Differential recruitment of brain networks during visuospatial and color processing: Evidence from ERP microstates

Recent functional magnetic resonance imaging (fMRI) studies consistently revealed contributions of fronto-parietal and related networks to the execution of a visuospatial judgment task, the so-called "Clock Task". However, due to the low temporal resolution of fMRI, the exact cortical dynamics and timing of processing during task performance could not be resolved until now. In order to clarify the detailed cortical activity and temporal dynamics, 14 healthy subjects performed an established version of the "Clock Task", which comprises a visuospatial task (angle discrimination) and a control task (color discrimination) with the same stimulus material, in an electroencephalography (EEG) experiment. Based on the time-resolved analysis of network activations (microstate analysis), differences in timing between the angle compared to the color discrimination task were found after sensory processing in a time window starting around 200ms. Significant differences between the two tasks were observed in an analysis window from 192ms to 776ms. We divided this window in two parts: an early phase - from 192ms to ∼440ms, and a late phase - from ∼440ms to 776ms. For both tasks, the order of network activations and the types of networks were the same, but, in each phase, activations for the two conditions were dominated by differing network states with divergent temporal dynamics. Our results provide an important basis for the assessment of deviations in processing dynamics during visuospatial tasks in clinical populations.

Mobility and Bandwidth Prediction as a Service in Virtualized LTE Systems

Recently telecommunication industry benefits from infrastructure sharing, one of the most fundamental enablers of cloud computing, leading to emergence of the Mobile Virtual Network Operator (MVNO) concept. The most momentous intents by this approach are the support of on-demand provisioning and elasticity of virtualized mobile network components, based on data traffic load. To realize it, during operation and management procedures, the virtualized services need be triggered in order to scale-up/down or scale-out/in an instance. In this paper we propose an architecture called MOBaaS (Mobility and Bandwidth Availability Prediction as a Service), comprising two algorithms in order to predict user(s) mobility and network link bandwidth availability, that can be implemented in cloud based mobile network structure and can be used as a support service by any other virtualized mobile network services. MOBaaS can provide prediction information in order to generate required triggers for on-demand deploying, provisioning, disposing of virtualized network components. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operation, as well. Through the preliminary experiments with the prototype implementation on the OpenStack platform, we evaluated and confirmed the feasibility and the effectiveness of the prediction algorithms and the proposed architecture.

Value Priorities and Content of Religiosity - New Research Perspectives

In this study the relationship of religiosity and value priorities is differentiated, based on a multidimensional measurement of different contents of religiosity. The structure of values is conceptualized using Schwartz’ (1992) two orthogonal dimensions of Self-transcendence vs. Self-enhancement and Openness to change vs. Conservation. The relations between these two dimensions and eight religious contents, ranging from open-minded to more close-minded forms of religiosity, were tested in a sample of church attenders (N = 685), gathered in Germany. The results show, that depending on the content of religiosity, different values are preferred (self-direction, universalism, benevolence, tradition and security values). The results indicate the importance of the content of religiosity for predicting value-loaded behaviors.

A Thalamic-Fronto-Parietal Structural Covariance Network Emerging in the Course of Recovery from Hand Paresis after Ischemic Stroke.

AIM To describe structural covariance networks of gray matter volume (GMV) change in 28 patients with first-ever stroke to the primary sensorimotor cortices, and to investigate their relationship to hand function recovery and local GMV change. METHODS Tensor-based morphometry maps derived from high-resolution structural images were subject to principal component analyses to identify the networks. We calculated correlations between network expression and local GMV change, sensorimotor hand function and lesion volume. To verify which of the structural covariance networks of GMV change have a significant relationship to hand function, we performed an additional multivariate regression approach. RESULTS Expression of the second network, explaining 9.1% of variance, correlated with GMV increase in the medio-dorsal (md) thalamus and hand motor skill. Patients with positive expression coefficients were distinguished by significantly higher GMV increase of this structure during stroke recovery. Significant nodes of this network were located in md thalamus, dorsolateral prefrontal cortex, and higher order sensorimotor cortices. Parameter of hand function had a unique relationship to the network and depended on an interaction between network expression and lesion volume. Inversely, network expression is limited in patients with large lesion volumes. CONCLUSION Chronic phase of sensorimotor cortical stroke has been characterized by a large scale co-varying structural network in the ipsilesional hemisphere associated specifically with sensorimotor hand skill. Its expression is related to GMV increase of md thalamus, one constituent of the network, and correlated with the cortico-striato-thalamic loop involved in control of motor execution and higher order sensorimotor cortices. A close relation between expression of this network with degree of recovery might indicate reduced compensatory resources in the impaired subgroup.

