Multisubject learning for common spatial patterns in motor-imagery BCI.

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern filter (CSP) as preprocessing step before feature extraction and classification. The CSP method is a supervised algorithm and therefore needs subject-specific training data for calibration, which is very time consuming to collect. In order to reduce the amount of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the goal of multisubject learning is to learn a spatial filter for a new subject based on its own data and that of other subjects. This paper outlines the details of the multitask CSP algorithm and shows results on two data sets. In certain subjects a clear improvement can be seen, especially when the number of training trials is relatively low.

On the role of cost-sensitive learning in multi-class brain-computer interfaces.

Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

Four-year follow-up of infliximab therapy in rheumatoid arthritis patients with long-standing refractory disease: attrition and long-term evolution of disease activity.

Although there is strong evidence supporting the short-term efficacy and safety of anti-tumour necrosis factor-alpha agents, few studies have examined the long-term effects. We evaluated 511 patients with long-standing refractory rheumatoid arthritis treated with intravenous infusions of infliximab 3 mg/kg at weeks 0, 2, 6, and 14 and every 8 weeks thereafter for 4 years. Among the initial 511 patients included in the study, 479 could be evaluated; of these, 295 (61.6%) were still receiving infliximab treatment at year 4 of follow-up. The most common reasons for treatment discontinuation were lack of efficacy (65 patients, 13.6%), safety (81 patients, 16.9%), and elective change (38 patients, 7.9%). Analysis of disease activity scores (DAS28 [disease activity score based on the 28-joint count]) over time showed that, after the initial rapid improvement during the first 6 to 22 weeks of therapy, a further decrease in disease activity of 0.2 units in the DAS28 score per year was observed. DAS28 scores, measured at week 14 or 22, were found to predict subsequent discontinuation due to lack of efficacy. In conclusion, long-term maintenance therapy with infliximab 3 mg/kg is effective in producing further reductions in disease activity. Disease activity measured by the DAS28 at week 14 or 22 of infliximab therapy was the best predictor of long-term attrition.

DAS28 best reflects the physician's clinical judgment of response to infliximab therapy in rheumatoid arthritis patients: validation of the DAS28 score in patients under infliximab treatment.

This study is based on an expanded access program in which 511 patients suffering from active refractory rheumatoid arthritis (RA) were treated with intravenous infusions of infliximab (3 mg/kg+methotrexate (MTX)) at weeks 0, 2, 6 and every 8 weeks thereafter. At week 22, 474 patients were still in follow-up, of whom 102 (21.5%), who were not optimally responding to treatment, received a dose increase from week 30 onward. We aimed to build a model to discriminate the decision to give a dose increase. This decision was based on the treating rheumatologist's clinical judgment and therefore can be considered as a clinical measure of insufficient response. Different single and composite measures at weeks 0, 6, 14 and 22, and their differences over time were taken into account for the model building. Ranking of the continuous variables based on areas under the curve of receiver-operating characteristic (ROC) curve analysis, displayed the momentary DAS28 (Disease Activity Score including a 28-joint count) as the most important discriminating variable. Subsequently, we proved that the response scores and the changes over time were less important than the momentary evaluations to discriminate the physician's decision. The final model we thus obtained was a model with only slightly better discriminative characteristics than the DAS28. Finally, we fitted a discriminant function using the single variables of the DAS28. This displayed similar scores and coefficients as the DAS28. In conclusion, we evaluated different variables and models to discriminate the treating rheumatologist's decision to increase the dose of infliximab (+MTX), which indicates an insufficient response to infliximab at 3 mg/kg in patients with RA. We proved that the momentary DAS28 score correlates best with this decision and demonstrated the robustness of the score and the coefficients of the DAS28 in a cohort of RA patients under infliximab therapy.

Diagnostic classification of spondylarthropathy and rheumatoid arthritis by synovial histopathology: a prospective study in 154 consecutive patients.

OBJECTIVE: To explore prospectively the value of synovial histopathology in comparison with the value of classic parameters for diagnostic classification of spondylarthropathy (SpA) and rheumatoid arthritis (RA) in patients with an atypical disease presentation. METHODS: Synovial biopsy samples were obtained from 154 consecutive patients presenting for diagnostic evaluation; 67 patients fulfilled the classification criteria for RA, SpA, or other well-defined disease at the time of arthroscopy (cohort 1), and an additional 53 patients were classified after a full diagnostic reevaluation at 6 months (cohort 2). Synovial parameters with diagnostic value were identified in cohort 1 and were compared prospectively with classic diagnostic parameters in cohort 2. RESULTS: Staining with anticitrulline, staining with monoclonal antibody 12A (recognizing HLA-DR shared epitope-human cartilage glycoprotein 39(263-275) complexes), and crystal deposition had positive predictive values (PPVs) for diagnosis of >90% in patients with an atypical disease presentation (cohort 2). Using these 3 parameters, a diagnosis was predicted by synovial histopathology in 39.6% of cohort 2 patients and turned out to be correct in 90.5% of these patients at 6 months of followup. Using a multiparameter model rather than single histopathologic parameters, even better results were obtained, with a diagnostic prediction in 79.2% of samples and a PPV of 81.0%. In comparison, a similar multiparameter model using classic diagnostic criteria rather than synovial histopathology performed poorly in cohort 2; the sensitivity was 56.6% and the PPV was 73.3%, with an inferior capacity to predict SpA. Especially for the presence of crystals and anticitrulline staining, the analysis of synovial tissue had a clear added value to the analysis of synovial fluid or serum in patients with an atypical presentation. CONCLUSION: This proof-of-concept study indicates that synovial histopathology can contribute to the multiparametric diagnostic classification of inflammatory arthritis in patients with an atypical presentation.