AI News Archive: May 20, 2026 — Part 27

Sourced from 500+ daily AI sources, scored by relevance.

• CandorMD: An AI-Assisted Audio Simulation and Feedback System for Training Clinicians for Medical Error Disclosure

  Clinicians are expected to disclose harmful medical errors to patients and families in line with ethical, regulatory, and patient care standards, yet these conversations remain challenging because of their emotional complexity and limited training opportunities. Most physicians still learn primarily...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20701v1
• Combating Harms of Generative AI in CS1 with Code Review Interviews and a Flipped Classroom

  Background and Context: Large Language Models (LLMs) are more accessible and accurate than ever before, raising significant concerns for computing educators. One major concern is students using LLMs to bypass the effort needed to understand concepts and metacognitive strategies essential for success...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21374v1
• The Quiet Path from Seemingly Minor Design Errors to Workplace AI Incidents

  Recent human-computer interaction (HCI) research has revealed a widespread misalignment between how developers design workplace artificial intelligence (AI) systems, and what workers actually need from them. Yet, little research has examined the effects of this gap, or how it may cause harm. We anal...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21035v1
• Gen-AI-tecture: using generative AI to support architectural students in design tasks

  The "Gen-AI-tecture" project embeds a locally executed, discipline-specific tool into a mixed-methods focus-group design, structured around three research objectives: (a) to evaluate how generative AI tools impact students' creativity in design-thinking processes and outcomes, (b) to assess whether ...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21361v1
• The Human-AI Delegation Dilemma: Individual Strategies, Collective Equilibria and Sociotechnical Lock-in

  This paper takes an ecological approach toward large-scale models of hybrid human-AI intelligence. Emerging models of human-AI interaction predominantly advance the complementarity thesis variously dubbed human-AI collaboration and human-AI hybrid intelligence. However, this constitutes an over-simp...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21351v1
• PaintCopilot: Modeling Painting as Autonomous Artistic Continuation

  We present PaintCopilot, a co-creative neural painting assistant that models painting as an open-ended autoregressive artistic behavior conditioned on evolving canvas states and prior brushstroke history, without requiring a target image. Unlike existing neural painting methods that frame painting a...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20941v1
• Toward 6G-enabled Brain Computer Interfaces: Technical Requirements, Use Cases, Challenges, and Future Trends

  Brain computer interface (BCI) enables the brain to directly control an external device by converting neural signals into actionable outputs. However, effective real-time translation of brain activity strongly depends on the quality of neural communication between the brain and the external device. ...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20939v1
• Design Principles and Observable Indicators for AI-Enabled Pedagogical Accompaniment: Evidence from the Amico Dual-Mode Prototype in Italy and China

  AI-enabled systems are increasingly introduced into educational contexts, yet their effectiveness depends less on technological sophistication than on the quality of pedagogical mediation, ethical constraints, and context-sensitive design. This paper proposes a replicable framework for AI-enabled pe...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20665v1
• VIPER-MCP: Detecting and Exploiting Taint-Style Vulnerabilities in Model Context Protocol Servers

  Model Context Protocol (MCP) has emerged as a standard interface for connecting LLM agents to external tools. Because MCP servers expose privileged operations such as shell execution, network access, and file-system manipulation to agent-driven invocation, implementation flaws in tool handlers can c...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21392v1
• Detecting Trojaned DNNs via Spectral Regression Analysis

  Modern DNNs are repeatedly fine-tuned to incorporate new data and functionality. This evolutionary workflow introduces a security risk when updated data cannot be fully trusted, as adversaries may implant Trojans during fine-tuning. We present MIST, a Trojan detection approach that analyzes how a mo...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21146v1
• Verifiable Provenance and Watermarking for Generative AI: An Evidentiary Framework for International Operational Law and Domestic Courts

  Generative artificial intelligence now synthesizes photorealistic imagery, audio, and video at a cost that defeats traditional forensic intuition. The legal consequences span three regimes studied so far in isolation: international operational law, domestic procedure, and product regulation. This ar...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21002v1
• Choose Wisely and Privately: Proactive Client Selection for Fair and Efficient Federated Learning

