Innovative MRI Technique Reveals Brain Metabolism and Disease Signatures

Researchers at the University of Illinois Urbana-Champaign have developed a groundbreaking MRI method that non-invasively maps metabolic activity across the entire brain, offering new insights into brain function and disease. This advanced imaging approach combines ultrafast data acquisition with machine learning, significantly reducing scan times to just over 12 minutes while providing high-resolution metabolic images. The technique measures signals from brain metabolites and neurotransmitters, as well as water molecules, utilizing magnetic resonance spectroscopic imaging (MRSI). This allows for detailed visualization of metabolic and neurotransmitter variations in different brain regions.

Traditional MRI captures structural details, and functional MRI (fMRI) monitors blood flow related to neural activity, but neither provides direct information about the brain's metabolic processes. The new method fills this gap, enabling early detection of abnormalities by revealing metabolic and physiological changes before they become apparent on conventional scans.

The research team demonstrated the technology's capabilities by analyzing healthy subjects, noting variations in metabolic activity across brain regions. In patients with brain tumors, they identified elevated levels of choline and lactate in tumors of different grades—changes that standard MRI could not detect. Similarly, in multiple sclerosis patients, early metabolic alterations related to neuroinflammation and reduced neuronal activity were identified up to 70 days ahead of typical MRI signs.

This innovation holds promise for advancing personalized medicine, allowing clinicians to monitor disease progression and treatment response accurately. Its ability to detect subtle metabolic changes may facilitate earlier interventions and tailored therapies for neurological conditions. The study's findings are published in Nature Biomedical Engineering (2025), underscoring the significance of this high-speed, high-resolution metabolic imaging in future clinical applications.

Led by Professor Zhi-Pei Liang, the team’s approach is considered a major stride towards integrating metabolic imaging into routine neurological diagnostics, ultimately enhancing our understanding and management of complex brain diseases.