What Causes Deep Gray Matter Atrophy in Multiple Sclerosis?

Schoonheim, Menno M.; Geurts, Jeroen J. G.

doi:10.3174/ajnr.a5942

Cited by 9 publications

(6 citation statements)

References 19 publications

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“… 41 Axonal loss within cortico-subcortical connections and hence network disconnection are considered the primary driving force behind deep gray matter atrophy, which is especially severe in progressive patients and might therefore be reflected by NfL. 42 Astrocyte activation, reflected by GFAP, might contribute to damage in gray matter structures via chronic activation of the CNS innate immune system, which could result in an inhibition of remyelination and/or axonal mitochondrial dysfunction. 43 Nevertheless, earlier studies have also showed the ability of NfL to predict WM atrophy, a result which we cannot confirm in our cohort.…”

Section: Discussionmentioning

confidence: 99%

Neuroaxonal and Glial Markers in Patients of the Same Age With Multiple Sclerosis

Loonstra

Ruiter

Koel-Simmelink

et al. 2023

Neurol Neuroimmunol Neuroinflamm

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Background and ObjectivesThe specificity of novel blood biomarkers for multiple sclerosis (MS)–related neurodegeneration is unclear because neurodegeneration also occurs during normal aging. To understand which aspects of neurodegeneration the serum biomarkers neurofilament light (sNfL), serum glial fibrillary acidic protein (sGFAP), and serum contactin-1 (sCNTN1) reflect, we here explore their cross-sectional association with disability outcome measures and MRI volumes in a unique cohort of people with MS (PwMS) of the same age.MethodssNfL, sGFAP (both singe-molecule array technology) and sCNTN1 (Luminex) were measured in serum samples of 288 PwMS and 125 healthy controls (HCs) of the Project Y cohort, a population-based cross-sectional study of PwMS born in the Netherlands in 1966 and age-matched HC.ResultssNfL (9.83 pg/mL [interquartile range {IQR}: 7.8–12.0]) and sGFAP (63.7 pg/mL [IQR: 48.5–84.5]) were higher in PwMS compared with HC (sNfL: 8.8 pg/mL [IQR: 7.0–10.5]; sGFAP: 51.7 pg/mL [IQR: 40.1–68.3]) (p< 0.001), whereas contactin-1 (7,461.3 pg/mL [IQR: 5,951.8–9,488.6]) did not significantly differ between PwMS compared with HC (7,891.2 pg/mL [IQR: 6,120.0–10,265.8]) (p= 0.068). sNfL and sGFAP levels were 1.2-fold higher in secondary progressive patients (SPMS) compared with relapsing remitting patients (p= 0.009 andp= 0.043). Stratified by MS subtype, no relations were seen for CNTN1, whereas sNfL and sGFAP correlated with the Expanded Disability Status Scale (ρ = 0.43 and ρ = 0.39), Nine-Hole Peg Test, Timed 25-Foot Walk Test, and Symbol Digit Modalities Test (average ρ = 0.38) only in patients with SPMS. Parallel to these clinical findings, correlations were only found for sNfL and sGFAP with MRI volumes. The strongest correlations were observed between sNfL and thalamic volume (ρ = −0.52) and between sGFAP with deep gray matter volume (ρ = − 0.56) in primary progressive patients.DiscussionIn our cohort of patients of the same age, we report consistent correlations of sNfL and sGFAP with a range of metrics, especially in progressive MS, whereas contactin-1 was not related to clinical or MRI measures. This demonstrates the potential of sNfL and sGFAP as complementary biomarkers of neurodegeneration, reflected by disability, in progressive MS.

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Section: Discussionmentioning

confidence: 99%

Neuroaxonal and Glial Markers in Patients of the Same Age With Multiple Sclerosis

Loonstra

Ruiter

Koel-Simmelink

et al. 2023

Neurol Neuroimmunol Neuroinflamm

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“…Other work similarly reports widespread atrophy across the cortex and subcortex, with the subcortex and especially thalamus most closely related with neurocognitive changes in a large, public sample (Lutkenhoff et al, 2020). Evidence of deep gray matter atrophy in other neurodegenerative disorders with primarily white‐matter involvement such as multiple sclerosis (MS; Schoonheim & Geurts, 2019) highlights white‐matter, and potentially WD, as central to this pattern of atrophy.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal profile of atrophy in the first year following moderate‐severe traumatic brain injury

Brennan,

Duda,

Ware

et al. 2023

Human Brain Mapping

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Traumatic brain injury (TBI) triggers progressive neurodegeneration resulting in brain atrophy that continues months‐to‐years following injury. However, a comprehensive characterization of the spatial and temporal evolution of TBI‐related brain atrophy remains incomplete. Utilizing a sensitive and unbiased morphometry analysis pipeline optimized for detecting longitudinal changes, we analyzed a sample consisting of 37 individuals with moderate‐severe TBI who had primarily high‐velocity and high‐impact injury mechanisms. They were scanned up to three times during the first year after injury (3 months, 6 months, and 12 months post‐injury) and compared with 33 demographically matched controls who were scanned once. Individuals with TBI already showed cortical thinning in frontal and temporal regions and reduced volume in the bilateral thalami at 3 months post‐injury. Longitudinally, only a subset of cortical regions in the parietal and occipital lobes showed continued atrophy from 3 to 12 months post‐injury. Additionally, cortical white matter volume and nearly all deep gray matter structures exhibited progressive atrophy over this period. Finally, we found that disproportionate atrophy of cortex along sulci relative to gyri, an emerging morphometric marker of chronic TBI, was present as early as 3 month post‐injury. In parallel, neurocognitive functioning largely recovered during this period despite this pervasive atrophy. Our findings demonstrate msTBI results in characteristic progressive neurodegeneration patterns that are divergent across regions and scale with the severity of injury. Future clinical research using atrophy during the first year of TBI as a biomarker of neurodegeneration should consider the spatiotemporal profile of atrophy described in this study.

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“…We calculated correlation coefficients for EDSS, inverse 9HPT and SDMT with the z-scores of lesion load, whole brain GM volume, DGM and thalamus volume. 34 35 To account for the number of comparisons performed, correlations were corrected for multiple comparisons using Bonferroni correction (a=0.05).…”

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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What Causes Deep Gray Matter Atrophy in Multiple Sclerosis?

Cited by 9 publications

References 19 publications

Neuroaxonal and Glial Markers in Patients of the Same Age With Multiple Sclerosis

Neuroaxonal and Glial Markers in Patients of the Same Age With Multiple Sclerosis

Spatiotemporal profile of atrophy in the first year following moderate‐severe traumatic brain injury

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

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