Where do bright ideas occur in our brain? Meta-analytic evidence from neuroimaging studies of domain-specific creativity

Many studies have assessed the neural underpinnings of creativity, failing to find a clear anatomical localization. We aimed to provide evidence for a multi-componential neural system for creativity. We applied a general activation likelihood estimation (ALE) meta-analysis to 45 fMRI studies. Three individual ALE analyses were performed to assess creativity in different cognitive domains (Musical, Verbal, and Visuo-spatial). The general ALE revealed that creativity relies on clusters of activations in the bilateral occipital, parietal, frontal, and temporal lobes. The individual ALE revealed different maximal activation in different domains. Musical creativity yields activations in the bilateral medial frontal gyrus, in the left cingulate gyrus, middle frontal gyrus, and inferior parietal lobule and in the right postcentral and fusiform gyri. Verbal creativity yields activations mainly located in the left hemisphere, in the prefrontal cortex, middle and superior temporal gyri, inferior parietal lobule, postcentral and supramarginal gyri, middle occipital gyrus, and insula. The right inferior frontal gyrus and the lingual gyrus were also activated. Visuo-spatial creativity activates the right middle and inferior frontal gyri, the bilateral thalamus and the left precentral gyrus. This evidence suggests that creativity relies on multi-componential neural networks and that different creativity domains depend on different brain regions.

The development of functional astrocyte-neuron networks derived from stem cells

There is currently great scientific and medical interest in the potential of tissue grown from stem cells. These cells present opportunities for generating model systems for drug screening and toxicological testing which would be expected to be more relevant to human outcomes than animal based tissue preparations. Newly realised astrocytic roles in the brain have fundamental implications within the context of stem cell derived neuronal networks. If the aim of stem cell neuroscience is to generate functional neuronal networks that behave as networks do in the brain, then it becomes clear that we must include and understand all the cellular components that comprise that network, and which are important to support synaptic integrity and cell to cell signalling. We have shown that stem cell derived neurons exhibit spontaneous and coordinated calcium elevations in clusters and in extended processes, indicating local and long distance signalling (1). Tetrodotoxin sensitive network activity could also be evoked by electrical stimulation. Similarly, astrocytes exhibit morphology and functional properties consistent with this glial cell type. Astrocytes also respond to neuronal activity and to exogenously applied neurotransmitters with calcium elevations, and in contrast to neurons, also exhibited spontaneous rhythmic calcium oscillations. Astroctyes also generate propagating calcium waves that are gap junction and purinergic signalling dependent. Our results show that stem cell derived astrocytes exhibit appropriate functionality and that stem cell neuronal networks interact with astrocytic networks in co-culture. Using mixed cultures of stem cell derived neurons and astrocytes, we have also shown both cell types also modulate their glucose uptake, glycogen turnover and lactate production in response to glutamate as well as increased neuronal activity (2). This finding is consistent with their neuron-astrocyte metabolic coupling thus demonstrating a tractable human model, which will facilitate the study of the metabolic coupling between neurons and astrocytes and its relationship with CNS functional issues ranging from plasticity to neurodegeneration. Indeed, cultures treated with oligomers of amyloid beta 1-42 (Aβ1-42) also display a clear hypometabolism, particularly with regard to utilization of substrates such as glucose (3). Both co-cultures of neurons and astrocytes and purified cultures of astrocytes showed a significant decrease in glucose uptake after treatment with 2 and 0.2 μmol/L Aβ at all time points investigated (p <0.01). In addition, a significant increase in the glycogen content of cells was also measured. Mixed neuron and astrocyte co-cultures as well as pure astrocyte cultures showed an initial decrease in glycogen levels at 6 hours compared with control at 0.2 μmol/L and 2 μmol/L P <0.01. These changes were accompanied by changes in NAD+/NADH (P<0.05), ATP (P<0.05), and glutathione levels (P<0.05), suggesting a disruption in the energy-redox axis within these cultures. The high energy demands associated with neuronal functions such as memory formation and protection from oxidative stress put these cells at particular risk from Aβ-induced hypometabolism. As numerous cell types interact in the brain it is important that any in vitro model developed reflects this arrangement. Our findings indicate that stem cell derived neuron and astrocyte networks can communicate, and so have the potential to interact in a tripartite manner as is seen in vivo. This study therefore lays the foundation for further development of stem cell derived neurons and astrocytes into therapeutic cell replacement and human toxicology/disease models. More recently our data provides evidence for a detrimental effect of Aβ on carbohydrate metabolism in both neurons and astrocytes. As a purely in vitro system, human stem cell models can be readily manipulated and maintained in culture for a period of months without the use of animals. In our laboratory cultures can be maintained in culture for up to 12 months months thus providing the opportunity to study the consequences of these changes over extended periods of time relevant to aspects of the disease progression time frame in vivo. In addition, their human origin provides a more realistic in vitro model as well as informing other human in vitro models such as patient-derived iPSC.

