Park Chang-Hyun, Kim Bori R, Lim Soo Mee, Kim Eun-Hee, Jeong Jee Hyang, Kim Geon Ha
Division of Artificial Intelligence and Software, College of Artificial Intelligence, Ewha Womans University, Seoul, Republic of Korea.
Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Geroscience. 2025 Jan 27. doi: 10.1007/s11357-025-01531-x.
Superagers, older adults with exceptional cognitive abilities, show preserved brain structure compared to typical older adults. We investigated whether superagers have biologically younger brains based on their structural integrity.
A cohort of 153 older adults (aged 61-93) was recruited, with 63 classified as superagers based on superior episodic memory and 90 as typical older adults, of whom 64 were followed up after two years. A deep learning model for brain age prediction, trained on 899 diverse-aged adults (aged 31-100), was adapted to the older adult cohort via transfer learning. Brain age gap (BAG), a metric based on brain structural patterns, defined as the difference between predicted and chronological age, and its annual rate of change were calculated to assess brain aging status and speed, respectively, and compared among subgroups.
Lower BAGs correlated with more favorable cognitive status in memory and general cognitive function. Superagers exhibited a lower BAG than typical older adults at both baseline and follow-up. Individuals who maintained or attained superager status at follow-up showed a slower annual rate of change in BAG compared to those who remained or became typical older adults.
Superaging brains manifested maintained neurobiological youthfulness in terms of a more youthful brain aging status and a reduced speed of brain aging. These findings suggest that cognitive resilience, and potentially broader functional resilience, exhibited by superagers during the aging process may be attributable to their younger brains.
超级老人是指认知能力出众的老年人，与普通老年人相比，他们的大脑结构得以保留。我们基于其结构完整性研究了超级老人的大脑是否在生物学上更年轻。
招募了153名老年人（年龄在61 - 93岁之间），其中63人基于卓越的情景记忆被归类为超级老人，90人为普通老年人，其中64人在两年后接受了随访。一个用于脑年龄预测的深度学习模型，在899名不同年龄的成年人（年龄在31 - 100岁之间）上进行训练，通过迁移学习应用于老年人群体。脑年龄差距（BAG）是一种基于脑结构模式的指标，定义为预测年龄与实际年龄之间的差异，计算其年变化率以分别评估脑老化状态和速度，并在亚组之间进行比较。
较低的BAG与记忆和一般认知功能方面更良好的认知状态相关。超级老人在基线和随访时的BAG均低于普通老年人。在随访时维持或达到超级老人状态的个体，与那些仍然是或变成普通老年人的个体相比，其BAG的年变化率较慢。
超级老人的大脑在脑老化状态更年轻和脑老化速度降低方面表现出神经生物学上的年轻状态得以维持。这些发现表明，超级老人在衰老过程中表现出的认知恢复力，以及可能更广泛的功能恢复力，可能归因于他们更年轻的大脑。
