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In the original publication of this article [1], Fig. 10 contained two panels "C" as panel "F" was accidentally omitted. The incorrect (Fig. 1) and correct (Fig. 2) versions are published in this correction article.

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The exact mechanisms and temporal sequence of neurodegeneration in multiple sclerosis are still unresolved. The visual pathway including its unmyelinated retinal axons, can serve as a prototypic model of neurodegeneration in experimental optic neuritis. We conducted a longitudinal study combining retinal imaging through optical coherence tomography (OCT) with immunohistochemical analyses of retinal and optic nerve tissue at various time points in experimental autoimmune encephalomyelitis (EAE).Inner retinal layer (IRL) thickness was measured in 30 EAE and 14 healthy control C57BL/6 J mice using OCT. Distribution of marker proteins was assessed by immunofluorescence staining and retinal mRNA levels were assayed using real-time PCR. Histological morphology was evaluated on light and electron microscopy images.Signs of inflammatory edema 11 days post immunisation coincided with IRL thickening, while neuro-axonal degeneration throughout the disease course contributed to IRL thinning observed after 20 days post immunisation. Retinal pathology, including axonal transport impairment, was observed early, prior to cellular infiltration (i.e. T-cells) in the optic nerve 11 days post immunisation. Yet, the effects of early retinal damage on OCT-derived readouts were outweighed by the initial inflammatory edema. Early microglial activation and astrocytosis was detected in the retina prior to retinal ganglion cell loss and persisted until 33 days post immunisation. Müller cell reactivity (i.e. aquaporin-4 and glutamine synthetase decrease) presented after 11 days post immunisation in the IRL. Severe neuro-axonal degeneration was observed in the optic nerve and retina until 33 days post immunisation.Initial signs of retinal pathology subsequent to early glial activity, suggests a need for prophylactic treatment of optic neuritis. Following early inflammation, Müller cells possibly respond to retinal pathology with compensatory mechanisms. Although the majority of the IRL damage observed is likely due to retrograde degeneration following optic neuritis, initial pathology, possibly due to gliosis, may contribute further to IRL thinning. These results add morphological substrate to our OCT findings. The extent and rapid onset of axonal and neuronal damage in this model appears relevant for testing interventions scaled to human optic neuritis.

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BACKGROUND: Neuro-axonal injury is a key contributor to non-reversible long-term disability in multiple sclerosis (MS). However, the underlying mechanisms are not yet fully understood. Visual impairment is common among MS patients, in which episodes of optic neuritis (ON) are often followed by structural retinal damage and sustained functional impairment. Alterations in the optic nerve and retina have also been described in experimental autoimmune encephalomyelitis (EAE), a rodent model of MS. Thus, investigating structural anterior visual pathway damage may constitute a unique model for assessing mechanisms and temporal sequence of neurodegeneration in MS. We used a multimodal imaging approach utilizing optical coherence tomography (OCT) and diffusion tensor imaging (DTI) to explore the mechanisms and temporal dynamics of visual pathway damage in the animal model of MS. METHODS: 7 EAE-MOG35-55 and 5 healthy female C57BL/6J mice were used in this study. Ganglion cell complex (GCC) thickness was derived from an OCT volume scan centred over the optic nerve head, while the structure of the optic nerve and tracts was assessed from DTI and co-registered T2-weighted sequences performed on a 7T MRI scanner. Data was acquired at baseline, disease onset, peak of disease and recovery. Linear mixed effect models were used to account for intra-subject, inter-eye dependencies, group and time point. Correlation analyses assessed the relationship between GCC thickness and DTI parameters. Immunofluorescence staining of retina and optic nerve sections was used to assess distribution of marker proteins for microglia and neurodegeneration (nerve filaments). RESULTS: In EAE mice, a significant increase in GCC thickness was observed at disease onset (p < 0.001) followed by a decrease at recovery (p < 0.001) compared to controls. The EAE group had significant GCC thinning at recovery compared to all other time points (p < 0.001 for each). Signal increase on T2-weighted images around the optic nerves indicative of inflammation was seen in most of the EAE mice but in none of the controls. A significant decrease in axial diffusivity (AD) and increase in radial diffusivity (RD) values in EAE optic nerves (AD: p = 0.02, RD: p = 0.01) and tract (AD: p = 0.02, RD: p = 0.006) was observed compared to controls. GCC at recovery was positively correlated with AD (optic nerve: rho = 0.74, p = 0.04, optic tract: rho = 0.74, p = 0.04) and negatively correlated with RD (optic nerve: rho = -0.80, p = 0.02, optic tract: rho = -0.75, p = 0.04). Immunofluorescence analysis indicated the presence of activated microglia in the retina and optic nerves in addition to astrocytosis and axonal degeneration in the optic nerve of EAE mice. CONCLUSION: OCT detected GCC changes in EAE may resemble what is observed in MS-related acute ON: an initial phase of swelling (indicative of inflammatory edema) followed by a decrease in thickness over time (representative of neuro-axonal degeneration). In line with OCT findings, DTI of the visual pathway identifies EAE induced pathology (decreased AD, and increased RD). Immunofluorescence analysis provides support for inflammatory pathology and axonal degeneration. OCT together with DTI can detect retinal and optic nerve damage and elucidate to the temporal sequence of neurodegeneration in this rodent model of MS in vivo.

