An accelerated degeneration of white matter microstructure and networks in the nondemented old-old

The nondemented old-old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion-weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young-old (between 72 and 79, mean = 76.25 +/- 2.00) and 219 old-old (between 80 and 92, mean = 83.98 +/- 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old-old compared with young-old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old-old but not in the young-old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.

Automated summary

The nondemented old-old population over the age of 80 is rapidly increasing and can be seen as examples of successful aging. However, there is limited understanding of successful aging and its neural foundations in advanced age. In this study, diffusion-weighted imaging scans were used to measure the microstructural and network-based topological properties of brain white matter in 419 community-dwelling nondemented older individuals. The results showed significant declines in white matter connectivity in both microstructure and brain networks with increasing age, with a faster decline rate in the old-old compared to the young-old. Mediation models indicated that cognitive decline in the old-old group was partly due to age-related declines in white matter connectivity. Machine learning predictive models further supported the importance of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population.