Motor networks, but also non-motor networks predict motor signs in Parkinson's disease

Anjanibhargavi Ragothaman; Martina Mancini; John G Nutt; Junping Wang; Damien A Fair; Fay B Horak; Oscar Miranda-Dominguez

doi:10.1016/j.nicl.2023.103541

Motor networks, but also non-motor networks predict motor signs in Parkinson's disease

Neuroimage Clin. 2023:40:103541. doi: 10.1016/j.nicl.2023.103541. Epub 2023 Nov 11.

Authors

Anjanibhargavi Ragothaman¹, Martina Mancini¹, John G Nutt¹, Junping Wang², Damien A Fair³, Fay B Horak⁴, Oscar Miranda-Dominguez⁵

Affiliations

¹ Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA.
² Department of Radiology, Tianjin Medical University General Hospital, China.
³ Masonic Institute for the Developing Brain (MIDB), University of Minnesota, Minneapolis, MN 55455, USA; Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN 55455, USA; Department of Pediatrics, University of Minnesota Medical School, University of Minnesota, Minneapolis, MN 55455, USA; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA.
⁴ Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA. Electronic address: horakf@ohsu.edu.
⁵ Masonic Institute for the Developing Brain (MIDB), University of Minnesota, Minneapolis, MN 55455, USA; Department of Pediatrics, University of Minnesota Medical School, University of Minnesota, Minneapolis, MN 55455, USA.

Abstract

Objective: Investigate the brain functional networks associated with motor impairment in people with Parkinson's disease (PD).

Background: PD is primarily characterized by motor dysfunction. Resting-state functional connectivity (RsFC) offers a unique opportunity to non-invasively characterize brain function. In this study, we hypothesized that the motor dysfunction observed in people with PD involves atypical connectivity not only in motor but also in higher-level attention networks. Understanding the interaction between motor and non-motor RsFC that are related to the motor signs could provide insights into PD pathophysiology.

Methods: We used data from 88 people with PD (mean age: 68.2(SD:10), 55 M/33F) coming from 2 cohorts. Motor severity was assessed in practical OFF-medication state, using MDS-UPDRS Part-III motor scores (mean: 49 (SD:10)). RsFC was characterized using an atlas of 384 regions that were grouped into 13 functional networks. Associations between RsFC and motor severity were assessed independently for each RsFC using predictive modeling.

Results: The top 5 % models that predicted the MDS-UPDRS-III motor scores with effect size >0.5 were the connectivity between (1) the somatomotor and Subcortical-Basal-ganglia, (2) somatomotor and Visual and (3) CinguloOpercular (CiO) and language/Ventral attention (Lan/VeA) network pairs.

Discussion: Our findings suggest that, along with motor networks, visual- and attention-related cortical networks are also associated with the motor symptoms of PD. Non-motor networks may be involved indirectly in motor-coordination. When people with PD have deficits in motor networks, more attention may be needed to carry out formerly automatic motor functions, consistent with compensatory mechanisms in parkinsonian movement disorders.

Keywords: MDS-UPDRS III; Motor networks; Non-motor networks; Parkinson’s disease; Predictive modeling; Resting-state functional connectivity (RsFC).

MeSH terms

Aged
Basal Ganglia
Brain / diagnostic imaging
Brain Mapping
Humans
Magnetic Resonance Imaging
Parkinson Disease* / complications
Parkinson Disease* / diagnostic imaging
Parkinson Disease* / drug therapy

Abstract

MeSH terms

Grants and funding