FADR: Functional-Anatomical Discriminative Regions for Rest fMRI Characterization

Modeling and augmenting of fMRI data using deep recurrent variational auto-encoder

Objective. Recently, deep learning models have been successfully applied in functional magnetic resonance imaging (fMRI) modeling and associated applications. However, there still exist at least two challenges. Firstly, due to the lack of sufficient data, deep learning models tend to suffer from overfitting in the training process. Secondly, it is still challenging to model the temporal dynamics from fMRI, due to that the brain state is continuously changing over scan time. In addition, existing methods rarely studied and applied fMRI data augmentation.Approach. In this work, we construct a deep recurrent variational auto-encoder (DRVAE) that combined variational auto-encoder and recurrent neural network, aiming to address all of the above mentioned challenges. The encoder of DRVAE can extract more generalized temporal features from assumed Gaussian distribution of input data, and the decoder of DRVAE can generate new data to increase training samples and thus partially relieve the overfitting issue. The recurrent layers in DRVAE are designed to effectively model the temporal dynamics of functional brain activities. LASSO (least absolute shrinkage and selection operator) regression is applied on the temporal features and input fMRI data to estimate the corresponding spatial networks.Main results. Extensive experimental results on seven tasks from HCP dataset showed that the DRVAE and LASSO framework can learn meaningful temporal patterns and spatial networks from both real data and generated data. The results on group-wise data and single subject suggest that the brain activities may follow certain distribution. Moreover, we applied DRVAE on four resting state fMRI datasets from ADHD-200 for data augmentation, and the results showed that the classification performances on augmented datasets have been considerably improved.Significance. The proposed method can not only derive meaningful temporal features and spatial networks from fMRI, but also generate high-quality new data for fMRI data augmentation and associated applications.

Identifying ADHD Individuals From Resting-State Functional Connectivity Using Subspace Clustering and Binary Hypothesis Testing

Objective: This study focused on the ADHD classification through functional connectivity (FC) analysis. Method: An ADHD classification method was proposed with subspace clustering and binary hypothesis testing, wherein partial information of test data was adopted for training. By hypothesizing the binary label (ADHD or control) for the test data, two feature sets of training FC data were generated during the feature selection procedure that employed both training and test data. Then, a multi-affinity subspace clustering approach was performed to obtain the corresponding subspace-projected feature sets. With the energy comparison of projected feature sets, we finally identified ADHD individuals for the test data. Results: Our method outperformed several state-of-the-art methods with the above 90% average identification accuracy. By the discriminative FC contribution analysis, it also proved the reliability of our method. Conclusion: Results demonstrate the remarkable classification performance of our method and reveal some useful brain circuits to identify ADHD.