Multiple Sclerosis (MS) is a chronic neurologic disease that represents a leading cause of disability in young adults. It affects the brain in both the myelin content and axonal integrity, causing structural and functional disconnection between various brain areas. Damage accumulation leads to cognitive and motor impairment.
How the structural disconnection and plasticity to reorganise the integration pathways are related is still matter of debate. Graph theory analysis is a straightforward tool to investigate how linkages are generated and integrated in a network, and the brain can be described as a complicated network of interconnected and interacting elements. Nodes denote neural elements (neurons or brain regions) that are linked by edges representing physical connections (synapses or axonal projections). Graph theory analysis applied to advanced magnetic resonance imaging (MRI), i.e. diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI), provides information on the anatomical configuration of the brain network and describes how brain regions are functionally related.
We aim to exploit network analysis to study how the structural disruption is combined with the functional reorganization in MS, since the two phenomena are intrinsically connected. We will analyse MR data of a large sample of patients with MS (n=100), who have recently underwent clinical evaluation and 3T MRI with advanced sequences, i.e. T1-3D (cortical thickness), DTI (structural connectivity) and rs-fMRI (functional connectivity). Data will be analysed on large-scale according to the graph theory.
We will analyse the relationships between structural and functional connectivity changes and the correlation between MRI and clinical data.
By determining the structural and functional substrates underlying motor and cognitive impairment in MS, the results of this research may help to guide the therapeutic approach, and in particular rehabilitative intervention strategies.
MS has been studied through multiple methodologies for years. From the beginning of the 2000s to today, numerous studies have shown that advanced magnetic resonance techniques were an excellent tool for assessing the ultra-structural damage of both the white and gray matter, outside the focal MS lesions. This made it possible to explain the so-called clinical-radiological paradox, i.e. the discrepancy between the lesion load and the clinical disability. However, the majority of studies has been conducted on small series of MS patients and/or has investigated only an aspect of the pathology, e.g. some studies focused on the ultra-structural white matter damage by means of DTI, while others focused on functional abnormalities by means of fMRI. When studying brain functional changes, functional systems (i.e. the motor, visual, verbal or mnemonic function) have been investigated separately; the functional connectivity has been often studied only on few networks.
Analytical techniques to process advanced MR images, obtained either in single mode or combining modalities, are under development and graph theory is at the cutting edge of brain studies. Graph theory analysis is a straightforward tool to investigate how linkages are generated and integrated in a network, and the brain can be described as a complicated network of interconnected and interacting elements. Nodes denote neural elements (neurons or brain regions) that are linked by edges representing physical connections (synapses or axonal projections). Graph theory analysis applied to advanced MRI provides global information on the anatomical configuration of the brain network and describes how all brain regions are functionally related.
So far graph theory have been exploited to study separately axonal damages or plasticity in MS. This project is conceived to exploit the existent knowledge in network analysis to investigate the combined structural and functional brain networks, in order to understand the relationships between demyelination and axonal truncation processes and functional reorganization.
Studies of MS have been often performed on small samples of patients; convewrsely, we count on our database of 100 MS patients. Datasets include complete coverage of pathology stages, clinical scores, MRI data from anatomical to diffusion-based and functional images. Further, we can access to a database of controls that includes 41 healthy subjects matched in age and gender with patients.
As well, we aim to have a global description of how brain networks are influenced by the pathology, thus not limiting our investigation to the most studied networks, e.g. the default mode or somatosensory networks, neither to those regions that are known to be hit by MS, as the cerebellum. We pursue the global path, because we speculate that the concatenation of structural and functional modification can run across pathways that include other areas.
We will apply the most sophisticated and modern computational tools to globally investigate the combined structural and functional brain response to the pathology, including the correlation between the network parameters and the clinical outcomes, in order to assure a better comprehension of the events leading the clinical disability and to guide rehabilitation strategies.