Gilles de la Tourette syndrome is a clinically heterogeneous disorder with poor known pathophysiology. Recent neuropathological and structural neuroimaging data pointed to the dysfunction of cortico-basal ganglia networks. Nonetheless, it is not clear how these structural changes alter the functional activity of the brain and lead to heterogeneous clinical expressions of the syndrome. The objective of this study was to evaluate global integrative state and organization of functional connections of sensori-motor, associative and limbic cortico-basal ganglia networks, which are likely involved in tics and behavioural expressions of Gilles de la Tourette syndrome. We also tested the hypothesis that specific regions and networks contribute to different symptoms. Data were acquired on 59 adult patients and 27 gender- and age-matched controls using a 3T magnetic resonance imaging scanner. Cortico-basal ganglia networks were constructed from 91 regions of interest. Functional connectivity was quantified using global integration and graph theory measures. We found a stronger functional integration (more interactions among anatomical regions) and a global functional disorganization of cortico-basal ganglia networks in patients with Gilles de la Tourette syndrome compared with controls. All networks were characterized by a shorter path length, a higher number of and stronger functional connections among the regions and by a loss of pivotal regions of information transfer (hubs). The functional abnormalities correlated to tic severity in all cortico-basal ganglia networks, namely in premotor, sensori-motor, parietal and cingulate cortices and medial thalamus. Tic complexity was correlated to functional abnormalities in sensori-motor and associative networks, namely in insula and putamen. Severity of obsessive-compulsive disorder was correlated with functional abnormalities in associative and limbic networks, namely in orbito-frontal and prefrontal dorsolateral cortices. The results suggest that the pattern of functional changes in cortico-basal ganglia networks in patients could reflect a defect in brain maturation. They also support the hypothesis that distinct regions of cortico-basal ganglia networks contribute to the clinical heterogeneity of this syndrome.

Normal aging is related to a decline in specific cognitive processes, in particular in executive functions and memory. In recent years a growing number of studies have focused on changes in brain functional connectivity related to cognitive aging. A common finding is the decreased connectivity within multiple resting state networks, including the default mode network (DMN) and the salience network. In this study, we measured resting state activity using fMRI and explored whether cognitive decline is related to altered functional connectivity. To this end we used a machine learning approach to classify young and old participants from functional connectivity data. The originality of the approach consists in the prediction of the performance and age of the subjects based on functional connectivity by using a machine learning approach. Our findings showed that the connectivity profile between specific networks predicts both the age of the subjects and their cognitive abilities. In particular, we report that the connectivity profiles between the salience and visual networks, and the salience and the anterior part of the DMN, were the features that best predicted the age. Moreover, independently of the age of the subject, connectivity between the salience network and various specific networks (i.e., visual, frontal) predicted episodic memory skills either based on a standard assessment or on an autobiographical memory task, and short-term memory binding. Finally, the connectivity between the salience and the frontal networks predicted inhibition and updating performance, but this link was no longer significant after removing the effect of age. Our findings confirm the crucial role of episodic memory and executive functions in cognitive aging and suggest a pivotal role of the salience network in neural reorganization in aging.