BACKGROUND AND PURPOSE: White matter (WM) tract disruption impacts volume loss in connected deep gray matter (DGM) over 5 years in people with multiple sclerosis (PwMS). However, the timeline of this phenomenon remains poorly characterized. MATERIALS AND METHODS: Annual serial MRI for 181 PwMS was retrospectively analyzed from a 10-year clinical trial database. Annualized thalamic atrophy, DGM atrophy, and disruption of connected WM tracts were measured. For time series analysis, ~700 epochs were collated using a sliding 5-year window, and regression models predicting 1-year atrophy were applied to characterize the influence of new tract disruption from preceding years, while controlling for whole brain atrophy and other relevant factors. RESULTS: Disruptions of WM tracts connected to the thalamus were significantly associated with thalamic atrophy 1 year later (β: 0.048-0.103). This effect was not observed for thalamic tract disruption concurrent with the time of atrophy nor for thalamic tract disruption preceding the atrophy by 2-4 years. Similarly, disruptions of white matter tracts connected to the DGM were significantly associated with DGM atrophy 1 year later (β: 0.078-0.111), but not for tract disruption concurrent with, nor preceding the atrophy by 2-4 years. CONCLUSION: Increased rates of thalamic and DGM atrophy were restricted to 1 year following newly developed disruption in connected WM tracts. In research and clinical settings, additional gray matter atrophy may be expected 1 year following new lesion growth in connected white matter.

Buffalo Neuroimaging Analysis Center Department of Neurology Jacobs School of Medicine and Biomedical Sciences University at Buffalo State University of New York Buffalo NY USA

Center for Biomedical Imaging at Clinical Translational Science Institute University at Buffalo State University of New York 100 High Street Buffalo NY 14203 USA

Department of Neurology and Center of Clinical Neuroscience 1st Faculty of Medicine Charles University and General University Hospital Prague Czech Republic

Department of Radiology 1st Faculty of Medicine Charles University General University Hospital Prague Czech Republic

IRCCS Fondazione Don Carlo Gnocchi Milan Italy

Jacobs Multiple Sclerosis Center Department of Neurology School of Medicine and Biomedical Sciences University at Buffalo State University of New York Buffalo NY USA

Zobrazit více v PubMed

Geurts JJ, Barkhof F (2008) Grey matter pathology in multiple sclerosis. Lancet Neurol. https://doi.org/10.1016/S1474-4422(08)70191-1 PubMed DOI

Stebbins GT, Nyenhuis DL, Wang C et al (2008) Gray matter atrophy in patients with ischemic stroke with cognitive impairment. Stroke. https://doi.org/10.1161/STROKEAHA.107.507392 PubMed DOI

Thompson PM, Hayashi KM, De Zubicaray G et al (2003) Dynamics of gray matter loss in Alzheimer’s disease. J Neurosci. https://doi.org/10.1523/jneurosci.23-03-00994.2003 PubMed DOI PMC

Sepulcre J, Goñi J, Masdeu JC et al (2008) Contribution of white matter lesions to gray matter atrophy in multiple sclerosis evidence from voxel-based analysis of T1 lesions in the visual pathway. Arch Neurol. https://doi.org/10.1001/archneurol.2008.562 DOI

Mühlau M, Buck D, Förschler A et al (2013) White-matter lesions drive deep gray-matter atrophy in early multiple sclerosis: support from structural MRI. Mult Scler J 19(11):1485–1492. https://doi.org/10.1177/1352458513478673 DOI

Thomalla G, Glauche V, Weiller C, Röther J (2004) Time course of wallerian degeneration after ischaemic stroke revealed by diffusion tensor imaging. J Neurol Neurosurg Psychiatry. https://doi.org/10.1136/jnnp.2004.046375 DOI

Lassmann H, Brück W, Lucchinetti CF (2007) The immunopathology of multiple sclerosis: an overview. Brain Pathol. https://doi.org/10.1111/j.1750-3639.2007.00064.x PubMed DOI PMC

Audoin B, Zaaraoui W, Reuter F et al (2010) Atrophy mainly affects the limbic system and the deep grey matter at the first stage of multiple sclerosis. J Neurol Neurosurg Psychiatry 81(6):690–695. https://doi.org/10.1136/jnnp.2009.188748 PubMed DOI

Deppe M, Krämer J, Tenberge JG et al (2016) Early silent microstructural degeneration and atrophy of the thalamocortical network in multiple sclerosis. Hum Brain Mapping. https://doi.org/10.1002/hbm.23144 DOI