Multiple roles for executive control in belief-desire reasoning:distinct neural networks are recruited for self perspective inhibition and complexity of reasoning

Belief-desire reasoning is a core component of 'Theory of Mind' (ToM), which can be used to explain and predict the behaviour of agents. Neuroimaging studies reliably identify a network of brain regions comprising a 'standard' network for ToM, including temporoparietal junction and medial prefrontal cortex. Whilst considerable experimental evidence suggests that executive control (EC) may support a functioning ToM, co-ordination of neural systems for ToM and EC is poorly understood. We report here use of a novel task in which psychologically relevant ToM parameters (true versus false belief; approach versus avoidance desire) were manipulated orthogonally. The valence of these parameters not only modulated brain activity in the 'standard' ToM network but also in EC regions. Varying the valence of both beliefs and desires recruits anterior cingulate cortex, suggesting a shared inhibitory component associated with negatively valenced mental state concepts. Varying the valence of beliefs additionally draws on ventrolateral prefrontal cortex, reflecting the need to inhibit self perspective. These data provide the first evidence that separate functional and neural systems for EC may be recruited in the service of different aspects of ToM.

Identifying spatially overlapping local cortical networks with MEG

Recent modelling studies (Hadjipapas et al. [2009]: Neuroimage 44:1290-1303) have shown that it may be possible to distinguish between different neuronal populations on the basis of their macroscopically measured (EEG/MEG) mean field. We set out to test whether the different orientation columns contributing to a signal at a specific cortical location could be identified based on the measured MEG signal. We used 1.5deg square, static, obliquely oriented grating stimuli to generate sustained gamma oscillations in a focal region of primary visual cortex. We then used multivariate classifier methods to predict the orientation (left or right oblique) of the stimuli based purely on the time-series data from this one location. Both the single trial evoked response (0-300 ms) and induced post-transient power spectra (300-2,300 ms, 20-70 Hz band) due to the different stimuli were classifiable significantly above chance in 11/12 and 10/12 datasets respectively. Interestingly, stimulus-specific information is preserved in the sustained part of the gamma oscillation, long after perception has occurred and all neuronal transients have decayed. Importantly, the classification of this induced oscillation was still possible even when the power spectra were rank-transformed showing that the different underlying networks give rise to different characteristic temporal signatures. © 2009 Wiley-Liss, Inc.

A research platform for artificial neural networks with applications in pediatric epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and nonepileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that (1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and (2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Glia-augmented artificial neural networks: foundations and applications

Information processing in the human brain has always been considered as a source of inspiration in Artificial Intelligence; in particular, it has led researchers to develop different tools such as artificial neural networks. Recent findings in Neurophysiology provide evidence that not only neurons but also isolated and networks of astrocytes are responsible for processing information in the human brain. Artificial neural net- works (ANNs) model neuron-neuron communications. Artificial neuron-glia networks (ANGN), in addition to neuron-neuron communications, model neuron-astrocyte con- nections. In continuation of the research on ANGNs, first we propose, and evaluate a model of adaptive neuro fuzzy inference systems augmented with artificial astrocytes. Then, we propose a model of ANGNs that captures the communications of astrocytes in the brain; in this model, a network of artificial astrocytes are implemented on top of a typical neural network. The results of the implementation of both networks show that on certain combinations of parameter values specifying astrocytes and their con- nections, the new networks outperform typical neural networks. This research opens a range of possibilities for future work on designing more powerful architectures of artificial neural networks that are based on more realistic models of the human brain.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

Neuronal networks with gap junctions: A study of piece-wise linear planar neuron models

