Introduction We and collaborators discovered that flickering lights and sound at gamma frequency (40 Hz) reduce Alzheimer's disease (AD) pathology and alter immune cells and signaling in mice. To determine the feasibility of this intervention in humans we tested the safety, tolerability, and daily adherence to extended audiovisual gamma flicker stimulation. Methods Ten patients with mild cognitive impairment due to underlying AD received 1‐hour daily gamma flicker using audiovisual stimulation for 4 or 8 weeks at home with a delayed start design. Results Gamma flicker was safe, tolerable, and adherable. Participants’ neural activity entrained to stimulation. Magnetic resonance imaging and cerebral spinal fluid proteomics show preliminary evidence that prolonged flicker affects neural networks and immune factors in the nervous system. Discussion These findings show that prolonged gamma sensory flicker is safe, tolerable, and feasible with preliminary indications of immune and network effects, supporting further study of gamma stimulation in AD.

Alzheimer’s disease (AD) is a central nervous system (CNS) disease characterized by loss of memory, cognitive functions, and neurodegeneration. Plasmin is an enzyme degrading many plasma proteins. In the CNS, plasmin may reduce the accumulation of beta amyloid (Aβ) and have other actions relevant to AD pathophysiology. Brain plasmin synthesis is regulated by two enzymes: one activating, the tissue plasminogen activator (tPA), and the other inhibiting, the plasminogen activator inhibitor-1 (PAI-1). We investigated the levels of tPA and PAI-1 in serum from 40 AD and 40 amnestic mild cognitively impaired (aMCI) patients compared to 10 cognitively healthy controls. Moreover, we also examined the PAI-1/tPA ratio in these patient groups. Venous blood was collected and the PAI-1 and tPA serum concentrations were quantified using sandwich ELISAs. The results showed that PAI-1 levels increased in AD and aMCI patients. This increase negatively correlated with cognitive performance measured using the Mini-Mental Status Exam (MMSE). Similarly, the ratio between tPA and PAI-1 gradually increases in aMCI and AD patients. This study demonstrates that AD and aMCI patients have altered PAI-1 serum levels and PAI-1/tPA ratio. Since these enzymes are CNS regulators of plasmin, PAI-1 serum levels could be a marker reflecting cognitive decline in AD.

Smart mobile phone use is increasing worldwide, as is the ability of mobile devices to monitor daily routines, behaviors, and even cognitive changes. There is a growing opportunity for users to share the data collected with their medical providers which may serve as an accessible cognitive impairment screening tool. Data logged or tracked in an app and analyzed with machine learning (ML) could identify subtle cognitive changes and lead to more timely diagnoses on an individual and population level. This review comments on existing evidence of mobile device applications designed to passively and/or actively collect data on cognition relevant for early detection and diagnosis of Alzheimer's disease (AD). The PubMed database was searched to identify existing literature on apps related to dementia and cognitive health data collection. The initial search deadline was December 1, 2022. Additional literature published in 2023 was accounted for with a follow-up search prior to publication. Criteria for inclusion was limited to articles in English which referenced data collection via mobile app from adults 50+ concerned, at risk of, or diagnosed with AD dementia. We identified relevant literature (n = 25) which fit our criteria. Many publications were excluded because they focused on apps which fail to collect data and simply provide users with cognitive health information. We found that although data collecting cognition-related apps have existed for years, the use of these apps as screening tools remains underdeveloped; however, it may serve as proof of concept and feasibility as there is much supporting evidence on their predictive utility. Concerns about the validity of mobile apps for cognitive screening and privacy issues remain prevalent. Mobile applications and use of ML is widely considered a financially and socially viable method of compiling symptomatic data but currently this large potential dataset, screening tool, and research resource is still largely untapped.