The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging

Abstract

Background Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying “lesion filling” by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling. Methods The study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling. Results Artefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients. Conclusion Lesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads.

Abnormal functional connectivity in radiologically isolated syndrome: A resting-state fMRI study

“…50 Specifically, the filled regions may not fully replicate the original data, which can introduce artifacts and inaccuracies, potentially affecting the identification of functional connectivity networks and the posterior statistical analysis. 50…”

“…Finally, we decided not to apply lesion filling, a controversial procedure given the difficulties observed in automatic segmentation, and that led some authors to suggest caution when choosing this approach, especially in individuals with higher lesion loads. 50 Specifically, the filled regions may not fully replicate the original data, which can introduce artifacts and inaccuracies, potentially affecting the identification of functional connectivity networks and the posterior statistical analysis. 50 In closing, our results indicate the existence of aberrant connectivity in RIS patients, suggesting that brain tissue damage may not be limited to focal white matter lesions.…”

“…50 Specifically, the filled regions may not fully replicate the original data, which can introduce artifacts and inaccuracies, potentially affecting the identification of functional connectivity networks and the posterior statistical analysis. 50 In closing, our results indicate the existence of aberrant connectivity in RIS patients, suggesting that brain tissue damage may not be limited to focal white matter lesions. Our RIS patients had abnormal brain connectivity in major resting-state neural networks that might be involved in non-motor symptoms (i.e.…”

Volumetric measurements of brain in multiple sclerosis: comparison of segmentation methods in simulated MRI data

“…This study did not use lesion filling to investigate the different software performances in native MS brains [9]. The following subsections present details of each segmentation software method.…”

Utilizing network analysis in blinded independent central review for clinical trials as adjudication agreement dashboard

“…It was concluded that lesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts. Thus, more care is to be taken for individuals with higher lesions loads (9).…”