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Project

Improved classification of Alzheimer's disease: differentiation of slow propagating waves of BOLD intensity of dynamic rsfMRI in AD mice models in pre-plaque and post-plaque stages.

There is increasing evidence that in neurodegenerative diseases (ND) synaptic defects affect synaptic transmission mechanisms. These synaptic transmission deficits may influence the functional connectivity (FC) in the brain, by impairing communication between brain regions. FC can be measured using resting state functional MRI (rsfMRI), and is defined as the temporal correlation between low frequency fluctuations in the blood-oxygen-level-dependent (BOLD) fMRI signal in distinct brain areas. Thus rsfMRI enables a nuanced appreciation of the system-scale network structure of the brain. It was reported that various topological properties of resting state functional networks correlate with higher cognitive functions and are susceptible to various pathological disruptions. Patients with Alzheimer's disease (AD) display aberrant brain function. RsfMRI has identified default mode (DMN) and task positive (TPN) network disruptions as promising biomarkers for AD. Recently, the rsfMRI field has seen a shift from 'static' BOLD signal analysis to more time-resolved dynamic analysis. Dynamic rsfMRI (drsfMRI) is a state-of-the-art approach, which has revealed many new insights into the macro-scale organization of functional networks and could already identify short-lasting large scale patterns of spatiotemporal BOLD activity, the 'Quasi- Periodic Patterns' (QPPs) in human and rats. Just recently, we observed the existence of QPPs of a set of large-scale Quasi-Periodic patterns in healthy anesthetized mice, similar as to what has been observed in other species, and which highly resemble known mouse resting state networks. The latter hints at a neuronal origin and a contribution to brain functional connectivity. We further illustrated how global signal regression affects the spatiotemporal dynamics, suggesting a potential role for its effect in conventional rsfMRI studies. Patterns could be observed reliably at the single subject level, marking promise in the advance towards more reliable rsfMRI research. Finally, the QPPs of neural activity, describe recurring spatiotemporal events that display DMN with TPN anti-correlation. QPPs therefore represent likely contributors to the DMN's and TPN's functional organisation. Therefore, we reason that QPPs could provide new insights into AD network dysfunction and improve disease diagnosis.We postulate the hypothesis that QPPs would help understand the aberrant DMN and TPN FC observed in Alzheimer's disease, and might serve as a more sensitive biomarker than conventional rsfMRI measures, improving AD classification both in an early pre-plaque stage as late post-plaque stage. In this project, we will use state-of-the-art MRI to investigate: a) how QPPs in a mouse models for AD (Tg2576), differs from control animals, b) how these QPPs might interact with sensory stimulation processing, c) how the QPP acquired at rest or sensory stimulation contribute to the DMN and DMN-TPN FC, and how they improve AD classification.
Date:1 Jan 2019 →  31 Dec 2020
Keywords:NEURODEGENERATIVE DISEASES, FUNCTIONAL-CONNECTIVITY MRI (FCMRI), IMAGE PROCESSING, MAGNETIC RESONANCE IMAGING (FUNCTIONAL)
Disciplines:Biomedical image processing, Neurological and neuromuscular diseases, Neuroimaging