Dementia with Lewy bodies (DLB) is recognized as the second most common form of dementia after Alzheimer?s disease (AD). At early stages, the clinical phenotypes of DLB and AD overlap and leaves many DLB patients undiagnosed. The importance of developing accurate imaging markers in DLB becomes imperative in order to improve the care of patients and to develop successful treatments for DLB patients. MRI can provide a large panel of information related to brain structure, tissue properties and functional activity obtained from structural, multiparametric and functional MRI sequences. The central hypothesis is that multimodal identification of DLB- specific biomarkers derived from resting state-fMRI, structural and multiparametric MRI have the potential to improve etiological diagnosis. The long-term goal is to gain a deeper understanding of functional connectivity mechanisms in DLB. The objective of this application is to provide a fast and accurate computational diagnosis support for the clinical neuroscientist to assist him in identifying DLB patients at an early stage. The objective of this project will be accomplished by three specific aims: (1) To identify and quantify disease-related alterations in dynamic functional connectivity of DLB and AD patients in rs-fMRI based on a probabilistic graph-modeling framework across subjects, and groups of individuals in terms of functional state space composition, functional state transition matrices, state and dependency graph structures, and dynamic FC patterns or scenarios. As input data, we will primarily consider pairwise correlations of the time courses associated with spontaneous co- activity maps, including RSNs, as revealed at the subject level by spatial independent component analysis.; (2) To construct and analyze the dynamical functional connectivity network in DLB and AD patients based on novel finite-time cluster synchronization of Markovian switching networks. We will use the input data from Aim 1 and will analyze and compare these networks in terms of time courses of graph measures, tracking of modular architectures and the proportion of time spent in relevant states and transitions; and (3) To implement and evaluate a CAD system based on imaging biomarkers derived from rs-fMRI, structural and multiparametric MRI. This study is innovative because it moves from traditional time-averaged quantitative markers to finer temporal variations of functional connectivity and thus represents an important step to understanding individual differences and internal state changes in DLB. The proposed project is significant because it will be the first computer-aided diagnosis system dedicated to the differential diagnosis between DLB and AD, and will help to select the most appropriate therapeutic strategy in the very early stage of the pathology course. Furthermore, this proposal will enhance the infrastructure of research and education at FAMU- FSU College of Engineering, introducing bioimaging and biomedical research experiences to underrepresented minority and female students, who would otherwise lack such opportunities. This would allow them to experience a broad spectrum of techniques, and acquire skills such as data and image analysis used in modern scientific investigations, while developing a vast network of partnerships among scientists from national and international institutions.
The emphasis of this project lies on the development of a computer-aided diagnosis system dedicated to the differential diagnosis between dementia with Lewy bodies (DLB) and Alzheimer?s dementia, especially in their prodromal stage, based on novel DLB-specific biomarkers derived from rs-fMRI, structural and multiparametric MRI. It will help to select the most appropriate therapeutic strategy in the very early stage of the pathology course, thus resulting in a better patient care and an improved quality of life for both patients and caregivers. In addition, this project will provide hands-on research exposure and undergraduate or professional school preparation opportunities for individuals who are from diverse backgrounds underrepresented in biomedical research
