Tract-specific MRI measures explain learning and recall differences in multiple sclerosis

Abstract

Cognitive difficulties are common and a key concern for people with multiple sclerosis. Advancing knowledge of the role of white matter pathology in multiple sclerosis-related cognitive impairment is essential as both occur early in the disease with implications for early intervention. Consequently, this cross-sectional study asked whether quantifying the relationships between lesions and specific white matter structures could better explain co-existing cognitive differences than whole brain imaging measures. Forty participants with relapse-onset multiple sclerosis underwent cognitive testing and MRI at 3 Tesla. They were classified as cognitively impaired (n = 24) or unimpaired (n = 16) and differed across verbal fluency, learning, and recall tasks corrected for intelligence and education (corrected p-values=0.007-0.04). The relationships between lesions and white matter were characterised across six measures: conventional voxel-based T2 lesion load, whole brain tractogram load (lesioned volume/whole tractogram volume), whole bundle volume, bundle load (lesioned volume/whole bundle volume), tractometry (diffusion-tensor and high angular resolution diffusion measures sampled from all bundle streamlines), and lesionometry (diffusion measures sampled from streamlines traversing lesions only). The tract-specific measures were extracted from corpus callosum segments (genu and isthmus), striato-prefrontal and -parietal pathways, and the superior longitudinal fasciculi (sections I, II, and III). White matter measure-task associations demonstrating at least moderate evidence against the null hypothesis (Bayes Factor threshold <0.2) were examined using independent t-tests and covariate analyses (significance level p<0.05). Tract-specific measures were significant predictors (all p-values <0.05) of task-specific clinical scores and diminished the significant effect of group as a categorical predictor in Story Recall (isthmus bundle load), Figure Recall (right striato-parietal lesionometry), and Design Learning (left superior longitudinal fasciculus III volume). Lesion load explained the difference in List Learning, whereas Letter Fluency was not associated with any of the imaging measures. Overall, tract-specific measures outperformed the global lesion and tractogram load measures. Variation in regional lesion burden translated to group differences in tract-specific measures, which in turn, attenuated differences in individual cognitive tasks. The structural differences converged in temporo-parietal regions with particular influence on tasks requiring visuospatial-constructional processing. We highlight that measures quantifying the relationships between tract-specific structure and multiple sclerosis lesions uncovered associations with cognition masked by overall tract volumes and global lesion and tractogram loads. These tract-specific white matter quantifications show promise for elucidating the relationships between neuropathology and cognition in multiple sclerosis.

Superficial white matter across the lifespan: volume, thickness, change, and relationship with cortical features

“…Within each tissue type, changes are heterogenous, with different growth trajectories in different cortical regions and white matter pathways (Lebel et al, 2012; Lebel et al, 2008; Schilling et al, 2022a), and different associations with axonal myelination (Grydeland et al, 2019) and densities (Beck et al, 2021; Cox et al, 2016; Giorgio et al, 2010; Schilling et al, 2022a). These studies have driven hypotheses that aim to relate structure and function and determine structural mediators or cognition (Armstrong et al, 2020; Frangou et al, 2022; Winter et al, 2021), or may serve as a benchmark of normative trajectories that might be used to reveal patterns of abnormal variation or vulnerability in disease and disorder (Bethlehem et al, 2022; Fjell and Walhovd, 2010; Shafer et al, 2022).…”

A tractometry principal component analysis of white matter tract network structure and relationships with cognitive function in relapsing-remitting multiple sclerosis

“…Indeed, Li et al, (2012) found different FA covariance patterns for different segments of the corpus callosum. In support of this point, a recent study found that WM tract metrics of volume and microstructural integrity from specific section of specific tracts, including subsections of the corpus callosum, superior longitudinal fasciculus and the striato-prefrontal and striato-parietal pathways, better predict cognitive test performance than global tractography and lesion measures, and also better than whole tract measures ( Winter et al, 2021 ). This lack of granularity in our data may therefore account for the weak component structure that emerged after rotation.…”

Modern Technology in Multi-Shell Diffusion MRI Reveals Diffuse White Matter Changes in Young Adults With Relapsing-Remitting Multiple Sclerosis

“…By producing streamlines-based statistics, tractometry was shown to have satisfactory reproducibility in both healthy subjects and individuals with Parkinson’s disease ( Cousineau et al, 2017 ). A recent study utilized tract-specific MRI measures to investigate the relationship between neuropathology and cognition in MS ( Winter et al, 2021 ). In this study, 40 patients with long-lasting MS were recruited.…”

“…It is thus impossible to determine if the increase in free water fraction for example is driven by the presence of focal lesions only. In future work, the use of intralesional measures and lesionometry ( Winter et al, 2021 ) would be helpful to better characterize diffusion MRI abnormalities caused specifically by MS focal lesions. It would also be interesting to separate gadolinium-enhancing lesions (considered as “active lesions”) from non-enhancing lesions.…”