  A novel unsupervised deep learning method is developed to identify
individual-specific large scale brain functional networks (FNs) from
resting-state fMRI (rsfMRI) in an end-to-end learning fashion. Our method
leverages deep Encoder-Decoder networks and conventional brain decomposition
models to identify individual-specific FNs in an unsupervised learning
framework and facilitate fast inference for new individuals with one forward
pass of the deep network. Particularly, convolutional neural networks (CNNs)
with an Encoder-Decoder architecture are adopted to identify
individual-specific FNs from rsfMRI data by optimizing their data fitting and
sparsity regularization terms that are commonly used in brain decomposition
models. Moreover, a time-invariant representation learning module is designed
to learn features invariant to temporal orders of time points of rsfMRI data.
The proposed method has been validated based on a large rsfMRI dataset and
experimental results have demonstrated that our method could obtain
individual-specific FNs which are consistent with well-established FNs and are
informative for predicting brain age, indicating that the individual-specific
FNs identified truly captured the underlying variability of individualized
functional neuroanatomy.
