E21 Resting-state Functional Mri: Studying Default Mode Network In Healthy Controls Versus Early Manifest Patients With Huntington's Disease

Abstract

Background Huntington’s disease (HD) is characterised by a progressive neuronal cell loss in the brain, which leads to impairment of various brain functions. Resting-state fMRI measures spontaneous low-frequency fluctuations of the BOLD signal and helps to investigate functional architecture of the brain. It allows to identify a number of resting-state networks (RSN) – brain regions with synchronous spontaneous BOLD signal fluctuations. One of the most stable RSNs is default mode network (DMN), which is thought to be linked to integration of cognitive and emotional processing and to mind-wandering. Objective To assess pattern of activation within DMN in healthy controls in comparison with early manifest HD patients. Methods Ten right-handed early manifest HD mutation carriers (including 4 females, mean age 32.0 years) of stages 1 and 2 (Shoulson and Fahn, 1979) and sixteen right-handed healthy controls (including 9 females, mean age 41.4 years) underwent 1.5 T MRI scanning. Participants were instructed to lie still and relaxed with closed eyes, not to think about anything in particular, and not to fall asleep. Standard MRI scans and resting-state fMRI scans were acquired. A group independent component analysis (ICA) was used to identify pattern of DMN. Obtained data was processed using GIFT 2.0, SPM5, and SPM8 (Matlab 2011 and 2013) software. Gender and age were taken as covariates in data analysis. Results Using ICA, we identified DMN activation pattern both in healthy controls and early manifest HD mutation carriers. With a two-sample t-test we found a decreased activation within DMN in early manifest HD mutation carriers in comparison with healthy controls. We observed significantly reduced activation (p Conclusions Our findings may indicate the ongoing neurodegenerative process. We are working on a further analysis of clinical data and on linking it with resting-state fMRI data to find any possible correlations.