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Results for “cognitive heterogeneity”

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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11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Genetic programming for understanding cognitive biases that generate polarization in social networks

Recent studies have applied agent-based models to infer human-interpretable explanations of individual-scale behaviors that generate macro-scale patterns in complex social systems. Genetic programming has proven to be an ideal explainable AI tool for this purpose, where primitives may be expressed in an interpretable fashion and assembled into agent rules. Evolutionary model discovery (EMD) is a tool that combines genetic programming and random forest feature importance analysis, to infer individual-scale, human-interpretable explanations from agent-based models. We deploy EMD to investigate the cognitive biases behind the emergence of ideological polarization within a population. An agent-based model is developed to simulate a social network, where agents are able to create or sever links with one another, and update an internal ideological stance based on their neighbors' stances. Agent rules govern these actions and constitute of cognitive biases. A set of 7 cognitive biases are included as genetic program primitives in the search for rules that generate hyper-polarization among the population of agents. We find that heterogeneity in cognitive biases is more likely to generate polarized social networks. Highly polarized social networks are likely to emerge when individuals with confirmation bias are exposed to those with either attentional bias, egocentric bias, or cognitive dissonance.

Gunaratne, Chathika↗
DOE OSTI2023

Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns

We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging-defined subgroups. We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid-negative controls. We used voxel-based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD-Memory. There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD-Memory, lower left hemisphere GMV for AD-Language, anterior lower GMV for AD-Executive, and posterior lower GMV for AD-Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD-Language group than any other group (p = 1.15 × 10 -10 ). For overlap between imaging-defined and cognitively defined subgroups, AD-Memory matched up with an imaging-defined limbic predominant group. MRI findings differ across cognitively defined AD subgroups.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

The POINTER Imaging baseline cohort: Associations between multimodal neuroimaging biomarkers, cardiovascular health, and cognition

Abstract INTRODUCTION The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is evaluating lifestyle interventions in older adults at risk for cognitive decline and dementia. Here we characterize the baseline data set of the POINTER Imaging ancillary study. METHODS Participants underwent health and cognitive assessments and neuroimaging with multimodal positron emission tomography (PET) (beta‐amyloid [Aβ] and tau) and magnetic resonance imaging (MRI). Framingham risk score (FRS) was used to quantify cardiovascular disease (CVD) risk. RESULTS A total of 1052 participants (31% from underrepresented ethnoracial groups) were enrolled. Compared to Aβ−, Aβ+ (29%) participants were older, had higher apolipoprotein E (APOE) ε4 carriage rate and white matter hyperintensity volume, and greater temporal tau. FRS was related to MRI measures, but not AD biomarkers. FRS and tau had independent effects on cognition. DISCUSSION In this heterogenous, at‐risk cohort, CVD risk was related to more abnormal brain structure and poorer cognition, representing a putative non‐AD (Alzheimer's disease) pathway to brain injury and cognitive decline. Highlights The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) cohort is enriched for cardiovascular disease (CVD) and poor lifestyle POINTER Imaging collected multimodal neuroimaging data in this unique, at‐risk cohort Amyloid burden was related to age, apolipoprotein E (APOE) ε4 carriage, and measures of disease progression Associations between amyloid and tau, and tau and cognition, were relatively weak CVD risk and tau pathology were independently related to memory

Neurosciences & Neurology↗
DOE OSTI2026

4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer’s Diagnosis

Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics. Recent studies demonstrate enhanced diagnostic sensitivity for Alzheimer’s disease (AD) through synergistic integration of neuroimaging data (e.g., sMRI, fMRI) with tabular data (e.g., behavioral and cognitive tests). However, the intrinsic heterogeneity across modalities (e.g., 4D spatiotemporal fMRI dynamics vs. 3D anatomical sMRI structure) presents critical challenges for discriminative feature fusion, often leading to information loss or biased fusion. To bridge this gap, we propose M2M-AlignNet: a multimodal co-attention network with latent alignment for early AD diagnosis using sMRI and fMRI. At the core of our approach is a multi-patch-to-multi-patch (M2M) contrastive loss function that quantifies and reduces representational discrepancies via weighted patch correspondence, explicitly aligning fMRI components across brain regions with their sMRI structural substrates without one-to-one constraints. Additionally, we propose a latent-as-query co-attention module to autonomously discover fusion patterns, circumventing modality prioritization biases while minimizing feature redundancy. We conduct extensive experiments to confirm the effectiveness of our method and highlight the correspondence between fMRI and sMRI as AD biomarkers.

