Although individual subjects can be identified with high accuracy using correlation matrices computed from resting-state functional MRI (rsfMRI) data, the performance significantly degrades as the scan duration is decreased. Recurrent neural networks can achieve high accuracy with short-duration (72 s) data segments but are designed to use temporal features not present in the correlation matrices. Here we show that shallow feedforward neural networks that rely solely on the information in rsfMRI correlation matrices can achieve state-of-the-art identification accuracies (≥99.5%) with data segments as short as 20 s and across a range of input data size combinations when the total number of data points (number of regions × number of time points) is on the order of 10,000.

尽管可以使用根据静止状态功能MRI（rsfMRI）数据计算出的相关矩阵来高精度地识别各个对象，但是随着扫描持续时间的减少，性能会显着下降。递归神经网络可以使用短时（72 s）数据段实现高精度，但被设计为使用相关矩阵中不存在的时间特征。在这里我们显示出仅依靠rsfMRI相关矩阵中的信息的浅层前馈神经网络可以实现最新的识别精度（≥99.5％），当数据点的总数（区域数×时间点数）约为s时，数据段短至20 s，并且跨越一系列输入数据大小组合 10，000。