A network science approach of the macroscopic organization of the brain: analysis of structural and functional brain networks in health and disease

Abstract

The brain is composed of massively connected elements arranged into modules that form hierarchical networks. Experimental evidence reveals a well-defined connectivity design, characterized by the presence of strategically connected core nodes that critically contribute to resilience and maintain stability in interacting brain networks. Certain brain pathologies, such as Alzheimer's disease and alcohol use disorder, are thought to be a consequence of cascading maladaptive processes that alter normal connectivity. These findings have greatly contributed to the development of network neuroscience to understand the macroscopic organization of the brain. This thesis focuses on the application of network science tools to investigate structural and functional brain networks in health and disease. To accomplish this goal, three specific studies are conducted using human and rodent data recorded with MRI and tracing technologies. In the first study, we examine the relationship between structural and functional connectivity in the rat cortical network. Using a detailed cortical structural matrix obtained from published histological tracing data, we first compare structural connections in the rat cortex with their corresponding spontaneous correlations extracted empirically from fMRI data. We then show the results of this comparison by relating structural properties of brain connectivity to the functional modularity of resting-state networks. Specifically, we study link reciprocity in both intra- and inter-modular connections as well as the structural motif frequency spectrum within functionally defined modules. Overall, our results provide further evidence that structural connectivity is coupled to and shapes functional connectivity in cortical networks. The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting pahtogenic seeding and subsequent prion-like spreading processes of neurofibrillary tangles and amyloid plaques. In the second study of this thesis, we investigate whether structural brain networks as measured with dMRI could serve as a complementary diagnostic tool in prodromal dementia. Using imaging data from the ADNI database, we first aim to implement machine learning techniques to extract centrality features that are altered in Alzheimer's dementia. We then incorporate data from the NKI database and create dynamical models of normal aging and Alzheimer's disease to estimate the earliest detectable stage associated with dementia in the simulated disease progression. Our model results suggest that changes associated with dementia begin to manifest structurally at early stages. Statistical dependence measures computed between BOLD signals can inform about brain functional states in studies of neurological and psychiatric disorders. Furthermore, its non-invasive nature allows comparable measurements between clinical and animal studies, providing excellent translational capabilities. In the last study, we apply the NBS method to investigate alterations in the resting-state functional connectivity of the rat brain in a PD state, an established animal model of clinical relevant features in alcoholism. The analysis reveal statistically significant differences in a connected subnetwork of structures with known relevance for addictive behaviors, hence suggesting potential targets for therapy. This thesis provides three novel contributions to understand the healthy and pathological brain connectivity under the perspective of network science. The results obtained in this thesis underscore that brain network models offer further insights into the structure-function coupling in the brain. More importantly, this network perspective provides potential applications for the diagnosis and treatment of neurological and psychiatric disorders.