Disease Biomarker? Perivascular Spaces Changes Across Neurological Diseases with Distinct Etiologies
Poster No:
1652
Submission Type:
Abstract Submission
Authors:
Thais Bezerra1, Lucas Scardua Silva1, Rafael Batista João1, Ricardo Brioschi1, Ítalo Karmann Aventurato1, Brunno Campos1, Marina Koutsodontis Machado Alvim1, John-Paul Nicolo2, Gernot Hlauschek2, Leonilda Santos3, Felipe von Glehn4, Thiago Rezende1, Marcondes Cavalcante França Junior1, Guilherme Ludwig5, Terence O'Brien2, Meng Law2, Patrick Kwan2, Fernando Cendes1, Ben Sinclair2, Clarissa Lin Yasuda1
Institutions:
1Department of Neurology, Faculty of Medical Sciences, UNICAMP, Campinas, Brazil, 2Monash University, Melbourne, Victoria, 3Institute of Biology, UNICAMP, Campinas, Brazil, 4UnB, Brasília, Brazil, 5Institute of Mathematics, Statistics and Scientific Computing, UNICAMP, Campinas, Brazil
First Author:
Co-Author(s):
Ítalo Karmann Aventurato
Department of Neurology, Faculty of Medical Sciences, UNICAMP
Campinas, Brazil
Department of Neurology, Faculty of Medical Sciences, UNICAMP
Campinas, Brazil
Marina Koutsodontis Machado Alvim
Department of Neurology, Faculty of Medical Sciences, UNICAMP
Campinas, Brazil
Department of Neurology, Faculty of Medical Sciences, UNICAMP
Campinas, Brazil
Marcondes Cavalcante França Junior
Department of Neurology, Faculty of Medical Sciences, UNICAMP
Campinas, Brazil
Department of Neurology, Faculty of Medical Sciences, UNICAMP
Campinas, Brazil
Guilherme Ludwig
Institute of Mathematics, Statistics and Scientific Computing, UNICAMP
Campinas, Brazil
Institute of Mathematics, Statistics and Scientific Computing, UNICAMP
Campinas, Brazil
Introduction:
Introduction: The glymphatic system is the brain's primary waste clearance mechanism, responsible for removing soluble metabolites and proteins from the central nervous system. It consists of cerebrospinal fluid, interstitial fluid, and perivascular spaces (PVS), which are channels formed by astroglial cells that surround blood vessels. These PVS can be identified using high-resolution T1-weighted MRI images, providing a representation of the system.
While some small studies have linked changes in PVS and the glymphatic system to the pathophysiology of various diseases, such as epilepsy(Pham et al., 2022), Parkinson's disease, and dementia, there have been fewer investigations into the commonalities and specific differences in PVS changes among conditions with different underlying causes. This study specifically examines the enlarged PVS burden in the white matter (WM) and basal ganglia (BG) of individuals with distinct pathologies, including neuromyelitis optica (NMO)(von Glehn et al., 2014), spinocerebellar ataxia type 3 (SCA3)(D'Abreu et al., 2012), and epilepsy(Whelan et al., 2018).
While some small studies have linked changes in PVS and the glymphatic system to the pathophysiology of various diseases, such as epilepsy(Pham et al., 2022), Parkinson's disease, and dementia, there have been fewer investigations into the commonalities and specific differences in PVS changes among conditions with different underlying causes. This study specifically examines the enlarged PVS burden in the white matter (WM) and basal ganglia (BG) of individuals with distinct pathologies, including neuromyelitis optica (NMO)(von Glehn et al., 2014), spinocerebellar ataxia type 3 (SCA3)(D'Abreu et al., 2012), and epilepsy(Whelan et al., 2018).
Methods:
Methods: We recruited 385 epilepsy patients (257 with Temporal Lobe Epilepsy with hippocampal sclerosis (TLE-HS), 64 with Generalized Epilepsy (GGE), 64 with extratemporal epilepsy (EXTRA)), and 472 healthy controls (CT_EPI). We also analyzed data from a group of NMO (38 patients and 43 healthy controls (CT_NMO) and SCA3 (90 patients and 90 controls (CT_SCA_3). All patients were followed at the University of Campinas, Brazil. Patients and healthy controls were scanned with T1-weighted MRI on the same 3T Phillips MRI scanner, resolution 1.0x1.0x1.0mm. A deep-learning algorithm (U-Net) was trained and applied to segment PVS(Sinclair et al., 2024). We used Freesurfer 7.1.1(Fischl, 2012) (combined with the JHU DTI atlas) to parcellate the T1 images into anatomical regions, necessary for separation of basal ganglia and white matter (excluding the BG). The volume of PVS in the White Matter (WM) and Basal Ganglia (BG) were calculated and normalized to the volume of the respective region, yielding volume fractions (vf). We built individual (one per each disease) multivariate analysis of covariance (age and sex were used as nuisance covariates) on SPSS20 with the two PVS measures as dependent variables (volume fraction of WM's PVS= pvsvf_WmexcBG; and volume fraction of BG's PVS= pvsvf_BG), and group as the independent variable of interest. We also performed non-parametric comparisons with Mann-Whitney and Kruskal-Wallis tests, which produced results similar to those of the GLMs.
Results:
Results: In the white matter, only the group with SCA3 presented differences between patients and controls (F(2,175) =8.75, p<0.001; Pillai's trace=0.09; partial eta squared 0.09), reduced fraction volume in SCA3 (Figure 1). Regarding the PVS in BG, both SCA3 (F(2,175) =8.75, p<0.001; Pillai's trace=0.09; partial eta squared 0.09) and epilepsy (F(6,1702) =34.8, p<0.001; Pillai's trace=0.22; partial eta squared=0.11) groups showed differences (p<0.05), although with opposite directions (reductions in the 3 subgroups of epilepsy and increase in patients with SCA3) Figure1. It is noteworthy that we did not identify significant changes in patients with NMO compared to paired controls.
Conclusions:
Conclusion: These findings suggest that the volume of PVS in the WM and BG present different patterns of alterations according to different etiologies. Interestingly, we did not observe changes in NMO, although it is related to a dysfunction of Aquaporin4 channels. While the spinocerebellar ataxia type 3 (which is genetically determined) presented a reduction of PVS (in WM and BG), we identified enlarged PVS burden in all subgroups of patients with epilepsy. Whether these changes could arise from a detrimental effect of each specific mechanism on the brain's glymphatic system or potentially that impaired glymphatics contribute to the dysfunction remains unclear and requires investigation.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Genetics:
Neurogenetic Syndromes 2
Modeling and Analysis Methods:
Multivariate Approaches
Segmentation and Parcellation 1
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics
Keywords:
Basal Ganglia
Epilepsy
MRI
Multivariate
Neurological
Segmentation
White Matter
Other - SCA3; Neuromyelitis Optica; perivascular spaces
1|2Indicates the priority used for review
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Provide references using APA citation style.
Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781. https://doi.org/10.1016/j.neuroimage.2012.01.021
Pham, W., Lynch, M., Spitz, G., O'Brien, T., Vivash, L., Sinclair, B., & Law, M. (2022). A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging. Front Neurosci, 16, 1021311. https://doi.org/10.3389/fnins.2022.1021311
Sinclair, B., Vivash, L., Moses, J., Lynch, M., Pham, W., Dorfman, K., Marotta, C., Koh, S., Bunyamin, J., & Rowsthorn, E. (2024). Perivascular space Identification Nnunet for Generalised Usage (PINGU). arXiv preprint arXiv:2405.08337.
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