  Federated Learning enables collaborative model training across decentralized data sources without data transfer. Averaging-based FL is limited by the presence of non-IID data, which negatively impacts convergence speed and final model accuracy. Conventional alternatives suffer from significant ineff...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20975v1
• Comparative Evaluation of Deep Learning Models for Fake Image Detection

  The growing sophistication of GAN-based image manipulation presents significant challenges for digital forensics. This study compares the performance of four pretrained CNN architectures including VGG16, ResNet50, EfficientNetB0, and XceptionNet for fake image detection using a unified preprocessing...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20971v1
• Rethinking Fraud Safety Evaluation: Multi-Round Attacks Reveal Safety-Utility Tradeoffs in Graph-Context LLM Defenders

  Single-turn safety evaluation is a poor proxy for real fraud defense, where attackers escalate across multiple rounds. This paper evaluates fraud defenders under replay and adaptive multi-round attacks and measures when a defender refuses, not just whether it eventually refuses. On a frozen multi-ro...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20759v1
• An Application-Layer Multi-Modal Covert-Channel Reference Monitor for LLM Agent Egress

  A large language model (LLM) agent that sends messages can leak data inside them. Destination allowlists and content scanners do not police whether an otherwise-benign payload is itself a covert channel: a compromised agent encodes bits in zero-width characters, homoglyphs, whitespace, base64, JavaS...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20734v1
• Trusted Weights, Treacherous Optimizations? Optimization-Triggered Backdoor Attacks on LLMs

  Inference optimization is a vital technique for deploying LLMs at scale. Compilation is the most widely adopted optimization technique for LLMs. While it assumes semantic equivalence between the original and compiled graphs, we first uncover its numerical side effects can be maliciously exploited to...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20641v1
• GenAI-Driven Threat Detection with Microsoft Security Copilot

  Defending against today's increasingly sophisticated cyberattacks requires security analysts to continuously translate evolving attacker tradecraft into detection logic. This places defenders in a reactive posture, requiring constantly updated expertise across an increasingly fragmented security lan...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20896v1
• Agentic Model Checking

  Verifying LLM-generated systems code is hard: bugs are prevalent, formal specifications are missing, and safety contracts are encoded implicitly at call sites rather than enforced at function boundaries. We propose agentic model checking, a paradigm that couples LLM agents with a bounded model check...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21434v1
• Domain-Adaptable Reinforcement Learning for Code Generation with Dense Rewards

  Large language models show strong potential for automated code generation, but lack guarantees for correctness, quality, safety, and domain-specific constraints. For instance in robotics, where code generation is increasingly being used for planning and executing actions, awareness of the environmen...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21180v1
• Software Product Line Engineering: Adoption, Tooling and AI Era Challenges

  Software Product Line Engineering enables systematic reuse across families of related software intensive systems. This survey synthesises key SPLE foundations, lifecycle concepts, adoption models, tooling and AI era challenges. Based on a structured review of the SPLE literature, we compare major ad...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21353v1
• Governance by Construction for Generalist Agents

  Enterprise agents are increasingly expected to operate autonomously across tools and interfaces, yet production deployments require governance by construction. Systems must specify which actions are allowed, when human oversight is required, and what information may be exposed, without rebuilding th...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20874v1
• Linearly Constrained Deep Beamformer for Multi-Speaker Scenarios

  We propose a deep beamforming framework for enhancing target speaker(s) in multi-speaker environments. A deep neural network (DNN) is trained to estimate beamforming weights directly from noisy multichannel inputs while satisfying linear spatial constraints through an adaptive multi-term loss inspir...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21141v1
• A Survey of Audio Reasoning in Multimodal Foundation Models

  Reasoning has become a defining capability of modern foundation models, yet its development in the audio modality remains limited. Audio poses challenges that are distinct from those of text and vision. It is continuous, temporally dense, and contains linguistic, paralinguistic, and environmental in...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21008v1
• From Numbers to Perception, Energy Decay Curves Prediction

  Predicting Room Impulse Responses (RIRs) remains a challenge due to the high dimensionality of audio signals and the need for perceptual accuracy. This paper introduces a neural network framework that predicts multi-band Energy Decay Curves (EDCs) directly from room geometry and material properties....