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BACKGROUND: Measurements of brain volume loss (BVL) in individual patients are currently discussed controversially. One concern is the impact of short-term biological noise, like hydration status. METHODS: Three publicly available reliability MRI datasets with scan intervals of days to weeks were used. An additional cohort of 60 early relapsing multiple sclerosis (MS) patients with MRI follow-ups was analyzed to test whether after 1 year pathological BVL is detectable in a relevant fraction of MS patients. BVL was determined using SIENA/FSL. Results deviating from zero in the reliability datasets were considered as within-patient fluctuation (WPF) consisting of the intrinsic measurement error as well as the short-term biological fluctuations of brain volumes. We provide an approach to interpret BVL measurements in individual patients taking the WPF into account. RESULTS: The estimated standard deviation of BVL measurements from the pooled reliability datasets was 0.28%. For a BVL measurement of x% per year in an individual patient, the true BVL lies with an error probability of 5% in the interval x% ± (1.96 × 0.28)/(scan interval in years)%. To allow a BVL per year of at least 0.4% to be identified after 1 year, the measured BVL needs to exceed 0.94%. The median BVL per year in the MS patient cohort was 0.44%. In 11 out of 60 MS patients (18%) we found a BVL per year equal or greater than 0.94%. CONCLUSION: The estimated WPF may be helpful when interpreting BVL results on an individual patient level in diseases such as MS.

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Brain volume loss (BVL) has gained increasing interest for monitoring tissue damage in neurodegenerative diseases including multiple sclerosis (MS). In this longitudinal study, 117 healthy participants (age range 37.3-82.6 years) received at least 2 magnetic resonance imaging examinations. BVL (in %) was determined with the Structural Image Evaluation using Normalisation of Atrophy/FMRIB Software Library and annualized. Mean BVL per year was 0.15%, 0.30%, 0.46%, and 0.61% at ages 45, 55, 65, and 75 years, respectively. The corresponding BVL per year values of the age-dependent 95th percentiles were 0.52%, 0.77%, 1.05% and 1.45%. Pathological BVL can be assumed if an individual BVL per year exceeds these thresholds for a given age. The mean BVL per year determined in this longitudinal study was consistent with results from a cross-sectional study that was published recently. The cut-off for a pathological BVL per year at the age of 45 years (0.52%) was consistent with the cut-off suggested previously to distinguish between physiological and pathological BVL in MS patients. Different cut-off values, however, need to be considered when interpreting BVL assessed in cohorts of higher ages.

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