Issue 35.3 SUMMER 2026 |
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Hosung Kim, PhD, associate professor
of research neurology at
the Keck School of Medicine of
USC and co-senior author of the
study.“ This pattern suggests the
brain may be reorganizing itself,
essentially rejuvenating undamaged
networks to compensate for
lost function.”
The research team used an
advanced form of artificial
intelligence known as a graph
convolutional network to predict
the biological age of 18 brain
regions from MRI data. The
difference between a person’ s
predicted brain age and their
actual chronological age, known
as the brain-predicted age difference
( brain-PAD), served as a
sensitive marker of neural health.
When the team associated these
measurements with motor performance
scores, they found a striking pattern: stroke survivors with severe movement deficits, even after more than 6 months of rehabilitation, showed younger-than-expected brain age in regions opposite the lesion, particularly within the frontoparietal
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network, a key system involved in motor planning, attention, and coordination.
“ These findings suggest that when stroke damage leads to greater movement loss, undamaged regions on the opposite side of the brain may adapt to help compensate,” Kim explained.“ We saw this in the contralesional frontoparietal network, which showed a more‘ youthful’ pattern and is known to support motor planning, attention, and coordination. Rather than indicating full recovery of movement, this pattern may reflect the brain’ s attempt to adjust when the damaged motor system can no longer function normally. This gives us a new way to see neuroplasticity that traditional imaging could not capture.”
The study was conducted through ENIGMA, a global alliance that unites data from more than 50 countries to better understand the brain across diseases. Researchers harmonized MRI data and clinical measures across dozens
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of cohorts to build the world’ s largest stroke neuroimaging dataset of its kind.
“ By pooling data from hundreds of stroke survivors worldwide and applying cutting-edge AI, we can detect subtle patterns of brain reorganization that would be invisible in smaller studies. These findings of regionally differential brain aging in chronic stroke could eventually guide personalized rehabilitation strategies,” said Arthur W. Toga, PhD, director of the Stevens INI and Provost Professor at USC.
The team plans to expand their work to include longitudinal studies tracking patients from the acute to chronic stages of stroke recovery. By observing how patterns of brain aging and reorganization develop over time, clinicians might be able to customize interventions based on each patient’ s unique neural adaptation process, ultimately improving recovery outcomes and quality of life in the near future. g
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“This gives us a new way to see neuroplasticity that traditional imaging could not capture.” |
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