Age distribution of the ADNI subjects.

In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.

本研究中，我们对来自人类连接组计划青年成人研究（Human Connectome Project Young Adult study）的1048名健康受试者，以及来自阿尔茨海默病神经影像倡议3期（ADNI-3）研究的94名健康受试者的结构像，采用自研纤维束追踪流程进行处理，并与来自braingraph.org的同批次受试者预处理数据联合开展分析。我们提取全脑结构连接组特征，构建基于相关性的回归机器学习模型，以预测健康受试者的智力水平与年龄。研究结果显示，通过弥散张量成像（diffusion tensor imaging）检测活体人脑内的解剖纤维束，可在一定程度上预测不同类型的智力水平与年龄。尽管未发现所研究特征的预测能力因影像特征提取方法不同而存在显著差异，但我们确实发现，在所有数据集的结构连接组数据中，晶态智力（crystallized intelligence）的预测效果始终优于液态智力（fluid intelligence）。本研究结果可为利用脑部磁共振成像（brain MR imaging）开展更广泛的研究主题提供一套实用且可扩展的处理与分析框架。