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

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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Identifying a whole‐brain connectome‐based model in drug‐naïve Parkinson's disease for predicting motor impairment

Human Brain Mapping ◽

10.1002/hbm.25768 ◽

2021 ◽

Author(s):

Haoting Wu ◽

Cheng Zhou ◽

Xueqin Bai ◽

Xiaocao Liu ◽

Jingwen Chen ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Motor Impairment ◽

Whole Brain ◽

Brain Connectome ◽

Drug Naïve

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

Journal of the Neurological Sciences ◽

10.1016/j.jns.2021.119459 ◽

2021 ◽

Vol 429 ◽

pp. 119459

Author(s):

Sara Satolli ◽

Federica Agosta ◽

Rosa De Micco ◽

Silvia Basaia ◽

Mattia Siciliano ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Brain Network ◽

Whole Brain ◽

Drug Naïve

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Primary disruption of the default mode network subsystems in drug-naïve Parkinson’s disease with mild cognitive impairments

Neuroradiology ◽

10.1007/s00234-020-02378-z ◽

2020 ◽

Vol 62 (6) ◽

pp. 685-692 ◽

Cited By ~ 1

Author(s):

Yanbing Hou ◽

Xiaoqin Yuan ◽

Qianqian Wei ◽

Ruwei Ou ◽

Jing Yang ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Default Mode Network ◽

Cognitive Impairments ◽

Default Mode ◽

Drug Naïve

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4302 Decreased structural basal ganglia motor loop connections in Vascular Parkinsonism compared to Parkinson’s disease and healthy aging

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.295 ◽

2020 ◽

Vol 4 (s1) ◽

pp. 94-94

Author(s):

Christine Cooper ◽

Federico Rodriguez-Porcel ◽

Travis Turner ◽

Gonzalo Revuelta ◽

Jens Jensen ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Basal Ganglia ◽

Healthy Aging ◽

Lower Body ◽

Vascular Parkinsonism ◽

Whole Brain ◽

Brain Connectome ◽

Clinical Scores ◽

Motor Loop

OBJECTIVES/GOALS: This study uses diffusion kurtosis imaging (DKI) to investigate the structural profiles of basal ganglia (BG) motor circuitry in Vascular Parkinsonism (VP), Parkinson’s disease (PD), and healthy aging controls (HC). VP is a clinical diagnosis of lower body predominant parkinsonism without significant benefit from levodopa. VP is distinct from PD, yet the concept of VP remains debated due to the inability of prior studies to identify specific causative changes. One reason for this may be limitations in measuring intricate BG connectivity in vivo. Given the predominant lower body parkinsonism symptoms in VP, we hypothesized that VP would be associated with decreased connectivity specifically within the BG motor loop. METHODS/STUDY POPULATION: We obtained DKI brain imaging in subjects with VP (N = 7), PD (N = 21), and HCs (N = 58), the latter of which had cardiovascular risk factors but no neurological symptoms. The VP and PD groups were evaluated by a parkinsonism-focused motor exam and brief cognitive testing. We compared BG motor loop connectivity between groups and investigated for correlation between connectivity and clinical scores. To account for differences in fiber counts due to the different imaging scanners and protocols between cohorts, we used a BG motor loop proportion, which was the ratio of the BG motor loop fiber count over a control loop, the visual processing pathway. We used Kruskal-Wallis rank sum test with post-hoc Dunn tests to assess imaging findings between subject groups, and Pearson’s correlation to look for correlation between clinical scores and fiber counts. RESULTS/ANTICIPATED RESULTS: The whole brain connectome showed the fewest number of fibers in VP, followed by PD, and then HC (p<0.0001). The BG motor loop proportion fiber count of the BG motor loop was lower in the VP group, compared to the PD and HC cohorts (p = 0.031). In the VP group, the whole brain connectome fiber count correlated with a gait and balance subscore of the Movement Disorders Society - Unified Parkinson Disease Rating Scale (R = −0.87, p = 0.01). DISCUSSION/SIGNIFICANCE OF IMPACT: This study indicates that VP is associated with decreased structural connectivity, with a disproportionate degree of loss in the BG motor circuitry. While the etiology for this susceptibility to injury and preferential damage to BG remains to be defined, these findings can provide an important starting point for a biological understanding of VP, and a potential future marker for diagnosis and tracking disease progression.

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Dysfunction of the Default Mode Network in Drug-Naïve Parkinson’s Disease with Mild Cognitive Impairments: A Resting-State fMRI Study

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2016.00247 ◽

2016 ◽

Vol 8 ◽

Cited By ~ 21

Author(s):

Yanbing Hou ◽

Jing Yang ◽

Chunyan Luo ◽

Wei Song ◽

Ruwei Ou ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Default Mode Network ◽

Resting State ◽

Cognitive Impairments ◽

Resting State Fmri ◽

Default Mode ◽

Fmri Study ◽

Drug Naïve

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Dopamine Depletion Alters Macroscopic Network Dynamics in Parkinson’s Disease

10.1101/382994 ◽

2018 ◽

Author(s):

James M. Shine ◽

Peter T. Bell ◽

Elie Matar ◽

Russell A. Poldrack ◽

Simon J.G. Lewis ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Large Scale ◽

Motor Symptom ◽

Grey Matter Volume ◽

Compensatory Mechanism ◽

Dopamine Depletion ◽

Network Integration ◽

Whole Brain ◽

The Impact

AbstractParkinson’s disease is primarily characterised by diminished dopaminergic function, however the impact of these impairments on large-scale brain dynamics remains unclear. It has been difficult to disentangle the direct effects of Parkinson’s disease from compensatory changes that reconfigure the functional signature of the whole brain network. To examine the causal role of dopamine depletion in network-level topology, we investigated time-varying network structure in 37 individuals with idiopathic Parkinson’s disease, both ‘On’ and ‘Off’ dopamine replacement therapy, along with 50 age-matched, healthy control subjects using resting-state functional MRI. By tracking dynamic network-level topology, we found that the Parkinson’s disease ‘Off’ state was associated with greater network-level integration than in the ‘On’ state. The extent of integration in the ‘Off’ state inversely correlated with motor symptom severity, suggesting that a shift toward a more integrated network topology may be a compensatory mechanism associated with preserved motor function in the dopamine depleted ‘Off’ state. Furthermore, we were able to demonstrate that measures of both cognitive and brain reserve (i.e., premorbid intelligence and whole brain grey matter volume) had a positive relationship with the relative increase in network integration observed in the dopaminergic ‘Off’ state. This suggests that each of these factors plays an important role in promoting network integration in the dopaminergic ‘Off’ state. Our findings provide a mechanistic basis for understanding the PD ‘Off’ state and provide a further conceptual link with network-level reconfiguration. Together, our results highlight the mechanisms responsible for pathological and compensatory change in Parkinson’s disease.

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