Researchers have identified key biomarkers of mild cognitive impairment (MCI) – considered an early stage of cognitive decline and a precursor to conditions such as Alzheimer’s disease – involving neurovascular coupling (NVC), functional connectivity (FC), and cerebrovascular endothelial extracellular vesicles (CEEVs). The study highlights changes in these measurements as a potential diagnostic tool for detecting early stages of cognitive decline.
Credit: University of Oklahoma
Using a combination of functional near-infrared spectroscopy (fNIRS) and flow cytometry, the study examined how brain function and cerebrovascular health differ between individuals with MCI and healthy controls. The research centered on neurovascular coupling – how neuronal activity changes with cerebral blood flow. Researchers found that NVC responses in the left dorsolateral prefrontal cortex (LDLPFC), a critical brain region for working memory, were significantly impaired in participants with MCI compared with controls. These impairments were associated with decreased blood flow and diminished FC in the brain, suggesting that these markers could help differentiate between healthy aging and MCI.
Additionally, the researchers found higher concentrations of CEEVs in individuals with MCI – and that those levels correlated with small vessel ischemic damage in the brain, measured via MRI. The increased presence of these vesicles suggests that they could serve as biomarkers for cerebrovascular pathology and cognitive decline.
"Every brain is different, and there may be differing reasons for cognitive impairment, but having these predictors – measuring neurovascular coupling, functional connectivity, and CEEVs – potentially opens opportunities to develop individualized interventions, whether it's a pharmacological therapy or non-invasive brain stimulation, or something as simple as cognitive behavioral therapy," said Andriy Yabluchanskiy, OU College of Medicine associate professor of neurosurgery and co-author of the study, in a press release.
Researchers also used a machine-learning model to classify MCI using a combination of these factors – NVC, FC, and CEEV concentration. The model demonstrated an 85 percent accuracy rate, highlighting the potential of these biomarkers in early diagnosis.
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