Association of Retinal and Macular Damage with Brain Atrophy in Multiple Sclerosis

Neuroaxonal degeneration in the central nervous system contributes substantially to the long term disability in multiple sclerosis (MS) patients. However, in vivo determination and monitoring of neurodegeneration remain difficult. As the widely used MRI-based approaches, including the brain parenchymal fraction (BPF) have some limitations, complementary in vivo measures for neurodegeneration are necessary. Optical coherence tomography (OCT) is a potent tool for the detection of MS-related retinal neurodegeneration. However, crucial aspects including the association between OCT- and MRI-based atrophy measures or the impact of MS-related parameters on OCT parameters are still unclear. In this large prospective cross-sectional study on 104 relapsing remitting multiple sclerosis (RRMS) patients we evaluated the associations of retinal nerve fiber layer thickness (RNFLT) and total macular volume (TMV) with BPF and addressed the impact of disease-determining parameters on RNFLT, TMV or BPF. BPF, normalized for subject head size, was estimated with SIENAX. Relations were analyzed primarily by Generalized Estimating Equation (GEE) models considering within-patient inter-eye relations. We found that both RNFLT (p = 0.019, GEE) and TMV (p = 0.004, GEE) associate with BPF. RNFLT was furthermore linked to the disease duration (p<0.001, GEE) but neither to disease severity nor patients' age. Contrarily, BPF was rather associated with severity (p<0.001, GEE) than disease duration and was confounded by age (p<0.001, GEE). TMV was not associated with any of these parameters. Thus, we conclude that in RRMS patients with relatively short disease duration and rather mild disability RNFLT and TMV reflect brain atrophy and are thus promising parameters to evaluate neurodegeneration in MS. Furthermore, our data suggest that RNFLT and BPF reflect different aspects of MS. Whereas BPF best reflects disease severity, RNFLT might be the better parameter for monitoring axonal damage longitudinally. Longitudinal studies are necessary for validation of data and to further clarify the relevance of TMV.

[1]  C. Pfueller,et al.  Patterns of retinal nerve fiber layer loss in multiple sclerosis patients with or without optic neuritis and glaucoma patients , 2010, Clinical Neurology and Neurosurgery.

[2]  Axel Petzold,et al.  Optical coherence tomography in multiple sclerosis: a systematic review and meta-analysis , 2010, The Lancet Neurology.

[3]  G. Ying,et al.  Longitudinal study of vision and retinal nerve fiber layer thickness in multiple sclerosis , 2010, Annals of neurology.

[4]  P. Calabresi,et al.  Macular volume determined by optical coherence tomography as a measure of neuronal loss in multiple sclerosis. , 2009, Archives of neurology.

[5]  E. Erdfelder,et al.  Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses , 2009, Behavior research methods.

[6]  W. Brück,et al.  Substantial early, but nonprogressive neuronal loss in multiple sclerosis (ms) spinal cord , 2009, Annals of neurology.

[7]  David H. Miller,et al.  Imaging outcomes for neuroprotection and repair in multiple sclerosis trials , 2009, Nature Reviews Neurology.

[8]  J. Sepulcre,et al.  RETINAL NERVE FIBER LAYER IS ASSOCIATED WITH BRAIN ATROPHY IN MULTIPLE SCLEROSIS , 2008, Neurology.

[9]  Alexander Klistorner,et al.  Axonal loss and myelin in early ON loss in postacute optic neuritis , 2008, Annals of neurology.

[10]  C. Cheung,et al.  Relationship between retinal nerve fiber layer measurement and signal strength in optical coherence tomography. , 2008, Ophthalmology.

[11]  L. Pablo,et al.  Axonal loss in the retinal nerve fiber layer in patients with multiple sclerosis , 2008, Multiple sclerosis.

[12]  Elliot Frohman,et al.  The role of optical coherence tomography in multiple sclerosis: Expert panel consensus , 2007, Journal of the Neurological Sciences.

[13]  David H. Miller,et al.  An investigation of the retinal nerve fibre layer in progressive multiple sclerosis using optical coherence tomography. , 2007, Brain : a journal of neurology.

[14]  A J Thompson,et al.  Voxel-based analysis of grey matter magnetization transfer ratio maps in early relapsing remitting multiple sclerosis , 2007, Multiple sclerosis.

[15]  Gary R Cutter,et al.  Relation of visual function to retinal nerve fiber layer thickness in multiple sclerosis. , 2006, Ophthalmology.

[16]  S. Reingold,et al.  Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” , 2005, Annals of neurology.

[17]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[18]  P. M. Matthews,et al.  Evidence of early cortical atrophy in MS , 2003, Neurology.

[19]  J H Simon,et al.  Eight-year follow-up study of brain atrophy in patients with MS , 2002, Neurology.

[20]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[21]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[22]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[23]  A J Thompson,et al.  Brain atrophy in clinically early relapsing-remitting multiple sclerosis. , 2002, Brain : a journal of neurology.

[24]  F Barkhof,et al.  Optimizing the association between disability and biological markers in MS , 2001, Neurology.

[25]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[26]  P. Matthews,et al.  Normalized Accurate Measurement of Longitudinal Brain Change , 2001, Journal of computer assisted tomography.

[27]  R. Rudick,et al.  Use of the brain parenchymal fraction to measure whole brain atrophy in relapsing-remitting MS , 1999, Neurology.

[28]  B D Trapp,et al.  Axonal pathology in multiple sclerosis: relationship to neurologic disability. , 1999, Current opinion in neurology.

[29]  P M Matthews,et al.  Axonal damage correlates with disability in patients with relapsing-remitting multiple sclerosis. Results of a longitudinal magnetic resonance spectroscopy study. , 1998, Brain : a journal of neurology.

[30]  J. Fujimoto,et al.  Optical coherence tomography of the human retina. , 1995, Archives of ophthalmology.

[31]  J. Kurtzke.  Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[32]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[33]  P M Matthews,et al.  Evidence of axonal damage in the early stages of multiple sclerosis and its relevance to disability. , 2001, Archives of neurology.

[34]  J. Nawrocki,et al.  Selmaj Optical coherence tomography in multiple sclerosis Thickness of the retinal nerve fiber layer as a potential measure of axonal loss and brain atrophy , 2022 .