How brain networks tic: predicting tic severity through rs-fMRI dynamics in Tourette syndrome

Abstract

Tourette syndrome (TS) is a neuropsychiatric disorder characterized by motor and phonic tics, which several different theories, such as basal ganglia-thalamo-cortical loop dysfunction and amygdala hypersensitivity, have sought to explain. Previous research has shown dynamic changes in the brain prior to tic onset leading to tics, and this study aims to investigate the contribution of network dynamics to them. For this, we have employed three methods of functional connectivity to resting-state fMRI data - namely the static, the sliding window dynamic and the ICA based estimated dynamic; followed by an examination of the static and dynamic network topological properties. A leave-one-out (LOO-) validated regression model with LASSO regularization was used to identify the key predictors. Most relevant predictors were related to regions belonging to the amygdala-mediated visual social processing network. This is in line with a recently proposed social decision-making dysfunction hypothesis, opening new horizons in understanding tic pathophysiology.

Tourette syndrome research highlights from 2023

“…A third research of interest compiled several metrics of functional connectivity based on three different approaches: the classic static functional connectivity, the dynamic one obtained with a sliding window, and independent component analysis (ICA) based connectivity (Ramkiran et al 2023). Altogether, and by compiling a large variety of statistical metrics and analyses, the authors underscored the particular significance of metrics obtained with the dynamical approaches, especially related to networks involving the primary motor cortex, the prefrontal-basal ganglia pathway and the amygdala.…”

Contribution of cortical layers to human dynamic functional connectivity

“…This allows periods of stronger or weaker connectivity between areas to be identified that would otherwise be missed in a static analysis. The connectivity patterns, their variability, and duration associated with the time-varying states have been the focus of numerous recent publications assessing differences between healthy subjects and diverse groups of patients (Niu et al, 2019, Jiao et al, 2021, Pang et al, 2020, Zhang et al, 2018, Ahmadi et al, 2021, Naro et al, 2018, Cao et al, 2019, Gu et al, 2020, Rabany et al, 2019, Wu et al, 2021, Fallahi et al, 2021, Fu et al, 2020, Wei et al, 2020, Kaiser et al, 2016, Wong et al, 2021, Demertzi et al, 2019, Ramkiran, 2022, demonstrating the clinical relevance of tracking such connectivity changes in the human brain. Moreover, the reconfiguration of these dynamic connectivity patterns upon stimulation further suggests that functional dynamic connectivity analysis may be of value in novel therapeutic approaches (Grami et al, 2021).…”