University of Illinois, Urbana-Champaign, IL, United States; Mayo Clinic, Rochester MN, United States.
Mayo Clinic, Rochester MN, United States.
Neuroimage. 2022 May 1;251:119020. doi: 10.1016/j.neuroimage.2022.119020. Epub 2022 Feb 20.
Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adults at risk of cognitive decline. Using deep learning models augmented with a model-interpretation technique on data from 1432 Mayo Clinic Study of Aging participants, we identified a subset of brain structures that were most predictive of individualized cognitive trajectories and indicative of cognitively resilient vs. vulnerable individuals. Specifically, these structures explained why some participants were resilient to the deleterious effects of elevated brain amyloid and poor vascular health. Of these, medial temporal lobe and fornix, reflective of age and pathology-related degeneration, and corpus callosum, reflective of inter-hemispheric disconnection, accounted for 60% of the heterogeneity explained by the most predictive structures. Our results are valuable for identifying cognitively vulnerable individuals and for developing interventions for cognitive decline.
特定的大脑结构（灰质区域和白质束）在决定认知能力下降方面起着主导作用，并解释了认知老化的异质性。这些结构的识别对于筛选有认知能力下降风险的老年人至关重要。我们使用深度学习模型，并结合 Mayo 诊所衰老研究 1432 名参与者的数据的模型解释技术，确定了一组最能预测个体认知轨迹的大脑结构，这些结构可以区分认知能力较强和较弱的个体。具体来说，这些结构解释了为什么一些参与者能够抵抗大脑淀粉样蛋白升高和血管健康状况不佳的有害影响。其中，内侧颞叶和穹窿，反映了年龄和与病理相关的退化，以及胼胝体，反映了半球间的断开，解释了最能预测结构所解释的异质性的 60%。我们的研究结果对于识别认知能力较弱的个体和开发认知能力下降的干预措施非常有价值。
