Unsupervised deep learning for individualized brain functional network identification
This method provides a fast and accurate way to identify individualized brain functional networks, which could be beneficial for neuroscience research and clinical applications by better understanding individual brain differences.
This paper introduces an unsupervised deep learning method to identify individual-specific brain functional networks (FNs) from resting-state fMRI (rsfMRI). The method successfully identifies FNs consistent with established networks and useful for predicting brain age, indicating it captures individualized functional neuroanatomy variability.
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.