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20968v1
• DuplexSLA: A Full-Duplex Spoken Language Model with Synchronized Speech, Language, and Action

  Recent advances in spoken dialogue language models have shifted from turn-based to full-duplex designs, where the model continuously listens to the user while generating responses. However, existing duplex backbones still lack a native channel for in-conversation planning and tool calling, leaving r...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20755v1
• Speech Quality Embeddings for Improved Detection and Classification of Degradations in Speech Signals

  Automatic subjective speech quality assessment (SSQA) traditionally estimates speech quality on an utterance or system level. While this resolution was adequate for older transmission or synthesis systems that produced speech signals of mediocre quality, modern systems generate high-quality speech w...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.21332v1
• Raon-OpenTTS: Open Models and Data for Robust Text-to-Speech

  Recent advances in text-to-speech (TTS) models show impressive speech naturalness and quality, yet the role of large-scale open data in driving this progress remains underexplored. In this work, we introduce Raon-OpenTTS, an open TTS model that performs competitively with state-of-the-art closed-dat...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20830v1
• MemConflict: Evaluating Long-Term Memory Systems Under Memory Conflicts

  Long-term memory systems enable conversational agents based on large language models (LLMs) to retain, retrieve, and apply user-specific information across multi-session interactions. However, existing evaluations mainly assess outcome-level performance or temporal updating, providing limited insigh...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20926v1
• CALMem : Application-Layer Dual Memory for Conversational AI

  Large language models (LLMs) operate within fixed context windows that fundamentally limit conversational continuity. When context fills, compaction discards history irreversibly; when sessions end, all memory resets to zero. Existing solutions-larger context windows, retrieval-augmented generation ...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20724v1
• DIVE: Embedding Compression via Self-Limiting Gradient Updates

  High-dimensional embeddings from large language models impose significant storage and computational costs on vector search systems. Recent embedding compression methods, including Matryoshka-Adaptor (EMNLP 2024), Search-Adaptor (ACL 2024), and SMEC (EMNLP 2025), enable dimensionality reduction throu...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20689v1
• Layer-wise Token Compression for Efficient Document Reranking

  Transformer-based document cross-encoder rerankers are a central component of modern information retrieval systems. Despite their success, these models suffer from high computational costs due to processing long query-document sequences at inference time. A known approach to improve efficiency is to...

  📄 ResearchMay 20, 2026http://arxiv.org/abs/2605.20683v1
• Decoding heterogeneous aging clocks and disease risk stratification using a metabolomic foundation model

  Metabolomic aging clocks estimate biological age by modeling metabolite concentrations, thereby capturing aging signals from healthspan and adverse outcomes. However, existing clocks generally assume homogeneous aging trajectories and yield only a single age acceleration metric, limiting their capacity to capture inter-individual metabolic heterogeneity and characterize nuanced individual-level representations. To address these limitations, we proposed MetFoundation, a metabolomic foundation model pre-trained on nuclear magnetic resonance (NMR) metabolomic profiles from over 430,000 participants in UK Biobank via self-supervised learning. This large-scale pre-training enables MetFoundation to learn a metabolomic representation space that captures the complex, nonlinear structure of systemic metabolism as reflected in NMR data. Building on MetFoundation, we developed a mortality-informed metabolomic aging clock by fine-tuning an attached survival module, deriving age acceleration that d

  📄 ResearchMay 20, 2026https://www.biorxiv.org/content/10.64898/2026.05.18.725977v1?rss=1
• Emergent Entrainment and Predictive Dynamics in Bio-Inspired Spiking Neural Networks

  Rhythm is a key building block of human music, speech and numerous other human activities. Understanding the computational substrates of rhythm perception requires models that bridge algorithmic function with biological implementation. We propose a physiologically grounded spiking neural network (SNN) framework to investigate the emergent representation and interpretation of auditory rhythms. Utilizing a recurrent SNN architecture trained on an auditory entrainment task, we characterize the network's latent dynamics through the analysis of firing rates and membrane potential fluctuations. Our results demonstrate that simulated neural populations exhibit phase-locking to the stimulus beat, with endogenous oscillations driven by rhythmic input. We further show that anticipatory dynamics--characterized by pre-stimulus depolarization--emerge naturally from the network's synaptic plasticity and temporal integration properties, rather than from explicitly defined oscillators. By treating net

