Application of mechanistic methods to clinical trials in multiple sclerosis: the simvastatin case

Eshaghi, Arman; Kievit, Rogier A.; Prados, Ferrán; Sudre, Carole H.; Nicholas, Jennifer M.; Cardoso, M. Jorge; Chan, Dennis; Nicholas, Richard; Ourselin, Sébastien; Greenwood, John A.; Thompson, Alan; Alexander, Daniel C.; Barkhof, Frederik; Chataway, Jeremy; Ciccarelli, Olga

doi:10.1101/343442

Cited by 2 publications

(3 citation statements)

References 51 publications

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“…We used an established pipeline as described elsewhere. 21 Briefly, this pipeline included N4 bias field correction, 22 lesion filling 23 (to reduce the effects of hypointense lesions in T1 scans during segmentation), and used the Geodesic Information Flows (GIF) V.3.0 24 to segment the lesion filled T1-weighted images into GM, WM and cerebrospinal fluid (CSF) probability maps, as well as to divide the brain into 120 parcellations according to the Neuromorphometrics atlas. 25 We used GIF because it allows the inclusion of 2D-MRI data and does not require additional manual editing, which for a cohort of this size would have been unfeasible.…”

Section: Methodsmentioning

confidence: 99%

Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes

Colato

Stutters

Tur

et al. 2021

J Neurol Neurosurg Psychiatry

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ObjectiveIn multiple sclerosis (MS), MRI measures at the whole brain or regional level are only modestly associated with disability, while network-based measures are emerging as promising prognostic markers. We sought to demonstrate whether data-driven patterns of covarying regional grey matter (GM) volumes predict future disability in secondary progressive MS (SPMS).MethodsWe used cross-sectional structural MRI, and baseline and longitudinal data of Expanded Disability Status Scale, Nine-Hole Peg Test (9HPT) and Symbol Digit Modalities Test (SDMT), from a clinical trial in 988 people with SPMS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA). We repeated ICA on 400 healthy controls. We used survival models to determine whether baseline patterns of covarying GM volume measures predict cognitive and motor worsening.ResultsWe identified 15 patterns of regionally covarying GM features. Compared with whole brain GM, deep GM and lesion volumes, some ICA components correlated more closely with clinical outcomes. A mainly basal ganglia component had the highest correlations at baseline with the SDMT and was associated with cognitive worsening (HR=1.29, 95% CI 1.09 to 1.52, p<0.005). Two ICA components were associated with 9HPT worsening (HR=1.30, 95% CI 1.06 to 1.60, p<0.01 and HR=1.21, 95% CI 1.01 to 1.45, p<0.05). ICA measures could better predict SDMT and 9HPT worsening (C-index=0.69–0.71) compared with models including only whole and regional MRI measures (C-index=0.65–0.69, p value for all comparison <0.05).ConclusionsThe disability progression was better predicted by some of the covarying GM regions patterns, than by single regional or whole-brain measures. ICA, which may represent structural brain networks, can be applied to clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect.

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Section: Methodsmentioning

confidence: 99%

Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes

Colato

Stutters

Tur

et al. 2021

J Neurol Neurosurg Psychiatry

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show abstract

“…We used an established pipeline as described elsewhere [18]. Briefly, this pipeline included N4 bias field correction [19], lesion filling [20] (to reduce the effects of hypointense lesions in T1 scans during segmentation), and used the Geodesic Information Flows (GIF) version 3.0 [21] to segment the lesion filled T1-weighted images into GM, white matter (WM), and CSF probability maps, as well as to parcellate the brain into 120 regions according to the Neuromorphometrics atlas [22].…”

Section: Image Processingmentioning

confidence: 99%

“…The 9 networks encompassed mainly the cerebellum, caudate, putamen, thalamus, precuneus, frontal, parietal, temporal and occipital brain regions (ICA-networks6,7,8,9,11,12,15,18,20, whole brain GM, and age). EDSS was significantly associated with component 6 (= -0.19, se= 0.07, t(817.87)= -2.76, p <0.01), component 8 (= 0.17, se= 0.07, t(809.09)= 2.37, p <0.05), component 11 (= 0.15, se= 0.07, t(776.81)= 2.10, p <0.05), component 20 (= 0.15, se= 0.07, t(815.87)= 2.08, p <0.05), and whole brain GM (= -0.17, se= 0.08, t(817.51)= -2.10, p <0.05) (Table 4 in Supplementary materials).…”

mentioning

confidence: 99%

Predicting disability progression and cognitive worsening in multiple sclerosis using grey matter network measures

Colato

Stutters

Tur

et al. 2021

Preprint

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Objective In multiple sclerosis (MS), magnetic resonance imaging (MRI) measures at the whole brain or regional level are only modestly associated with disability, while network-based measures are emerging as promising prognostic markers. We sought to demonstrate whether data-driven network-based measures of regional grey matter (GM) volumes predict future disability in secondary progressive MS (SPMS). Methods We used cross-sectional structural MRI, and baseline and longitudinal data of Expanded Disability Status Scale [EDSS], 9-Hole Peg Test [9HPT], and Symbol Digit Modalities Test [SDMT], from a clinical trial in 988 people with progressive MS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA) to identify co-varying patterns of GM volume change. We used survival models to determine whether baseline GM network measures predict cognitive and motor worsening. Results We identified 15 networks of regionally co-varying GM features. Compared with whole brain GM, deep GM, and lesion volumes, ICA-components correlated more closely with clinical outcomes. A mainly basal ganglia component had the highest correlations at baseline with the SDMT and was associated with cognitive worsening (HR= 1.29, 95% CI [1.09-1.52], p< 0.005). Two ICA-components were associated with 9HPT worsening (HR=1.30, 95% CI [1.06:1.60], p<0.01; and HR= 1.21, 95%CI [1.01:1.45], p<0.05). Post-hoc analyses revealed that for 9HPT and SDMT survival models including network-based measures reported a higher discrimination power (respectively, C-index= 0.69, se= 0.03; C-index= 0.71, se= 0.02) compared to models including only whole and regional MRI measures (respectively, C-index= 0.65, se= 0.03; C-index= 0.69, se= 0.02). Conclusions The disability progression was better predicted by networks of covarying GM regions, rather than by single regional or whole-brain measures. Network analysis can be applied in future clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect.

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Application of mechanistic methods to clinical trials in multiple sclerosis: the simvastatin case

Cited by 2 publications

References 51 publications

Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes

Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes

Predicting disability progression and cognitive worsening in multiple sclerosis using grey matter network measures

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