Imagine your brain is a complex highway system, with different regions connected by roads. In Parkinson’s disease (PD) patients, the effectiveness of levodopa, a common treatment, can vary. To identify which patients will benefit most from deep brain stimulation (DBS), researchers turned to brain imaging. They reconstructed each patient’s brain network using magnetic resonance imaging and analyzed the differences between those who responded well to levodopa and those who didn’t. Surprisingly, they found no significant differences in overall connectivity between the two groups. However, certain connections were impacted in both groups. In patients who didn’t respond well, these connections were decreased, while connections related to specific brain regions were increased. The study concluded that assessing the morphologic brain network can be a valuable method for predicting levodopa responsiveness in PD patients and helping determine DBS candidates. Researchers have now opened up exciting possibilities for tailoring treatment plans based on neuroimaging data, potentially improving outcomes for individuals with PD.

BackgroundThe levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson’s Disease Rating Scale (UPDRS)-III score > 33% in the LCT receive DBS treatment. However, LCT results are affected by many factors, and only provide information on the immediate effectiveness of dopamine. The aim of the present study was to investigate the relationship between LCT outcome and brain imaging features of PD patients to determine whether the latter can be used to identify candidates for DBS.MethodsA total of 38 PD patients were enrolled in the study. Based on improvement in UPDRS-III score in the LCT, patients were divided into low improvement (PD-LCT-L) and high improvement (PD-LCT-H) groups. Each patient’s neural network was reconstructed based on T1-weighted magnetic resonance imaging data using the Jensen–Shannon divergence similarity estimation method. The network was established with the multiple kernel support vector machine technique. We analyzed differences in individual morphologic brain networks and their global and local metrics to determine whether there were differences in the connectomes of PD-LCT-L and PD-LCT-H groups.ResultsThe 2 groups were similar in terms of demographic and clinical characteristics. Mean ± SD levodopa responsiveness was 26.52% ± 3.47% in the PD-LCT-L group (N = 13) and 58.66% ± 4.09% in the PD-LCT-H group (N = 25). There were no significant differences between groups in global and local metrics. There were 43 consensus connections that were affected in both groups; in PD-LCT-L patients, most of these connections were decreased whereas those related to the dorsolateral superior frontal gyrus and left cuneus were significantly increased.ConclusionMorphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS.

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