Abstract
Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective invasive treatment for advanced Parkinson's disease (PD) at present. Due to the invasiveness and cost of operations, a reliable tool is required to predict the outcome of therapy in the clinical decision-making process. This work aims to investigate whether the topological network of functional connectivity states can predict the outcome of DBS without medication. Fifty patients were recruited to extract the features of the brain related to the improvement rate of PD after STN-DBS and to train the machine learning model that can predict the therapy's effect. The functional connectivity analyses suggested that the GBRT model performed best with Pearson's correlations of r = 0.65, p = 2.58E−07 in medication-off condition. The connections between middle frontal gyrus (MFG) and inferior temporal gyrus (ITG) contribute most in the GBRT model.
Highlights
Parkinson’s disease (PD) is a common neurodegenerative disorder with a wide range of motor and non-motor symptoms, such as cognitive impairment, autonomic dysfunction, disorders of sleep, depression, or hyposmia, which lead to a severe burden for the patients and their caregivers (Poewe et al, 2017)
This study included 50 patients aged from 50 to 77 with a final clinic diagnosis of PD. They were recruited from Tsinghua University Yuquan Hospital, Beijing, China, and their disease severities were assessed according to the motor section of the Movement Disorder Society (MDS) UPDRS-III (Antonini et al, 2013)
The electrode contacts were well-sited within the subthalamic nucleus (STN), and Deep brain stimulation (DBS) lead localization was reconstructed using Lead-DBS
Summary
Parkinson’s disease (PD) is a common neurodegenerative disorder with a wide range of motor and non-motor symptoms, such as cognitive impairment, autonomic dysfunction, disorders of sleep, depression, or hyposmia, which lead to a severe burden for the patients and their caregivers (Poewe et al, 2017). To cure PD, highly efficacious therapies, such as pharmacological dopamine substitution, have been adapted widely (Poewe et al, 2017). The use of levodopa as dopamine-replacement therapy is highly effective in ameliorating the symptoms of the disease (Fahn et al, 2004) through changing the motor cortex hypoactivation in the supplementary motor area and the primary motor cortex (Buhmann et al, 2003). It is reported that neurostimulation of STN was more effective than medical management alone (Deuschl et al, 2006)
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