CRII: III: A Spatio-Temporal Data Mining Framework For Functional Neuroimaging Data

University Of Cincinnati Main Campus, Cincinnati OH

Investigators

Abstract

The human brain is an interconnected web of billions of neurons that enables humans to memorize, reason, perceive, imagine, and act. Understanding the relation between neuronal activity in the brain and the functionality it enables is crucial to the characterization, early diagnosis, and effective treatment of mental illness. Advances in brain imaging technologies allow researchers to collect large volumes of brain activity data while subjects are resting or while working on a task. Multiple such brain imaging datasets that are publicly available present a tremendous opportunity to study the relationship between brain activity and brain functionality. However, a major factor limiting progress is the lack of suitable computational data mining tools that can sift through large volumes of data with challenging properties to discover insights about brain functionality. One major challenge in developing the necessary tools is due to the properties of the brain activity data that are different from traditionally studied data for which majority of the computational tools are originally developed. Another challenge is due to the manner in which desired insights are represented in the brain activity data. This project will result in novel computational tools and techniques that will address these two general challenges. This work is expected to accelerate progress towards effective treatment procedures for mental illness. The overall goals of this project will be accomplished by defining original neuroimaging data analytic problems without shoe-horning them into existing frameworks, tackling the unique spatio-temporal characteristics of neuroimaging data, and leveraging domain knowledge in neuroscience. The driving neuroscience questions include: 1) What are the representations of the functional activity that adheres to the underlying structure of the brain connections? 2) What are the brain activation maps that can be used to represent a variety of brain functions and to study relationships among them? 3) What is the utility of transient brain states in uniquely identifying subjects, in comparison to a static representation? The corresponding computational research involves developing techniques for: 1) Determining the brain parcellation such that the resultant parcels reflect the underlying topographic connectivity; 2) Simultaneously learning the dictionary as well as classification models for multiple task-fMRI datasets; 3) Discovering and using transient brain states and their transitions to uniquely identify subjects based on their fMRI data. The resultant tools and techniques will enable the investigation of hypotheses relevant to personalized neuroscience -- understanding the neurological processes that are shared and unique to individual subjects. This will help achieve the clinically relevant goals of personalized neuroscience and eventually alleviate the huge societal burden of mental illness. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.