Alexander De Cock

Objective: Cognitive impairment is common in multiple sclerosis (MS), significantly impacts daily functioning, is time-consuming to assess, and is prone to practice effects. We examined whether the alpha band power measured with magnetoencephalography (MEG) is associated with the different cognitive domains affected by MS.Methods: Sixty-eight MS patients and 47 healthy controls underwent MEG, T1- and FLAIR-weighted magnetic resonance imaging (MRI), and neuropsychological testing. Alpha power in the occipital cortex was quantified in the alpha1 (8-10 Hz) and alpha2 (10-12 Hz) bands. Next, we performed best subset regression to assess the added value of neurophysiological measures to commonly available MRI measures.Results: Alpha2 power significantly correlated with information processing speed (p < 0.001) and was always retained in all multilinear models, whereas thalamic volume was retained in 80% of all models. Alpha1 power was correlated with visual memory (p < 0.001) but only retained in 38% of all models.Conclusions: Alpha2 (10-12 Hz) power in rest is associated with IPS, independent of standard MRI parameters. This study stresses that a multimodal assessment, including structural and functional biomarkers, is likely required to characterize cognitive impairment in MS. Resting-state neurophysiology is thus a promising tool to understand and follow up changes in IPS.

As assembly systems move into the era of mass customisation, the complexity of design processes, (re)configurations and operations rises. Well-structured data are key in keeping this complexity manageable. Here to, this paper presents a multidimensional formal skill model designed to deliver generic descriptions of needs and capacities with skills as the connector between products, processes and resources. The model formalises resource structures in relation to the processes they master and products they can produce. This paper discusses the case-based evaluation in a reconfigurable assembly system and highlights the added-value of a skill-based modelling approach. The presented formal model combines concepts coming from both offline and online modelling perspectives and allows for various applications and levels of detail. The resource structures embedded in the prerequisites of a skill enable matchmaking of resources for workspace design and reconfigurations. The mapping of the model to standardised ISA-95 models couples production needs to the resources allowing for more optimal production planning, control and a structured interface between enterprise and control systems. The possibility to couple states to the assembly environment allows for optimal runtime orchestration.

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.

Background 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. Objectives To investigate the relationship between brain age and information processing speed in MS. Methods A ridge-regression model was trained to predict age from brain MRI volumetric features and sex in a healthy control dataset (HC_train, n=1690). 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. Information processing speed was assessed with the Symbol Digit Modalities Test (SDMT). Results Brain age was significantly related to SDMT scores in the MS_test dataset (r=-0.44, p<.001), and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r=-0.21, p=0.003) and a significant weight (−0.21, p=0.011) in a multivariate regression equation with age. Conclusions Brain age is a candidate biomarker for information processing speed in MS and an easy to grasp metric for brain health.

Dysphagia is common in persons with multiple sclerosis (MS). Speech and language therapists give dysphagia recommendations to persons with MS and caregivers. Nonadherence to these recommendations can increase the risk of aspiration. We investigated current compliance with dysphagia recommendations among caregivers and kitchen staff and assessed improvement in compliance by increasing knowledge through tailored training. An observational cohort study was conducted over 4 weeks during which the compliance of the caregivers and kitchen staff in a rehabilitation center was monitored. A questionnaire was used to assess reasons for noncompliance. A 2-hour training session was provided for all caregivers and kitchen staff to improve their knowledge and skills. The compliance rate was observed again 1 and 6 months after the training. Compliance was defined by whether recommendations were followed. Results showed a significant improvement after training for overall compliance by caregivers (from 58% to >81%, P < .001). This improvement was still observed 6 months later (80%). After training, significant differences were found in compliance with the following recommendations (P ≤ .001): consistency of soup, consistency of liquids, food preparation, alertness, speed, amount, posture, and supervision. Recommendation for utensils did not improve (P = .44). Compliance with diet modifications made by the kitchen staff improved significantly (from 74% to >86%, P = .002), and even more during follow-up (to >95%, P = .009). Dysphagia training tailored to the needs of caregivers to improve knowledge significantly improves compliance with dysphagia recommendations and the quality of care.