Robust artifactual independent component classification for BCI practitioners

Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain–computer interfaces (BCIs). Approach. Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. Main results. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Significance. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.EC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBIBMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, ZentrumDFG, 194657344, EXC 1086: BrainLinks-BrainTool

Automating the search for a patent's prior art with a full text similarity search

More than ever, technical inventions are the symbol of our society's advance. Patents guarantee their creators protection against infringement. For an invention being patentable, its novelty and inventiveness have to be assessed. Therefore, a search for published work that describes similar inventions to a given patent application needs to be performed. Currently, this so-called search for prior art is executed with semi-automatically composed keyword queries, which is not only time consuming, but also prone to errors. In particular, errors may systematically arise by the fact that different keywords for the same technical concepts may exist across disciplines. In this paper, a novel approach is proposed, where the full text of a given patent application is compared to existing patents using machine learning and natural language processing techniques to automatically detect inventions that are similar to the one described in the submitted document. Various state-of-the-art approaches for feature extraction and document comparison are evaluated. In addition to that, the quality of the current search process is assessed based on ratings of a domain expert. The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality

Humoral response to mRNA vaccines against SARS-CoV-2 in patients with humoral immunodeficiency disease.

OBJECTIVES Although mRNA-based vaccines against SARS-CoV-2 induce a robust immune response and prevent infections and hospitalizations, there are limited data on the antibody response in individuals with humoral immunodeficiency. The aim of this study was to evaluate the humoral immune response after two vaccine doses with BNT162b2 or mRNA-1273 in patients with humoral immunodeficiency disease. METHODS This cross-sectional study assessed 39 individuals with hypogammaglobulinemia under immunoglobulin replacement therapy. IgG anti-SARS-CoV-2 spike protein antibodies (anti-S) were measured 4 weeks to 4 months after two doses of an mRNA vaccine against SARS-CoV-2. The proportion of patients, who developed a humoral immune response to the spike protein were evaluated and compared to 19 healthy controls. RESULTS After vaccination with two vaccine doses, 26/39 patients (66.7%) with humoral immunodeficiency disease and all healthy controls developed anti-S. In subjects with baseline IgG 5 g/l: 151.5 AU/ml (95%CI 109.0-400.0), healthy controls 250.0 AU/ml (95%CI 209.0-358.0), p = 0.007. CONCLUSION In most patients with mild to moderate humoral immunodeficiency we found only slightly lower anti-S antibodies compared with healthy controls after two vaccine doses with BNT162b2 and mRNA-1273. However, in patients with a decreased baseline IgG below 3 g/l and/or under immunosuppressive drugs, we found severely impaired humoral immune responses

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

Context: Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective: To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design: Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting: Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients: Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions: Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures: Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results: Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions: The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient

Solidarity during the COVID-19 pandemic: evidence from a nine-country interview study in Europe

Calls for solidarity have been an ubiquitous feature in the response to the COVID-19 pandemic. However, we know little about how people have thought of and practised solidarity in their everyday lives since the beginning of the pandemic. What role does solidarity play in people’s lives, how does it relate to COVID-19 public health measures and how has it changed in different phases of the pandemic? Situated within the medical humanities at the intersection of philosophy, bioethics, social sciences and policy studies, this article explores how the practice-based understanding of solidarity formulated by Prainsack and Buyx helps shed light on these questions. Drawing on 643 qualitative interviews carried out in two phases (April–May 2020 and October 2020) in nine European countries (Austria, Belgium, France, Germany, Ireland, Italy, The Netherlands, German-speaking Switzerland and the UK), the data show that interpersonal acts of solidarity are important, but that they are not sustainable without consistent support at the institutional level. As the pandemic progressed, respondents expressed a longing for more institutionalised forms of solidarity. We argue that the medical humanities have much to gain from directing their attention to individual health issues, and to collective experiences of health or illness. The analysis of experiences through a collective lens such as solidarity offers unique insights to understandings of the individual and the collective. We propose three essential advances for research in the medical humanities that can help uncover collective experiences of disease and health crises: (1) an empirical and practice-oriented approach alongside more normative approaches; (2) the confidence to make recommendations for practice and policymaking and (3) the pursuit of cross-national and multidisciplinary research collaborations