Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development
Causal terminology is often introduced in the interpretation of encoding and decoding models trained on neuroimaging data. In this article, we investigate which causal statements are warranted and which ones are not supported by empirical evidence. We argue that the distinction between encoding and decoding models is not sufficient for this purpose: relevant features in encoding and decoding models carry a different meaning in stimulus-and in response-based experimental paradigms. We show that only encoding models in the stimulus-based setting support unambiguous causal interpretations. By combining encoding and decoding models trained on the same data, however, we obtain insights into causal relations beyond those that are implied by each individual model type. We illustrate the empirical relevance of our theoretical findings on EEG data recorded during a visuo-motor learning task.
Coarse-grained modeling of molecular fluids is often based on nonspherical convex rigid bodies like ellipsoids or spherocylinders representing rodlike or platelike molecules or groups of atoms, with site-site interaction potentials depending both on the distance among the particles and the relative orientation. In this category of potentials, the Gay-Berne family has been studied most extensively. However, conventional molecular graphics programs are not designed to visualize such objects. Usually the basic units are atoms displayed as spheres or as vertices in a graph. Atomic aggregates can be highlighted through an increasing amount of stylized representations, e.g., Richardson ribbon diagrams for the secondary structure of proteins, Connolly molecular surfaces, density maps, etc., but ellipsoids and spherocylinders are generally missing, especially as elementary simulation units. We fill this gap providing and discussing a customized OpenGL-based program for the interactive, rendered representation of large ensembles of convex bodies, useful especially in liquid crystal research. We pay particular attention to the performance issues for typical system sizes in this field. The code is distributed as open source.
BackgroundResearch on the neurophysiological correlates of visuomotor integration and learning (VMIL) has largely focused on identifying learning-induced activity changes in cortical areas during motor execution. While such studies have generated valuable insights into the neural basis of VMIL, little is known about the processes that represent the current state of VMIL independently of motor execution. Here, we present empirical evidence that a subject’s performance in a 3D reaching task can be predicted on a trial-to-trial basis from pre-trial electroencephalographic (EEG) data. This evidence provides novel insights into the brain states that support successful VMIL.MethodsSix healthy subjects, attached to a seven degrees-of-freedom (DoF) robot with their right arm, practiced 3D reaching movements in a virtual space, while an EEG recorded their brain’s electromagnetic field. A random forest ensemble classifier was used to predict the next trial’s performance, as measured by the time needed to reach the goal, from pre-trial data using a leave-one-subject-out cross-validation procedure.ResultsThe learned models successfully generalized to novel subjects. An analysis of the brain regions, on which the models based their predictions, revealed areas matching prevalent motor learning models. In these brain areas, the α/μ frequency band (8–14 Hz) was found to be most relevant for performance prediction.ConclusionsVMIL induces changes in cortical processes that extend beyond motor execution, indicating a more complex role of these processes than previously assumed. Our results further suggest that the capability of subjects to modulate their α/μ bandpower in brain regions associated with motor learning may be related to performance in VMIL. Accordingly, training subjects in α/μ-modulation, e.g., by means of a brain-computer interface (BCI), may have a beneficial impact on VMIL.
Abstract-Despite intensive efforts, no significant benefit of rehabilitation robotics in post-stroke motor-recovery has yet been demonstrated in large-scale clinical trials. The present work is based on the premise that future advances in rehabilitation robotics require an enhanced understanding of the neural processes involved in motor learning after stroke. We present a system that combines a Barret WAM TM seven degreeof-freedom robot arm with neurophysiological recordings for the purpose of studying post-stroke motor learning. We used this system to conduct a pilot study on motor learning during reaching movements with two stroke patients. Preliminary results indicate that pre-trial brain activity in ipsilesional sensorimotor areas may be a neural correlate of the current state of motor learning. These results are discussed in terms of their relevance for future rehabilitation strategies that combine rehabilitation robotics with real-time analyses of neurophysiological recordings.
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