Psychological factors and brain magnetic resonance imaging metrics associated with fatigue in persons with multiple sclerosis.

BACKGROUND: Besides demographics and clinical factors, psychological variables and brain-tissue changes have been associated with fatigue in persons with multiple sclerosis (pwMS). Identifying predictors of fatigue could help to improve therapeutic approaches for pwMS. Therefore, we investigated predictors of fatigue using a multifactorial approach. METHODS: 136 pwMS and 49 normal controls (NC) underwent clinical, neuropsychological, and magnetic resonance imaging examinations. We assessed fatigue using the "Fatigue Scale for Motor and Cognitive Functions", yielding a total, motor, and cognitive fatigue score. We further analyzed global and subcortical brain volumes, white matter lesions and microstructural changes (examining fractional anisotropy; FA) along the cortico striatal thalamo cortical (CSTC) loop. Potential demographic, clinical, psychological, and magnetic resonance imaging predictors of total, motor, and cognitive fatigue were explored using multifactorial linear regression models. RESULTS: 53% of pwMS and 20% of NC demonstrated fatigue. Besides demographics and clinical data, total fatigue in pwMS was predicted by higher levels of depression and reduced microstructural tissue integrity in the CSTC loop (adjusted R2 = 0.52, p < 0.001). More specifically, motor fatigue was predicted by lower education, female sex, higher physical disability, higher levels of depression, and self-efficacy (adjusted R2 = 0.54, p < 0.001). Cognitive fatigue was also predicted by higher levels of depression and lower self-efficacy, but in addition by FA reductions in the CSTC loop (adjusted R2 = 0.45, p < 0.001). CONCLUSIONS: Our results indicate that depression and self-efficacy strongly predict fatigue in MS. Incremental variance in total and cognitive fatigue was explained by microstructural changes along the CSTC loop, beyond demographics, clinical, and psychological variables.

Relevance of Cognition and Emotion for Patient-Reported Quality of Life After Stroke in Working Age: An Observational Cohort Study.

Background: Patient-reported quality of life (QoL) may help to capture sequela of stroke more comprehensively. We aimed to investigate QoL in working age persons with ischemic stroke regarding impaired domains and identify factors associated with better QoL. Methods: We invited persons with stroke aged 18-55 years to participate in this prospective observational study. We assessed QoL and self-rated health using the EuroQol 5 Dimension questionnaire (EQ-5D) during hospital stay (baseline) and at 3-months follow-up (FU). Additionally, the National Institute of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS), cognition (Montreal Cognitive assessment, MOCA), emotion (Hospital Anxiety and Depression Scale), and return to work were evaluated. We used hierarchical regression to identify predictors of QoL (self-rated health and QoL Index score) at FU. Results: We included 138 persons with stroke (mean age = 43.6 ± 10 years; 41% female; median admission NIHSS = 2), of whom 99 participated at FU. QoL Index and self-rated health were correlated with NIHSS, mRS, anxiety, and depression at both timepoints. Although 80% had favorable functional outcome at FU (mRS < 2), high proportions of these persons reported problems in the "Pain and/or Discomfort" (25.3%) and "Anxiety/Depression" (22.8%) dimensions. Only discharge NIHSS and baseline MOCA independently predicted self-rated health at FU. Female sex, higher discharge NIHSS, and higher baseline depression scores predicted worse QoL Index scores at FU. Conclusions: Three months post-stroke, working age persons with stroke frequently reported problems in dimensions not assessed by the routinely used mRS. Despite correlations between clinical scales and QoL, patient-reported outcomes and screening for cognition and emotion ensure a more comprehensive assessment of post-stroke consequences relevant for QoL.

MRI correlates of cognitive improvement after home-based EEG neurofeedback training in patients with multiple sclerosis: a pilot study.

OBJECTIVE: Neurofeedback training may improve cognitive function in patients with neurological disorders. However, the underlying cerebral mechanisms of such improvements are poorly understood. Therefore, we aimed to investigate MRI correlates of cognitive improvement after EEG-based neurofeedback training in patients with MS (pwMS). METHODS: Fourteen pwMS underwent ten neurofeedback training sessions within 3-4 weeks at home using a tele-rehabilitation system. Half of the pwMS (N = 7, responders) learned to self-regulate sensorimotor rhythm (SMR, 12-15 Hz) by visual feedback and improved cognitively after training, whereas the remainder (non-responders, n = 7) did not. Diffusion-tensor imaging and resting-state fMRI of the brain was performed before and after training. We analyzed fractional anisotropy (FA) and functional connectivity (FC) of the default-mode, sensorimotor (SMN) and salience network (SAL). RESULTS: At baseline, responders and non-responders were comparable regarding sex, age, education, disease duration, physical and cognitive impairment, and MRI parameters. After training, compared to non-responders, responders showed increased FA and FC within the SAL and SMN. Cognitive improvement correlated with increased FC in SAL and a correlation trend with increased FA was observed. CONCLUSIONS: This exploratory study suggests that successful neurofeedback training may not only lead to cognitive improvement, but also to increases in brain microstructure and functional connectivity.

