Effects of synaptic and myelin plasticity on learning in a network of Kuramoto phase oscillators.

Models of learning typically focus on synaptic plasticity. However, learning is the result of both synaptic and myelin plasticity. Specifically, synaptic changes often co-occur and interact with myelin changes, leading to complex dynamic interactions between these processes. Here, we investigate the implications of these interactions for the coupling behavior of a system of Kuramoto oscillators. To that end, we construct a fully connected, one-dimensional ring network of phase oscillators whose coupling strength (reflecting synaptic strength) as well as conduction velocity (reflecting myelination) are each regulated by a Hebbian learning rule. We evaluate the behavior of the system in terms of structural (pairwise connection strength and conduction velocity) and functional connectivity (local and global synchronization behavior). We find that adaptive myelination is able to both functionally decouple structurally connected oscillators as well as to functionally couple structurally disconnected oscillators. With regard to the latter, we find that for conditions in which a system limited to synaptic plasticity develops two distinct clusters both structurally and functionally, additional adaptive myelination allows for functional communication across these structural clusters. These results confirm that network states following learning may be different when myelin plasticity is considered in addition to synaptic plasticity, pointing toward the relevance of integrating both factors in computational models of learning.

Noninvasive reconstruction of cardiac electrical activity: update on current methods, applications and challenges.

Electrical activity at the level of the heart muscle can be noninvasively reconstructed from body-surface electrocardiograms (ECGs) and patient-specific torso-heart geometry. This modality, coined electrocardiographic imaging, could fill the gap between the noninvasive (low-resolution) 12-lead ECG and invasive (high-resolution) electrophysiology studies. Much progress has been made to establish electrocardiographic imaging, and clinical studies appear with increasing frequency. However, many assumptions and model choices are involved in its execution, and only limited validation has been performed. In this article, we will discuss the technical details, clinical applications and current limitations of commonly used methods in electrocardiographic imaging. It is important for clinicians to realise the influence of certain assumptions and model choices for correct and careful interpretation of the results. This, in combination with more extensive validation, will allow for exploitation of the full potential of noninvasive electrocardiographic imaging as a powerful clinical tool to expedite diagnosis, guide therapy and improve risk stratification.

A torsional eye movement calculation algorithm for low contrast images in video-oculography.

Video-oculography (VOG) is a frequently used clinical technique to detect eye movements. In this research, head mounted small video-cameras and IR-illumination are employed to image the eye. Many algorithms have been developed to extract horizontal and vertical eye movements from the video images. Designing a method to determine torsional eye movements is a more complex task. The use of IR-wavelengths required for illumination in certain clinical tests results in a very low image contrast. In such images, iris textures are almost invisible, making them unsuited for direct application of standard matching algorithms, which are used to calculate torsional eye movements. This research presents the design and implementation of a robust torsional eye movement detection algorithm for VOG. This algorithm uses a new approach to measure the torsional eye movement and is suitable for low contrast videos. The algorithm is implemented in a clinical device and its performance is compared to that of alternative techniques.

A novel stepwise analysis procedure of genome-wide expression profiles identifies transcript signatures of thiamine genes as classifiers of mitochondrial mutants.

To extract functional information on genes and processes from large expression datasets, analysis methods are required that can computationally deal with these amounts of data, are tunable to specific research questions, and construct classifiers that are not overspecific to the dataset at hand. To satisfy these requirements, a stepwise procedure that combines elements from principal component analysis and discriminant analysis, was developed to specifically retrieve genes involved in processes of interest and classify samples based upon those genes. In a global expression dataset of 300 gene knock-outs in Saccharomyces cerevisiae, the procedure successfully classified samples with similar 'cellular component' Gene Ontology annotations of the knock-out gene by expression signatures of limited numbers of genes. The genes discriminating 'mitochondrion' from the other subgroups were evaluated in more detail. The thiamine pathway turned out to be one of the processes involved and was successfully evaluated in a logistic model to predict whether yeast knock-outs were mitochondrial or not. Further, this pathway is biologically related to the mitochondrial system. Hence, this strongly indicates that our approach is effective and efficient in extracting meaningful information from large microarray experiments and assigning functions to yet uncharacterized genes.

Measures for the evaluation of bias field removal procedures in MRI and the design of wavelets for suppression of RF inhomogeneities.

RF inhomogeneities are illumination artifacts in MR images which manifest as a multiplicative bias field. To measure the quality of an MR image with respect to RF inhomogeneities, existing multi-valued criteria are in use. Here we propose a useful conversion of these multi-valued criteria into a single measure of quality which simplifies image quality evaluation and comparison. Next, to remove such a bias field, a novel wavelet based approach is employed, that extends a previous 1D wavelet design methodology to a 2D setting. This method is found to perform well on images with strong small details. The results for brain MR images are subject to improvement, however our results hint to a future scenario for improved image quality.

Multiwavelet design for cardiac signal processing.

An approach for designing multiwavelets is introduced, for use in cardiac signal processing. The parameterization of the class of multiwavelets is in terms of associated FIR polyphase all-pass filters. Orthogonality and a balanced vanishing moment of order 1 are built into the parameterization. An optimization criterion is developed to associate the wavelets with different meaningful segments of a signal. This approach is demonstrated on the simultaneous detection of QRS-complexes and T-peaks in ECG signals.