A Multimodal brain analysis of Miyoshi myopathy patients
Poster No:
700
Submission Type:
Late-Breaking Abstract Submission
Authors:
Kanji Cho1, Ivica Just1, Eva Niess2, Martin Krssak1, Petra Hnilicova3, Martin Kolisek3
Institutions:
1Department of Internal Medicine Ⅲ, Medical University of Vienna, Vienna, Austria, 2Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, 3Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovakia
First Author:
Co-Author(s):
Eva Niess
Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna
Vienna, Austria
Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna
Vienna, Austria
Petra Hnilicova
Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava
Martin, Slovakia
Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava
Martin, Slovakia
Martin Kolisek
Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava
Martin, Slovakia
Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava
Martin, Slovakia
Introduction:
Miyoshi myopathy/dysferlinopathy (MMD) is a rare muscular disorder caused by mutations in the dysferlin gene (DYSF). Muscle weakness and atrophy in the lower extremities are prominent features of MMD. While the DYSF gene implicated in MMD is also expressed in the brain, the potential effects of MMD-related DYSF variants on brain structure and function remain largely unexplored. Notably, molecular changes due to mutations in the DYSF gene affecting dysferlin resemble those observed in Alzheimer's disease involving Amyloid ß (Hnilicova, 2024). To investigate this, we conducted longitudinal multimodal magnetic resonance (MR) study , including MR volumetry and MR Spectroscopy Imaging (MRSI) on a family with seven children, four of whom are affected by the disease.
Methods:
Family with mutation in DYSF gene with 4 siblings (2f/2m) with manifested dystrophy symptomatically, age 36.0±5.1 years, and 3 siblings (2m/1f) being asymptomatic carriers only, age 34.0±5.3 years, and both parents, age 64.5±0.7 years, participated in the study. The average duration between the measurements was 9.4 months.
Study was performed by 3T MR system (PrismaFit) using a 64-channel head coil (Siemens Healthcare, Germany). T1-weighted MPRAGE was acquired for the volumetry of the brain with the voxel size 0.83mm3. Freesurfer v.7.4.1 (https://surfer.nmr.mgh.harvard.edu) with Desikan-Killiany atlas was used for the segmentation and parcellation. The longitudinal analyses of the volumetry was performed by Freesurfer's the longitudinal two stage model. We extracted volumes and symmetrized percent change (SPC) of the volumetry between two time points. 3D echo-less MRSI(Moser et al., 2020) was conducted (TR= 850ms, AD=0.8ms, matrix size 50x50x21, FOV 220x220x126mm3, TA=5:40min) and postprocessing has been done using in-house developed software for reconstruction and LCmodel spectral fitting. The precentral cortex was selected based on the segmented image created by Freesurfer. 1 MMD patient and 2 control data from follow-up measurement were excluded due to the poor quality. The Mann Whitney U test by SPSS (v.29.0.2.0) was conducted to compare the extracted metabolites concentrations the volumetry results between two groups.
Study was performed by 3T MR system (PrismaFit) using a 64-channel head coil (Siemens Healthcare, Germany). T1-weighted MPRAGE was acquired for the volumetry of the brain with the voxel size 0.83mm3. Freesurfer v.7.4.1 (https://surfer.nmr.mgh.harvard.edu) with Desikan-Killiany atlas was used for the segmentation and parcellation. The longitudinal analyses of the volumetry was performed by Freesurfer's the longitudinal two stage model. We extracted volumes and symmetrized percent change (SPC) of the volumetry between two time points. 3D echo-less MRSI(Moser et al., 2020) was conducted (TR= 850ms, AD=0.8ms, matrix size 50x50x21, FOV 220x220x126mm3, TA=5:40min) and postprocessing has been done using in-house developed software for reconstruction and LCmodel spectral fitting. The precentral cortex was selected based on the segmented image created by Freesurfer. 1 MMD patient and 2 control data from follow-up measurement were excluded due to the poor quality. The Mann Whitney U test by SPSS (v.29.0.2.0) was conducted to compare the extracted metabolites concentrations the volumetry results between two groups.
