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Measuring Stress

One of the great mysteries of the brain is how it perceives pain and stress. By studying the brains of youth and adults enduring types of social discrimination and physical pain, UMBC researchers hope to make in-roads into early intervention practices for patients.

Raimi Quiton, an assistant professor of psychology who was trained as a neuroscientist, is interested in understanding how pain perception can be impacted by biopsychosocial factors, such as race/ethnicity, gender, age, and socioeconomic status.

“There are large disparities in social group membership or socioeconomic group membership in terms of who develops chronic pain,” says Quiton, explaining that discrimination and marginalization can affect the brain, how the brain processes pain, and how people experience pain.

While studies show that women, African-Americans, and older people experience chronic pain at higher rates, Quiton says that the underlying brain mechanisms of why the risk may be higher for those groups of people is unclear, and the impact of social disparities on the brain have not been widely studied. “The overarching goal of the research is to characterize what is happening in the brain that may predispose certain individuals to developing chronic pain later in life, and to try to identify mediating social factors so that there can be early intervention for at-risk individuals,” she explains.

Quiton also studies how people with healthy brains process pain compared to people with post-traumatic stress disorder. To study the effect of pain on the brain, Quiton uses short, harmless bursts of heat pain and pressure pain on human subjects, evoking a response in the brain that can be observed by Quiton on the screens outside of the fMRI machine. (These types of pain stimuli do not harm the human subject or damage the tissue where the pain stimulus is applied.)

“Lived experience really impacts how the brain functions and how pain is processed,” Quiton said.

Tinoosh Mohsenin, assistant professor of computer science and electrical engineering and director of Energy Efficient High Performance Computing (EEHPC) Lab, recently received a CAREER Award from the National Science Foundation for her work developing deep-learning technology and machine-learning algorithms that can be used in medical and non-medical applications. Deep-learning algorithms are modeled and developed to potentially function like the brain.

Mohsenin is also developing battery powered and cost-effective computing techniques that can monitor brain signals and recognize patterns in brain function. These wearable devices can monitor stress, detect seizures, and possibly identify cancer by analyzing brain waves and image data. “The deep-learning technology can potentially help detect cancer with greater accuracy by providing more information than is found in standard imaging,” she says.

Mohsenin hopes deep-learning technologies will help physicians and medical professionals detect seizures and cancer more quickly and accurately by improving the analysis of highly complex brain signals and data images, beyond what can be gleaned from today’s standard brain monitoring and analysis techniques.

“Current deep-learning models have not been explored for power-constrained smart devices, and this research can potentially revolutionize several fields, including health care, transportation, ecology, surveillance, and public utilities,” she explains.

Mohsenin hopes her work will also help people with significant mobility limitations who use small multi-modal sensors on their tongues as well as other methods to maneuver wheelchairs or command other technologies. More complex algorithms and their efficient hardware implementation can notably improve the responsiveness of such tools for users.