The presence of gap junction coupling among neurons of the central nervous systems has been appreciated for some time now. In recent years there has been an upsurge of interest from the mathematical community in understanding the contribution of these direct electrical connections between cells to large-scale brain rhythms. Here we analyze a class of exactly soluble single neuron models, capable of producing realistic action potential shapes, that can be used as the basis for understanding dynamics at the network level. This work focuses on planar piece-wise linear models that can mimic the firing response of several different cell types. Under constant current injection the periodic response and phase response curve (PRC) is calculated in closed form. A simple formula for the stability of a periodic orbit is found using Floquet theory. From the calculated PRC and the periodic orbit a phase interaction function is constructed that allows the investigation of phase-locked network states using the theory of weakly coupled oscillators. For large networks with global gap junction connectivity we develop a theory of strong coupling instabilities of the homogeneous, synchronous and splay state. For a piece-wise linear caricature of the Morris-Lecar model, with oscillations arising from a homoclinic bifurcation, we show that large amplitude oscillations in the mean membrane potential are organized around such unstable orbits.

Depolarization induced suppression of excitation and the emergence of ultra-slow rhythms in neural networks

Ultra-slow fluctuations (0.01-0.1 Hz) are a feature of intrinsic brain activity of as yet unclear origin. We propose a candidate mechanism based on retrograde endocannabinoid signaling in a synaptically coupled network of excitatory neurons. This is known to cause depolarization-induced suppression of excitation (DISE), which we model phenomenologically. We construct emergent network oscillations in a globally coupled network and show that for strong synaptic coupling DISE can lead to a synchronized population burst at the frequencies of resting brain rhythms.

Legal Framework for Cross-Regional Networks: The Case of Services and Migration

Paper prepared by Marion Panizzon and Charlotte Sieber-Gasser for the International Conference on the Political Economy of Liberalising Trade in Services, Hebrew University of Jerusalem, 14-15 June 2010 Recent literature has shed light on the economic potential of cross-border networks. These networks, consisting of expatriates and their acquaintances from abroad and at home, provide the basis for the creation of cross-border value added chains and therewith the means for turning brain drain into brain circulation. Both aspects are potentially valuable for economic growth in the developing world. Unilateral co-development policies operating through co-funding of expatriate business ventures, but also bilateral agreements liberalising circular migration for a limited set of per-sons testify to the increasing awareness of governments about the potential, which expatriate networks hold for economic growth in developing countries. Whereas such punctual efforts are valuable, viewed from a long term perspective, these top-down, government mandated Diaspora stimulation programs, will not replace, this paper argues, the market-driven liberalisation of infrastructure and other services in developing countries. Nor will they carry, in the case of circular labour migration, the political momentum to liberalise labour market admission for those non-nationals, who will eventually emerge as the future transnational entrepreneurs. It will take a combination of mode 4 and infrastructure services openings-cum regulation for countries at both sides of the spectrum to provide the basis and precondition for transnational business and entrepreneurial networks to emerge and translate into cross-border, value added production chains. Two key issues are of particular relevance in this context: (i) the services sector, especially in infrastructure, tends to suffer from inefficiencies, particularly in developing countries, and (ii) labour migration, a highly complex issue, still faces disproportionately rigid barriers despite well-documented global welfare gains. Both are hindrances for emerging markets to fully take advantage of the potential of these cross-border networks. Adapting the legal framework for enhancing the regulatory and institutional frameworks for services trade, especially in infrastructure services sectors (ISS) and labour migration could provide the incentives necessary for brain circulation and strengthen cross-border value added chains by lowering transaction costs. This paper analyses the shortfalls of the global legal framework – the shallow status quo of GATS commitments in ISS and mode 4 particular – in relation to stimulating brain circulation and the creation of cross-border value added chains in emerging markets. It highlights the necessity of adapting the legal framework, both on the global and the regional level, to stimulate broader and wider market access in the four key ISS sectors (telecommunications, transport, professional and financial services) in developing countries, as domestic supply capacity, global competitiveness and economic diversification in ISS sectors are necessary for mobilising expatriate re-turns, both physical and virtual. The paper argues that industrialised, labour receiving countries need to offer mode 4 market access to wider categories of persons, especially to students, graduate trainees and young professionals from abroad. Further-more, free trade in semi-finished products and mode 4 market access are crucial for the creation of cross-border value added chains across the developing world. Finally, the paper discusses on the basis of a case study on Jordan why the key features of trade agreements, which promote circular migration and the creation of cross-border value added chains, consist of trade liberalisation in services and liberal migration policies.

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.