Assessing cognitive changes in multiple sclerosis: criteria for a reliable decision

Cognitive progression independent of relapse in multiple sclerosis

BACKGROUND

Substantial physical-disability worsening in relapsing-remitting multiple sclerosis (RRMS) occurs outside of clinically recorded relapse. This phenomenon, termed progression independent of relapse activity (PIRA), is yet to be established for cognitive decline.

METHODS

Retrospective analysis of RRMS patients. Cognitive decline was defined using reliable-change-index cut-offs for each test (Symbol Digit Modalities Test, Brief Visuospatial Memory Test-Revised, California Verbal Learning Test-II). Decline was classified as PIRA if the following conditions were met: no relapse observed between assessments nor within 9 months of cognitive decline.

RESULTS

The study sample (n = 336) was 80.7% female with a mean (standard deviation (SD)) age, disease duration, and observation period of 43.1 (9.5), 10.8 (8.4), and 8.1 (3.1) years, respectively. A total of 169 (50.3%) subjects were cognitively impaired at baseline relative to age-, sex-, and education-matched HCs. Within subjects who experienced cognitive decline (n = 167), 89% experienced cognitive PIRA. A total of 141 (68.1%) cognitive decline events were observed independent of EDSS worsening. Cognitive PIRA was more likely to be observed with increased assessments (p < 0.001) and lower assessment density (p < 0.001), accounting for baseline clinical factors.

CONCLUSION

These results establish the concept of cognitive PIRA and further our understanding of progressive cognitive decline in RRMS.

Cognitive impairment in multiple sclerosis: from phenomenology to neurobiological mechanisms

Multiple sclerosis (MS) is an autoimmune-mediated disease of the central nervous system characterized by inflammation, demyelination and chronic progressive neurodegeneration. Among its broad and unpredictable range of clinical symptoms, cognitive impairment (CI) is a common and disabling feature greatly affecting the patients' quality of life. Its prevalence is 20% up to 88% with a wide variety depending on the phenotype of MS, with highest frequency and severity in primary progressive MS. Involving different cognitive domains, CI is often associated with depression and other neuropsychiatric symptoms, but usually not correlated with motor and other deficits, suggesting different pathophysiological mechanisms. While no specific neuropathological data for CI in MS are available, modern research has provided evidence that it arises from the disease-specific brain alterations. Multimodal neuroimaging, besides structural changes of cortical and deep subcortical gray and white matter, exhibited dysfunction of fronto-parietal, thalamo-hippocampal, default mode and cognition-related networks, disruption of inter-network connections and involvement of the γ-aminobutyric acid (GABA) system. This provided a conceptual framework to explain how aberrant pathophysiological processes, including oxidative stress, mitochondrial dysfunction, autoimmune reactions and disruption of essential signaling pathways predict/cause specific disorders of cognition. CI in MS is related to multi-regional patterns of cerebral disturbances, although its complex pathogenic mechanisms await further elucidation. This article, based on systematic analysis of PubMed, Google Scholar and Cochrane Library, reviews current epidemiological, clinical, neuroimaging and pathogenetic evidence that could aid early identification of CI in MS and inform about new therapeutic targets and strategies.

Meaningful cognitive change for the Minimal Assessment of Cognitive Function in Multiple Sclerosis

BACKGROUND

There is limited information on interpretation of cognitive changes over time in multiple sclerosis (MS).

OBJECTIVE

This study aimed to provide normative data for the assessment of statistically meaningful change in all tests of the Minimal Assessment of Cognitive Function in MS (MACFIMS).

METHODS

We applied the reliable change methodology to a healthy Italian cohort, assessed with two alternate versions of the MACFIMS 1 year apart. We calculated confidence intervals of retest score variance using the reliable change index (RCI). Moreover, multivariable linear regression models adjusted for age, sex, education, and baseline score were built to calculate the regression-based change index (RB-CI).

RESULTS

Overall, 200 healthy individuals were enrolled. Thresholds for interpreting change in each test were calculated. In the multivariable models, baseline score was associated with retest score in all tests (B from 0.439 to 0.760; p < 0.001). RB-CI can be calculated with data of the multivariable models.

CONCLUSION

We provide normative data for reliable cognitive change evaluation for all the tests of the MACFIMS, which includes the Symbol Digit Modalities Test and Brief International Cognitive Assessment in MS, two widely used tools for screening and monitoring cognition in MS. Our findings can significantly improve the interpretation of cognitive changes in MS.

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.