In vivo Assessment of the Cerebellum by Novel MRI Techniques and Application to Hereditary Ataxias: Morphological, Pathoanatomical and Clinical Aspects
Medical Physics, Biomedical Technology
Final Report Abstract
The aim of this collaborative project was to obtain deeper insight into the pathoanatomy of cerebellar nuclei in common forms of degenerative ataxias by using novel MRI techniques including quantitative susceptibility mapping (QSM), neurite orientation dispersion and density mapping, and diffusion-based fiber tractography. To this end, 157 patients and 137 controls were scanned at 3T as well as 32 patients (spinocerebellar ataxia type 6 [SCA6], Friedreich ataxia [FRDA]) and 40 controls at 7T. Clinical history and ataxia-specific clinical scores (e.g. SARA, SCAFI) were also obtained. We collected 3T-MRI data from patients with SCA1 (n = 16), SCA2 (n = 14), SCA3 (n = 24), SCA6 (n = 25), FRDA (n = 15), autosomal dominant cerebellar ataxia type III (ADCAIII, n = 13), multiple system atrophy - cerebellar type (MSA-C, n = 18) and sporadic onset adult ataxia of unknown etiology (SAOA, n=14). A small number of patients with other ataxias types were also collected (e.g. SCA14 (n=4), SCA17 (n=3)). Patients with SCA6 and FRDA as well as selected controls were scanned at baseline and after 1 year with 3T- and 7T-MRI. Analysis of features of the cerebellar nuclei were carried out based on manual delineations and voxel-based analysis. We were able to show that susceptibility maps enabled us to reliably assess the dentate nuclei already at a magnetic field strength of 3 Tesla and that volume measures of the dentate nuclei based on both 3T and 7T susceptibility maps were in good accordance with histological data. Since the smaller cerebellar nuclei (globose, emboliform, fastigial nuclei) could only be identified with reduced reliability, we excluded them from further analysis. From the clinical perspective, we initially concentrated on studying susceptibility patterns and volumetric changes. Our findings show that alterations in susceptibility revealed by QSM are common in the dentate nuclei in different types of cerebellar ataxias. The most striking changes in susceptibility were found in SCA1, MSA-C, and SCA6. The higher susceptibility in SCA1 and MSA-C may be explained by a reduction of neurons (increase in iron concentration) and/or an increase in iron-rich glial cells, e.g. microgliosis. The lower susceptibility in SCA6 suggests a loss of iron-rich glial cells. Lower volume of the dentate nuclei was found to varying degrees in all ataxia types and was most pronounced in SCA6 patients and least prominent in SCA3 patients. After 1 year, there seemed to be a trend to lower volumes and susceptibilities of the dentate nuclei in SCA6 patients, while these features were relatively constant across FRDA patients and selected controls. In healthy controls, we observed that dentate nuclei’s size remained relatively stable across the lifespan and that their susceptibility increases with age. Compared to controls, we found a faster age-related tissue decline in cerebellum and cerebellar cortex volumes in SCA6 patients. Clinical and structural MRI data collected during this DFG project were also processed and provided for different meta-analyses to the ENIGMA – Ataxia working group to study disease related atrophy patterns in the brain, cerebellum and spinal cord across a large cohort. To improve future data analyses, we created an atlas of the dentate nuclei parcellation according to their connectivity to the cerebellar lobules and a probabilistic atlas of fiber trajectories between the dentate nuclei and the cerebellar lobules. In addition, we implemented neural network-based approaches for cerebellum segmentation of strongly atrophied brains as well as for dentate nuclei segmentation in controls and patients. While diffusion weighted images were collected within the MRI data acquisition phase, we have not yet performed detailed data analysis across patient groups. We will investigate diffusionassociated patterns across ataxia subgroups in the near future. Results of the current project have laid the foundation to answer further questions to better understand pathological alterations of degenerative ataxia.
Publications
- Structural and Functional Magnetic Resonance Imaging of the Cerebellum: Considerations for Assessing Cerebellar Ataxias. Cerebellum. 2016;15(1):21-25
Deistung A, Stefanescu MR, Ernst TM, Schlamann M, Ladd ME, Reichenbach JR, Timmann D
(See online at https://doi.org/10.1007/s12311-015-0738-9) - An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM). Magn Reson Imaging. 2017;39:110-122
Feng X, Deistung A, Dwyer MG, Hagemeier J, Polak P, Lebenberg J, Frouin F, Zivadinov R, Reichenbach JR, Schweser F
(See online at https://doi.org/10.1016/j.mri.2017.02.002) - Overview of quantitative susceptibility mapping. NMR Biomed. 2017;30(4)
Deistung A, Schweser F, Reichenbach JR
(See online at https://doi.org/10.1002/nbm.3569) - Quantitative susceptibility mapping (QSM) and R2* in the human brain at 3T: Evaluation of intra-scanner repeatability. Z Med Phys. 2018;28(1):36-48
Feng X, Deistung A, Reichenbach JR
(See online at https://doi.org/10.1016/j.zemedi.2017.05.003) - The influence of brain iron and myelin on magnetic susceptibility and effective transverse relaxation - A biochemical and histological validation study. Neuroimage. 2018;179:117-133
Hametner S, Endmayr V, Deistung A, Palmrich P, Prihoda M, Haimburger E, Menard C, Feng X, Haider T, Leisser M, Köck U, Kaider A, Höftberger R, Robinson S, Reichenbach JR, Lassmann H, Traxler H, Trattnig S, Grabner G
(See online at https://doi.org/10.1016/j.neuroimage.2018.06.007) - A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and a multi-scale continuous flow model. PLoS Comput Biol. 2019;15(6):e1007073
Hodneland E, Hanson E, Sævareid O, Nævdal G, Lundervold A, Šoltészová V, Munthe-Kaas AZ, Deistung A, Reichenbach JR, Nordbotten JM
(See online at https://doi.org/10.1371/journal.pcbi.1007073) - Analysis of intensity normalization for optimal segmentation performance of a fully convolutional neural network. Z Med Phys. 2019;29(2):128-138
Jacobsen N, Deistung A, Timmann D, Goericke SL, Reichenbach JR, Güllmar D
(See online at https://doi.org/10.1016/j.zemedi.2018.11.004) - Superficial white matter imaging: Contrast mechanisms and whole-brain in vivo mapping. Sci Adv. 2020;6(41):eaaz9281
Kirilina E, Helbling S, Morawski M, Pine K, Reimann K, Jankuhn S, Dinse J, Deistung A, Reichenbach JR, Trampel R, Geyer S, Müller L, Jakubowski N, Arendt T, Bazin PL, Weiskopf N
(See online at https://doi.org/10.1126/sciadv.aaz9281)
