Sex-specific whole-brain network topologic organization in drug naïve Parkinson's disease patients

Abstract Background The functional alternation of distinct brain networks contribute to motor impairment in Parkinson’s disease (PD) remains unclear. Identifying a whole-brain connectome-based predictive model (CPM) in drug-naïve patients and verifying its predictability among drug-managed patients would be helpful to detect generalizable brain-behavior association and reflect intrinsic functional underpinning of motor impairment. Methods Resting-state functional data of 47 drug-naïve patients were enrolled to construct a predictive model by using the CPM approach, which was subsequently validated in 115 drug-managed patients. The severity of motor impairment was assessed by calculating Unified Parkinson’s Disease Rating Scale part III (UPDRS III) scores. Predictive performance was evaluated with the correlation coefficient(rtrue) and the mean squared error (MSE) between observed and predicted scores. Results A CPM for predicting individual motor impairment in drug-naïve PD was identified with significant performance (rtrue=0.845, p < 0.001, MSE = 137.57). Two connection patterns were recognized according to the correlation coefficients between the connections’ strength and motor impairment severity. The negative motor-impairment-related network contained more within-network connections in the motor, visual-related, and default mode networks, while the positive motor-impairment-related network was constructed mostly with between-network connections coupled motor-visual, motor-limbic, and motor-basal ganglia networks. The predictability of constructed model was further confirmed in drug-managed patients (r = 0.209, p = 0.025, MSE = 182.96), suggesting generalizability in PD patients with lasting dopaminergic medication influence. Conclusions This study identified a whole-brain connectome-based model that could predict the severity of motor impairment for PD. The connection patterns generated from the model reflected that functional segregation of motor, visual-related, and default mode networks play an important role in PD motor impairment, and higher connections coupling motor and non-motor regions might demonstrate a compensatory mechanism to overcome motor impairment.

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Identifying a Whole-brain Connectome-based Model in Drug-naïve Parkinson’s Disease for Predicting Motor Impairment

10.21203/rs.3.rs-779169/v2 ◽

2021 ◽

Author(s):

Haoting Wu ◽

Cheng Zhou ◽

Tao Guo ◽

Jingjing Wu ◽

Xueqin Bai ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Predictive Model ◽

Rating Scale ◽

Motor Impairment ◽

Predictive Performance ◽

Whole Brain ◽

Brain Connectome ◽

Network Connections ◽

Drug Naïve

Abstract Identifying a whole-brain connectome-based predictive model in drug-naïve patients with Parkinson’s disease and verifying its predictions on drug-managed patients would be useful in determining the intrinsic functional underpinnings of motor impairment and establishing general brain-behavior associations. In this study, we constructed a predictive model from the resting-state functional data of 47 drug-naïve patients by using a connectome-based approach. This model was subsequently validated in 115 drug-managed patients. The severity of motor impairment was assessed by calculating Uniﬁed Parkinson’s Disease Rating Scale Part III scores. The predictive performance of model was evaluated using the correlation coefficient (rtrue) between predicted and observed scores. As a result, a connectome-based model for predicting individual motor impairment in drug-naïve patients was identified with significant performance (rtrue = 0.845, p < 0.001, ppermu = 0.002). Two patterns of connection were identified according to correlations between connection strength and the severity of motor impairment. The negative motor-impairment-related network contained more within-network connections in the motor, visual-related, and default mode networks, whereas the positive motor-impairment-related network was constructed mostly with between-network connections coupling the motor-visual, motor-limbic, and motor-basal ganglia networks. Finally, this predictive model constructed around drug-naïve patients was confirmed with significant predictive efficacy on drug-managed patients (r = 0.209, p = 0.025), suggesting a generalizability in Parkinson’s disease patients under long-term drug influence. In conclusion, this study identified a whole-brain connectome-based model that could predict the severity of motor impairment in Parkinson’s patients and furthers our understanding of the functional underpinnings of the disease.

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