Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, independent component analysis (ICA) and sparse dictionary learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps. As growing evidence shows that human brain function is hierarchically organized, new approaches that can infer and model the hierarchical structure of brain networks are widely called for. Recently, deep convolutional neural network (CNN) has drawn much attention, in that deep CNN has proven to be a powerful method for learning high-level and mid-level abstractions from low-level raw data. Inspired by the power of deep CNN, in this paper, we developed a new neural network structure based on CNN, called deep convolutional auto-encoder (DCAE), in order to take the advantages of both data-driven approach and CNN's hierarchical feature abstraction ability for the purpose of learning mid-level and high-level features from complex, large-scale tfMRI time series in an unsupervised manner. The DCAE has been applied and tested on the publicly available human connectome project tfMRI data sets, and promising results are achieved.

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Current functional magnetic resonance imaging (fMRI) data modeling techniques, such as independent component analysis and sparse coding methods, can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective, and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications. However, this task is still a challenging and open problem due to the tremendous variability of various types of functional brain networks and the presence of various sources of noises. In recognition of the fact that convolutional neural networks (CNN) has superior capability of representing spatial patterns with huge variability and dealing with large noises, in this paper, we design, apply, and evaluate a deep 3-D CNN framework for automatic, effective, and accurate classification and recognition of large number of functional brain networks reconstructed by sparse representation of whole-brain fMRI signals. Our extensive experimental results based on the Human Connectome Project fMRI data showed that the proposed deep 3-D CNN can effectively and robustly perform functional networks classification and recognition tasks, while maintaining a high tolerance for mistakenly labeled training instances. This study provides a new deep learning approach for modeling functional connectomes based on fMRI data.

Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions. Second, the temporal dynamics of spatial network interactions is modeled by a weighted time-evolving graph, and then a data-driven unsupervised learning algorithm based on the dynamic behavioral mixed-membership model (DBMM) is adopted to identify behavioral patterns of brain networks during the temporal evolution process of spatial overlaps/interactions. Experimental results on the Human Connectome Project (HCP) task fMRI data showed that our methods can reveal meaningful, diverse behavior patterns of connectome-scale network interactions. In particular, those networks’ behavior patterns are distinct across HCP tasks such as motor, working memory, language and social tasks, and their dynamics well correspond to the temporal changes of specific task designs. In general, our framework offers a new approach to characterizing human brain function by quantitative description for the temporal evolution of spatial overlaps/interactions of connectome-scale brain networks in a standard reference space.