RESUMO
BACKGROUND: Research work has shown that hippocampal subfields are atrophic to varying extents in multiple sclerosis (MS) patients. However, studies examining the functional implications of subfield-specific hippocampal damage in early MS are limited. We aim to gain insights into the relationship between hippocampal atrophy and memory function by investigating the correlation between global and regional hippocampal atrophy and memory performance in early MS patients. METHODS: From the Italian Neuroimaging Network Initiative (INNI) dataset, we selected 3D-T1-weighted brain MRIs of 219 early relapsing remitting (RR)MS and 246 healthy controls (HC) to identify hippocampal atrophic areas. At the time of MRI, patients underwent Selective-Reminding-Test (SRT) and Spatial-Recall-Test (SPART) and were classified as mildly (MMI-MS: n.110) or severely (SMI-MS: n:109) memory impaired, according to recently proposed cognitive phenotypes. RESULTS: Early RRMS showed lower hippocampal volumes compared to HC (p < 0.001), while these did not differ between MMI-MS and SMI-MS. In MMI-MS, lower hippocampal volumes correlated with worse memory tests (r = 0.23-0.37, p ≤ 0.01). Atrophic voxels were diffuse in the hippocampus but more prevalent in cornu ammonis (CA, 79%) than in tail (21%). In MMI-MS, decreased subfield volumes correlated with decreases in memory, particularly in the right CA1 (SRT-recall: r = 0.38; SPART: r = 0.34, p < 0.01). No correlations were found in the SMI-MS group. CONCLUSION: Hippocampal atrophy spreads from CA to tail from early disease stages. Subfield hippocampal atrophy is associated with memory impairment in MMI-MS, while this correlation is lost in SMI-MS. This plays in favor of a limited capacity for an adaptive functional reorganization of the hippocampi in MS patients.
RESUMO
In this work we present BIANCA-MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA-MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA-MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA-MS to other widely used tools. Second, we tested how BIANCA-MS performs in separate datasets. Finally, we evaluated BIANCA-MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA-MS clearly outperformed other available tools in both high- and low-resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA-MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each individual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA-MS is a robust and accurate approach for automated MS lesion segmentation.
