Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations.

Journal: NeuroImage
Published Date:

Abstract

We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.

Authors

  • N S D'Souza
    Department of Electrical and Computer Engineering, Johns Hopkins University, USA. Electronic address: Shimona.Niharika.Dsouza@jhu.edu.
  • M B Nebel
    Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA.
  • D Crocetti
    Department of Surgery "Pietro Valdoni", Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy.
  • J Robinson
    Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA.
  • N Wymbs
    Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA.
  • S H Mostofsky
    Center for Neurodevelopmental & Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins School of Medicine, USA; Department of Psychiatry and Behavioral Science, Johns Hopkins School of Medicine, USA.
  • A Venkataraman
    Department of Electrical and Computer Engineering, Johns Hopkins University, USA.