Cortical and white matter lesion topology influences focal corpus callosum atrophy in multiple sclerosis

Abstract

Background and purpose: Corpus callosum (CC) atrophy is a strong predictor of multiple sclerosis (MS) disability but the contributing pathological mechanisms remain uncertain. We aimed to apply advanced MRI to explore what drives the often nonuniform callosal atrophy.

Methods: Prospective brain 7 Tesla and 3 Tesla Human Connectom Scanner MRI were performed in 92 MS patients. White matter, leukocortical, and intracortical lesions were manually segmented. FreeSurfer was used to segment the CC and topographically classify lesions per lobe or as deep white matter lesions. Regression models were calculated to predict focal CC atrophy.

Results: The frontal and parietal lobes contained the majority (≥80%) of all lesion classifications in both relapsing-remitting and secondary progressive MS subtypes. The anterior subsection of the CC had the smallest proportional volume difference between subtypes (11%). Deep, temporal, and occipital white matter lesions, and occipital intracortical lesions were the strongest predictors of middle-posterior callosal atrophy (adjusted R² = .54-.39, P < .01).

Conclusions: Both white matter and cortical lesions contribute to regional corpus callosal atrophy. The lobe-specific lesion topology does not fully explain the inhomogeneous CC atrophy.

Keywords: atrophy; corpus callosum; magnetic resonance imaging; multiple sclerosis.

FIGURE 1

Lobe‐specific white matter and cortex segmentation pipeline. An example of the output from the pipeline segmenting the white matter and cortex according to frontal, parietal, temporal, and occipital lobes. Additionally, a segmentation of the deep white matter was applied to cover the white matter that was not defined as part of a specific lobe. FreeSurfer 7.0.0 (http://surfer.nmr.mgh.harvard.edu, Harvard University, Boston, MA, USA) was used to create the segmentation pipeline.

FIGURE 2

Manual segmentation of lesions. All patients underwent a 7 T T₂*‐weighted gradient‐echo sequence that was manually segmented by two trained raters. The first rater performed an independent evaluation and segmentation of lesions. The second rater performed quality control of the segmentations. Where there was disagreement, the two raters discussed to find consensus. The lesions appeared as focal hyperintensities and had to extend for at least three voxels and two consecutive slices.

FIGURE 3

Lesion distribution by lobe. The frontal and parietal lobes contained the majority of lesions, of all lesion classifications. Depending on lesion classification, the temporal and occipital lobes jointly accounted for 6%‐20% of the total lesion load. RRMS, relapsing‐remitting multiple sclerosis; SPMS, secondary progressive multiple sclerosis.

FIGURE 4

Corpus callosum volume difference between multiple sclerosis subtypes. The most significant difference between relapsing‐remitting multiple sclerosis and secondary progressive multiple sclerosis exists between the mid and posterior subsections of the corpus callosum. CI, confidence interval; RRMS, relapsing‐remitting multiple sclerosis; SPMS, secondary progressive multiple sclerosis. *P < .05, by Independent Samples T‐Test, two‐tailed. **P < .01, by Independent Samples T‐Test, two‐tailed. ^† P < .05, by Mann‐Whitney U‐test, two‐tailed. ^†† P < .01, by Mann‐Whitney U‐test, two‐tailed. Specific test choice was dictated by Levene's test for equality of variance of the respective variables.