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.

Parkinson’s Disease (PD) is a common neurodegenerative disease which impacts millions of people around the world. In clinical treatments, freezing of gait (FoG) is used as the typical symptom to assess PD patients’ condition. Currently, the assessment of FoG is usually performed through live observation or video analysis by doctors. Considering the aging societies, such a manual inspection based approach may cause serious burdens on the healthcare systems. In this study, we propose a pure video-based method to automatically detect the shuffling step, which is the most indistinguishable type of FoG. Firstly, the RGB silhouettes which only contain legs and feet are fed into the feature extraction module to obtain multi-level features. 3D convolutions are used to aggregate both temporal and spatial information. Then the multi-level features are aggregated by the feature fusion. Skip connections are implemented to reserve information of high resolution and period-wise horizontal pyramid pooling is utilized to fuse both global context and local features. To validate the efficacy of our method, a dataset containing 268 normal gait samples and 362 shuffling step samples is built, on which our method achieves an average detection accuracy of 90.8%. Besides shuffling step detection, we demonstrate that our method can also assess the severity of walking abnormity. Our proposal facilitates a more frequent assessment of FoG with less manpower and lower cost, leading to more accurate monitoring of the patients’ condition.

Parkinson’s disease (PD) is a neurodegenerative disease that is associated with motor and non-motor symptoms and caused by lack of dopamine in the substantia nigra of the brain. Subthalamic nucleus deep brain stimulation (STN-DBS) is a widely accepted therapy of PD that mainly inserts electrodes into both sides of the brain. The effect of STN-DBS was mainly for motor function, so this study focused on the recovery of motor function for PD after DBS. Hemispherical asymmetry in the brain network is considered to be a potential indicator for diagnosing PD patients. This study investigated the value of hemispheric brain connection asymmetry in predicting the DBS surgery outcome in PD patients. Four types of brain connections, including left intra-hemispheric (LH) connection, right intra-hemispheric (RH) connection, inter-hemispheric homotopic (Ho) connection, and inter-hemispheric heterotopic (He) connection, were constructed based on the resting state functional magnetic resonance imaging (rs-fMRI) performed before the DBS surgery. We used random forest for selecting features and the Ridge model for predicting surgical outcome (i.e., improvement rate of motor function). The functional connectivity analysis showed that the brain has a right laterality: the RH networks has the best correlation (r = 0.37, p = 5.68E-03) between the predicted value and the true value among the above four connections. Moreover, the region-of-interest (ROI) analysis indicated that the medioventral occipital cortex (MVOcC)–superior temporal gyrus (STG) and thalamus (Tha)–precentral gyrus (PrG) contributed most to the outcome prediction model for DBS without medication. This result provides more support for PD patients to evaluate DBS before surgery.

Parkinson's disease (PD) is a multi-systemic disease in the brain arising from the dysfunction of several neural networks. The diagnosis and treatment of PD have gained more attention for clinical researchers. While there have been many fMRI studies about functional topological changes of PD patients, whether the dynamic changes of functional connectivity can predict the drug therapy effect is still unclear. The primary objective of this study was to assess whether large-scale functional efficiency changes of topological network are detectable in PD patients, and to explore whether the severity level (UPDRS-III) after drug treatment can be predicted by the pre-treatment resting-state fMRI (rs-fMRI). Here, we recruited 62 Parkinson's disease patients and calculated the dynamic nodal efficiency networks based on rs-fMRI. With connectome-based predictive models using the least absolute shrinkage and selection operator, we demonstrated that the dynamic nodal efficiency properties predict drug therapy effect well. The contributed regions for the prediction include hippocampus, post-central gyrus, cingulate gyrus, and orbital gyrus. Specifically, the connections between hippocampus and cingulate gyrus, hippocampus and insular gyrus, insular gyrus, and orbital gyrus are positively related to the recovery (post-therapy severity level) after drug therapy. The analysis of these connection features may provide important information for clinical treatment of PD patients.

Background. Deep brain stimulation of the subthalamic nuclei (STN-DBS) is a standard treatment option for advanced Parkinsons disease (PD) patients. Delirium following DBS electrode implantation is common, by several studies, and cognitive impairment is a risk factor for developing postoperative delirium (POD). This prospective observational study was conducted to identify whether preoperative baseline cognitive status has an association with POD in PD patients undergoing DBS surgery. Methods. Preoperatively, neuropsychiatric and neuropsychological assessments of the patients were performed including clinical dementia rating (CDR) score, instrumental activities of daily living (IADL) score, mini-mental state exam (MMSE) score, Montreal cognitive assessment (MoCA) score, Hamilton anxiety (HAMA) and Hamilton depression (HAMD) scores, and numerical cancellation test. POD was identified by the confusion assessment method (CAM) twice per day on postoperative day 1 until discharge. Results. Twenty-seven (21.6) of 125 patients developed POD. Among the variables screened, age, CDR score, MMSE score, and HAMA score were indicated to be independent influence factors of POD. The cutoff score, AUC, sensitivity, and specificity of age, CDR score, MMSE score, and HAMA score associated with POD was 58.5, 0.751, 92.6, 52.0; 0.5, 0.848, 77.8, 91.8; 27.5, 0.827, 88.9, 62.2; and 12.5, 0.706, 85.2, 54.1, respectively. Conclusions. We observed age, CDR score, MMSE score, and HAMA score were independent influence factors of POD in PD patients who received DBS. It is necessary to assess the cognitive status of PD patients before surgery to identify high-risk patients.