A multivariate approach for processing magnetization effects in triggered event-related functional magnetic resonance imaging time series

Triggered event-related functional magnetic resonance imaging requires sparse intervals of temporally resolved functional data acquisitions, whose initiation corresponds to the occurrence of an event, typically an epileptic spike in the electroencephalographic trace. However, conventional fMRI time series are greatly affected by non-steady-state magnetization effects, which obscure initial blood oxygen level-dependent (BOLD) signals. Here, conventional echo-planar imaging and a post-processing solution based on principal component analysis were employed to remove the dominant eigenimages of the time series, to filter out the global signal changes induced by magnetization decay and to recover BOLD signals starting with the first functional volume. This approach was compared with a physical solution using radiofrequency preparation, which nullifies magnetization effects. As an application of the method, the detectability of the initial transient BOLD response in the auditory cortex, which is elicited by the onset of acoustic scanner noise, was used to demonstrate that post-processing-based removal of magnetization effects allows to detect brain activity patterns identical with those obtained using the radiofrequency preparation. Using the auditory responses as an ideal experimental model of triggered brain activity, our results suggest that reducing the initial magnetization effects by removing a few principal components from fMRI data may be potentially useful in the analysis of triggered event-related echo-planar time series. The implications of this study are discussed with special caution to remaining technical limitations and the additional neurophysiological issues of the triggered acquisition.

Emotion recognition: The role of featural and configural face information

Several studies investigated the role of featural and configural information when processing facial identity. A lot less is known about their contribution to emotion recognition. In this study, we addressed this issue by inducing either a featural or a configural processing strategy (Experiment 1) and by investigating the attentional strategies in response to emotional expressions (Experiment 2). In Experiment 1, participants identified emotional expressions in faces that were presented in three different versions (intact, blurred, and scrambled) and in two orientations (upright and inverted). Blurred faces contain mainly configural information, and scrambled faces contain mainly featural information. Inversion is known to selectively hinder configural processing. Analyses of the discriminability measure (A′) and response times (RTs) revealed that configural processing plays a more prominent role in expression recognition than featural processing, but their relative contribution varies depending on the emotion. In Experiment 2, we qualified these differences between emotions by investigating the relative importance of specific features by means of eye movements. Participants had to match intact expressions with the emotional cues that preceded the stimulus. The analysis of eye movements confirmed that the recognition of different emotions rely on different types of information. While the mouth is important for the detection of happiness and fear, the eyes are more relevant for anger, fear, and sadness.

Highly accurate evaluation of GPS synchronization for TDOA localization

Clock synchronization is critical for the operation of a distributed wireless network system. In this paper we investigate on a method able to evaluate in real time the synchronization offset between devices down to nanoseconds (as needed for positioning). The method is inspired by signal processing algorithms and relies on fine-grain time information obtained during the reconstruction of the signal at the receiver. Applying the method to a GPS-synchronized system show that GPS-based synchronization has high accuracy potential but still suffers from short-term clock drift, which limits the achievable localization error.

Approximate decoding approaches for network coded correlated data

This paper considers a framework where data from correlated sources are transmitted with the help of network coding in ad hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth variations. We show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples showing how the proposed algorithm can be deployed in sensor networks and distributed imaging applications.

White matter matters: a multimodal approach to investigate white matter microstructure and metabolism

White matter connects different brain areas and applies electrical insulation to the neuron’s axons with myelin sheaths in order to enable quick signal transmission. Due to its modulatory properties in signal conduction, white matter plays an essential role in learning, cognition and psychiatric disorders (Fields, 2008a). In respect thereof, the non-invasive investigation of white matter anatomy and function in vivo provides the unique opportunity to explore the most complex organ of our body. Thus, the present thesis aimed to apply a multimodal neuroimaging approach to investigate different white matter properties in psychiatric and healthy populations. On the one hand, white matter microstructural properties were investigated in a psychiatric population; on the other hand, white matter metabolic properties were assessed in healthy adults providing basic information about the brain’s wiring entity. As a result, three research papers are presented here. The first paper assessed the microstructural properties of white matter in relation to a frequent epidemiologic finding in schizophrenia. As a result, reduced white matter integrity was observed in patients born in summer and autumn compared to patients born in winter and spring. Despite the large genetic basis of schizophrenia, accumulating evidence indicates that environmental exposures may be implicated in the development of schizophrenia (A. S. Brown, 2011). Notably, epidemiologic studies have shown a 5–8% excess of births during winter and spring for patients with schizophrenia on the Northern Hemisphere at higher latitudes (Torrey, Miller, Rawlings, & Yolken, 1997). Although the underlying mechanisms are unclear, the seasonal birth effect may indicate fluctuating environmental risk factors for schizophrenia. Thus, exposure to harmful factors during foetal development may result in the activation of pathologic neural circuits during adolescence or young adulthood, increasing the risk of schizophrenia (Fatemi & Folsom, 2009). While white matter development starts during the foetal period and continues until adulthood, its major development is accomplished by the age of two years (Brody, Kinney, Kloman, & Gilles, 1987; Huang et al., 2009). This indicates a vulnerability period of white matter that may coincide with the fluctuating environmental risk factors for schizophrenia. Since microstructural alterations of white matter in schizophrenia are frequently observed, the current study provided evidence for the neurodevelopmental hypothesis of schizophrenia. In the second research paper, the perfusion of white matter showed a positive correlation between white matter microstructure and its perfusion with blood across healthy adults. This finding was in line with clinical studies indicating a tight coupling between cerebral perfusion and WM health across subjects (Amann et al., 2012; Chen, Rosas, & Salat, 2013; Kitagawa et al., 2009). Although relatively little is known about the metabolic properties of white matter, different microstructural properties, such as axon diameter and myelination, might be coupled with the metabolic demand of white matter. Furthermore, the ability to detect perfusion signal in white matter was in accordance with a recent study showing that technical improvements, such as pseudo-continuous arterial spin labeling, enabled the reliable detection of white matter perfusion signal (van Osch et al., 2009). The third paper involved a collaboration within the same department to assess the interrelation between functional connectivity networks and their underlying structural connectivity.

