A new study lead by a team of researchers at the Keck School of Medicine of USC shows that younger “brain age,” a neuroimaging-based assessment of global brain health, is associated with better post-stroke outcomes. The findings could lead to better ways to predict post-stroke outcomes and offer insight on new potential treatment targets to improve recovery.

Understanding why some stroke survivors show better recovery than others despite similar damage to the brain has been a critical goal in stroke research, since it could help researchers develop better stroke rehabilitation therapies. During a stroke, blood flow to part of the brain is cut off. Without oxygen, brain cells are damaged and eventually die, resulting in brain damage known as a lesion. Studies have shown that people with similar amounts of lesion damage can experience varying amounts of recovery. Much research in the past two decades has focused on the specific location of brain damage and how the lesion affects connected networks in the brain.

This study, published April 4, 2023 in Neurology®, takes into consideration global brain health, a new way of analyzing the health of the brain based on its cellular, vascular, and structural integrity. Although global brain health has been widely examined in aging and neurodegenerative disease such as Alzheimer’s disease, it had not previously been studied in relation to stroke rehabilitation outcomes. Led by Sook-Lei Liew, PhD, of the Keck School of Medicine’s Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI), the team of researchers focused on a specific measure of global brain health known as brain age, which examines the biology of the nervous system through whole brain structural neuroimaging, hypothesizing that the integrity of residual brain tissue, or what is left after the stroke, may be critical for post-stroke outcomes.
Brain age is a biomarker that predicts chronological age based on neuroimaging of structures such as regional thickness, surface area, and volumes, and is calculated using advanced machine learning algorithms, which have been widely studied at the Stevens INI. A higher brain predicted age difference, calculated as the difference between a person’s predicted brain age minus their chronological age, suggests that the brain appears to be older than the person’s chronological age. An older-appearing brain has been associated with Alzheimer’s disease, major depression, traumatic brain injury, and more.

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