Early Differentiation of dementia with Lewy bodies fromAlzheimer's disease based on novel dynamic imaging biomarkers - Project Summary:
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.