  📄 ResearchMay 20, 2026https://www.biorxiv.org/content/10.64898/2026.05.18.725874v1?rss=1
• Predicting and Elucidating Peptide Retention Mechanisms with Graph Attention Networks

  Liquid chromatography (LC) is a key technology in bottom-up proteomics, separating proteolytic peptides to decrease sample complexity, enhance coverage, and increase the robustness of protein identification and quantification. Although high-resolution mass spectrometry has advanced significantly, comparable progress in LC has lagged, primarily due to a limited understanding of peptide-column interactions. To bridge this knowledge gap, we introduce a novel deep learning model (PeptideGNN) based on a Graph Neural Network (GNN) architecture to model and elucidate peptide behaviors across various separation conditions. Trained to accurately predict peptide retention times on ten diverse proteomic datasets, the model subsequently employed a saliency mapping technique to interpret the underlying retention mechanisms. Our model consistently outperformed existing retention-time predictors across multiple datasets, while the saliency mapping, importantly, revealed insights into peptide-stationa

  📄 ResearchMay 20, 2026https://www.biorxiv.org/content/10.64898/2026.05.18.725893v1?rss=1
• From 3D Time-of-Flight Angiography to Accelerated 4D Arterial Spin Labeling Angiography: A Fast Few-Shot Transfer Learning Approach

  Purpose: To develop a data-efficient deep learning framework for rapid reconstruction of highly accelerated 4D arterial spin labeling (ASL) magnetic resonance angiography (MRA) with robust generalization using extremely limited acquired data, addressing the challenges of prolonged acquisition and reconstruction time. Methods: A simulation-driven, few-shot transfer learning approach was adopted by leveraging publicly available 3D time-of-flight (TOF)-MRA data to generate realistic multi-coil complex-valued pseudo-ASL k-space datasets for large-scale pre-training. A 3D unrolled reconstruction network was trained on this simulated data using a histogram-weighted loss and subsequently extended to 4D using lightweight temporal fusion modules. Fine-tuning was performed using only two experimentally acquired 4D ASL-MRA datasets. The method was evaluated on retrospectively and prospectively undersampled Cartesian 4D ASL-MRA data acquired at 3T and compared with compressed sensing (CS) and loca

  📄 ResearchMay 20, 2026https://www.biorxiv.org/content/10.64898/2026.05.18.725892v1?rss=1
• Incomplete letter recognition is limited by cortical and not optical factors: Simulating the visual deficits of dementia in healthy adults

  Incomplete letter recognition tasks are frequently used to detect visual deficits arising from neurodegenerative syndromes, including Posterior Cortical Atrophy (PCA; 'visual-variant Alzheimer's disease'). A recent development of this approach is the Graded Incomplete Letters Test (GILT), which measures recognition thresholds for letters degraded by removing pixelated sections (decreasing 'completeness'). Although GILT thresholds are strongly elevated in PCA relative to typical adults, the precise cortical visual impairments underlying these deficits are unclear, as is the potential contribution from age-related optical limitations. We compared candidate cortical factors (crowding and global integration) with optical limitations (blur and low contrast) by simulating these factors in typical adults (n=6) viewing incomplete letter stimuli. Participants identified foveally presented letters (12 alternatives), with completeness varied using QUEST. At baseline, thresholds averaged ~5% compl

  📄 ResearchMay 20, 2026https://www.biorxiv.org/content/10.64898/2026.05.18.725904v1?rss=1
• Gut microbiota signatures differentiate trajectory-defined response phenotypes and predict self-management outcomes in irritable bowel syndrome

  Background: Heterogeneity in symptom presentation and treatment response in irritable bowel syndrome (IBS) remains poorly understood. The gut microbiota may contribute to this variability, but its role in shaping symptom trajectories and responses to self-management interventions is unclear. Objective: To identify symptom trajectory phenotypes and determine whether gut microbiota composition and function distinguish these phenotypes and predict multidimensional responses to pain self-management interventions in young adults with IBS. Design: Ancillary data analysis from a randomized control trial (NCT03332537). Methods: Participants with longitudinal data (n = 62) were analyzed using longitudinal k-means clustering (KML) based on trajectories of measures in IBS quality of life (QOL), Brief Pain Inventory (BPI), and psychoneurological outcomes (anxiety, applied cognition, depression, fatigue, global health, positive affect, and sleep disturbance) over 12 weeks. Baseline differences betw

