在递归神经网络中用于神经病理生理异质性建模的人口导向关注。

Nicha C Dvornek, Xiaoxiao Li, Juntang Zhuang, Pamela Ventola, James S Duncan
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引用次数: 0

摘要

神经系统疾病的异质表现表明在大脑中发生的潜在病理生理变化的潜在差异。我们建议使用人口导向注意力(DGA)机制来模拟功能网络差异的异质模式,用于从功能磁共振成像(fMRI)时间序列数据进行预测的递归神经网络模型。从DGA头部计算的上下文用于帮助关注基于个人人口统计信息的适当功能网络。我们在发表的研究中使用了先前产生最先进结果的ABIDE I数据集的3个子集上演示了改进的分类,在留下一个站点的交叉验证框架下评估性能,以更好地推广到新数据。最后,我们提供了基于个体人口变量解释功能网络差异的例子。
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Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity.

Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heterogeneous patterns of functional network differences using a demographic-guided attention (DGA) mechanism for recurrent neural network models for prediction from functional magnetic resonance imaging (fMRI) time-series data. The context computed from the DGA head is used to help focus on the appropriate functional networks based on individual demographic information. We demonstrate improved classification on 3 subsets of the ABIDE I dataset used in published studies that have previously produced state-of-the-art results, evaluating performance under a leave-one-site-out cross-validation framework for better generalizeability to new data. Finally, we provide examples of interpreting functional network differences based on individual demographic variables.

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