Laminar analysis of the cortical T1/T2-weighted ratio at 7T

Objective: In this observational study, we explored cortical structure as function of cortical depth through a laminar analysis of the T1/T2-weighted (T1w/T2w) ratio, which has been related to dendrite density in ex vivo brain tissue specimens of patients with MS. Methods: In 39 patients (22 relapsing-remitting, 13 female, age 41.1 ± 10.6 years; 17 progressive, 11 female, age 54.1 ± 9.9 years) and 21 healthy controls (8 female, , age 41.6 ± 10.6 years), we performed a voxel-wise analysis of T1w/T2w ratio maps from high-resolution 7T images from the subpial surface to the gray matter/white matter boundary. Six layers were sampled to ensure accuracy based on mean cortical thickness and image resolution. Results: At the voxel-wise comparison (p < 0.05, family wise error rate corrected), the whole MS group showed lower T1w/T2w ratio values than controls, both when considering the entire cortex and each individual layer, with peaks occurring in the fusiform, temporo-occipital, and superior and middle frontal cortex. In relapsing-remitting patients, differences in the T1w/T2w ratio were only identified in the subpial layer, with the peak occurring in the fusiform cortex, whereas results obtained in progressive patients mirrored the widespread damage found in the whole group. Conclusions: Laminar analysis of T1w/T2w ratio mapping confirms the presence of cortical damage in MS and shows a variable expression of intracortical damage according to the disease phenotype. Although in the relapsing-remitting stage, only the subpial layer appears susceptible to damage, in progressive patients, widespread cortical abnormalities can be observed, not only, as described before, with regard to myelin/iron concentration but, possibly, to other microstructural features

SUITer: An automated method for improving segmentation of infratentorial structures at ultra‐high‐field MRI

BACKGROUND AND PURPOSE: The advent of high and ultra-high-field MRI has significantly improved the investigation of infratentorial structures by providing high-resolution images. However, none of the publicly available methods for cerebellar image analysis has been optimized for high-resolution images yet. METHODS: We present the implementation of an automated algorithm—SUITer (spatially unbiased infratentorial for enhanced resolution) method for cerebellar lobules parcellation on high-resolution MR images acquired at both 3 and 7T MRI. SUITer was validated on five manually segmented data and compared with SUIT, FreeSurfer, and convolutional neural networks (CNN). SUITer was then applied to 3 and 7T MR images from 10 multiple sclerosis (MS) patients and 10 healthy controls (HCs). RESULTS: The difference in volumes estimation for the cerebellar grey matter (GM), between the manual segmentation (ground truth), SUIT, CNN, and SUITer was reduced when computed by SUITer compared to SUIT (5.56 vs. 29.23 mL) and CNN (5.56 vs. 9.43 mL). FreeSurfer showed low volumes difference (3.56 mL). SUITer segmentations showed a high correlation (R 2 = .91) and a high overlap with manual segmentations for cerebellar GM (83.46%). SUITer also showed low volumes difference (7.29 mL), high correlation (R 2 = .99), and a high overlap (87.44%) for cerebellar GM segmentations across magnetic fields. SUITer showed similar cerebellar GM volume differences between MS patients and HC at both 3T and 7T (7.69 and 7.76 mL, respectively). CONCLUSIONS: SUITer provides accurate segmentations of infratentorial structures across different resolutions and MR fields

SUITer: An Automated Method for Improving Segmentation of Infratentorial Structures at Ultra-High-Field MRI

none6siBACKGROUND AND PURPOSE: The advent of high and ultra-high-field MRI has significantly improved the investigation of infratentorial structures by providing high-resolution images. However, none of the publicly available methods for cerebellar image analysis has been optimized for high-resolution images yet. METHODS: We present the implementation of an automated algorithm—SUITer (spatially unbiased infratentorial for enhanced resolution) method for cerebellar lobules parcellation on high-resolution MR images acquired at both 3 and 7T MRI. SUITer was validated on five manually segmented data and compared with SUIT, FreeSurfer, and convolutional neural networks (CNN). SUITer was then applied to 3 and 7T MR images from 10 multiple sclerosis (MS) patients and 10 healthy controls (HCs). RESULTS: The difference in volumes estimation for the cerebellar grey matter (GM), between the manual segmentation (ground truth), SUIT, CNN, and SUITer was reduced when computed by SUITer compared to SUIT (5.56 vs. 29.23 mL) and CNN (5.56 vs. 9.43 mL). FreeSurfer showed low volumes difference (3.56 mL). SUITer segmentations showed a high correlation (R2 =.91) and a high overlap with manual segmentations for cerebellar GM (83.46%). SUITer also showed low volumes difference (7.29 mL), high correlation (R2 =.99), and a high overlap (87.44%) for cerebellar GM segmentations across magnetic fields. SUITer showed similar cerebellar GM volume differences between MS patients and HC at both 3T and 7T (7.69 and 7.76 mL, respectively). CONCLUSIONS: SUITer provides accurate segmentations of infratentorial structures across different resolutions and MR fields.mixedEl Mendili M.M.; Petracca M.; Podranski K.; Fleysher L.; Cocozza S.; Inglese M.El Mendili, M. M.; Petracca, M.; Podranski, K.; Fleysher, L.; Cocozza, S.; Inglese, M

