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Functional connectivity analysis of cerebellum’s network during resting-state using functional Magnetic Resonance Imaging (fMRI) data

Pezoulas Vasileios

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Year 2017
Type of Item Master Thesis
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During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research exploring cerebellum's relationship with cognitive processes and gender as well. The current thesis consists of two fundamental parts. In the first part of this thesis, a lobular network analysis of cerebellum was conducted with the purpose of investigating its overall organization in individuals with low and high crystallized Intelligence Quotient (IQ). In order to do so, resting-state fMRI (rs-fMRI) data were collected from 136 healthy subjects from the well-known Human Connectome Project (HCP) database. Cerebellum was anatomically parcellated, in the Montreal Neurological Institute (MNI) coordinate space, into 28 lobules-Regions of Interest (ROIs) and thereafter correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD timeseries for each pair of ROIs. Afterwards, Minimum Spanning Trees (MSTs) were constructed in order to retain only the strongest connections within each network. Subsequently, six global and three local metrics were calculated in order to retrieve features concerning the functional and structural characteristics of each MST. Moreover, a hub analysis was conducted in order to identify nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results suggest that both male and female networks have small-world properties with significant differences only in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. In addition, an increased effort dedicated by the low-IQ population is detected in three specific lobules. In the final part of this study, instead of performing a lobular analysis of cerebellum, a voxel-wise clustering analysis approach was adopted based on Spectral Graph Theory. The main goal of this venture is to define a larger number of functional cerebellar regions and thus provide a much more accurate and data-driven gender-based network analysis of cerebellum’s activity. The recruited clustering approach was based on a spatially constrained version of the conventional spectral clustering algorithm by combining the average correlation matrix across 100 subjects with an appropriately thresholded Euclidean distance matrix. The procedure was first tested on synthetic data prior to any application on the original data. In order to find the most stable threshold as well as the optimal number of clusters, a repeated cross-validation procedure was executed on randomly defined subsets of the original population by assessing two basic clustering evaluation indices. The estimated parameters were then used to apply the SCSC procedure on the original data and extract a resting-state network atlas which was combined with the anatomical one, to define a functional atlas of cerebellum with 46 ROIs. To our knowledge, this atlas is the first resting-state functional cerebellar atlas based on the HCP data. This atlas was finally used to perform a gender-based network analysis of cerebellum, similar to the one described previously. Our results suggest the existence of significant differences in the optimal organization of the MSTs between the two genders. Finally, the dominant hub that was found in functional region 10 supports the dominance of the Left VI lobule in cerebellum’s functional connectivity as it was already reported in the first part of this study.

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