Measuring and validating the levels of brain-derived neurotrophic factor in human serum

Brain-derived neurotrophic factor (BDNF) secreted by neurons is a significant component of synaptic plasticity. In humans, it is also present in blood platelets where it accumulates following its biosynthesis in megakaryocytes. BDNF levels are thus readily detectable in human serum and it has been abundantly speculated that they may somehow serve as an indicator of brain function. However, there is a great deal of uncertainty with regard to the range of BDNF levels that can be considered normal, how stable these values are over time and even whether BDNF levels can be reliably measured in serum. Using monoclonal antibodies and a sandwich ELISA, this study reports on BDNF levels in the serum of 259 volunteers with a mean value of 32.69 ± 8.33 ng/ml (SD). The mean value for the same cohort after 12 months was not significantly different (N = 226, 32.97 ± 8.36 ng/ml SD, p = 0.19). Power analysis of these values indicates that relatively large cohorts are necessary to identify significant differences, requiring a group size of 60 to detect a 20% change. The levels determined by ELISA could be validated by Western blot analyses using a BDNF monoclonal antibody. While no association was observed with gender, a weak, positive correlation was found with age. The overall conclusions are that BDNF levels can be reliably measured in human serum, that these levels are quite stable over one year, and that comparisons between two populations may only be meaningful if cohorts of sufficient sizes are assembled

Genomics and proteomics: role in the management of multiple sclerosis

Epidemiological studies and neuro-imaging have provided important insights into the natural course and prognostic factors of multiple sclerosis (MS), but our ability to predict different courses of the disease, and especially its response to treatment, is still very limited. Pharmacogenetic, pharmacogenomic and proteomic studies aim to assess gene and protein function in disease and promise to help to fill this important gap in our knowledge. Such studies may increase our understanding of disease mechanisms and responses to therapeutic compounds. Large-scale transcriptional expression profiling can be performed using gene chip microarrays; this technology allows screening for differentially expressed genes without having well-defined underlying hypotheses ("discovery-driven research”). To complement the technique, real time reverse transcription and polymerase chain reaction (RT-PCR) can be used for more targeted profiling and provides quantitative data on pre-selected genes. However, to maximise their clinical utility, expression profiling results need to be combined with well-documented clinical and imaging data. Two forthcoming studies will investigate the long-term effects of early treatment with interferon beta-1b (IFNβ) on the course of MS. The BENEFIT (BEtaseron®/Betaferon® in Newly Emerging MS for Initial Treatment) study will incorporate pharmacogenetic and pharmacogenomic analyses to determine the genetic elements controlling treatment response. BEST-PGx (Betaferon®/Betaseron® in Early relapsing-remitting MS Surveillance Trial—Pharmacogenomics) is an exploratory 2-year study that will investigate the value of RNA expression profiling and pharmacogenetics in predicting treatment response to IFNβ in patients with early relapsing MS. The main goal of BEST-PGx is the identification of differences in gene expression profiles of patients showing differential treatment responses. In addition, this study may reveal new information relevant to the mechanism of action of interferon treatment in MS and also to differences in the underlying pathology of the immune system. These data may help us approach the goal of a really "individualised therapy” with increased efficacy, reduced adverse drug reactions and more efficient use of healthcare resource

Non-communicating syringomyelia: a feature of spinal cord involvement in multiple sclerosis

In patients with multiple sclerosis (MS) non-communicating syringomyelia (NCS) has been described as an incidental finding in case studies and small case series. NCS in MS patients commonly leads to uncertainty particularly as the clinical picture of NCS is variable and surgical therapy may be considered. Up to date little is known about the prevalence and clinical importance of NCS in MS. We report the imaging and clinical characteristics of NCS formations in nine MS patients from a 1 year follow-up study in a representative group of 202 MS (4.5%) patients. Brain and spinal cord MRI was performed as part of a genetic study. NCS did commonly extend the central canal and the cord was slightly distended at the level of the syrinx. The cord and syrinx showed no tendency to change in size or shape over 1 year. Despite thorough search into the clinical history and current clinical status no definite but only minimal indications of symptoms potentially related to the NCS were found. We confirm that NCS may occur in MS patients with spinal cord pathology. It can be a subtle finding without clinical correlates. Syrinx formations are more likely to be a consequence of MS cord pathology than a coincidental findin

Investigating Employees’ Concerns and Wishes Regarding Digital Stress Management Interventions With Value Sensitive Design: Mixed Methods Study

