Networks of microstructural damage predict disability in multiple sclerosis

Background: Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods. // Methods: We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures. // Results: We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001). // Conclusions: GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials

Colournamer, a synthetic observer for colour communication

Colour specification is not only the domain of technologists but is also an important process for anyone who needs to communicate about colour in the multilingual Internet environment. We have developed an online application Colournamer, a synthetic observer ‘trained’ by the participants’ responses, to facilitate colour communication between different cultures. At present it supports English, Greek, Spanish and German

Navigation technologies for autonomous underwater vehicles

Formålet med denne masteroppgaven har vært å få innsikt i om en spesiell undervisningsmetode, Sirkelen for undervisning og læring, kan ha positiv effekt på språk- og skriveopplæring av elever i ungdomsskolen med norsk som andrespråk og med kort botid i Norge. Undervisningsmetoden er tidligere blitt studert på voksne andrespråkselever og på elever i barnetrinnet. Det foreligger lite forskning som kan si noe om Sirkelmodellen kan være et godt verktøy for andrespråkselever som begynner rett i ungdomsskolen. Videre finnes det ifølge Golden og Hvistendal (2013) lite forskning på skriving spesielt hos andrespråkselever ettersom dette kommer innunder generell skriveforskning. Studien er gjennomført på en ungdomsskole på Østlandet. Empirien består av elevtekster som er analysert etter bestemte kriterier i lys av teori om andrespråkslæring, mellomspråksfaser og aspekter ved det norske språket som kan være utfordrende for andrespråkselever. Tekstene er skrevet i ulike sjangre og er samlet inn over tid. Det har vært mulig å spore språklig- og skriftlig utvikling i tekstene. Det har også vært mulig å konkludere med at undervisningsmetoden gir et godt grunnlag for språk- og skriveopplæring av andrespråkselever med kort botid i Norge, men at det trengs videre forskning for å kunne generalisere til å gjelde en større elevgruppe for å tette kunnskapshullet fullstendig

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

Objective: In 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). Methods: We 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. Results: We 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). Conclusions: The 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

Remyelination varies between and within lesions in multiple sclerosis following bexarotene.

Funder: MS Society of the United KingdomFunder: NIHR UCLH Biomedical Research CentreFunder: UCLFunder: University College LondonFunder: NIHRFunder: Thorne Family FoundationFunder: Adeslon Medical Research FoundationOBJECTIVE: In multiple sclerosis chronic demyelination is associated with axonal loss, and ultimately contributes to irreversible progressive disability. Enhancing remyelination may slow, or even reverse, disability. We recently trialled bexarotene versus placebo in 49 people with multiple sclerosis. While the primary MRI outcome was negative, there was converging neurophysiological and MRI evidence of efficacy. Multiple factors influence lesion remyelination. In this study we undertook a systematic exploratory analysis to determine whether treatment response - measured by change in magnetisation transfer ratio - is influenced by location (tissue type and proximity to CSF) or the degree of abnormality (using baseline magnetisation transfer ratio and T1 values). METHODS: We examined treatment effects at the whole lesion level, the lesion component level (core, rim and perilesional tissues) and at the individual lesion voxel level. RESULTS: At the whole lesion level, significant treatment effects were seen in GM but not WM lesions. Voxel-level analyses detected significant treatment effects in WM lesion voxels with the lowest baseline MTR, and uncovered gradients of treatment effect in both WM and CGM lesional voxels, suggesting that treatment effects were lower near CSF spaces. Finally, larger treatment effects were seen in the outer and surrounding components of GM lesions compared to inner cores. INTERPRETATION: Remyelination varies markedly within and between lesions. The greater remyelinating effect in GM lesions is congruent with neuropathological observations. For future remyelination trials, whole GM lesion measures require less complex post-processing compared to WM lesions (which require voxel level analyses) and markedly reduce sample sizes