/kg (female); and PND 70, 87 mg/kg (male) and 91 mg/kg (female). VCD ‐ ED 50 values in the sarin model were the following: PND 21, 45 mg/kg (male), 48 mg/kg (female); PND 28, 152 mg/kg (male) 79 mg/kg (female); and PND 70, 97 mg/kg (male) 79 mg/kg (female). Phenobarbital‐ ED 50 values in the VX model were the following: PND 21, 43 mg/kg (male) and 18 mg/kg (female); PND 28, 48 mg/kg (male) and 97 mg/kg (female). Phenobarbital‐ ED 50 values in the sarin model were the following: PND 21, 32 mg/kg (male) and 32 mg/kg (female); PND 28, 58 mg/kg (male) and 97 mg/kg (female); and PND 70, 65 mg/kg (female). Significance SPD and VCD demonstrated anticonvulsant activity in both immature and adult rats in the sarin‐ and VX ‐induced status epilepticus models. Phenobarbital was effective in immature rats, whereas in adult rats, higher doses were required that were accompanied by toxicity. Overall, significantly less drug was required to stop seizures in PND 21 animals than in the older animals, and overall, males required higher amounts of drug than females.

Haines, Kari M.↗
DOE OSTI2023

NREM sleep as a novel protective cognitive reserve factor in the face of Alzheimer's disease pathology

Alzheimer’s disease (AD) pathology impairs cognitive function. Yet some individuals with high amounts of AD pathology suffer marked memory impairment, while others with the same degree of pathology burden show little impairment. Why is this? One proposed explanation is cognitive reserve i.e., factors that confer resilience against, or compensation for the effects of AD pathology. Deep NREM slow wave sleep (SWS) is recognized to enhance functions of learning and memory in healthy older adults. However, that the quality of NREM SWS (NREM slow wave activity, SWA) represents a novel cognitive reserve factor in older adults with AD pathology, thereby providing compensation against memory dysfunction otherwise caused by high AD pathology burden, remains unknown. Here, we tested this hypothesis in cognitively normal older adults (N = 62) by combining 11 C-PiB (Pittsburgh compound B) positron emission tomography (PET) scanning for the quantification of β-amyloid (Aβ) with sleep electroencephalography (EEG) recordings to quantify NREM SWA and a hippocampal-dependent face-name learning task. We demonstrated that NREM SWA significantly moderates the effect of Aβ status on memory function. Specifically, NREM SWA selectively supported superior memory function in individuals suffering high Aβ burden, i.e., those most in need of cognitive reserve (B = 2.694, p = 0.019). In contrast, those without significant Aβ pathological burden, and thus without the same need for cognitive reserve, did not similarly benefit from the presence of NREM SWA (B = -0.115, p = 0.876). This interaction between NREM SWA and Aβ status predicting memory function was significant after correcting for age, sex, Body Mass Index, gray matter atrophy, and previously identified cognitive reserve factors, such as education and physical activity (p = 0.042). These findings indicate that NREM SWA is a novel cognitive reserve factor providing resilience against the memory impairment otherwise caused by high AD pathology burden. Furthermore, this cognitive reserve function of NREM SWA remained significant when accounting both for covariates, and factors previously linked to resilience, suggesting that sleep might be an independent cognitive reserve resource. Beyond such mechanistic insights are potential therapeutic implications. Unlike many other cognitive reserve factors (e.g., years of education, prior job complexity), sleep is a modifiable factor. As such, it represents an intervention possibility that may aid the preservation of cognitive function in the face of AD pathology, both present moment and longitudinally.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

The Suisun Summit 2022: Emerging Themes and Open Questions in qEEG and Neurotherapies

The Suisun Summit 2022 was a gathering of 70 clinicians, educators, and researchers in quantitative electroencephalography and neurofeedback. During this 5-day event, several themes emerged in talks or discussion groups: EEG/qEEG Reading Skills; Medication Effects and Pharmaco-EEG; Technological Advancements; Emerging Concerns; and Growing Community Prestige through Research. Participants were asked to summarize what they believed to be the most important messages from the event to share with colleagues who were not in attendance, resulting in this review. A unifying concept for all the themes was a desire for higher quality, standardized EEG/qEEG education that provides depth as well as breadth. Models of clinical care that encourage open communication with prescribers and functional medicine specialists were strongly emphasized. Abstracts from all presentations are attached in Addendum B.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Physiological Characterization of Language Comprehension

