In earlier work, WPI-ICReDD scientists showed that a palladium catalyst paired with phosphine ligands could drive light-activated (reaction activated by shining light) transformations of aryl ketones, but the same system did not work for alkyl ketones. Their data indicated that alkyl ketyl radicals did form briefly. However, these radicals immediately returned an electron to the palladium center, a phenomenon known as back electron transfer (BET), before any useful reaction could proceed. As a result, the starting material remained unchanged.

Similar to traditional palladium-based catalysis, the behavior of photoexcited palladium catalysts is highly dependent on the phosphine ligand attached to the metal. The team suspected that choosing the correct ligand might unlock reactivity with alkyl ketones. The difficulty was scale: thousands of phosphine ligands exist, and experimentally screening them for an unfamiliar reaction would be slow, labor-intensive, and generate unnecessary chemical waste.

To overcome these limitations, the researchers turned to computational chemistry to narrow down the field of candidate ligands. They used the Virtual Ligand-Assisted Screening (VLAS) approach developed by Associate Professor Wataru Matsuoka and Professor Satoshi Maeda at WPI-ICReDD. Applying VLAS to 38 phosphine ligands, the method produced a heat map that predicted how well each ligand might promote the desired reactivity by analyzing electronic and steric properties.

Guided by these predictions, the team selected three ligands for laboratory testing and ultimately identified L4 as the most effective option — tris(4-methoxyphenyl)phosphine (P(p-OMe-C₆H₄)₃). This ligand successfully suppressed BET, allowing alkyl ketones to generate ketyl radicals and participate in high-yield transformations.

The resulting method provides chemists with an accessible way to work with alkyl ketyl radicals and demonstrates how VLAS can rapidly guide the development and optimization of new chemical reactions.

To better understand how ATP functions in this area, the team examined signaling patterns in the hippocampus and how they changed under stress. The hippocampus has long been associated with mood disorders, in part because it is sensitive to prolonged stress and is involved in shaping emotional behavior. disruptions in this region can affect how the brain processes stress, which may set the stage for depression or anxiety.

Stress, ATP Loss, and the Role of Connexin 43

The researchers found that male mice prone to developing depressive- and anxiety-like behaviors after long-term stress had lower levels of ATP. These mice also produced less of a key protein required for ATP release (connexin 43). Connexin 43 forms channels that allow ATP to move between certain cells, making it an important part of how the brain maintains healthy energy and signaling levels.

To test whether reduced ATP release contributed to mood-related symptoms, the team genetically decreased or removed connexin 43 in cells that normally release ATP. This experiment was done in another group of mice that had not been exposed to prolonged stress. Even without a stressful environment, lowering connexin 43 triggered depressive- and anxiety-like behaviors and reduced ATP levels. This finding suggested that disruptions in ATP release alone could influence emotional behavior.

When the researchers restored connexin 43 in the hippocampus of stressed mice, ATP levels returned to normal and the animals showed noticeable improvements in their behavior. This recovery helped reinforce the idea that ATP signaling plays a central role in regulating mood.

A Shared Biological Pathway for Depression and Anxiety

Gao explains, “This is the first direct evidence that deficient ATP release in [a region of the] hippocampus drives both depressive- and anxiety-like behaviors, revealing a shared molecular pathway [for these conditions].” Identifying such a pathway is important, as depression and anxiety often occur together and can be difficult to treat simultaneously with existing therapies.

Gao notes that the link between connexin 43 and ATP release highlights a possible target for future treatments. By improving or restoring ATP signaling, scientists may eventually be able to develop interventions that address both conditions at once. The research team also plans to include both male and female mice in upcoming studies to determine whether these mechanisms operate similarly across sexes, which could broaden the relevance of their findings.

“Healthier bodies with more muscle mass and less hidden belly fat are more likely to have healthier, youthful brains,” said senior study author Cyrus Raji, M.D., Ph.D., associate professor of radiology and neurology in the Department of Radiology at Mallinckrodt Institute of Radiology at Washington University School of Medicine in St. Louis, Missouri. “Better brain health, in turn, lowers the risk for future brain diseases, such as Alzheimer’s.”

How MRI Measures Brain Age and Body Composition

Brain age is an estimate of how old the brain appears biologically, based on its structure as seen through MRI. Body MRI can track muscle mass, which serves as a marker for efforts to reduce frailty and strengthen overall health. Estimated brain age from structural scans may also shed light on risk factors for Alzheimer’s disease, including muscle loss.

“While it is commonly known that chronological aging translates to loss of muscle mass and increased hidden belly fat, this work shows that these health measures relate to brain aging itself,” Dr. Raji said. “It shows muscle and fat mass quantified in the body are key reflectors of brain health, as tracked with brain aging.”

Study Details: Imaging, AI Analysis, and Participant Profile

The study evaluated 1,164 healthy adults (52% women) across four research sites using whole-body MRI. Participants had a mean chronological age of 55.17 years. Imaging included T1-weighted MRI sequences, which highlight fat as bright and fluid as dark, providing a clear view of muscle, fat, and brain tissue. An artificial intelligence (AI) algorithm measured total normalized muscle volume, visceral fat (hidden belly fat), subcutaneous fat (fat under the skin) and predicted brain age.

The data indicated that individuals with a higher visceral fat to muscle ratio had higher predicted brain age. Subcutaneous fat showed no meaningful association with how old the brain appeared.

“The participants with more muscle tended to have younger-looking brains, while those with more hidden belly fat relative to their muscle had older-looking brains,” Dr. Raji said. “The fat just under the skin wasn’t related to brain aging. In short, more muscle and a lower visceral fat to muscle ratio were linked to a younger brain.”

Implications for Health, Prevention, and Future Interventions

Dr. Raji explained that focusing on building muscle and reducing visceral fat are realistic and actionable goals. Whole-body MRI and AI-based brain age estimates can offer clear benchmarks for programs designed to lower visceral fat while maintaining or increasing muscle.

He also noted that the results highlight the close connection between physical health and brain health.

“This research has validated widely held hypotheses about the association between body composition biomarkers and brain health and provides a foundation for those biomarkers to be included in future trials of various metabolic interventions and treatments,” he said.

What the Findings Mean for GLP-1 Weight Loss Drugs

Commonly prescribed glucagon-like peptide-1 (GLP-1) weight loss medications, including Ozempic, are effective at reducing body fat but may also contribute to muscle loss. Dr. Raji suggested that the study’s findings could help guide the development of next-generation therapies. These future treatments may aim to reduce visceral fat more than subcutaneous fat while protecting muscle mass.

“Losing fat — especially visceral fat — while preserving muscle volume would have the best benefit on brain aging and brain health based on insights from our work,” he said. “Thus, our study can inform future treatments by promoting research that quantifies MRI of body fat, muscle and brain age, which can help determine the optimal dosing regimens for GLP-1s to achieve the best outcomes in body and brain health.”

Co-authors are Somayeh Meysami, M.D., Soojin Lee, Ph.D., Saurabh Garg, M.Sc., Nasrin Akbari, M.Sc., Rodrigo Solis Pompa M.D., M.H.Sc., Ahmed Gouda, M.Sc., Thanh Duc Nguyen, Ph.D., Saqib Abdullah Basar, M.B.B.S., M.P.H., Yosef G. Chodakiewitz, M.D., David A. Merrill, M.D., Ph.D., Alex Exuzides, Ph.D., M.D., Amar P. Patel, M.D., Daniel J. Durand, M.D., M.B.A., and Sam Hashemi, M.Sc.