Neuroimaging predictors of longitudinal disability and cognition outcomes in multiple sclerosis patients: A systematic review and meta-analysis

Pike, Alexandra C.; James, G. Andrew; Drew, Paul D.; Archer, Robert L.

doi:10.1016/j.msard.2021.103452

Cited by 9 publications

(6 citation statements)

References 48 publications

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“…37 The correlation between k D and SDMT yielded an effect size similar to some previously reported structural imaging biomarkers of cognitive processing speed, including fractional anisotropy in the superior longitudinal fascicle 16 or gray matter atrophy-based brain age gap, 38 but was lower than overall effect size in a recent meta-analysis of multimodal structural MRI data. 39 In our dataset, a superior correlation with SDMT was achieved using regional degree centrality in the MPFC hub of the DMN, yielding an effect size comparable with structural imaging biomarkers. 39,40 Correspondingly, cognitive impairment in MS has been shown to be associated with increased degree or eigenvector centrality in the DMN [19][20][21][22][23] and decreased centrality in the visual 20,22,23 and sensorimotor network, 20 while eigenvector and degree centrality show high agreement even within the same group.…”

Section: Correlation With Cognitive Processing Speedmentioning

confidence: 70%

Section: Correlation With Cognitive Processing Speedmentioning

confidence: 70%

“…In our dataset, a superior correlation with SDMT was achieved using regional degree centrality in the MPFC hub of the DMN, yielding an effect size comparable with structural imaging biomarkers. 39,40 Correspondingly, cognitive impairment in MS has been shown to be associated with increased degree or eigenvector centrality in the DMN 19–23 and decreased centrality in the visual 20,22,23 and sensorimotor network, 20 while eigenvector and degree centrality show high agreement even within the same group. 21 Nevertheless, our results add novel evidence for the strength of the relationship between degree centrality in the DMN and cognitive processing speed since no such correlation in MS has been reported before.…”

Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

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Global Rearrangement of Degree Centrality Reflects Cognitive Impairment and Fatigue in Multiple Sclerosis

Hok,

Thai,

Rehák Bučková

et al. 2024

Preprint

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Background and Objectives: The aim of this secondary data analysis was to determine whether multiple sclerosis (MS) is associated with changes in global degree rank order disruption index (kD), a graph theory-based functional connectivity measure representing shift in overall distribution of nodal degree centrality. Additionally, we tested the relationship between kD and MS symptoms (cognitive and motor impairment, fatigue, and global disability). Methods: Global kD was computed in a pre-existing cross-sectional fMRI dataset and compared between patients with MS (PwMS) and healthy controls (HCs). Group differentiation was tested against other known biomarkers in MS (regional degree centrality, structural MRI with volumetry, diffusion-weighted imaging, lesion mapping) using receiver operating characteristic and logistic regression analysis. Associations between kD and cognitive processing speed (Symbol Digit Modalities Test), fatigue (Fatigue Scale for Motor and Cognitive Functions), gait (Timed Up and Go Test), and disability (Expanded Disability Status Scale [EDSS]) were evaluated using Spearman correlation coefficient and ordinal regression adjusted for structural imaging, age, sex, and disease duration. Results: Analysis included 56 PwMS and 58 HCs (35/27 women, median age 45.1/40.5 years). Global kD was lower in PwMS (median −0.30, inter-quartile range [IQR] 0.55) than in HCs (median −0.06, IQR 0.54; p = 0.009, Mann-Whitney U test). kD yielded acceptable differentiation between groups (area under curve 0.64), but did not improve such differentiation on top of structural imaging. Both kD and regional degree in medial prefrontal cortex (MPFC) were correlated with cognitive decline (kD: Spearman's ρ = 0.32, p = 0.019; MPFC: ρ = −0.45, p = 0.001, n = 55), while kD was also correlated with fatigue (ρ = −0.34, p = 0.010, n = 56), but not with EDSS (ρ = −0.06, p = 0.674, n = 56) or gait (ρ = −0.18, p = 0.211, n = 52). kD significantly explained cognitive impairment (χ2 = 4.49, p = 0.034) and fatigue (χ2 = 7.18, p = 0.007). Discussion: Our data provide evidence that kD is a potential biomarker of cognitive decline and fatigue. Further cross-validations are required to assess its generalizability.

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Section: Correlation With Cognitive Processing Speedmentioning

confidence: 70%

Section: Correlation With Cognitive Processing Speedmentioning

confidence: 70%

Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

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Global Rearrangement of Degree Centrality Reflects Cognitive Impairment and Fatigue in Multiple Sclerosis

Hok,

Thai,

Rehák Bučková

et al. 2024

Preprint

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“…Different MRI predictors of MS disability which can be easily implemented in clinics have been proposed. [6][7][8][9] To date, these predictors have not been approved to be implemented in clinical practice. Predictors include white matter lesions (WMLs) accumulation 10,11 and linear measurements of brain atrophy.…”

Section: Introductionmentioning

confidence: 99%

Predictors of long-term disability in multiple sclerosis patients using routine magnetic resonance imaging data: A 15-year retrospective study

et al. 2023

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Introduction Early identification of patients at high risk of progression could help with a personalised treatment strategy. Magnetic resonance imaging (MRI) measures have been proposed to predict long-term disability in multiple sclerosis (MS), but a reliable predictor that can be easily implemented clinically is still needed. Aim Assess MRI measures during the first 5 years of the MS disease course for the ability to predict progression at 10+ years. Methods Eighty-two MS patients (53 females), with ≥10 years of clinical follow-up and having two MRI scans, were included. Clinical data were obtained at baseline, follow-up and at ≥10 years. White matter lesion (WML) counts and volumes, and four linear brain sizes were measured on T2/FLAIR ‘Fluid-Attenuated-Inversion-Recovery’ and T1-weighted images. Results Baseline and follow-up inter-caudate diameter (ICD) and third ventricular width (TVW) measures correlated positively with Expanded Disability Status Scale, ≥10 or more of WMLs showed a high sensitivity in predicting progression, at ≥10 years. A steeper rate of lesion volume increase was observed in subjects converting to secondary progressive MS. The sensitivity and specificity of both ICD and TVW, to predict disability at ≥10 years were 60% and 64%, respectively. Conclusion Despite advances in brain imaging and computerised volumetric analysis, ICD and TVW remain relevant as they are simple, fast and have the potential in predicting long-term disability. However, in this study, despite the statistical significance of these measures, the clinical utility is still not reliable.

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Multi-modal neuroimaging signatures predict cognitive decline in multiple sclerosis: A 5-year longitudinal study

Al-iedani,

Lea,

Alshehri

et al. 2024

Multiple Sclerosis and Related Disorders

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Neuroimaging predictors of longitudinal disability and cognition outcomes in multiple sclerosis patients: A systematic review and meta-analysis

Cited by 9 publications

References 48 publications

Global Rearrangement of Degree Centrality Reflects Cognitive Impairment and Fatigue in Multiple Sclerosis

Global Rearrangement of Degree Centrality Reflects Cognitive Impairment and Fatigue in Multiple Sclerosis

Predictors of long-term disability in multiple sclerosis patients using routine magnetic resonance imaging data: A 15-year retrospective study

Multi-modal neuroimaging signatures predict cognitive decline in multiple sclerosis: A 5-year longitudinal study

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