Background: Work stress places a heavy economic and disease burden on society. Recent technological advances include digital health interventions for helping employees prevent and manage their stress at work effectively. Although such digital solutions come with an array of ethical risks, especially if they involve biomedical big data, the incorporation of employees' values in their design and deployment has been widely overlooked. Objective: To bridge this gap, we used the value sensitive design (VSD) framework to identify relevant values concerning a digital stress management intervention (dSMI) at the workplace, assess how users comprehend these values, and derive specific requirements for an ethics-informed design of dSMIs. VSD is a theoretically grounded framework that front-loads ethics by accounting for values throughout the design process of a technology. Methods: We conducted a literature search to identify relevant values of dSMIs at the workplace. To understand how potential users comprehend these values and derive design requirements, we conducted a web-based study that contained closed and open questions with employees of a Swiss company, allowing both quantitative and qualitative analyses. Results: The values health and well-being, privacy, autonomy, accountability, and identity were identified through our literature search. Statistical analysis of 170 responses from the web-based study revealed that the intention to use and perceived usefulness of a dSMI were moderate to high. Employees' moderate to high health and well-being concerns included worries that a dSMI would not be effective or would even amplify their stress levels. Privacy concerns were also rated on the higher end of the score range, whereas concerns regarding autonomy, accountability, and identity were rated lower. Moreover, a personalized dSMI with a monitoring system involving a machine learning-based analysis of data led to significantly higher privacy (P=.009) and accountability concerns (P=.04) than a dSMI without a monitoring system. In addition, integrability, user-friendliness, and digital independence emerged as novel values from the qualitative analysis of 85 text responses. Conclusions: Although most surveyed employees were willing to use a dSMI at the workplace, there were considerable health and well-being concerns with regard to effectiveness and problem perpetuation. For a minority of employees who value digital independence, a nondigital offer might be more suitable. In terms of the type of dSMI, privacy and accountability concerns must be particularly well addressed if a machine learning-based monitoring component is included. To help mitigate these concerns, we propose specific requirements to support the VSD of a dSMI at the workplace. The results of this work and our research protocol will inform future research on VSD-based interventions and further advance the integration of ethics in digital health

An interpretable machine learning approach to multimodal stress detection in a simulated office environment

Background and objective: Work-related stress affects a large part of today’s workforce and is known to have detrimental effects on physical and mental health. Continuous and unobtrusive stress detection may help prevent and reduce stress by providing personalised feedback and allowing for the development of just-in-time adaptive health interventions for stress management. Previous studies on stress detection in work environments have often struggled to adequately reflect real-world conditions in controlled laboratory experiments. To close this gap, in this paper, we present a machine learning methodology for stress detection based on multimodal data collected from unobtrusive sources in an experiment simulating a realistic group office environment (N=90). Methods: We derive mouse, keyboard and heart rate variability features to detect three levels of perceived stress, valence and arousal with support vector machines, random forests and gradient boosting models using 10-fold cross-validation. We interpret the contributions of features to the model predictions with SHapley Additive exPlanations (SHAP) value plots. Results: The gradient boosting models based on mouse and keyboard features obtained the highest average F1 scores of 0.625, 0.631 and 0.775 for the multiclass prediction of perceived stress, arousal and valence, respectively. Our results indicate that the combination of mouse and keyboard features may be better suited to detect stress in office environments than heart rate variability, despite physiological signal-based stress detection being more established in theory and research. The analysis of SHAP value plots shows that specific mouse movement and typing behaviours may characterise different levels of stress. Conclusions: Our study fills different methodological gaps in the research on the automated detection of stress in office environments, such as approximating real-life conditions in a laboratory and combining physiological and behavioural data sources. Implications for field studies on personalised, interpretable ML-based systems for the real-time detection of stress in real office environments are also discussed

Changes in the Cerebrospinal Fluid and Plasma Lipidome in Patients with Rett Syndrome

