Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study

https://doi.org/10.1016/j.nicl.2020.102549Get rights and content
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Highlights

  • Thalamus atrophy is associated with cognitive impairment in multiple sclerosis.

  • This was confirmed by automated and manual segmentations, but effect sizes varied.

  • The algorithms work in a multi-center setting.

  • Automated techniques exhibit proportional bias with respect to thalamus size.

  • Differences between vendors can affect the robustness of these associations.

Abstract

Background and rationale

Thalamus 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.

Methods

Standardized 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.

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 performance was significantly associated with smaller thalamus volumes bilaterally using all methods. Vendor significantly affected the findings.

Conclusion

Automated 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.

Keywords

Multiple Sclerosis
MRI
Cognition
Thalamus
Deep grey matter
Atrophy
Segmentation

Abbreviations

BRB-N
Brief Repeatable Battery of Neuropsychological Tests
CAT12
Computational Anatomy Toolbox for Statistical Parametric Mapping 12
CI
cognitively impaired and preserved (CP)
CII
cognitive impairment index
CNR
contrast-to-noise ratio
CP
cognitively preserved
EDSS
Expanded Disability Status Scale
eTIV
estimated total intracranial volume
FSL-FIRST
FMRIB Integrated Registration and Segmentation Tool
GIF
Geodesic Information Flows
GM
grey matter
GMV
grey matter volume
ICC
intraclass correlation coefficient
MS
Multiple Sclerosis
NBV
Normalized brain volume
NGMV
Normalized grey matter volume
NWMV
Normalized white matter volume
IPS
information processing speed
HC
healthy control
PASAT
Paced Auditory Serial Addition Test
RRMS
Relapsing-Remitting Multiple Sclerosis
SD
standard deviations
SDMT
Symbol Digit Modalities Test
SPM12
Statistical Parametric Mapping 12
SRT
Selective Reminding Test
10/36 SRT
10/36 Spatial Recall Test
WCST
Wisconsin Card Sorting Test
WLG
Word List Generation
WM
white matter
WMV
white matter volume
VolBrain
MRI Brain Volumetry System

Cited by (0)

1

Hugo Vrenken and Charles R.G. Guttmann - Both authors contributed equally.