Decrement in processing speed (PS) is a primary cognitive morbidity in clinical populations and could significantly influence other cognitive functions, such as attention and memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translational implications on clinical and aging research. In this study, we verified that resting-state functional connectivity could be used to predict PS in 99 older adults by using connectome-based predictive modeling (CPM). We identified two distinct connectome patterns across the whole brain: the Fast-PS and Slow-PS networks. Relative to the slow-PS network, the fast-PS network showed more within-network connectivity in the motor and visual networks and less between-network connectivity in the motor-visual, motor-subcortical/cerebellum and motor-frontoparietal networks. We further verified that the connectivity patterns for prediction of PS were also useful for predicting attention and memory in the same sample. To test the generalizability and specificity of the connectome-based predictive models, we applied these two connectome models to an independent sample of three age groups (101 younger adults, 103 middle-aged adults and 91 older adults) and confirmed these models could specifically be generalized to predict PS of the older adults, but not the younger and middle-aged adults. Taking all the findings together, the identified connectome-based predictive models are strong for predicting PS in older adults. The application of CPM to predict neurocognitive abilities can complement conventional neurocognitive assessments, bring significant clinical benefits to patient management and aid the clinical diagnoses, prognoses and management of people undergoing the aging process.

中文翻译：

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基于连接组的模型可以预测老年人的处理速度

处理速度 (PS) 降低是临床人群中的主要认知疾病，可能会显着影响其他认知功能，例如注意力和记忆力。验证基于连接组的模型在预测神经认知能力方面的有效性对临床和衰老研究具有重要的转化意义。在这项研究中，我们通过使用基于连接组的预测模型 (CPM) 验证了静息状态功能连接可用于预测 99 名老年人的 PS。我们在整个大脑中确定了两种不同的连接组模式：Fast-PS 和 Slow-PS 网络。相对于慢 PS 网络，快速 PS 网络在运动和视觉网络中显示出更多的网络内连接性，而在运动视觉中显示出更少的网络间连接性，运动-皮层下/小脑和运动-额顶网络。我们进一步验证了用于预测 PS 的连接模式对于预测同一样本中的注意力和记忆也很有用。为了测试基于连接组的预测模型的普遍性和特异性，我们将这两个连接组模型应用于三个年龄组（101 名年轻人、103 名中年人和 91 名老年人）的独立样本，并确认这些模型可以专门用于泛化预测老年人的 PS，但不能预测年轻人和中年人的 PS。综合所有发现，确定的基于连接组的预测模型非常适合预测老年人的 PS。应用 CPM 预测神经认知能力可以补充传统的神经认知评估，