Wei, Yuxiang [Georgia Institute of Technology]↗
DOE OSTI2021

Therapeutic targeting of membrane-associated proteins in central nervous system tumors

The activity of the most complex system, the central nervous system (CNS) is profoundly regulated by a huge number of membrane-associated proteins (MAP). A minor change stimulates immense chemical changes and the elicited response is organized by MAP, which acts as a receptor of that chemical or channel enabling the flow of ions. Slight changes in the activity or expression of these MAPs lead to severe consequences such as cognitive disorders, memory loss, or cancer. CNS tumors are heterogeneous in nature and hard-to-treat due to random mutations in MAPs; like as overexpression of EGFRvIII/TGFβR/VEGFR, change in adhesion molecules α5β3 integrin/SEMA3A, imbalance in ion channel proteins, etc. Extensive research is under process for developing new therapeutic approaches using these proteins such as targeted cytotoxic radiotherapy, drug-delivery, and prodrug activation, blocking of receptors like GluA1, developing viral vector against cell surface receptor. The combinatorial approach of these strategies along with the conventional one might be more potential. Henceforth, our review focuses on in-depth analysis regarding MAPs aiming for a better understanding for developing an efficient therapeutic approach for targeting CNS tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Developing a Disaster-Ready Power Grid Agent Through Geophysically-Informed Fault Event Scenarios

Management of the nation’s power grid over the coming decades will need to factor multiple climate-driven threats to the power system that can be stressful or even detrimental to its operations. During disasters, grid operators suffer from cognitive overload where the electric grid is severely impacted by the weather, yet grid operators have limited awareness of these factors. Meanwhile, the electric grid is facing an explosion of data coming from a variety of sources which can enable the operator to evaluate the risks and develop mitigation strategies against hazardous events. In this work, we processed heterogeneous environmental and power grid data to learn and model grid behavior caused by extreme weather events. In this study, we focused on two weather-driven hazards, hurricanes and wildfires, which were analysed for the electric grid of Texas. We the used this data to train a Reinforcement Learning (RL) agent by analysing and predicting future behaviour of the grid during those hazard events. All these parts are incorporated in one framework to have a geophysically informed power simulators that can be used to train RL agents.

electic grid, reinforcement learning, climate adap↗
DOE OSTI2024

GraphCH: A Deep Framework for Assessing Cyber-Human Aspects in Insider Threat Detection

Insider threat is one of the most damaging cyber attacks that could cause the loss of intellectual property and enterprise data security breaches. Action sequence data such as host logs are used to investigate such threats and develop anomaly-based AI detectors. However, insider threat actions are similar to legitimate user activities, causing AI detectors to fail and suffer from high false alarm rates. Therefore, user cyber activity logs are inadequate to fully unfold insider threats. In this study, we adopt human psychological principles of risk-taking and impulsiveness along with host data to assess the influence and usefulness of human behavioral aspects in insider threat detection. Here, we hypothesize that individuals' impulsive and risk-taking behavior correlates with cyberspace activities. To validate our hypothesis, we conducted an IRB-approved study recruiting 35 participants who work in a large U.S. university and collected their cyber and psychological data for 90 days. Host and human-behavioral data analysis and mapping indicate that impulsive and risk-taking users trigger more system errors causing (un)intentional insider threats and are susceptible to attackers' social engineering and cognitive hacking. Utilizing cyber-human aspects, we introduce a Cyber-Human Graph Neural Network (GNN) based framework GraphCH to identify abnormal user behaviors and detect insider threats.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML

