Mechanisms and parameters of photobiomodulation for neurological and neuropsychiatric disorders: whether and how to apply?
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Qi Cao
,
Zhongqi Liu
and
Lifeng Wang
Abstract
Neurological and neuropsychiatric disorders are among the leading causes of mortality and disability worldwide, with current treatment modalities including traditional therapies, psychological and supportive interventions, and emerging therapeutic approaches. Photobiomodulation (PBM), a neuromodulatory technique using lasers and light-emitting diodes (LEDs), has emerged as a promising intervention for enhancing brain function by stimulating neural activity, thereby protecting brain tissue and restoring function. Despite its widespread application, the precise mechanisms underlying the selection of critical parameters and their associated therapeutic effects remain incompletely understood. This systematic review synthesizes data from multiples studies over the past decade, investigating the effects of PBM on neurological and neuropsychiatric disorders, including traumatic brain injury (TBI), spinal cord injury (SCI), Alzheimer’s disease (AD), Parkinson’s disease (PD), generalized anxiety disorder (GAD), major depressive disorder (MDD), and healthy subjects. Emerging evidence suggests that the therapeutic mechanisms of PBM may involve enhanced energy metabolism, increased cerebral blood flow (CBF), modulation of oxidative stress, anti-inflammatory effects, neuroprotection and regeneration, enhanced synaptic plasticity, and regulation of resting-state brain networks. Regarding parameter selection, wavelength has emerged as a critical factor influencing penetration depth and the specific chromophore responsible for photon absorption and therapeutic efficacy. This review focuses on the characteristics of diverse wavelengths, as well as the roles of multiple chromophores and associated signaling pathways. Different irradiation modalities, including both non-invasive and invasive approaches, are examined, alongside optimal treatment windows for power and fluence. Additionally, less frequently addressed aspects, such as spot area and power density patterns, are considered.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Conceptualization, Q.C.; literature search, Q.C. and Z.L.; software, Q.C. and Z.L.; writing – original draft preparation, Q.C.; writing – review and editing, L.W. and H.K.; visualization, Q.C. and Z.L.; supervision, L.W. and H.K.; project administration, L.W. and H.K.; funding acquisition, L.W. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work the authors employed ChatGPT and Kimi to refine language including grammar and sentence structure. Following the use of these tools, the authors thoroughly reviewed and edited the content as necessary and take full responsibility for the content of the publication.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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