There is considerable evidence that disruption of time-varying connectivity in mental illness is more sensitive than static connectivity, however we are only scratching the surface of identifying and characterizing potential imaging biomarkers. Existing approaches to estimating and characterizing whole brain time-varying connectivity from fMRI data have shown considerable promise, with exponential growth in research in this field. We have shown that multivariate data-driven methods are well suited to estimate the changes of interest in a robust man- ner and have developed a powerful set of tools as part of the initial funded project that are now in wide use in the community. However there are still some important limitations including 1) the use of a two-step process to estimate features and then characterize dynamics, 2) methods often assume linear relationships either within or between networks over time, and 3) methods assume spatially fixed nodes and ignore the possibility of spatially fluid evolution of networks over time. We propose a novel family of models that builds on the well-structured framework of joint blind source separation to capture a more complete characterization of (potentially nonlinear) spatio-temporal dynamics, avoiding a two-step process. Our models will also produce a rich set of metrics to characterize the available dynamics and enable in depth comparison with currently available models including those that are model-based. We will extensively validate our approaches in a variety of ways including simula- tions, concurrent EEG/fMRI data, application to resting, movie, and task fMRI data, and comparison to model- based dynamical systems methods. We will apply the developed tools to study dynamic properties across mul- tiple mental illnesses including schizophrenia, bipolar disorder, and the autism spectrum. There is considerable evidence of disruption of dynamics in all three disorders, however little work has attempted to synthesize across, e.g. the mood, psychosis, and social cognition spectrum. Nor have there been concerted efforts to evaluate the evidence for predictive categorical and dimensional changes within both patients and controls. We will provide open source tools and release data throughout the duration of the project via a web portal and the NITRC re- pository, hence enabling other investigators to compare their own methods with our own as well as to apply them to a large variety of brain disorders. Our tools have wide application to the study of the healthy brain as well as many other diseases such as Alzheimer?s disease and attention deficit hyperactivity disorder. 37
There is considerable interest in approaches that capture time-varying connectivity, however existing ap- proaches suffer from three major limitations: 1) extraction of networks and estimation of dynamic features is typically done in separate steps, thus not fully optimizing for dynamic fluctuations or online prediction of full spatiotemporal relationships, 2) methods typically assume relatively simple linear relationships, and 3) most ap- proaches assume the spatial nodes are fixed in time, effectively ignoring the possibility of functionally fluid nodes. We propose to address these limitations by developing a family of flexible spatio-temporal models that leverage and blend the concepts of joint blind source separation and deep learning, which we show can reveal important information missed by existing models. We will apply the proposed models to study spatial and temporal aspects of dynamic connectivity in a large set of transdiagnostic data including schizophrenia, bipolar disorder, and au- tism spectrum. 36
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