SUITer: an automated method for improving segmentation of infratentorial structures at ultra-high field MRI

Background and purpose: The advent of high and ultra-high-field MRI has significantly improved the investigation of infratentorial structures by providing high-resolution images. However, none of the publicly available methods for cerebellar image analysis has been optimized for high-resolution images yet. Methods: We present the implementation of an automated algorithm-SUITer (spatially unbiased infratentorial for enhanced resolution) method for cerebellar lobules parcellation on high-resolution MR images acquired at both 3 and 7T MRI. SUITer was validated on five manually segmented data and compared with SUIT, FreeSurfer, and convolutional neural networks (CNN). SUITer was then applied to 3 and 7T MR images from 10 multiple sclerosis (MS) patients and 10 healthy controls (HCs). Results: The difference in volumes estimation for the cerebellar grey matter (GM), between the manual segmentation (ground truth), SUIT, CNN, and SUITer was reduced when computed by SUITer compared to SUIT (5.56 vs. 29.23 mL) and CNN (5.56 vs. 9.43 mL). FreeSurfer showed low volumes difference (3.56 mL). SUITer segmentations showed a high correlation (R2 = .91) and a high overlap with manual segmentations for cerebellar GM (83.46%). SUITer also showed low volumes difference (7.29 mL), high correlation (R2 = .99), and a high overlap (87.44%) for cerebellar GM segmentations across magnetic fields. SUITer showed similar cerebellar GM volume differences between MS patients and HC at both 3T and 7T (7.69 and 7.76 mL, respectively). Conclusions: SUITer provides accurate segmentations of infratentorial structures across different resolutions and MR fields

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Streamline density and lesion volume reveal a postero-anterior gradient of corpus callosum damage in multiple sclerosis

Background and purpose: Although interhemispheric disconnection significantly contributes to disability in multiple sclerosis (MS), the topography, timeline and relationship of callosal damage accrual with hemispheric damage are still unclear. Methods: Streamline density and the presence of focal lesions in five callosal subregions were computed in 55 people with MS [13 relapsing-remitting (RRMS), 20 secondary progressive (SPMS), 22 primary progressive (PPMS)] and 24 healthy controls. Results: Streamline density decrease was identified in SPMS in all corpus callosum (CC) subregions, in PPMS in the posterior CC and mid-posterior CC and in RRMS in the posterior CC. CC density was independently predicted by CC lesion volume and hemispheric lesion volume and independently predicted visuospatial memory, Expanded Disability Status Scale, manual dexterity and ambulation. Conclusions: The reduction in CC density across phenotypes suggests an earlier involvement of the posterior regions, followed only at a later stage by involvement of the anterior portions of the CC. Such interhemispheric disconnection seems to develop as a consequence of white matter macroscopic damage and exerts a relevant impact on motor and, to a lesser extent, cognitive disability

Sensory-motor network topology in multiple sclerosis: Structural connectivity analysis accounting for intrinsic density discrepancy

Graph theory and network modelling have been previously applied to characterize motor network structural topology in multiple sclerosis (MS). However, between-group differences disclosed by graph analysis might be primarily driven by discrepancy in density, which is likely to be reduced in pathologic conditions as a consequence of macroscopic damage and fibre loss that may result in less streamlines properly traced. In this work, we employed the convex optimization modelling for microstructure informed tractography (COMMIT) framework, which, given a tractogram, estimates the actual contribution (or weight) of each streamline in order to optimally explain the diffusion magnetic resonance imaging signal, filtering out those that are implausible or not necessary. Then, we analysed the topology of this \u2018COMMIT-weighted sensory-motor network\u2019 in MS accounting for network density. By comparing with standard connectivity analysis, we also tested if abnormalities in network topology are still identifiable when focusing on more \u2018quantitative\u2019 network properties. We found that topology differences identified with standard tractography in MS seem to be mainly driven by density, which, in turn, is strongly influenced by the presence of lesions. We were able to identify a significant difference in density but also in network global and local properties when accounting for density discrepancy. Therefore, we believe that COMMIT may help characterize the structural organization in pathological conditions, allowing a fair comparison of connectomes which considers discrepancies in network density. Moreover, discrepancy-corrected network properties are clinically meaningful and may help guide prognosis assessment and treatment choice