Spring Research Roundup 2026

Scientists at the Yale School of Public Health (YSPH) have developed a process that could lead to a better understanding of how brain function is connected to behavior.

In a study published in Nature Methods, the researchers introduce LatentSNA, a novel analytical method which helps scientists find clearer links between patterns in the brain and things like emotions, thinking, and mental health.

Brain scans, such as MRI, produce very complex data, but they tend to isolate brain performance and miss larger brain networks acting in unison. The LatentSNA approach looks at the brain as a whole system, identifying broader patterns and allowing researchers to capture how different regions work together.

Using data from thousands of people across multiple large studies, the researchers found that their method was much more accurate than existing tools. In some cases, it improved the ability to detect meaningful brain–behavior links by more than 100% and made results more consistent across studies.

The method also revealed new insights into mental health. For example, in children, the researchers found that emotional problems like anxiety and depression are linked to disruptions across many parts of the brain, not just one area. These disruptions often form “hub-and-spoke” patterns, where a few key brain regions play a central role in communication.

“Brain networks have long been recognized as central to behavior and disease, yet our analytical tools have fallen behind in capturing their meaningful neurobiological topology. LatentSNA closes that gap,” said senior author Dr. Yize Zhao, PhD, YSPH associate professor of biostatistics. “By embedding network science into a Bayesian inference framework, we can now detect brain–behavior relationships with more than double the accuracy and reliability of existing tools. This opens a new window into how disruptions in brain network organization give rise to mental health conditions and neurological disease, bringing us meaningfully closer to biomarkers that can transform diagnosis and care."

Lead author Dr. Selena Wang, PhD, an assistant professor at the Indiana University School of Medicine, conducted the study when she was a YSPH postdoctoral student.

A study published in JAMA Network Open examined whether a 2020 change in Medicare Advantage payment policy improved care experiences for older adults living with Alzheimer disease and related dementias (ADRD).

Medicare Advantage plans, which now enroll more than half of all Medicare beneficiaries, are private health options that receive fixed monthly payments from the Centers for Medicare & Medicaid Services (CMS) to provide care for their members. These payments are adjusted using a risk-adjustment model, which takes into account beneficiaries’ demographic characteristics and medical conditions. From 2014 to 2019, ADRD was excluded from this payment model. As a result, payments may not have fully reflected the care needs of beneficiaries living with dementia and may have served as a disincentive for Medicare Advantage plans to provide sufficient or high-quality coverage.

In 2020, CMS restored ADRD to the risk-adjustment model. The current study found that this policy change was associated with better care experiences among Medicare Advantage beneficiaries with ADRD, including fewer reported problems getting needed care and lower medical financial burden.

These findings suggest that when payment policies better account for the needs of people living with dementia, Medicare Advantage may be better positioned to serve this population. This could help reduce health disparities and improve care for vulnerable populations.

“This study highlights the importance of accurately measuring patients’ needs,” said Dr. Xi Chen, PhD, associate professor of public health (health policy) at the Yale School of Public Health and the study’s senior author. “Better risk adjustment can help ensure that people with dementia receive the care and support they truly need.”