Background and Purpose-Numerous preclinical findings and a clinical pilot study suggest that recombinant human erythropoietin (EPO) provides neuroprotection that may be beneficial for the treatment of patients with ischemic stroke. Although EPO has been considered to be a safe and well-tolerated drug over 2 decades, recent studies have identified increased thromboembolic complications and/or mortality risks on EPO administration to patients with cancer or chronic kidney disease. Accordingly, the double-blind, placebo-controlled, randomized German Multicenter EPO Stroke Trial (Phase II/III; ClinicalTrials.gov Identifier: NCT00604630) was designed to evaluate efficacy and safety of EPO in stroke. Methods-This clinical trial enrolled 522 patients with acute ischemic stroke in the middle cerebral artery territory (intent-to-treat population) with 460 patients treated as planned (per-protocol population). Within 6 hours of symptom onset, at 24 and 48 hours, EPO was infused intravenously (40 000 IU each). Systemic thrombolysis with recombinant tissue plasminogen activator was allowed and stratified for. Results-Unexpectedly, a very high number of patients received recombinant tissue plasminogen activator (63.4%). On analysis of total intent-to-treat and per-protocol populations, neither primary outcome Barthel Index on Day 90 (Pϭ0.45) nor any of the other outcome parameters showed favorable effects of EPO. There was an overall death rate of 16.4% (nϭ42 of 256) in the EPO and 9.0% (nϭ24 of 266) in the placebo group (OR, 1.98; 95% CI, 1.16 to 3.38; Pϭ0.01) without any particular mechanism of death unexpected after stroke. Conclusions-Based on analysis of total intent-to-treat and per-protocol populations only, this is a negative trial that also raises safety concerns, particularly in patients receiving systemic thrombolysis. (Stroke. 2009;40:e647-e656.)

Which facial features allow human observers to successfully recognize expressions of emotion? While the eyes and mouth have been frequently shown to be of high importance, research on facial action units has made more precise predictions about the areas involved in displaying each emotion. The present research investigated on a fine-grained level, which physical features are most relied on when decoding facial expressions. In the experiment, individual faces expressing the basic emotions according to Ekman were hidden behind a mask of 48 tiles, which was sequentially uncovered. Participants were instructed to stop the sequence as soon as they recognized the facial expression and assign it the correct label. For each part of the face, its contribution to successful recognition was computed, allowing to visualize the importance of different face areas for each expression. Overall, observers were mostly relying on the eye and mouth regions when successfully recognizing an emotion. Furthermore, the difference in the importance of eyes and mouth allowed to group the expressions in a continuous space, ranging from sadness and fear (reliance on the eyes) to disgust and happiness (mouth). The face parts with highest diagnostic value for expression identification were typically located in areas corresponding to action units from the facial action coding system. A similarity analysis of the usefulness of different face parts for expression recognition demonstrated that faces cluster according to the emotion they express, rather than by low-level physical features. Also, expressions relying more on the eyes or mouth region were in close proximity in the constructed similarity space. These analyses help to better understand how human observers process expressions of emotion, by delineating the mapping from facial features to psychological representation.

BackgroundCognitive deterioration is a core symptom of many neuropsychiatric disorders and target of increasing significance for novel treatment strategies. Hence, its reliable capture in long-term follow-up studies is prerequisite for recording the natural course of diseases and for estimating potential benefits of therapeutic interventions. Since repeated neuropsychological testing is required for respective longitudinal study designs, occurrence, time pattern and magnitude of practice effects on cognition have to be understood first under healthy good-performance conditions to enable design optimization and result interpretation in disease trials.MethodsHealthy adults (N = 36; 47.3 ± 12.0 years; mean IQ 127.0 ± 14.1; 58% males) completed 7 testing sessions, distributed asymmetrically from high to low frequency, over 1 year (baseline, weeks 2-3, 6, 9, months 3, 6, 12). The neuropsychological test battery covered 6 major cognitive domains by several well-established tests each.ResultsMost tests exhibited a similar pattern upon repetition: (1) Clinically relevant practice effects during high-frequency testing until month 3 (Cohen's d 0.36-1.19), most pronounced early on, and (2) a performance plateau thereafter upon low-frequency testing. Few tests were non-susceptible to practice or limited by ceiling effects. Influence of confounding variables (age, IQ, personality) was minor.ConclusionsPractice effects are prominent particularly in the early phase of high-frequency repetitive cognitive testing of healthy well-performing subjects. An optimal combination and timing of tests, as extractable from this study, will aid in controlling their impact. Moreover, normative data for serial testing may now be collected to assess normal learning curves as important comparative readout of pathological cognitive processes.

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.