Morphologic brain network predicts levodopa responsiveness in Parkinson disease

Yongsheng Xie; Chunyan Gao; Bin Wu; Liling Peng; Jianjun Wu; Liqin Lang

doi:10.3389/fnagi.2022.990913

Morphologic brain network predicts levodopa responsiveness in Parkinson disease

Front Aging Neurosci. 2023 Jan 5:14:990913. doi: 10.3389/fnagi.2022.990913. eCollection 2022.

Authors

Yongsheng Xie^{1

2

3

4}, Chunyan Gao⁵, Bin Wu⁶, Liling Peng⁷, Jianjun Wu⁶, Liqin Lang^{1

2

3

4}

Affiliations

¹ Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
² National Center for Neurological Disorders, Shanghai, China.
³ Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China.
⁴ State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, China.
⁵ Department of Nursing, Huashan Hospital, Fudan University, Shanghai, China.
⁶ Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
⁷ Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China.

Abstract

Background: The 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.

Methods: A 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.

Results: The 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.

Conclusion: Morphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS.

Keywords: Levodopa challenge test; Parkinson disease; connectome; deep brain stimulation; morphologic brain network.