ArticleNeurophotonics2025
Quantitative simulation of near-infrared light treatment for Alzheimer's disease using patient-individualized optical-parametric phantoms.
Article in Neurophotonics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Photobiomodulation for skeletal muscle regeneration: mechanisms, dosimetry, and translational challenges.Frontiers in bioengineering and biotechnology · 2026Review
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Authors and funding
5 authors.
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Abstract
Significance: Alzheimer's disease (AD) is a brain disorder characterized by its multifactorial nature and complex pathogenesis, highlighting the necessity for multimodal and individualized interventions. Among emerging therapies, near-infrared (NIR) light treatment shows promise as a therapeutic modality for AD. However, existing clinical studies lack sufficient data on light dosimetry, parameter optimization, and dose-response. Aim: A versatile framework was developed to enable patient-individualized Monte Carlo simulation. A standardized dataset was established, including digital phantoms derived from 20 AD patients who received NIR light treatment. Approach: The phantoms were synthesized and mapped with multispectral optical parameters, integrating cortical parcellation, subcortical segmentation, and sparse annotation. Structure-related light fluence pathways and dose-response relationships were elucidated using simulation results and cognitive/functional assessments. Results: The capability for enhancing simulation fidelity and exploring dose-response relationships was verified using standard templates and clinical data. Linear independence was identified between changes in activities of daily living scale scores and energy deposition in gray matter. Conclusions: The framework offers a solution toward dose-response analysis, parameter optimization, and safety control in the clinical translation for multiple treatment paradigms, demonstrating promise for individualized, standardized, and precise intervention planning.
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