Paper List
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MCP-AI: Protocol-Driven Intelligence Framework for Autonomous Reasoning in Healthcare
This paper addresses the critical gap in healthcare AI systems that lack contextual reasoning, long-term state management, and verifiable workflows by...
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Model Gateway: Model Management Platform for Model-Driven Drug Discovery
This paper addresses the critical bottleneck of fragmented, ad-hoc model management in pharmaceutical research by providing a centralized, scalable ML...
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Tree Thinking in the Genomic Era: Unifying Models Across Cells, Populations, and Species
This paper addresses the fragmentation of tree-based inference methods across biological scales by identifying shared algorithmic principles and stati...
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SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
This paper addresses the core challenge of training robust multi-label EEG artifact classifiers by overcoming the scarcity and limited diversity of ma...
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Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening
This paper addresses the core challenge of objectively quantifying listeners' selective attention to specific musical components (e.g., vocals, drums,...
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Physics-Guided Surrogate Modeling for Machine Learning–Driven DLD Design Optimization
This paper addresses the core bottleneck of translating microfluidic DLD devices from research prototypes to clinical applications by replacing weeks-...
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Mechanistic Interpretability of Antibody Language Models Using SAEs
This work addresses the core challenge of achieving both interpretability and controllable generation in domain-specific protein language models, spec...
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Fluctuating Environments Favor Extreme Dormancy Strategies and Penalize Intermediate Ones
This paper addresses the core challenge of determining how organisms should tune dormancy duration to match the temporal autocorrelation of their envi...
Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
Southern University of Science and Technology | Brown University | University of Illinois Urbana-Champaign | University of Warwick | Carle Foundation Hospital
30秒速读
IN SHORT: This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization framework that accounts for developmental anatomical variability and tissue conductivity uncertainty.
核心创新
- Methodology First dual-objective optimization framework for pediatric HD-tDCS that generates personalized Pareto fronts balancing target intensity and focality, enabling systematic trade-off analysis.
- Methodology Introduction of two clinically actionable strategies: dose-consistency (enforcing fixed target intensity across individuals) and target-engagement (maximizing intensity under safety limits), both robust to conductivity variations.
- Biology First systematic quantification of depth-dependent tissue conductivity sensitivity: superficial targets dominated by scalp/bone conductivities (R² up to 0.85), while deep targets shaped by gray/white matter conductivities.
主要结论
- Conventional montages show significant age-dependent reductions in target intensity (p<0.05, FDR-corrected) and systematic sex differences mediated by scalp volume (mediation effect p<0.05).
- Optimized solutions Pareto-dominate conventional approaches, achieving 15-25% higher focality at matched intensity and 20-30% higher intensity at matched focality (Mann-Whitney U, p<0.001).
- Both optimization strategies remain robust under large conductivity variations (1,800 optimizations across 600 perturbed models), with sparse electrode configurations (<0.1 mA threshold) preserving performance (n.s., Mann-Whitney U).
摘要: High-definition transcranial direct current stimulation (HD-tDCS) dosing in children remains largely empirical, relying on one-size-fits-all protocols despite rapid developmental changes in head anatomy and tissue properties that strongly modulate how transcranial currents reach the developing brain. Using 70 pediatric head models (ages 6–17) and commonly used cortical targets (primary motor cortex and left dorsolateral prefrontal cortex), our forward simulations find that standard montages produce marked age-dependent reductions in target electric-field intensity and systematic sex differences linked to tissue-volume covariation, underscoring the profound limitations of conventional uniform montages. To overcome these limitations, we introduce a developmentally informed, dual-objective optimization framework designed to generate personalized Pareto fronts summarizing the trade-off between electric-field intensity and focality. These subject-specific fronts reveal systematic performance improvements over conventional montages, yielding both higher focality at matched target intensity and higher intensity at matched focality. From these optimized solutions, we derive two clinically practical dosing prescriptions: a dose-consistency strategy that, for the first time, explicitly enforces fixed target intensity across individuals to implicitly mitigate demographic effects, and a target-engagement strategy that maximizes target intensity under safety limits. Both strategies remain robust to large conductivity variations, and we further show that dense HD-tDCS solutions admit sparse equivalents without performance loss under the target-engagement strategy. Across 1,800 optimizations in 600 conductivity-perturbed head models, we also find that tissue conductivity sensitivity is depth-dependent, with Pareto-front distributions for superficial cortical targets most influenced by gray matter, scalp, and bone conductivities, and those for a deep target predominantly shaped by gray and white matter conductivities. Together, these results establish a principled framework for pediatric HD-tDCS planning that explicitly accounts for developmental anatomy and physiological uncertainty, enabling reliable and individualized neuromodulation dosing in vulnerable pediatric populations.