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A Joint Model for Predicting Structural and Functional Brain Health in Elderly Individuals

机译:预测老年人结构和功能性大脑健康的联合模型

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This paper presents a machine-learning-based joint model of brain age and cognitive performance, and demonstrates its superior performance relative to isolated models. Previous studies have chosen to study those two measures of brain health separately for two reasons: 1) although cognition can be measured regardless of an individual's health, brain-age ground-truth can be defined only for healthy individuals; and 2) while brain-age models are developed using neuroimaging data alone, modeling of cognitive performance additionally requires measures of cognitive reserve and biomarkers of cognitive disorders. However, those two measures are biologically related to each other, because they both depend on brain structure. Hence, we developed a joint model by 1) explicitly defining the commonalities and differences between them in a graph, and 2) converting that graph into a multitask-learning model to facilitate learning from population-level data. Our model took as inputs structural neuroimaging data and information related to cognitive reserve and disorders, and predicted brain age and cognitive performance in terms of a Mini-Mental State Examination (MMSE) score. We implemented linear and nonlinear joint models and compared them against isolated models. Our results indicate that joint modeling substantially improves the accuracy of the modeling of individual measures, relative to isolated models.
机译:本文提出了一种基于机器学习的大脑年龄和认知表现的联合模型,并证明了其相对于孤立模型的优越表现。先前的研究选择分别研究这两种对大脑健康的测量,原因有两个:1)尽管可以测量认知而不管个人的健康状况如何,但只能为健康的个体定义大脑年龄的真实性;和2)虽然仅使用神经影像数据来开发大脑年龄模型,但是对认知表现的建模还需要认知储备和认知障碍生物标志物的测量。但是,这两种方法在生物学上是相互关联的,因为它们都取决于大脑的结构。因此,我们通过以下方式开发了一个联合模型:1)在图中明确定义它们之间的共性和区别,以及2)将该图转换为多任务学习模型以促进从人口级别数据中学习。我们的模型以与认知储备和疾病相关的结构性神经影像数据和信息作为输入,并根据迷你精神状态检查(MMSE)得分预测了大脑的年龄和认知表现。我们实现了线性和非线性联合模型,并将其与孤立模型进行了比较。我们的结果表明,相对于孤立模型,联合建模可以大大提高单个度量建模的准确性。

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