Dataset Information

The association between white matter tract structural connectivity and information processing speed in relapsing-remitting multiple sclerosis.

ABSTRACT:

Background

Information processing speed (IPS) deterioration is common in relapsing-remitting multiple sclerosis (RRMS) patients [1] and might severely affect quality of life and occupational activity. However, understanding of its neural substrate is not fully elucidated. We aimed to investigate the associations between MRI-derived metrics of neuroanatomical structures, including the tracts, and IPS.

Methods

Symbol Digit Modalities Test (SDMT), Paced Auditory Serial Addition Test (PASAT), and Color Trails Test (CTT) were used to evaluate IPS in 73 RRMS consecutive patients, all undergoing only interferon beta (IFN-β) therapy during the study. At the same time, 1.5T MRI including diffusion tensor imaging (DTI) data was acquired for each recruited subject. We analyzed volumetric and diffusion MRI measures (FreeSurfer 6.0) including normalized brain volume (NBV), cortical thickness (thk), white matter hypointensities (WMH), volume (vol), diffusion parameters: mean (MD), radial (RD), axial (AD) diffusivities, and fractional anisotropy (FA) of 18 major white-matter (WM) tracts. Multiple linear regression model with interaction resulted in distinguishing the neural substrate of IPS deficit in the IPS impaired subgroup of patients.

Results

The most significant tract abnormalities contributing to IPS deficit were right inferior longitudinal fasciculus (R ILF) FA, forceps major (FMAJ) FA, forceps minor (FMIN) FA, R uncinate fasciculus (UNC) AD, R corticospinal tract (CST) FA, and left superior longitudinal fasciculus FA (L SLFT). Among volumetric MRI metrics, IPS deficit was associated with L and R thalamic vol. and cortical thickness of insular regions.

Conclusion

In this study, we showed that disconnection of the selected WM tracts, in addition to cortical and deep gray matter (GM) atrophy, might underlie IPS deficit in RRMS patients but more extensive studies are needed for precise associations.

SUBMITTER: Chylinska M

PROVIDER: S-EPMC10415523 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

The association between white matter tract structural connectivity and information processing speed in relapsing-remitting multiple sclerosis.

Chylińska Magdalena M Karaszewski Bartosz B Komendziński Jakub J Wyszomirski Adam A Hałas Marek M Szurowska Edyta E Sabisz Agnieszka A

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 20230427 9

<h4>Background</h4>Information processing speed (IPS) deterioration is common in relapsing-remitting multiple sclerosis (RRMS) patients [1] and might severely affect quality of life and occupational activity. However, understanding of its neural substrate is not fully elucidated. We aimed to investigate the associations between MRI-derived metrics of neuroanatomical structures, including the tracts, and IPS.<h4>Methods</h4>Symbol Digit Modalities Test (SDMT), Paced Auditory Serial Addition Test ...[more]

PMID: 37103603

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Project description:Understanding the brain changes underlying cognitive dysfunction is a key priority in multiple sclerosis (MS) to improve monitoring and treatment of this debilitating symptom. Functional connectivity network changes are associated with cognitive dysfunction, but it is less well understood how changes in normal appearing white matter relate to cognitive symptoms. If white matter tracts have network structure it would be expected that tracts within a network share susceptibility to MS pathology. In the present study, we used a tractometry approach to explore patterns of variance in white matter metrics across white matter (WM) tracts, and assessed how such patterns relate to neuropsychological test performance across cognitive domains. A sample of 102 relapsing-remitting MS patients and 27 healthy controls underwent MRI and neuropsychological testing. Tractography was performed on diffusion MRI data to extract 40 WM tracts and microstructural measures were extracted from each tract. Principal component analysis (PCA) was used to decompose metrics from all tracts to assess the presence of any co-variance structure among the tracts. Similarly, PCA was applied to cognitive test scores to identify the main cognitive domains. Finally, we assessed the ability of tract co-variance patterns to predict test performance across cognitive domains. We found that a single co-variance pattern which captured microstructure across all tracts explained the most variance (65% variance explained) and that there was little evidence for separate, smaller network patterns of pathology. Variance in this pattern was explained by effects related to lesions, but one main co-variance pattern persisted after this effect was regressed out. This main WM tract co-variance pattern contributed to explaining a modest degree of variance in one of our four cognitive domains in MS. These findings highlight the need to investigate the relationship between the normal appearing white matter and cognitive impairment further and on a more granular level, to improve the understanding of the network structure of the brain in MS.

Project description:ImportanceMultiple sclerosis (MS) is characterized by progressive gray matter (GM) atrophy that strongly correlates with clinical disability. However, whether localized GM atrophy correlates with specific disabilities in patients with MS remains unknown.ObjectiveTo understand the association between localized GM atrophy and clinical disability in a biology-driven analysis of MS.Design, setting, and participantsIn this cross-sectional study, magnetic resonance images were acquired from 133 women with relapsing-remitting MS and analyzed using voxel-based morphometry and volumetry. A regression analysis was used to determine whether voxelwise GM atrophy was associated with specific clinical deficits. Data were collected from June 28, 2007, to January 9, 2014.Main outcomes and measuresVoxelwise correlation of GM change with clinical outcome measures (Expanded Disability Status Scale and Multiple Sclerosis Functional Composite scores).ResultsAmong the 133 female patients (mean [SD] age, 37.4 [7.5] years), worse performance on the Multiple Sclerosis Functional Composite correlated with voxelwise GM volume loss in the middle cingulate cortex (P < .001) and a cluster in the precentral gyrus bilaterally (P = .004). In addition, worse performance on the Paced Auditory Serial Addition Test correlated with volume loss in the auditory and premotor cortices (P < .001), whereas worse performance on the 9-Hole Peg Test correlated with GM volume loss in Brodmann area 44 (Broca area; P = .02). Finally, voxelwise GM loss in the right paracentral lobulus correlated with bowel and bladder disability (P = .03). Thus, deficits in specific clinical test results were directly associated with localized GM loss in clinically eloquent locations.Conclusions and relevanceThese biology-driven data indicate that specific disabilities in MS are associated with voxelwise GM loss in distinct locations. This approach may be used to develop disability-specific biomarkers for use in future clinical trials of neuroprotective treatments in MS.

Dataset Information

The association between white matter tract structural connectivity and information processing speed in relapsing-remitting multiple sclerosis.

Background

Methods

Results

Conclusion

Publications

The association between white matter tract structural connectivity and information processing speed in relapsing-remitting multiple sclerosis.

Similar Datasets

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