Evaluation of a Newly Developed Smartphone App for Risk Factor Management in Young Patients With Ischemic Stroke: A Pilot Study.

Background: Efficient treatment of modifiable vascular risk factors decreases reoccurrence of ischemic stroke, which is of uttermost importance in younger patients. In this longitudinal pilot study, we thus assessed the effect of a newly developed smartphone app for risk factor management in such a cohort. Methods: The app conveys key facts about stroke, provides motivational support for a healthy lifestyle, and a reminder function for medication intake and blood pressure measurement. Between January 2019 and February 2020, we consecutively invited patients with ischemic stroke aged between 18 and 55 years to participate. Patients in the intervention group used the app between hospital discharge and 3-month follow-up. The control group received standard clinical care. Modifiable risk factors (physical activity, nutrition, alcohol consumption, smoking behavior, obesity, and hypertension) were assessed during the initial hospital stay and at a dedicated stroke outpatient department three months post-stroke. Results: The study cohort comprised 21 patients in the app intervention group (62% male; age = 41 ± 11 years; education = 12 ± 3 years) and 21 sex-, age- and education-matched control patients with a comparable stroke risk factor profile. Baseline stroke severity was comparable between groups (intervention: median NIHSS = 3; control: median NIHSS = 4; p = 0.604). Three months post-stroke, patients in the intervention group reported to be physically almost twice as active (13 ± 9 h/week) compared to controls (7 ± 5 h/week; p = 0.022). More intense app usage was strongly associated with higher physical activity (r = 0.60, p = 0.005) and lower consumption of unhealthy food (r = -0.51, p = 0.023). Smoking behavior (p = 0.001) and hypertension (p = 0.003) improved in all patients. Patients in the intervention group described better self-reported health-related quality of life three months post-stroke (p = 0.003). Conclusions: Specifically designed app interventions can be an easily to implement and cost-efficient approach to promote a healthier lifestyle in younger patients with a stroke.

Long-term course and morphological MRI correlates of cognitive function in multiple sclerosis.

BACKGROUND: Cognitive impairment frequently occurs in patients with MS (pwMS). Magnetic resonance imaging (MRI) markers could help to identify patients at risk for decline. OBJECTIVE: To characterize the long-term course and morphological MRI correlates of cognitive function in pwMS. METHODS: We invited 116 pwMS who had undergone clinical, cognitive, and MRI evaluations between 2006 and 2012 (baseline, BL) to attend follow-up (FU) testing between 2016 and 2018. Disability (expanded disability status scale (EDSS)), cognition (brief repeatable battery of neuropsychological test (BRB-N)), global and regional T2-lesion load (T2-LL), brain volumes, and cortical thickness were assessed. RESULTS: Sixty-three pwMS were willing to attend the FU (54%; median EDSS = 2, interquartile range (IQR) = 2) and did not differ from non-participating pwMS regarding BL characteristics. At BL, half of the participants showed cognitive deficits in at least one domain. Across the entire group, we observed no relevant changes in physical disability and cognition over 10 years. BL thalamic volume best predicted cognitive function at FU, in addition to age and BL cognition, explaining 67% of variance. Cognitive decliners (23.8%) were older, had longer disease duration, and a tendency for lower thalamic volume at BL. CONCLUSION: Thalamic volume predicted FU cognitive function and distinguished declining from stable pwMS, underlining the potential of MRI to define risk groups.

[Use of smartphone apps in secondary stroke prevention]. / Apps in der Sekundärprävention nach Schlaganfall.

Modifiable risk factors such as hypertension, obesity or smoking have been reported to explain up to 90% of risk for ischemic stroke. Treatment of these risk factors is known to decrease the risk of recurrent stroke events. We performed a computer-based literature research from June to August 2018 using the electronic database PubMed to investigate the effect of smartphone apps on risk factor control for secondary stroke prevention as well as feasibility and patient satisfaction with mobile health. Studies evaluating interventions by smartphone or tablet devices in stroke patients and reported results regarding risk factors, feasibility or patient satisfaction were considered (n = 10). Identified data showed significant improvement regarding the control of risk factors hypertension and diabetes as well as significant improvements of the lifestyle risk factors physical inactivity and obesity. Stroke patients perceive smartphone apps mostly as useful and are open-minded regarding mHealth, provided that these complement rather than replace personal medical care.