Background and purpose: Data from neuro‐imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as ‘how old the brain looks’ and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS).Methods: A linear regression model was trained to predict age from brain magnetic resonance imaging volumetric features and sex in a healthy control dataset (HC_train, n = 1673). This model was used to predict brain age in two test sets: HC_test (n = 50) and MS_test (n = 201). Brain‐predicted age difference (BPAD) was calculated as BPAD = brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT).Results: Brain age was significantly related to SDMT scores in the MS_test dataset (r = −0.46, p < 0.001) and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r = −0.24, p < 0.001) and a significant weight (−0.25, p = 0.002) in a multivariate regression equation with age.Conclusions: Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health.

Multi‐item working memory (WM) is a complex cognitive function thought to arise from specific frequency band oscillations and their interactions. While some theories and consistent findings have been established, there is still a lot of unclarity about the sources, temporal dynamics, and roles of event‐related fields (ERFs) and theta, alpha, and beta oscillations during WM activity. In this study, we performed an extensive whole‐brain ERF and time‐frequency analysis on n‐back magnetoencephalography data from 38 healthy controls. We identified the previously unknown sources of the n‐back M300, the right inferior temporal and parahippocampal gyrus and left inferior temporal gyrus, and frontal theta power increase, the orbitofrontal cortex. We shed new light on the role of the precuneus during n‐back activity, based on an early ERF and theta power increase, and suggest it to be a crucial link between lower‐level and higher‐level information processing. In addition, we provide strong evidence for the central role of the hippocampus in multi‐item WM behavior through the dynamics of theta and alpha oscillatory changes. Almost simultaneous alpha power decreases observed in the hippocampus and occipital fusiform gyri, regions known to be involved in letter processing, suggest that these regions together enable letter recognition, encoding and storage in WM. In summary, this study offers an extensive investigation into the spatial, temporal, and spectral characteristics of n‐back multi‐item WM activity.

In multiple sclerosis (MS), the early detection of disease activity or progression is key to inform treatment changes and could be supported by digital tools. We present a novel CE-marked and FDA-cleared digital care management platform consisting of (1) a patient phone/web application and healthcare professional portal (icompanion) including validated symptom, disability, cognition, and fatigue patient-reported outcomes; and (2) clinical brain magnetic resonance imaging (MRI) quantifications (icobrain ms). We validate both tools using their ability to detect (sub)clinical disease activity (known-groups validity) and real-world data insights. Surveys showed that 95.6% of people with MS (PwMS) were interested in using an MS app, and 98.2% were interested in knowing about MRI changes. The icompanion measures of disability (p < 0.001) and symptoms (p = 0.005) and icobrain ms MRI parameters were sensitive to (sub)clinical differences between MS subtypes. icobrain ms also decreased intra- and inter-rater lesion count variability and increased sensitivity for detecting disease activity/progression from 24% to 76% compared to standard radiological reading. This evidence shows PwMS’ interest, the digital care platform’s potential to improve the detection of (sub)clinical disease activity and care management, and the feasibility of linking different digital tools into one overarching MS care pathway.

Working memory (WM) problems are frequently present in people with multiple sclerosis (MS). Even though hippocampal damage has been repeatedly shown to play an important role, the underlying neurophysiological mechanisms remain unclear. This study aimed to investigate the neurophysiological underpinnings of WM impairment in MS using magnetoencephalography (MEG) data from a visual‐verbal 2‐back task. We analysed MEG recordings of 79 MS patients and 38 healthy subjects through event‐related fields and theta (4–8 Hz) and alpha (8–13 Hz) oscillatory processes. Data was source reconstructed and parcellated based on previous findings in the healthy subject sample. MS patients showed a smaller maximum theta power increase in the right hippocampus between 0 and 400 ms than healthy subjects (p = .014). This theta power increase value correlated negatively with reaction time on the task in MS (r = −.32, p = .029). Evidence was provided that this relationship could not be explained by a ‘common cause’ confounding relationship with MS‐related neuronal damage. This study provides the first neurophysiological evidence of the influence of hippocampal dysfunction on WM performance in MS.