Functional connectome fingerprinting using shallow feedforward neural networks

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 x number of time points) is on the order of 10,000.

Automated summary

Shallow feedforward neural networks relying solely on rsfMRI correlation matrices can achieve high accuracy in individual identification within short data segments, and perform well when the total number of data points is large.