Zivadinov R, Uher T, Hagemeier J et al (2016) A serial 10-year follow-up study of brain atrophy and disability progression in RRMS patients. Mult Scler 22(13):1709–1718. https://doi.org/10.1177/1352458516629769 PubMed DOI

Houtchens MK, Benedict RHB, Killiany R et al (2007) Thalamic atrophy and cognition in multiple sclerosis. Neurology. https://doi.org/10.1212/01.wnl.0000276992.17011.b5 PubMed DOI

Eshaghi A, Prados F, Brownlee WJ et al (2018) Deep gray matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol. https://doi.org/10.1002/ana.25145 PubMed DOI PMC

Zivadinov R, Havrdová E, Bergsland N et al (2013) Thalamic atrophy is associated with development of clinically definite multiple sclerosis. Radiology. https://doi.org/10.1148/radiol.13122424 PubMed DOI

Kuceyeski AF, Vargas W, Dayan M et al (2015) Modeling the relationship among gray matter atrophy, abnormalities in connecting white matter, and cognitive performance in early multiple sclerosis. Am J Neuroradiol. https://doi.org/10.3174/ajnr.A4165 PubMed DOI PMC

Fuchs TA, Carolus K, Benedict RHB et al (2018) Impact of focal white matter damage on localized subcortical gray matter atrophy in multiple sclerosis: a 5-year study. Am J Neuroradiol. https://doi.org/10.3174/ajnr.A5720 PubMed DOI PMC

Havrdova E, Zivadinov R, Krasensky J et al (2009) Randomized study of interferon beta-1a, low-dose azathioprine, and low-dose corticosteroids in multiple sclerosis. Multiple Sclerosis. https://doi.org/10.1177/1352458509105229 PubMed DOI

Horakova D, Cox JL, Havrdova E et al (2008) Evolution of different MRI measures in patients with active relapsing-remitting multiple sclerosis over 2 and 5 years: a case-control study. J Neurol Neurosurg Psychiatry. https://doi.org/10.1136/jnnp.2007.120378 PubMed DOI

Zivadinov R, Bergsland N, Dolezal O et al (2013) Evolution of cortical and thalamus atrophy and disability progression in early relapsing-remitting MS during 5 years. Am J Neuroradiol. https://doi.org/10.3174/ajnr.A3503 PubMed DOI PMC

Zivadinov R, Horakova D, Bergsland N et al (2019) A serial 10-year follow-up study of atrophied brain lesion volume and disability progression in patients with relapsing-remitting MS. Am J Neuroradiol. https://doi.org/10.3174/ajnr.A5987 PubMed DOI PMC

Bergsland N, Horakova D, Dwyer MG et al (2018) Gray matter atrophy patterns in multiple sclerosis: a 10-year source-based morphometry study. NeuroImage Clin 17:444–451 DOI

Coles AJ, Cox A, le Page E et al (2006) The window of therapeutic opportunity in multiple sclerosis. J Neurol 253(1):98–108 DOI

Zivadinov R, Rudick RA, de Masi R et al (2001) Effects of IV methylprednisolone on brain atrophy in relapsing-remitting MS. Neurology 57(7):1239–1247. https://doi.org/10.1212/WNL.57.7.1239 PubMed DOI

Gelineau-Morel R, Tomassini V, Jenkinson M, Johansen-Berg H, Matthews PM, Palace J (2012) The effect of hypointense white matter lesions on automated gray matter segmentation in multiple sclerosis. Hum Brain Mapp 33(12):2802–2814. https://doi.org/10.1002/hbm.21402 PubMed DOI

Smith SM, Zhang Y, Jenkinson M et al (2002) Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 17(1):479–489. https://doi.org/10.1006/nimg.2002.1040 PubMed DOI

Patenaude B, Smith SM, Kennedy DN, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage. https://doi.org/10.1016/j.neuroimage.2011.02.046 PubMed DOI

Fuchs TA, Dwyer M, Jakimovski D et al (2021) Quantifying disease pathology and predicting diseaffse progression in multiple sclerosis with only clinical routine T2-FLAIR MRI. NeuroImage Clin 31:102705 DOI

Ashton K, Fuchs TA, Oship D et al (2021) Diagnosis of depression in multiple sclerosis is predicted by frontal–parietal white matter tract disruption. J Neurol 268(1):169–177 DOI

Fuchs TA, Ziccardi S, Benedict RHB et al (2020) Functional connectivity and structural disruption in the default-mode network predicts cognitive rehabilitation outcomes in multiple sclerosis. J Neuroimaging 30(4):523–530 DOI

