Abstract
Resting-state functional magnetic resonance imaging (RS-fMRI) studies have revealed correlated neuronal activity from spatially distributed brain regions that is altered in neurogenerative disorders. Recent advances in RS-fMRI analyses reveal that these correlations are not necessarily constant and show dynamic interplay between transient brain functional connectivity (FC) states occurring at short timescales. Co-activation patterns (CAPs) are examples of such transient states that have now been observed in multiple species, have neuronal correlates, and can accurately distinguish between rodents of transgenic models of Alzheimer's disease (AD) and their wild-type littermates. However, whether they can statistically predict individual disease severity measured with behavioural readouts or pathological signatures of AD has not been tested. In the last decade, blood-based biomarkers (BBMs) of AD pathology have emerged as a cost-effective and reliable option to more invasive approaches such as cerebrospinal fluid or amyloid positron emission tomography. However, their relationship to the brain functional network alterations in AD has not been investigated.
The primary objective of this proposal is to investigate the relationship between alterations in RS-CAPs, performance on behavioural tasks of working memory and blood-based biomarkers of amyloid, tau and neurodegeneration measured in the same animals. In an ongoing study I am co-supervising at the Bio-Imaging Lab, we have already acquired RS-fMRI data in TgF344-AD model rats and their wild-type littermates at 4 and 10 months of age and performed the CAP analysis. We have found significant changes in functional co-activations of key regions implicated in AD in one of the CAPs. Working memory, assessed in these animals at 10-10.5 months of age, was found to be impaired. Finally, we have also acquired blood samples in these animals at the 11-month time-point which remain to be analysed. We propose, with this project, to analyse the blood samples for markers of AD pathology and then investigate if CAP alterations in individual animals can statistically predict/explain the levels of markers in them using a cross-validated machine-learning approach. We also aim to investigate the combined capability of CAPs and BBMs to predict working memory deficits in these animals. The findings from this project will not only add another dimension to the ongoing study by relating AD pathology, behaviour and dynamic brain functional connectivity but also serve as a reference for future RS-fMRI studies of other neurodegenerative disorders involving CAP analysis.
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