Results:
Volumetric data from baseline measurement revealed a significant increase in the volume of the right inferior lateral ventricle compared to healthy controls (p = 0.032), whereas the follow-up data showed no significant differences. SPC values and p-values for the right inferior lateral ventricle and surrounding structures are presented in Table 1(a). The two-stage longitudinal model identified a significant difference in the left inferior parietal cortex (p = 0.035; SPC = 3.799). For the MRSI, significant decrease (p=0.016) was observed in mIns in right hemisphere in MMD group in the follow-up measurement. Table 1(b) shows the Myo-Inositol concentration in Precentral Cortex of each Hemisphere. Figure 1 shows the spectroscopic ratio map of the MMD patient.
Conclusions:
Analysis of the baseline dataset revealed an increase in the right inferior lateral ventricle volume compared to healthy controls. This may be due to shrinkage of surrounding structures, such as the hippocampus and amygdala, a feature also seen in Alzheimer's. However, this may be more related to τ-protein pathology, which starts in the limbic system, rather than amyloid-β pathology, which develops in the cortex (van der Kant et al., 2020). Longitudinal analysis revealed an increase in the left inferior parietal cortex, suggesting MMD may involve activities stimulating language function (Ravizza et al., 2004; Seghier, 2013).
Follow-up MRSI data showed a significant decrease in mIns in the right hemisphere. Since mIns alterations are seen in psychiatric and metabolic diseases, this suggests potential glial dysfunction(Elberling et al., 2003; Tran et al., 2020).
For future studies, larger cohorts and multimodal approaches, such as Aβ-PET, may help clarify the impact of dysferlin on the brain.
Follow-up MRSI data showed a significant decrease in mIns in the right hemisphere. Since mIns alterations are seen in psychiatric and metabolic diseases, this suggests potential glial dysfunction(Elberling et al., 2003; Tran et al., 2020).
For future studies, larger cohorts and multimodal approaches, such as Aβ-PET, may help clarify the impact of dysferlin on the brain.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Genetics:
Neurogenetic Syndromes 1
Modeling and Analysis Methods:
Segmentation and Parcellation
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Subcortical Structures
Physiology, Metabolism and Neurotransmission Other
Novel Imaging Acquisition Methods:
Anatomical MRI
MR Spectroscopy 2
Keywords:
Glia
Magnetic Resonance Spectroscopy (MRS)
STRUCTURAL MRI
Other - Miyoshi myopathy
1|2Indicates the priority used for review
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Provide references using APA citation style.
Hnilicova, P. (2024). Brain of miyoshi myopathy/dysferlinopathy patients presents with structural and metabolic anomalies. Scientific Reports.
Moser, P. (2020). Intra-session and inter-subject variability of 3D-FID-MRSI using single-echo volumetric EPI navigators at 3T. Magnetic Resonance in Medicine, 83(6), 1920–1929. https://doi.org/10.1002/mrm.28076
Ravizza, S. M. (2004). Functional dissociations within the inferior parietal cortex in verbal working memory. NeuroImage, 22(2), 562–573. https://doi.org/10.1016/j.neuroimage.2004.01.039
Seghier, M. L. (2013). The Angular Gyrus. The Neuroscientist, 19(1), 43–61. https://doi.org/10.1177/1073858412440596
Tran, T. T. (2020). Brain MR Spectroscopy Markers of Encephalopathy Due to Nonalcoholic Steatohepatitis. Journal of Neuroimaging, 30(5), 697–703. https://doi.org/10.1111/jon.12728
van der Kant, R. (2020). Amyloid-β-independent regulators of tau pathology in Alzheimer disease. Nature Reviews Neuroscience, 21(1), 21–35. https://doi.org/10.1038/s41583-019-0240-3