Paper List
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Ill-Conditioning in Dictionary-Based Dynamic-Equation Learning: A Systems Biology Case Study
This paper addresses the critical challenge of numerical ill-conditioning and multicollinearity in library-based sparse regression methods (e.g., SIND...
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Hybrid eTFCE–GRF: Exact Cluster-Size Retrieval with Analytical pp-Values for Voxel-Based Morphometry
This paper addresses the computational bottleneck in voxel-based neuroimaging analysis by providing a method that delivers exact cluster-size retrieva...
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abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance
This paper addresses the critical challenge of quantitatively evaluating antibiotic prescribing policies under realistic uncertainty and partial obser...
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PesTwin: a biology-informed Digital Twin for enabling precision farming
This paper addresses the critical bottleneck in precision agriculture: the inability to accurately forecast pest outbreaks in real-time, leading to su...
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Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation
This paper addresses the core challenge of generating physically plausible 3D molecular structures by bridging the gap between autoregressive methods ...
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Omics Data Discovery Agents
This paper addresses the core challenge of making published omics data computationally reusable by automating the extraction, quantification, and inte...
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Single-cell directional sensing at ultra-low chemoattractant concentrations from extreme first-passage events
This work addresses the core challenge of how a cell can rapidly and accurately determine the direction of a chemoattractant source when the signal is...
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SDSR: A Spectral Divide-and-Conquer Approach for Species Tree Reconstruction
This paper addresses the computational bottleneck in reconstructing species trees from thousands of species and multiple genes by introducing a scalab...
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.