  📄 ResearchMay 20, 2026https://www.medrxiv.org/content/10.64898/2026.05.18.26353470v1?rss=1
• A pan-cancer regulatory atlas of 6,983 GWAS variants prioritizes recurrent regulatory annotations and candidate programs at cancer risk loci

  Genome-wide association studies have identified thousands of cancer risk variants in non-coding regions, yet their regulatory mechanisms remain largely uncharacterized. Here we present a regulatory annotation atlas of 6,983 genome-wide significant variants across 23 cancer types, scored using multimodal AlphaGenome predictions and integrated with ENCODE-4, Roadmap Epigenomics, and JASPAR 2024 annotations. Most variants (70.5%) fall outside annotated cis-regulatory elements; 27.7% overlap enhancers and 1.4% overlap promoters. Comparison with 6,626 position-matched eQTL control variants suggests that enhancer-classified variants carry 1.86-fold higher predicted effects (P = 1e-94) and promoter variants 7.84-fold (P = 2.5e-19). A composite prioritization score (RegVar-basic, excluding GWAS-derived pleiotropy and TF disruption, AUC = 0.650; RegVar-full, AUC = 0.675) outperforms CADD (0.499) and LINSIGHT (0.558) in this cancer-gene discrimination benchmark. Within-locus ranking across 2,626

  📄 ResearchMay 20, 2026https://www.medrxiv.org/content/10.64898/2026.05.16.26353369v1?rss=1
• Non-invasive Transcriptomic Cell Profiling of the Human Endometrium with Generative Deep Learning

  Background Delineating the cellular origins of extracellular vesicles (EVs) enables the detection of clinically relevant changes in dynamic and complex tissues, such as the endometrium, which are not characterizable through single biomarker assays. Transcriptome deconvolution into cellular composition using deep learning methods provides a means to explore this complexity. However, such computational methods have not been previously applied to EV bulk transcriptomes, and their efficacy in profiling EV population changes and concordance to tissue throughout the menstrual cycle remains unknown. Methods This observational cross-sectional study utilized a deconvolutional generative deep learning algorithm, BulkTrajBlend, trained on a comprehensive human endometrial single-cell RNA sequencing (scRNA-seq) atlas. The model was applied to deconvolve paired bulk transcriptomes from endometrial tissue and uterine fluid EVs (UF-EVs) across the proliferative (P, n=4), early-secretory (ES, n=5), mi

  📄 ResearchMay 20, 2026https://www.medrxiv.org/content/10.64898/2026.05.18.26352867v1?rss=1
• Benchmarking General-Purpose and Medical AI Large Language Models for Clinical Assessment and Management in Parkinson's Disease

  Background: The clinical applicability of large language models (LLMs) in Parkinson's disease (PD) management remains insufficiently characterized, particularly in generative responses to clinical vignette scenarios. Objective: To evaluate the quality of clinical assessments and management plans generated by a general-purpose LLM (Gemini 1.5 Pro) and a medically specialized LLM (OpenEvidence), and to compare their performance. Methods: Models generated free-text responses to 45 open clinical queries, focused on assessment of the situation, and recommended management plan. Two movement disorders fellows rated outputs using 5-point Likert scales, dichotomized into clinically appropriate ([≥]4) versus inappropriate ([≤]3). Discrepancies were adjudicated by a senior movement disorders specialist. Paired comparisons used McNemar's test; qualitative analysis examined severe errors. Results: Gemini 1.5 Pro and OpenEvidence showed high rates of clinically appropriate assessments (80.0% vs. 86.

