There has been great progress in the use of functional connectivity measures to study the healthy and dis- eased brain. The fMRI community has now realized that assessment of functional connectivity has been limited by an implicit assumption of spatial and temporal stationarity throughout the measurement period. Dynamics are potentially even more prominent in the resting-state, during which mental activity is unconstrained. There is a need for new methods to both estimate and quantify these changes. We propose to develop and compare a diverse but unified family of multivariate methods to address important aspects of dynamic connectivity that are not presently captured with existing approaches. Pilot data with initial approaches show robust changes in mental illness. Using a powerful framework that builds on the well-structured framework of joint blind source separation, we will make use of all available prior and statistical information-higher-order-statistics, sparsity, smoothness, sample and dataset dependence to derive a class of novel and effective dynamic models for full characterization of static and dynamic brain connectivity. We will validate these new methods while determining their properties and robustness to noise and other factors. We show preliminary work suggesting that there are important changes in dynamic properties that are not detectable in the static results and vice versa. Thus, we also propose models that can simultaneously capture stationary and non-stationary activity. We will apply our new set of methods to evaluate the common and distinct aspects of two patient groups (schizophrenia and bipolar disorder) as well as comorbid conditions (smoking and drinking). We will provide open source tools and release data throughout the duration of the project via a web portal and the NITRC repository, 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 and attention deficit hyperactivity disorder. 37
There is considerable interest in approaches that capture time-varying connectivity, however existing approaches are largely ad-hoc and hard to compare with one another. Existing approaches have shown consider- able promise, but no method currently provides a comprehensive view of the time-varying changes we know exist. For example, most studies focus on time-varying correlation but ignore time-varying nodes. Most approaches produce state matrices, but it's not clear how to relate these or even if they are meaningful. Multivariate methods are well suited to estimate the changes of interested in a unified and robust manner but such tools are still in their infancy. We thus propose to develop and validate a family of multivariate methods to estimate dynamic connectivity states (CS) and use these methods to study CS in overlapping psychosis patients (schizophrenia/bipolar disorder) and comorbid conditions (smoking/drinking). Our tools have wide application to the study of the healthy brain as well as many other diseases such as Alzheimer's and attention deficit hyperactivity disorder.
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