Parameter-Free Centralized Multi-Task Learning for Characterizing Developmental Sex Differences in Resting State Functional Connectivity
Parameter-Free Centralized Multi-Task Learning for Characterizing Developmental Sex Differences in Resting State Functional Connectivity
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DOI：
10.1609/aaai.v32i1.11907
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
Xiaofeng Zhu;Hongming Li;Yong Fan
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文献类型：
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作者：
Xiaofeng Zhu;Hongming Li;Yong Fan
与大多数现有研究通常使用方差分析或等效的多元线性回归来表征发育性别差异不同，我们提出了一种无参数集中式多任务学习方法，以静息状态功能MRI （rs-fMRI）数据为基础，识别大脑发育的性别特异性和常见静息状态功能连接（RSFC）模式。具体而言，我们设计了一种新的多任务学习模型，通过将男性和女性的年龄预测作为单独的任务，在年龄预测框架中表征性别特异性和常见的RSFC模式。并且，自动学习每个任务的重要性和这两种模式的平衡，以使多任务学习具有鲁棒性和无可调参数，即无参数。我们在合成数据集上的实验结果验证了我们的方法在预测性能方面的有效性，并且在费城神经发育队列（PNC）的1041名受试者（651名男性）的rs-fMRI扫描上的实验结果表明，除了表征RSFC模式的发育性别差异外，我们的方法比比较的最佳替代方法平均可以提高5.82%的年龄预测，具有统计学意义。
In contrast to most existing studies that typically characterize the developmental sex differences using analysis of variance or equivalently multiple linear regression, we present a parameter-free centralized multi-task learning method to identify sex specific and common resting state functional connectivity (RSFC) patterns underlying the brain development based on resting state functional MRI (rs-fMRI) data. Specifically, we design a novel multi-task learning model to characterize sex specific and common RSFC patterns in an age prediction framework by regarding the age prediction for males and females as separate tasks. Moreover, the importance of each task and the balance of these two patterns, respectively, are automatically learned in order to make the multi-task learning robust as well as free of tunable parameters, i.e., parameter-free for short. Our experimental results on synthetic datasets verified the effectiveness of our method with respect to prediction performance, and experimental results on rs-fMRI scans of 1041 subjects (651 males) of the Philadelphia Neurodevelopmental Cohort (PNC) showed that our method could improve the age prediction on average by 5.82% with statistical significance than the best alternative methods under comparison, in addition to characterizing the developmental sex differences in RSFC patterns.