  📄 ResearchMay 20, 2026https://www.medrxiv.org/content/10.64898/2026.05.13.26353021v1?rss=1
• Psychological Stress-Associated Ceramide and Diacylglyceride Lipotoxicity as Contributors to First Episode Depression Pathophysiology: A neuroimmune-Metabolic-Oxidative Stress (NIMETOX) Perspective

  Background: Aberrations in neuro-immune, metabolic, and oxidative stress (NIMETOX) pathways are implicated in major depressive disorder (MDD). First-episode simple dysmood disorder (FE-SDMD) without metabolic syndrome offers a unique model to investigate early lipid alterations underlying NIMETOX pathophysiology. Methods: Plasma samples were collected from 88 university students (44 FE-SDMD, 44 healthy controls). Participants underwent comprehensive psychiatric and psychological assessments, including adverse childhood experiences (ACEs), negative life events (NLEs), depression, anxiety, suicidal behaviors, and insomnia. Untargeted lipid profiling was performed using LC-QTOF-MS, while indices of oxidative and nitrosative stress (ONS) and lecithin-cholesterol acyltransferase (LCAT) activity were assessed. Data was analyzed using machine learning approaches with recursive feature elimination and cross-validation. Results: FE-SDMD was characterized by increased ceramides (CER), diacylglyc

  📄 ResearchMay 20, 2026https://www.medrxiv.org/content/10.64898/2026.05.18.26353450v1?rss=1
• ALARM-Net: An Event-Level False-Alarm Suppression Framework for Clinical EEG Seizure Detection on TUSZ v2.0.6

  Automated electroencephalography (EEG) seizure detection systems support clinical monitoring through alarm-driven workflows, in which the practical utility of a detector is determined by its event-level false-alarm rate. We examine the false-alarm structure produced by a strong window-level seizure detector on the Temple University Hospital Seizure Corpus (TUSZ) v2.0.6 and find that the false-alarm burden is unevenly distributed across subjects, with worst-decile subjects carrying substantially higher FA/24h than the cohort median. We propose ALARM-Net (Alarm-Level Adaptive Rejection Module), a detector-agnostic event-level alarm-suppression framework. ALARM-Net treats the window-level detector as a frozen black box, generates high-recall event proposals from its per-second probability timeline, and applies a regularized CatBoost classifier over 14 causal features summarizing each proposal's probability morphology, local pre-context, and alarm history. Operating-point selection is gove

  📄 ResearchMay 20, 2026https://www.medrxiv.org/content/10.64898/2026.05.17.26353436v1?rss=1
• Schedly

  AI Social Media Management Platform

  🧰 ToolsMay 20, 2026https://www.producthunt.com/products/schedly-2?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+the500feed+%28ID%3A+283491%29
• Frontal Cortex-Subthalamic Nucleus Beta Oscillations Exhibit Phase Locking and Granger Causality in Parkinson's Disease

  Objective. Pathological beta oscillations are a hallmark of Parkinson's Disease (PD) and are linked with symptom severity and therapeutic efficacy of deep brain stimulation (DBS). Although some studies suggest that beta oscillations may propagate from the frontal cortex to the subthalamic nucleus (STN), direct evidence based on cortical and subcortical neural recordings remains limited. This study investigates synchrony and directionality of beta-band interactions between the frontal cortex and STN in PD. Approach. Simultaneous electrocorticography and STN local field potential recordings were obtained from three PD patients undergoing awake DBS lead placement surgery. Cortical-STN beta phase synchrony was quantified using phase locking value, and directed functional connectivity was analyzed using time-resolved bivariate Granger causality. Main results. Phase locking value mapping revealed a spatially non-uniform distribution of beta phase synchrony, with the strongest coupling locali

  📄 ResearchMay 20, 2026https://www.medrxiv.org/content/10.64898/2026.05.13.26348975v1?rss=1
• SoloScripter

  Privacy first AI emails that sound human

  🧰 ToolsMay 20, 2026https://www.producthunt.com/products/soloscripter?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+the500feed+%28ID%3A+283491%29
• New report: U.S. Government is using AI more, but still has a long way to go

  New report: U.S. Government is using AI more, but still has a long way to go EurekAlert!

  🌐 MovesMay 20, 2026https://www.eurekalert.org/news-releases/1129060
• AI system automates coding for scientific research

  AI system automates coding for scientific research EurekAlert!

  🌐 MovesMay 20, 2026https://www.eurekalert.org/news-releases/1128963