Multi-modal neuroimaging signatures predict cognitive decline in multiple sclerosis: A 5-year longitudinal study

BACKGROUND

Cognitive impairment is a hallmark of multiple sclerosis (MS) but is usually an under-recorded symptom of disease progression. Identifying the predictive signatures of cognitive decline in people with MS (pwMS) over time is important to ensure effective preventative treatment strategies. Structural and functional brain characteristics as measured by various magnetic resonance (MR) methods have been correlated with variation in cognitive function in MS, but typically these studies are limited to a single MR modality and/or are cross-sectional designs. Here we assess the predictive value of multiple different MR modalities in relation to cognitive decline in pwMS over 5 years.

METHODS

A cohort of 43 pwMS was assessed at baseline and 5 years follow-up. Baseline (input) data consisted of 70 multi-modal MRI measures for different brain regions including magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and standard volumetrics. Age, sex, disease duration and treatment were included as clinical inputs. Cognitive function was assessed using the Audio Recorded Cognitive Screen (ARCS) and the Symbol Digit Modalities Test (SDMT). Prediction modelling was performed using the machine learning package - GLMnet, where a penalised regression was applied to identify multi-modal signatures with the most predictive value (and the least error) for each outcome.

RESULTS

The multi-modal approach to neuroimaging was able to accurately predict cognitive decline in pwMS. The best performing model for change in total ARCS (tARCS) included 16 features from across the various MR modalities and explained 54 % of the variation in change over time (R2=0.54, 95 % CI=0.48-0.51). The features included nine MRS, four volumetric and two DTI parameters. The model also selected disease duration, but not treatment, as a predictive feature. By comparison, the best model for SDMT included several of the same above features and explained 39 % of the change over time (R2=0.39, 95 % CI=0.48-0.51). Conventional volumetric measures were about half as good at predicting change in tARCS score compared to the best multi-modal model (R2=0.26 95 % CI:0.22-0.29). The clinical interpretation of the best predictive model for change in tARCS showed that cognitive decline could be predicted with >90 % accuracy in this cohort (AUC=0.92, SE=0.86 - 0.94).

CONCLUSION

Multi-modal MRI signatures can predict cognitive decline in a cohort of pwMS over 5 years with high accuracy. Future studies will benefit from the inclusion of even more MR modalities e.g., functional MRI, quantitative susceptibility mapping, magnetisation transfer imaging, as well as other potential predictors e.g., genetic and environmental factors. With further validation, this signature could be used in future trials with high-risk patients to personalise the management of cognitive decline in pwMS, even in the absence of relapses.

Do people with multiple sclerosis receive appropriate support from the National Disability Insurance Scheme matching their level of disability? A description of disease 'burden and societal cost in people with multiple sclerosis in Australia

ObjectiveThis study is the first to assess if the National Disability Insurance Scheme (NDIS) package allocated to people with multiple sclerosis (pwMS) is correlated with the disability level measured by standardised neurological assessment.MethodsWe aimed to recruit 10 pwMS per expanded disability status score (EDSS) step, including EDSS 0 (no disability) up to 9 (bedridden), and requested information about their NDIS application. Value of their packages was compared with mobility, cognition and psychological impact.ResultsOut of 186 pwMS, only 49% of all patients had an NDIS package approved. The mean values of the annual allowance were AU$30318 for patients with mild disability, AU$38361 for moderate disability and AU$115113 for severe disability. There was a striking variability in packages approved, but restricted mobility seems to be the driving factor. Rejection rates were <20% in patients with mild and moderate disability and none in those with severe disability. The package value correlated with EDSS steps, cognitive impairment and physical impact, but not psychological impact.ConclusionsThis is the first study to assess if NDIS packages correlate with internationally accepted disability scales. The NDIS support was correlated with disability measured by EDSS steps and cognition, but not psychological impact of the disease.What is known about the topic?There are over 25000 Australians living with multiple sclerosis, which is one of the most common neurological diseases leading to disability in early age. The National Disability Insurance Scheme has been introduced since 2013 to particularly assist young disabled Australians to participate in the community. Whether the approved package correlates with internationally accepted disability scores has not yet been assessed.What does this paper add?This study is the first to correlate disability, as assessed by the Expanded Disability Severity Scale (EDSS), with the approved package value.What are the implications for practitioners?Multiple sclerosis is a very variable disease affecting quality of life not only due to impairment of mobility, but also cognition and mental health. Although the NDIS package value was correlated with an EDSS and cognition, the psychological impact of the disease is often neglected.