In this project, our goal was to develop methods that would allow us to make accurate predictions about individual differences in human cognition. Understanding such differences is important for maximizing human and human-system performance. There is a large body of research on individual differences in the academic literature. Unfortunately, it is often difficult to connect this literature to applied problems, where we must predict how specific people will perform or process information. In an effort to bridge this gap, we set out to answer the question: can we train a model to make predictions about which people understand which languages? We chose language processing as our domain of interest because of the well- characterized differences in neural processing that occur when people are presented with linguistic stimuli that they do or do not understand. Although our original plan to conduct several electroencephalography (EEG) studies was disrupted by the COVID-19 pandemic, we were able to collect data from one EEG study and a series of behavioral experiments in which data were collected online. The results of this project indicate that machine learning tools can make reasonably accurate predictions about an individual?s proficiency in different languages, using EEG data or behavioral data alone.

42 ENGINEERING↗
DOE OSTI2023

Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces

Objective In recent years, motor imagery-based brain–computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement. Approach Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an auditory prompt of 1 Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm. Main results As a result, both the MRCP and ERD features showed the specific temporal–spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively. Significance This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.

Liu, Chang↗
DOE OSTI2020

The Sylvester Graphical Lasso (SyGlasso)

This paper introduces the Sylvester graphical lasso (SyGlasso) that captures multiway dependencies present in tensor-valued data. The model is based on the Sylvester equation that defines a generative model. The proposed model complements the tensor graphical lasso that imposes a Kronecker sum model for the inverse covariance matrix by providing an alternative Kronecker sum model that is generative and interpretable. A nodewise regression approach is adopted for estimating the conditional independence relationships among variables. The statistical convergence of the method is established, and empirical studies are provided to demonstrate the recovery of meaningful conditional dependency graphs. Here we apply the SyGlasso to an electroencephalography (EEG) study to compare the brain connectivity of alcoholic and nonalcoholic subjects. Here, we demonstrate that our model can simultaneously estimate both the brain connectivity and its temporal dependencies.

Wang, Yu↗
DOE OSTI2022

Transfer Learning-Based Independent Component Analysis

Understanding the underlying component structure is crucial for multivariate signal analysis. Among all the techniques that try to learn the latent structure, independent component analysis (ICA) is one of the most important and popular methods, which aims to extract independent components from multivariate signals and enables further analysis. For example, in electroencephalogram (EEG) analysis, artifacts filtering and disease detection are conducted based on the independent components of the signals. One critical challenge in existing ICA approaches is that the component extraction accuracy may degrade when the available data of a unit are limited. To address this issue, this paper proposes a transfer learning-based ICA method by innovatively transferring component distribution from a source domain, so that accurate component extraction results can be achieved even when only limited data are available in the target domain. To the best of our knowledge, this is the first work that leverages transfer learning to improve ICA accuracy with limited available data. In particular, we first extract all the independent components from the source domain by maximizing the log-likelihood function with a Newton-like method on a smooth manifold. Then for the target domain, the component with the largest negentropy is extracted in each round. To effectively leverage the knowledge from the source domain and to prevent the negative transfer, we try to find a component in the source domain that matches the component we are extracting. The probability density function of the matched component will then be used to improve the component extraction accuracy if such matched component can be found; otherwise, no knowledge will be transferred. Finally, numerical simulations and a case study with electrocardiogram (ECG) data are conducted, showing the effectiveness of the proposed method in transferring knowledge and reducing negative transfer.

42 ENGINEERING↗
DOE OSTI2022

MIDAS: Modeling Individual Differences using Advanced Statistics

This research explores novel methods for extracting relevant information from EEG data to characterize individual differences in cognitive processing. Our approach combines expertise in machine learning, statistics, and cognitive science, advancing the state-of-the art in all three domains. Specifically, by using cognitive science expertise to interpret results and inform algorithm development, we have developed a generalizable and interpretable machine learning method that can accurately predict individual differences in cognition. The output of the machine learning method revealed surprising features of the EEG data that, when interpreted by the cognitive science experts, provided novel insights to the underlying cognitive task. Additionally, the outputs of the statistical methods show promise as a principled approach to quickly find regions within the EEG data where individual differences lie, thereby supporting cognitive science analysis and informing machine learning models. This work lays methodological ground work for applying the large body of cognitive science literature on individual differences to high consequence mission applications.

97 MATHEMATICS AND COMPUTING↗