Abstract
Background and rationaleThalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. MethodsStandardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. ResultsIn smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings. ConclusionAutomated and manual thalamus segmentation consistently demonstrated an association between thalamus atrophy and cognitive impairment in MS. However, a proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings.
Highlights
Cognitive deficits are present in up to 70% of patients with multiple sclerosis (MS) and have a significant effect on their activities of daily living and quality of life (Amato et al, 2010; Chiaravalloti & DeLuca, 2008; Rao et al, 1991)
Similar results were found for the normalized FreeSurfer thalamus volume measurements, except that a significant correlation between left thalamus volume loss and verbal memory was found using this method (p = 0.03). In this multi-center cohort, relapsing-remitting MS (RRMS) patients with relatively mild physical disability and overt cognitively impaired (CI) showed severe thalamus atrophy based on all automated segmentation techniques, as was evidenced by a unique set of manually defined reference outlines in which the whole thalamus was segmented
In this work we addressed the potential effect of between-center heterogeneity in magnetic resonance imaging (MRI) acquisition in the regression analyses, remaining differences between scanners can systematically affect the robustness of the association between deep grey matter (GM) atrophy measurements and cognition across methods (Amiri et al, 2018)
Summary
Cognitive deficits are present in up to 70% of patients with multiple sclerosis (MS) and have a significant effect on their activities of daily living and quality of life (Amato et al, 2010; Chiaravalloti & DeLuca, 2008; Rao et al, 1991). We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. Thalamus segmentations were generated manually and using five automated methods. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. Results: In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values < 0.001). All methods significantly distinguished CI from CP MS patients, except manual outlines of the left thalamus (p = 0.23). Poorer global neuropsychological test perfor mance was significantly associated with smaller thalamus volumes bilaterally using all methods. A proportional bias in smaller thalami and choice of MRI acquisition system might impact the effect size of these findings
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