Methodology for GPS Synchronization Evaluation with High Accuracy

Clock synchronization in the order of nanoseconds is one of the critical factors for time-based localization. Currently used time synchronization methods are developed for the more relaxed needs of network operation. Their usability for positioning should be carefully evaluated. In this paper, we are particularly interested in GPS-based time synchronization. To judge its usability for localization we need a method that can evaluate the achieved time synchronization with nanosecond accuracy. Our method to evaluate the synchronization accuracy is inspired by signal processing algorithms and relies on fine grain time information. The method is able to calculate the clock offset and skew between devices with nanosecond accuracy in real time. It was implemented using software defined radio technology. We demonstrate that GPS-based synchronization suffers from remaining clock offset in the range of a few hundred of nanoseconds but the clock skew is negligible. Finally, we determine a corresponding lower bound on the expected positioning error.

Variable effects of the conserved RNA hairpin element upon 3' end processing of histone pre-mRNA in vitro.

We have studied the requirements for efficient histone-specific RNA 3' processing in nuclear extract from mammalian tissue culture cells. Processing is strongly impaired by mutations in the pre-mRNA spacer element that reduce the base-pairing potential with U7 RNA. Moreover, by exchanging the hairpin and spacer elements of two differently processed H4 genes, we find that this difference is exclusively due to the spacer element. Finally, processing is inhibited by the addition of competitor RNAs, if these contain a wild-type spacer sequence, but not if their spacer element is mutated. Conversely, the importance of the hairpin for histone RNA 3' processing is highly variable: A hairpin mutant of the H4-12 gene is processed with almost wild-type efficiency in extract from K21 mouse mastocytoma cells but is strongly affected in HeLa cell extract, whereas an identical hairpin mutant of the H4-1 gene is affected in both extracts. The hairpin defect of H4-12-specific RNA in HeLa cells can be overcome by a compensatory mutation that increases the base complementarity to U7 snRNA. Very similar results were also obtained in RNA competition experiments: processing of H4-12-specific RNA can be competed by RNA carrying a wild-type hairpin element in extract from HeLa, but not K21 cells, whereas processing of H4-1-specific RNA can be competed in both extracts. With two additional histone genes we obtained results that were in one case intermediate and in the other similar to those obtained with H4-1. These results suggest that hairpin binding factor(s) can cooperatively support the ability of U7 snRNPs to form an active processing complex, but is(are) not directly involved in the processing mechanism.

Information-Centric Content Retrieval for Delay-Tolerant Networks

The shift from host-centric to information-centric networking (ICN) promises seamless communication in mobile networks. However, most existing works either consider well-connected networks with high node density or introduce modifications to {ICN} message processing for delay-tolerant Networking (DTN). In this work, we present agent-based content retrieval, which provides information-centric {DTN} support as an application module without modifications to {ICN} message processing. This enables flexible interoperability in changing environments. If no content source can be found via wireless multi-hop routing, requesters may exploit the mobility of neighbor nodes (called agents) by delegating content retrieval to them. Agents that receive a delegation and move closer to content sources can retrieve data and return it back to requesters. We show that agent-based content retrieval may be even more efficient in scenarios where multi-hop communication is possible. Furthermore, we show that broadcast communication may not be necessarily the best option since dynamic unicast requests have little overhead and can better exploit short contact times between nodes (no broadcast delays required for duplicate suppression).