What do brain network interactions tell about cognitive processes? How to seek causality?

Optimal adjustment of brain networks allows the biased processing of information in response to the demand of environments and is therefore prerequisite for adaptive behaviour. It is widely shown that a biased state of networks is associated with a particular cognitive process. However, those associations were identified by backward categorization of trials and cannot provide a causal association with cognitive processes. This problem still remains a big obstacle to advance the state of our field in particular human cognitive neuroscience. In my talk, I will present two approaches to address the causal relationships between brain network interactions and behaviour. Firstly, we combined connectivity analysis of fMRI data and a machine leaning method to predict inter-individual differences of behaviour and responsiveness to environmental demands. The connectivity-based classification approach outperforms local activation-based classification analysis, suggesting that interactions in brain networks carry information of instantaneous cognitive processes. Secondly, we have recently established a brand new method combining transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and EEG. We use the method to measure signal transmission between brain areas while introducing extrinsic oscillatory brain activity and to study causal association between oscillatory activity and behaviour. We show that phase-matched oscillatory activity creates the phase-dependent modulation of signal transmission between brain areas, while phase-shifted oscillatory activity blunts the phase-dependent modulation. The results suggest that phase coherence between brain areas plays a cardinal role in signal transmission in the brain networks. In sum, I argue that causal approaches will provide more concreate backbones to cognitive neuroscience.

Sharing evidence of sustainable land management impacts

Ensuring sustainable use of natural resources is crucial for maintaining the basis for our livelihoods. With threats from climate change, disputes over water, biodiversity loss, competing claims on land, and migration increasing worldwide, the demands for sustainable land management (SLM) practices will only increase in the future. For years already, various national and international organizations (GOs, NGOs, donors, research institutes, etc.) have been working on alternative forms of land management. And numerous land users worldwide – especially small farmers – have been testing, adapting, and refining new and better ways of managing land. All too often, however, the resulting SLM knowledge has not been sufficiently evaluated, documented and shared. Among other things, this has often prevented valuable SLM knowledge from being channelled into evidence-based decision-making processes. Indeed, proper knowledge management is crucial for SLM to reach its full potential. Since more than 20 years, the international WOCAT network documents and promotes SLM through its global platform. As a whole, the WOCAT methodology comprises tools for documenting, evaluating, and assessing the impact of SLM practices, as well as for knowledge sharing, analysis and use for decision support in the field, at the planning level, and in scaling up identified good practices. In early 2014, WOCAT’s growth and ongoing improvement culminated in its being officially recognized by the UNCCD as the primary recommended database for SLM best practices. Over the years, the WOCAT network confirmed that SLM helps to prevent desertification, to increase biodiversity, enhance food security and to make people less vulnerable to the effects of climate variability and change. In addi- tion, it plays an important role in mitigating climate change through improving soil organic matter and increasing vegetation cover. In-depth assessments of SLM practices from desertification sites enabled an evaluation of how SLM addresses prevalent dryland threats. The impacts mentioned most were diversified and enhanced production and better management of water and soil degradation, whether through water harvesting, improving soil moisture, or reducing runoff. Among others, favourable local-scale cost-benefit relationships of SLM practices play a crucial role in their adoption. An economic analysis from the WOCAT database showed that land users perceive a large majority of the technologies as having benefits that outweigh costs in the long term. The high investment costs associated with some practices may constitute a barrier to adoption, however, where appropriate, short-term support for land users can help to promote these practices. The increased global concerns on climate change, disaster risks and food security redirect attention to, and trigger more funds for SLM. To provide the necessary evidence-based rationale for investing in SLM and to reinforce expert and land users assessments of SLM impacts, more field research using inter- and transdisciplinary approaches is needed. This includes developing methods to quantify and value ecosystem services, both on-site and off-site, and assess the resilience of SLM practices, as currently aimed at within the EU FP7 projects CASCADE and RECARE.