Modifying Alzheimer's disease pathophysiology with photobiomodulation: model, evidence, and future with EEG-guided...

This manuscript outlines a model of Alzheimer's Disease (AD) pathophysiology in progressive layers, from its genesis to the development of biomarkers and then to symptom expression. Genetic predispositions are the major factor that leads to mitochondrial dysfunction and subsequent amyloid and tau protein accumulation, which have been identified as hallmarks of AD. Extending beyond these accumulations, we explore a broader spectrum of pathophysiological aspects, including the blood-brain barrier, blood flow, vascular health, gut-brain microbiodata, glymphatic flow, metabolic syndrome, energy deficit, oxidative stress, calcium overload, inflammation, neuronal and synaptic loss, brain matter atrophy, and reduced growth factors. Photobiomodulation (PBM), which delivers near-infrared light to selected brain regions using portable devices, is introduced as a therapeutic approach. PBM has the potential to address each of these pathophysiological aspects, with data provided by various studies. They provide mechanistic support for largely small published clinical studies that demonstrate improvements in memory and cognition. They inform of PBM's potential to treat AD pending validation by large randomized controlled studies. The presentation of brain network and waveform changes on electroencephalography (EEG) provide the opportunity to use these data as a guide for the application of various PBM parameters to improve outcomes. These parameters include wavelength, power density, treatment duration, LED positioning, and pulse frequency. Pulsing at specific frequencies has been found to influence the expression of waveforms and modifications of brain networks. The expression stems from the modulation of cellular and protein structures as revealed in recent studies. These findings provide an EEG-based guide for the use of artificial intelligence to personalize AD treatment through EEG data feedback.