Internet of Things (IoT) and Edge Computing (EC) are rapidly becoming an integral part of the modern society. By 2030, there is estimated to be over 40 billion active and connected IoT devices [1]. This rapid progress also comes with a significant implication on cybersecurity. Back-end infrastructure and systems have a much broader attack than they did previously due to vulnerable IoT/EC devices being connected to wireless networks. This expanding attack surface is a growing concern because IoT/EC are increasingly being used in critical systems such as power grids, health care, and smart homes. To effectively address a problem of this scale, cognitive cyber methods—which can autonomously detect and react to cyber attacks as they develop—are needed. To address this, we bring Artificial Intelligence (AI) and Machine Learning (ML) to IoT/EC devices, using tinyML to monitor voluminous IoT data against cyber threats, and using Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. We propose a novel three-layer architecture: (1) an IoT layer for tinyML-based inference, (2) an edge layer for ML model training, and (3) a cloud layer for FL operations. Using the publicly available 11-class N-BaIoT dataset [2], we demonstrate that this architecture mitigates resource constraints at the IoT layer while improving detection accuracy over standard two-layer designs. An outlier-resistant scaler, feature reduction, and quantization enable the tinyML model to maintain detection accuracy with a reduced model size. Additionally, federated learning that only utilizes the intersection (across heterogenous devices) of the reduced feature set achieves superior detection accuracy compared to locally trained models.

Li, Mingyan [ORNL] (ORCID:0009000569532640)↗
DOE OSTI2025

Discovery of Proteoforms Associated With Alzheimer's Disease Through Quantitative Top-Down Proteomics

The complex nature of Alzheimer's disease (AD) and its heterogenous clinical presentation has prompted numerous large-scale - omic analyses aimed at providing a global understanding of the pathophysiological processes involved. AD involves isoforms, proteolytic products, and posttranslationally modified proteins such as amyloid beta (Aβ) and microtubule-associated protein tau. Top-down proteomics directly measures these species and thus, offers a comprehensive view of pathologically relevant proteoforms that are difficult to analyze using traditional proteomic techniques. Here, we broadly explored associations between proteoforms and clinicopathological traits of AD by deploying a quantitative top-down proteomics approach across frontal cortex of 103 subjects selected from the ROS and MAP cohorts. The approach identified 1213 proteins and 11,782 proteoforms, of which 154 proteoforms had at least one significant association with a clinicopathological phenotype. One important finding included identifying Aβ C-terminal truncation state as the key property for differential association between amyloid plaques and cerebral amyloid angiopathy. Furthermore, various N-terminally truncated forms of Aβ had noticeably stronger association with amyloid plaques and global cognitive function. Additionally, we discovered six VGF neuropeptides that were positively associated with cognitive function independent of pathological burden. The database of brain cortex proteoforms provides a valuable context for functional characterization of the proteins involved in AD and other late-onset brain pathologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Designing resilient IoT and Edge Computing with federated tinyML

The rapid growth of the Internet of Things (IoT) and Edge Computing (EC) has brought significant conveniences to modern society but has also greatly expanded the cyber attack surfaces, particularly as these technologies are being increasingly integrated into critical systems such as power grids, healthcare, and smart homes. Here, to improve IoT/EC’s cybersecurity posture, we leveraged Artificial Intelligence (AI) and Machine Learning (ML) by employing tinyML to monitor voluminous IoT data for cyber threats while addressing devices’ resource constraints, and utilizing Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. Building on our three-layer architecture combining tinyML and FL to enhance autonomous cyber attack detection, this paper demonstrated that the architecture improves detection accuracy, reduces resource consumption, and enables lightweight, secure IoT device monitoring. These results were validated using the public N-BaIoT dataset as well as real IoT network traffic data collected under multiple attack scenarios from our testbeds. Additionally, we introduced an enhanced FL methodology with a novel preprocessing stage, including federated feature selection and global preprocessor construction, to address IoT/EC data heterogeneity. We developed a physical IoT testbed for attack simulations and data collection, implemented a tinyML-powered detector for realistic model validation, and also built a virtual testbed for scalable evaluations of FL models across diverse network environments.

Cognitive cyber↗