Brain complex network analysis by resting-state functional magnetic resonance imaging and graph theory

Functional magnetic resonance is one of the functional neuroimaging techniques that exploits the hemodynamic variations produced by neuronal activity to identify the brain areas of activation. Being a non-invasive technique, it is applied in the clinical setting for the study of brain activity in subjects with different neuropathologies. Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly employed to study changes in functional brain connectivity in a vast number of conditions, including neurodegenerative diseases such as Parkinson’s or Alzheimer’s disease. The interest in the so-called functional connectome (i.e., the complex network of crosstalk between brain areas) is ever increasing [1, 2, 3]. To this end, recently several methods which stem from the realms of graph theory and network science have emerged as useful tools to study both local and global properties of complex brain networks. In detail, the brain is conceptualized as a graph, in which brain regions represent nodes and the relationships between the regions, defined through a variety of association measures rs-fMRI time-series, represent edges which connect the nodes within the graph [4]. Then, topological properties that highlight brain organization can be extracted [5]. Recently, various studies have shown that graph theoretical indices are sensitive to changes in brain network measures in both psychiatric and neurological diseases [1]. Since global and local variables were not always able to capture the differences between patients with pathology and controls, a new the disruption index k was introduced in Achard et al. (2012) [6] to capture it. Disruption index k summarizes graph metric changes at the nodal level in a single value. It is thus a global index capturing changes at the nodal level. For a specified graph metric, disruption index k is calculated as the slope of the linear regression model between the mean nodal metric value of a reference group and the differential nodal metric value between a given subject (patient or control) and that reference. If the subject’s nodal values are close to those of the reference group, the disruption index k will be close to 0. Contrary, if the subject’s nodal values are different from those of the reference group, with reduced values in nodes with high metric values in the reference group, the disruption index k will be negative. 6 Introduction Once the reference group is computed, the disruption index k can be calculated for each control and each patient individually and statistical tests can be applied to compare the differences between groups [6, 7]. In this thesis, we employed resting state functional magnetic resonance imaging (rs-fMRI), in conjunction with graph- theoretical analysis and a newly developed functional “disruption index”, to study whole-brain and local functional changes in different types of patients: primary open angle glaucoma patients, sudden unilateral sensorineural hearing loss patients and human immunodeficiency virus patients. We also assessed the potential of graph theoretical measures as biomarkers of disease in terms of their relationship to clinically parameters. The results presented here are obtained from patients and healthy controls that underwent rs-fMRI examination at 3T. The data was collected thanks multidisciplinary collaborations between: Clinical Infectious Diseases, Medical Physics, Neuroradiology, Ophthalmology and Otorhinolaryngology Unit