Introduction

Multiple system atrophy (MSA) is a sporadic, neurodegenerative disorder characterized by available combination of progressive autonomic dysfunction, parkinsonism, cerebellar and pyramidal features¹. MSA can be categorized based on the predominant motor symptoms as MSA with predominant parkinsonism (MSA-P) and MSA with predominant cerebellardysfunction (MSA-C)². MSA-C represents a distinct motor subtype of MSA, and is characterized by gaitataxia, limbataxia, scanning dysarthria, and cerebellar oculomotor dysfunction in addition to autonomicdysfunction and parkinsonism3. This work focuses on the motor features of MSA-C and hypothesizes that patients with MSA-C would demonstrate significantly higher abnormality in the cerebellar network in comparison to the basal ganglia network. This subnetwork comprising of the precentral cortex, basal ganglia, thalamus, ventral diencephalon and cerebellum has been previously utilized in connectivity-based studies in Parkinson’s disease^4,5.

Method

All subjects (23 MSA-C, 25 HC, age and gender matched) were scanned on a 3T Philips Achieva MRI scanner using a 32-channel head coil. 3D-T1 structural image with high resolution were obtained with echo-time(TE)=3.7ms, repetition-time(TR)=8.1ms, field-of-view(FOV)=256x256x155mm, slice-thickness=1mm, voxel-size=1x1x1mm, sense-factor=3.5, acquisition matrix=256x256, flip-angle=8⁰. Diffusion imaging(DWI) were acquired using a single-shot spin-echo, echo-planner sequence in axial section with TE=62ms. TR=8783ms, FOV=224x224mm, voxel-size=1.75x1.75x2mm; diffusion gradient direction=15, b-value=1000s/mm2 and single b=0s/mm2 image. To obtain complete connectome and determine the connectivity from various cerebellar nodes, two atlases Desikan atlas from Freesurfer-6.0(http://surfer.nmr.mgh.harvard.edu) and AAL atlas were fused to delineate the complete brain and multiple region of interest (ROIs) within cerebellum (from AAL). Using FSL-5.0.9(https://fsl.fmrib.ox.ac.uk), probabilistic tractography⁶ was implemented to track fibers from each node to every other node. Based on hypothesis of differences in the motor subnetwork, we extracted 38 ROIs to create a 38*38 subnetwork as shown in Figure-1. The ROIs selected included bilateral pre-central cortex, basal ganglia, thalamus, ventral diencephalon and cerebellum. Global, nodal measures were computed and analyzed for group difference using MANCOVAN model while co-varying for age and gender and performed False Discovery Rate(FDR) with significant q-value<0.05. Edge-wise analysis was performed using NBS for 10000 permutation with significant p-value<0.01. Further, global, nodal and edge strength were correlated with duration of illness, total-UMSARS and motor component scores. Pearson’s correlation was computed with age, gender and cerebellum volumes as co-variates and the significance threshold was maintained at p-value<0.05.

Results

Significant decrease in density (corrected p-value<0.01), transitivity (corrected p-value<0.01), characteristic path length (corrected p-value<0.01) and clustering coefficient (corrected p-value<0.01) was observed in motor subnetwork of patients with MSA-C when compared to HC. Several regions with significantly reduced nodal strength within the motor subnetwork were observed in patients with MSA-C in comparison to HC (Figure-1). Edge-wise analysis revealed significantly lower connectivity in patients with MSA-C involving 40 connections of the subnetwork (Figure-2). A significant negative correlation was observed between connectivity of the thalamus and total UMSARS scores and UMSARS-II score (Figure-3). At the edge-level, a few connections showed significant correlations with the total UMSARS score, and motor severity score (part-II). The connections involved were between the left cerebellum_8 to vermis_7, left thalamus to left ventral diencephalon, left-cerebellum_10 to vermis_4,5, and left-cerebellum_8 to vermis_7. Duration of illness showed a negative correlation between vermis_6 to vermis_7. Only one connection from left-cerebellum_4,5 to right-cerebellum_4,5 demonstrated a positive correlation with duration of illness.

Discussion

Our results provide definitive evidence of damage to connections which may be causally implicated in the motor features of cerebellar dysfunction and Parkinsonism observed in MSA-C. Abnormal global metrics of the motor subnetwork in the MSA-C are suggestive of impairment in the structural segregation, integration and network resilience of the motor subnetwork in MSA-C. At a nodal level, we observed significantly reduced nodal strength in several regions (Figure 1), indicating poor connectivity with other nodes. In edge-wise analysis, a higher proportion of abnormal connections involved nodes within the cerebellum and this observation probably substantiates the predominance of cerebellar dysfunction in MSA-C. The observed negative correlation between the UMSARS scores and thalamus connectivity suggests a definitive involvement of the motor subnetwork in MSA-C.

Conclusion

Significant alterations in the structural connectivity of the motor subnetwork at the global,nodal and edge levels are observed in MSA-C. The higher degree of abnormality observed in inter and intra-cerebellar connectivity corroborate with the predominant motor symptom of cerebellar dysfunction. Future studies exploring the structural connectivity of non-motor subnetworks are crucial to aid in a better understanding of the overall disease process in MSA-C.

Acknowledgements

Department of Science and Technology – Science Education and Research Board (DSTSERB)(ECR/2016/000808) provided partial funding for setting up the computing facility.