Application of Photostimulation Therapy in Cognitive Function of Alzheimer's Disease Patients: A Scoping Review

Alzheimer's disease patients often experience cognitive decline during disease progression, impacting their quality of life. Photostimulation therapy, as a non-invasive neuromodulation method, has been increasingly used in the adjunctive management of Alzheimer's patients in recent years. Despite the increasing number of related studies, a systematic review and comprehensive evaluation are still lacking.

This review systematically summarizes the current application of photostimulation therapy in Alzheimer's disease patients, outlines its implementation characteristics, outcome indicators, intervention effects and mechanisms of action in cognitive function intervention, and identifies evidence gaps in current research.

Relevant studies were systematically retrieved from PubMed, Web of Science, Cochrane Library, Embase, CINAHL, CNKI, CBM, WanFang Database, and VIP Database from their inception to January 5, 2026. Data from the included literature were extracted and analyzed.

A total of 21 studies were included. Photostimulation therapy primarily includes light stimulation, 40 Hz rhythm-related light stimulation, photobiological modulation and their combined therapies, as well as transcatheter intracranial laser therapy. This therapy has potential value in improving cognitive function or delaying cognitive decline and may affect sleep/rhythm and behavioral symptoms. Its potential mechanisms involve neural oscillation modulation, circadian rhythm reconstruction, and improvement of synaptic plasticity. However, existing evidence exhibits significant heterogeneity in study design, sample size, intervention parameters, and outcome indicators.

Photostimulation therapy has shown promising potential in cognitive function intervention for Alzheimer's disease patients, but current evidence is still largely in the exploratory stage. Future research should focus on multi-center, large-sample, and standardized studies to determine the optimal parameter combinations for different stages and scenarios, and to optimize individualized intervention protocols to improve clinical efficacy.