Kuceyeski A, Monohan E, Morris E, Fujimoto K, Vargas W, Gauthier SA (2018) Baseline biomarkers of connectome disruption and atrophy predict future processing speed in early multiple sclerosis. NeuroImage Clin 19:417–424 DOI

Kuceyeski A, Maruta J, Relkin N, Raj A (2013) The Network Modification (NeMo) Tool: elucidating the effect of white matter integrity changes on cortical and subcortical structural connectivity. Brain Connect 3(5):451–463 DOI

Kuceyeski A, Navi BB, Kamel H et al (2016) Structural connectome disruption at baseline predicts 6-months post-stroke outcome. Hum Brain Mapp 37(7):2587–2601 DOI

Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54(3):2033–2044. https://doi.org/10.1016/j.neuroimage.2010.09.025 PubMed DOI

Pierpaoli C, Barnett A, Pajevic S et al (2001) Water diffusion changes in wallerian degeneration and their dependence on white matter architecture. NeuroImage. https://doi.org/10.1006/nimg.2001.0765 PubMed DOI

Xu SY, Li CX, Li LY, Song Y, Sui Y (2020) Wallerian degeneration of bilateral cerebral peduncles after acute carbon monoxide poisoning. BMC Neurol. https://doi.org/10.1186/s12883-020-01677-5 PubMed DOI PMC

Bruijn LI, Miller TM, Cleveland DW (2004) Unraveling the mechanisms involved in motor neuron degeneration in ALS. Ann Rev Neurosci. https://doi.org/10.1146/annurev.neuro.27.070203.144244 PubMed DOI

Dinkin M (2017) Trans-synaptic retrograde degeneration in the human visual system: slow, silent, and real. Curr Neurol Neurosci Reports. https://doi.org/10.1007/s11910-017-0725-2 DOI

Kolbe S, Bajraszewski C, Chapman C et al (2012) Diffusion tensor imaging of the optic radiations after optic neuritis. Hum Brain Mapping. https://doi.org/10.1002/hbm.21343 DOI

Tian DC, Su L, Fan M et al (2018) Bidirectional degeneration in the visual pathway in neuromyelitis optica spectrum disorder (NMOSD). Multiple Sclerosis J. https://doi.org/10.1177/1352458517727604 DOI

Jindahra P, Petrie A, Plant GT (2012) The time course of retrograde trans-synaptic degeneration following occipital lobe damage in humans. Brain. https://doi.org/10.1093/brain/awr324 PubMed DOI

Dziedzic T, Metz I, Dallenga T et al (2010) Wallerian degeneration: a major component of early axonal pathology in multiple sclerosis. Brain Pathol 20(5):976–985. https://doi.org/10.1111/j.1750-3639.2010.00401.x PubMed DOI PMC

Sepulcre J, Sastre-Garriga J, Cercignani M, Ingle GT, Miller DH, Thompson AJ (2006) Regional gray matter atrophy in early primary progressive multiple sclerosis: A voxel-based morphometry study. Arch Neurol. https://doi.org/10.1001/archneur.63.8.1175 PubMed DOI

Henry RG, Shieh M, Amirbekian B, Chung SW, Okuda DT, Pelletier D (2009) Connecting white matter injury and thalamic atrophy in clinically isolated syndromes. J Neurol Sci. https://doi.org/10.1016/j.jns.2009.02.379 PubMed DOI

Hannoun S, Durand-Dubief F, Roch JA, Sappey-Marinier D, Cotton F (2016) Tracking successive Wallerian degenerations in a relapsing-remitting multiple sclerosis patient. J Neuroradiol. https://doi.org/10.1016/j.neurad.2016.05.004 PubMed DOI

Garcia-Martin E, Ara JR, Martin J et al (2017) Retinal and optic nerve degeneration in patients with multiple sclerosis followed up for 5 years. Ophthalmology. https://doi.org/10.1016/j.ophtha.2017.01.005 PubMed DOI

Absinta M, Vuolo L, Rao A et al (2015) Gadolinium-based MRI characterization of leptomeningeal inflammation in multiple sclerosis. Neurology. https://doi.org/10.1212/WNL.0000000000001587 PubMed DOI PMC

Cooze BJ, Dickerson M, Loganathan R et al (2022) The association between neurodegeneration and local complement activation in the thalamus to progressive multiple sclerosis outcome. Brain Pathol. https://doi.org/10.1111/bpa.13054 PubMed DOI

Smith SM, De Stefano N, Jenkinson M, Matthews PM (2001) Normalized accurate measurement of longitudinal brain change. J Comput Assist Tomogr 25(3):466–475. https://doi.org/10.1097/00004728-200105000-00022 PubMed DOI