Graph theoretical analysis of functional magnetic resonance imaging (fMRI) time series has revealed a small-world organization of slow-frequency blood oxygen level-dependent (BOLD) signal ...fluctuations during wakeful resting. In this study, we used graph theoretical measures to explore how physiological changes during sleep are reflected in functional connectivity and small-world network properties of a large-scale, low-frequency functional brain network. Twenty-five young and healthy participants fell asleep during a 26.7 min fMRI scan with simultaneous polysomnography. A maximum overlap discrete wavelet transformation was applied to fMRI time series extracted from 90 cortical and subcortical regions in normalized space after residualization of the raw signal against unspecific sources of signal fluctuations; functional connectivity analysis focused on the slow-frequency BOLD signal fluctuations between 0.03 and 0.06 Hz. We observed that in the transition from wakefulness to light sleep, thalamocortical connectivity was sharply reduced, whereas corticocortical connectivity increased; corticocortical connectivity subsequently broke down in slow-wave sleep. Local clustering values were closest to random values in light sleep, whereas slow-wave sleep was characterized by the highest clustering ratio (gamma). Our findings support the hypothesis that changes in consciousness in the descent to sleep are subserved by reduced thalamocortical connectivity at sleep onset and a breakdown of general connectivity in slow-wave sleep, with both processes limiting the capacity of the brain to integrate information across functional modules.
Applying graph theoretical analysis of spontaneous BOLD fluctuations in functional magnetic resonance imaging (fMRI), we investigated whole-brain functional connectivity of 11 healthy volunteers ...during wakefulness and propofol-induced loss of consciousness (PI-LOC). After extraction of regional fMRI time series from 110 cortical and subcortical regions, we applied a maximum overlap discrete wavelet transformation and investigated changes in the brain's intrinsic spatiotemporal organization. During PI-LOC, we observed a breakdown of subcortico-cortical and corticocortical connectivity. Decrease of connectivity was pronounced in thalamocortical connections, whereas no changes were found for connectivity within primary sensory cortices. Graph theoretical analyses revealed significant changes in the degree distribution and local organization metrics of brain functional networks during PI-LOC: compared with a random network, normalized clustering was significantly increased, as was small-worldness. Furthermore we observed a profound decline in long-range connections and a reduction in whole-brain spatiotemporal integration, supporting a topological reconfiguration during PI-LOC. Our findings shed light on the functional significance of intrinsic brain activity as measured by spontaneous BOLD signal fluctuations and help to understand propofol-induced loss of consciousness.
Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical ...parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.
Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, ...for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.
Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Current labeling and optical imaging methods, however, cannot be used for ...continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescent indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended time scales. We report on a dual-mode high-density microelectrode array, which can simultaneously record in (i) full-frame mode with 19,584 recording sites and (ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we develop reliable analysis tools, which drastically increase the throughput to infer axonal morphology and conduction speed.
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•Increasing number of ecosystem services indicators generated, but impact questioned.•Credibility, salience and legitimacy (CSL) are crucial for informing decision making.•In ...addition, feasibility (F) criteria ensure continued assessment and use of indicators.•Sixteen selection criteria synthesized from practical experience and the literature.•A checklist to develop effective ecosystem service indicators.
Decision makers are increasingly interested in information from ecosystem services (ES) assessments. Scientists have for long recognised the importance of selecting appropriate indicators. Yet, while the amount and variety of indicators developed by scientists seems to increase continuously, the extent to which the indicators truly inform decision makers is often unknown and questioned. In this viewpoint paper, we reflect and provide guidance on how to develop appropriate ES indicators for informing decision making, building on scientific literature and practical experience collected from researchers involved in seven case studies. We synthesized 16 criteria for ES indicator selection and organized them according to the widely used categories of credibility, salience, legitimacy (CSL). We propose to consider additional criteria related to feasibility (F), as CSL criteria alone often seem to produce indicators which are unachievable in practice. Considering CSLF together requires a combination of scientific knowledge, communication skills, policy and governance insights and on-field experience. In conclusion, we present a checklist to evaluate CSLF of your ES indicators. This checklist helps to detect and mitigate critical shortcomings in an early phase of the development process, and aids the development of effective indicators to inform actual policy decisions.
