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| Home > Publications database > How brain networks tic: Predicting tic severity through rs‐fMRI dynamics in Tourette syndrome > print |
| 001 | 1007800 | ||
| 005 | 20231103080308.0 | ||
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| 100 | 1 | _ | |a Ramkiran, Shukti |0 P:(DE-Juel1)169201 |b 0 |
| 245 | _ | _ | |a How brain networks tic: Predicting tic severity through rs‐fMRI dynamics in Tourette syndrome |
| 260 | _ | _ | |a New York, NY |c 2023 |b Wiley-Liss |
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| 520 | _ | _ | |a 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. The relevant predictors pointed to dysfunction of the primary motor cortex, the prefrontal-basal ganglia loop and 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. |
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| 700 | 1 | _ | |a Gaebler, Arnim Johannes |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Rajkumar, Ravichandran |0 P:(DE-Juel1)164396 |b 4 |
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| 700 | 1 | _ | |a Neuner, Irene |0 P:(DE-Juel1)131781 |b 6 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1002/hbm.26341 |g p. hbm.26341 |0 PERI:(DE-600)1492703-2 |n 11 |p 4225-4238 |t Human brain mapping |v 44 |y 2023 |x 1065-9471 |
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