Alzheimer’s affects millions of people around the world, but it remains one of the most perplexing diseases. It materializes in the brain, sometimes progressing rapidly even before behavioral signs or symptoms are discovered.
Recent developments show that it is possible to detect it more quickly and effectively. A team led by the University of Texas at Arlington (UTA) is working on a dual-mode brain detection device that can quickly and non-invasively detect the disease. Funded by a grant from the National Institutes of Health, the new technology uses broadband near-infrared spectroscopy (bbNIRS), a noninvasive technology that can quantify metabolic, neurovascular, or hemodynamic functions and activities in the human brain. It is based on the absorption and emission of near-infrared light through the cortex, and is combined with multichannel electroencephalograms (EEG) of the scalp, which can provide quantitative mapping of electrophysiological activities in the human brain (see video).
“Over the past two years, we have worked to develop novel devices and data analysis algorithms that allow us to quantify the optical properties of brain tissue using a combination of dry EEG and optical headband systems at the same time,” says Hanli Liu, Professor bioengineering at UTA. and the principal investigator of this work.
With bbNIRS, changes in brain concentrations of oxidized cytochrome-c-oxidase (oxCcO), oxygenated hemoglobin (HbO), and total hemoglobin (HbT) in the human forehead Live it can be quantified with excellent reproducibility and reliability, he says, adding that CcO facilitates oxygen utilization for cellular energy metabolism.
As oxygenated blood in the brain reflects vascular physiology and hemodynamic conditions and is therefore closely associated with brain health. Neurophysiology-based parameters such as brain oxCcO, HbO, and HbT may be altered or impaired in the human Alzheimer’s brain compared to that of normal older adults. Liu notes that researchers have found bbNIRS Live is able to distinguish patients with the disease from age-matched controls.
Currently, methods for detecting Alzheimer’s disease rely on MRIs and positron emission tomography (PET) scans to view and assess behaviors. But these techniques are not as comprehensive when it comes to identifying symptoms, particularly in the early stages.
“The weaknesses of behavioral assessments include that they are not quantitative or sensitive for early detection,” says Liu. “It’s common for early-stage Alzheimer’s patients to not display any behavioral signs or symptoms.”
Since the Alzheimer’s brain forms significant degenerations in all anatomical, biological, and pathological aspects, such impairments must be accompanied by brain dysfunctions of metabolic, hemodynamic, and electrophysiological (MHE) activities.
“Our dual-mode system is a high-risk, high-impact, proof-of-principle approach to demonstrate that non-invasive spectroscopy measurements of brain tissue can contain key neurophysiological signature information,” says Liu.
These signatures can serve as digital biomarkers to identify and cross-validate. They are also used for the accurate detection of Alzheimer’s patients at different levels of severity. With the new device, researchers will be able to identify neurophysiological biomarkers that can ultimately be used in the detection of Alzheimer’s disease for earlier, faster and more accurate detection in mild or moderate to severe stages.
The team hopes that their study will enable the identification of digital biomarkers critical for the early detection of Alzheimer’s disease at various stages. And, in theory, the new technology could one day be applied to other diseases of the brain.
“The success of the study will allow us to conduct a larger clinical trial that will validate and promote digital biomarkers for early, rapid, low-cost, and accurate identification of disease,” Liu says, noting that early detection is key to an effective diagnosis. treatment.