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We propose a novel integrated framework that jointly models complementaryinformation from resting-state functional MRI (rs-fMRI) connectivity anddiffusion tensor imaging (DTI) tractography to extract biomarkers of brainconnectivity predictive of behavior. Our framework couples a generative modelof the connectomics data with a deep network that predicts behavioral scores.The generative component is a structurally-regularized Dynamic DictionaryLearning (sr-DDL) model that decomposes the dynamic rs-fMRI correlationmatrices into a collection of shared basis networks and time varyingsubject-specific loadings. We use the DTI tractography to regularize thismatrix factorization and learn anatomically informed functional connectivityprofiles. The deep component of our framework is an LSTM-ANN block, which usesthe temporal evolution of the subject-specific sr-DDL loadings to predictmultidimensional clinical characterizations. Our joint optimization strategycollectively estimates the basis networks, the subject-specific time-varyingloadings, and the neural network weights. We validate our framework on adataset of neurotypical individuals from the Human Connectome Project (HCP)database to map to cognition and on a separate multi-score prediction task onindividuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold crossvalidation setting. Our hybrid model outperforms several state-of-the-artapproaches at clinical outcome prediction and learns interpretable multimodalneural signatures of brain organization.

Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning  to Integrate Multimodal and Dynamic Functional Connectomics data for  Multidimensional Clinical Characterizations