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.

虽然可以使用从静息状态功能 MRI (rsfMRI) 数据计算的相关矩阵以高精度识别个体受试者，但随着扫描持续时间的减少，性能显着下降。循环神经网络可以通过短时 (72 s) 数据段实现高精度，但旨在使用相关矩阵中不存在的时间特征。在这里，我们展示了仅依赖 rsfMRI 相关矩阵中的信息的浅层前馈神经网络可以实现最先进的识别精度。≥99.5%) 当数据点总数（区域数 × 时间点数）为 10,000.