Rett syndrome (RTT) is defined as a rare disease caused by mutations of the methyl-CpG binding protein 2 (MECP2). It is one of the most common causes of genetic mental retardation in girls, characterized by normal early psychomotor development, followed by severe neurologic regression. Hitherto, RTT lacks a specific biomarker, but altered lipid homeostasis has been found in RTT model mice as well as in RTT patients. We performed LC-MS/MS lipidomics analysis to investigate the cerebrospinal fluid (CSF) and plasma composition of patients with RTT for biochemical variations compared to healthy controls. In all seven RTT patients, we found decreased CSF cholesterol levels compared to age-matched controls (n = 13), whereas plasma cholesterol levels were within the normal range in all 13 RTT patients compared to 18 controls. Levels of phospholipid (PL) and sphingomyelin (SM) species were decreased in CSF of RTT patients, whereas the lipidomics profile of plasma samples was unaltered in RTT patients compared to healthy controls. This study shows that the CSF lipidomics profile is altered in RTT, which is the basis for future (functional) studies to validate selected lipid species as CSF biomarkers for RTT

The effectiveness and user experience of a biofeedback intervention program for stress management supported by virtual reality and mobile technology: a randomized controlled study

Background: Heart rate variability biofeedback (HRV-BF) can be used for stress management. Recent feasibility studies suggest that delivering HRV-BF in virtual reality (VR) is associated with better user experience (UX) and might yield more beneficial changes in HRV than two-dimensional screens. The effectiveness of a VR-supported HRV-BF intervention program has, however, not been investigated yet. Methods: In this study, 87 healthy women and men were assigned to a VR-supported HRV-BF intervention (INT; $n=44$) or a wait-list control (WLC; $n=43$) group. The INT came to the lab for four weekly HRV-BF sessions in VR using a head-mounted display. Between lab sessions, participants were asked to perform breathing exercises without biofeedback supported by a mobile application. Stress-related psychological and psychophysiological outcomes were assessed pre- and post-intervention and at a follow-up four weeks after the intervention in both groups. A psychosocial stress test was conducted post-intervention to investigate changes in stress reactivity. UX was assessed after each HRV-BF session in the INT. Results: Analysis revealed that LF increased significantly from pre- to post-, whereas pNN50 increased and chronic stress decreased significantly from pre-intervention to follow-up in the INT compared to the WLC. Anxiety and mental fatigue decreased significantly, while mindfulness and health-related quality of life increased significantly from pre- to post- and from pre-intervention to follow-up in the INT compared to the WLC (all small effects). The two groups did not differ in their stress reactivity post-intervention. As for UX in the INT, the degree of feeling autonomous concerning technology adoption significantly decreased over time. Competence, involvement, and immersion, however, increased significantly from the first to the last HRV-BF session, while hedonic motivation significantly peaked in the second session and then gradually returned to first-session levels. Conclusions: This HRV-BF intervention program, supported by VR and mobile technology, was able to significantly improve stress indicators and stress-related symptoms and achieved good to very good UX. Future studies should control for potential placebo effects and emphasize higher degrees of personalization and adaptability to increase autonomy and, thereby, long-term health and well-being. These findings may serve as a first step towards future HRV-BF applications of cutting-edge, increasingly accessible technologies, such as wearables, VR, and smartphones, in the service of mental health and healthcare

Evaluation of a new approach for semi-automatic segmentation of the cerebellum in patients with multiple sclerosis

Cerebellar dysfunction is an important contributor to disability in patients with multiple sclerosis (MS), however, few in vivo studies focused on cerebellar volume loss so far. This relates to technical challenges regarding the segmentation of the cerebellum. In this study, we evaluated the semi-automatic ECCET software for performing cerebellar volumetry using high-resolution 3D T1-MR scans in patients with MS and healthy volunteers. We performed test-retest as well as inter-observer reliability testing of cerebellar segmentation and compared the ECCET results with a fully automatic cerebellar segmentation using the FreeSurfer software pipeline in 15 MS patients. In a pilot matched-pair analysis with another data set from 15 relapsing-remitting MS patients and 15 age- and sex-matched healthy controls (HC), we assessed the feasibility of the ECCET approach to detect MS-related cerebellar volume differences. For total normalized cerebellar volume as well as grey and white matter volumes, intrarater (intraclass correlation coefficient (ICC)=0.99, 95% CI=0.98-0.99) and interobserver agreement (ICC=0.98, 95% CI=0.74-0.99) were strong. Comparison between ECCET and FreeSurfer results likewise yielded a good intraclass correlation (ICC=0.86, 95% CI=0.58-0.95). Compared to HC, MS patients had significantly reduced normalized total brain, total cerebellar, and grey matter volumes (p≤0.05). ECCET is a suitable tool for cerebellar segmentation showing excellent test-retest and inter-observer reliability. Our matched-pair analysis between MS patients and healthy volunteers suggests that the method is sensitive and reliable in detecting cerebellar atrophy in M