In imaging functional connectivity (FC) analyses of the resting brain, alterations of FC during unconsciousness have been reported. These results are in accordance with recent electroencephalographic ...studies observing impaired top-down processing during anesthesia. In this study, simultaneous records of functional magnetic resonance imaging (fMRI) and electroencephalogram were performed to investigate the causality of neural mechanisms during propofol-induced loss of consciousness by correlating FC in fMRI and directional connectivity (DC) in electroencephalogram.
Resting-state 63-channel electroencephalogram and blood oxygen level-dependent 3-Tesla fMRI of 15 healthy subjects were simultaneously registered during consciousness and propofol-induced loss of consciousness. To indicate DC, electroencephalographic symbolic transfer entropy was applied as a nonlinear measure of mutual interdependencies between underlying physiological processes. The relationship between FC of resting-state networks of the brain (z values) and DC was analyzed by a partial correlation.
Independent component analyses of resting-state fMRI showed decreased FC in frontoparietal default networks during unconsciousness, whereas FC in primary sensory networks increased. DC indicated a decline in frontal-parietal (area under the receiver characteristic curve, 0.92; 95% CI, 0.68-1.00) and frontooccipital (0.82; 0.53-1.00) feedback DC (P<0.05 corrected). The changes of FC in the anterior default network correlated with the changes of DC in frontal-parietal (rpartial=+0.62; P=0.030) and frontal-occipital (+0.63; 0.048) electroencephalographic electrodes (P<0.05 corrected).
The simultaneous propofol-induced suppression of frontal feedback connectivity in the electroencephalogram and of frontoparietal FC in the fMRI indicates a fundamental role of top-down processing for consciousness.
We discuss the role of heterodox economics in opening new perspectives, the question of scalability of socio-economic order, the heritage of the “socialist calculation debate” and its ongoing ...relevance for discussions on “post-capitalism” today and finally the potentials of computational simulation and agent-based modelling for the exploration of alternative socio-economic approaches. The contributions to our special issue address these aspects and topics in different ways and therefore underline the fruitfulness of these discussions, especially in regard to the development of more just and sustainable socio-economic structures. Faced with the contemporary polycrisis, we can no longer afford “capitalist realism”.
Recent advances in the field of cellular reprogramming have opened a route to studying the fundamental mechanisms underlying common neurological disorders. High‐density microelectrode‐arrays ...(HD‐MEAs) provide unprecedented means to study neuronal physiology at different scales, ranging from network through single‐neuron to subcellular features. In this work, HD‐MEAs are used in vitro to characterize and compare human induced‐pluripotent‐stem‐cell‐derived dopaminergic and motor neurons, including isogenic neuronal lines modeling Parkinson's disease and amyotrophic lateral sclerosis. Reproducible electrophysiological network, single‐cell and subcellular metrics are used for phenotype characterization and drug testing. Metrics, such as burst shape and axonal velocity, enable the distinction of healthy and diseased neurons. The HD‐MEA metrics can also be used to detect the effects of dosing the drug retigabine to human motor neurons. Finally, it is shown that the ability to detect drug effects and the observed culture‐to‐culture variability critically depend on the number of available recording electrodes.
CMOS‐based high‐density microelectrode arrays (HD‐MEAs) are used, in combination with iPSC‐derived neurons, to compare the electrical phenotypes of multiple iPSC‐derived neuronal cell lines and the respective disease lines (modelling amyotrophic lateral sclerosis and Parkinson's disease). The work includes a comprehensive characterization across scales, including neuronal‐network, single‐cell and subcellular levels.
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