Multivariate classification provides a neural signature of Tourette disorder

Giuseppe A Zito (1, 2) , Andreas Hartmann (1, 3) , Benoît Béranger (4, 1) , Samantha Weber (5) , Selma Aybek (5) , Johann Faouzi (6, 1) , Emmanuel Roze (1) , Marie Vidailhet (1) , Yulia Worbe (1, 3, 7)

1 ICM - Institut du Cerveau = Paris Brain Institute
2 Inselspital - Bern University Hospital [Berne]
3 CHU Pitié-Salpêtrière [AP-HP]
4 CENIR - Plateforme de neuroimagerie CENIR = Center for NeuroImaging Research-Human MRI Neuroimaging core facility for clinical research [ICM Paris]
5 Inselspital Bern
6 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]
7 CHU Saint-Antoine [AP-HP]

Background: Tourette disorder (TD), hallmarks of which are motor and vocal tics, has been related to functional abnormalities in large-scale brain networks. Using a fully-data driven approach in a prospective, case-control study, we tested the hypothesis that functional connectivity of these networks carries a neural signature of TD. Our aim was to investigate (i) the brain networks that distinguish adult patients with TD from controls, and (ii) the effects of antipsychotic medication on these networks. Methods: Using a multivariate analysis based on support vector machine (SVM), we developed a predictive model of resting state functional connectivity in 48 patients and 51 controls, and identified brain networks that were most affected by disease and pharmacological treatments. We also performed standard univariate analyses to identify differences in specific connections across groups. Results: SVM was able to identify TD with 67% accuracy (p=0.004), based on the connectivity in widespread networks involving the striatum, fronto-parietal cortical areas and the cerebellum. Medicated and unmedicated patients were discriminated with 69% accuracy (p=0.019), based on the connectivity among striatum, insular and cerebellar networks. Univariate approaches revealed differences in functional connectivity within the striatum in patients vs. controls, and between the caudate and insular cortex in medicated vs. unmedicated TD. Conclusions: SVM was able to identify a neuronal network that distinguishes patients with TD from control, as well as medicated and unmedicated patients with TD, holding a promise to identify imaging-based biomarkers of TD for clinical use and evaluation of the effects of treatment.

Domaines: Sciences du Vivant [q-bio]; Neurosciences [q-bio.NC]

Giuseppe A Zito, Andreas Hartmann, Benoît Béranger, Samantha Weber, Selma Aybek, et al.. Multivariate classification provides a neural signature of Tourette disorder: Running head: Multivariate analysis of Tourette disorder. Psychological Medicine, 2021. ⟨hal-03480739⟩