Inter-area Connections Research Articles

A coupled neural field model for the standard consolidation theory

The standard consolidation theory states that short-term memories located in the hippocampus enable the consolidation of long-term memories in the neocortex. In other words, the neocortex slowly learns long-term memories with a transient support of the hippocampus that quickly learns unstable memories. However, it is not clear yet what could be the neurobiological mechanisms underlying these differences in learning rates and memory time-scales. Here, we propose a novel modeling approach of the standard consolidation theory, that focuses on its potential neurobiological mechanisms. In addition to synaptic plasticity and spike frequency adaptation, our model incorporates adult neurogenesis in the dentate gyrus as well as the difference in size between the neocortex and the hippocampus, that we associate with distance-dependent synaptic plasticity. We also take into account the interconnected spatial structure of the involved brain areas, by incorporating the above neurobiological mechanisms in a coupled neural field framework, where each area is represented by a separate neural field with intra- and inter-area connections. To our knowledge, this is the first attempt to apply neural fields to this process. Using numerical simulations and mathematical analysis, we explore the short-term and long-term dynamics of the model upon alternance of phases of hippocampal replay and retrieval cue of an external input. This external input is encodable as a memory pattern in the form of a multiple bump attractor pattern in the individual neural fields. In the model, hippocampal memory patterns become encoded first, before neocortical ones, because of the smaller distances between the bumps of the hippocampal memory patterns. As a result, retrieval of the input pattern in the neocortex at short time-scales necessitates the additional input delivered by the memory pattern of the hippocampus. Neocortical memory patterns progressively consolidate at longer times, up to a point where their retrieval does not need the support of the hippocampus anymore. At longer times, perturbation of the hippocampal neural fields by neurogenesis erases the hippocampus pattern, leading to a final state where the memory pattern is exclusively evoked in the neocortex. Therefore, the dynamics of our model successfully reproduces the main features of the standard consolidation theory. This suggests that neurogenesis in the hippocampus and distance-dependent synaptic plasticity coupled to synaptic depression and spike frequency adaptation, are indeed critical neurobiological processes in memory consolidation.

Knowns and unknowns about the neurobiology of stuttering.

Stuttering occurs in early childhood during a dynamic phase of brain and behavioral development. The latest studies examining children at ages close to this critical developmental period have identified early brain alterations that are most likely linked to stuttering, while spontaneous recovery appears related to increased inter-area connectivity. By contrast, therapy-driven improvement in adults is associated with a functional reorganization within and beyond the speech network. The etiology of stuttering, however, remains enigmatic. This Unsolved Mystery highlights critical questions and points to neuroimaging findings that could inspire future research to uncover how genetics, interacting neural hierarchies, social context, and reward circuitry contribute to the many facets of stuttering.

Case Report: Modulation of Effective Connectivity in Brain Networks after Prosthodontic Tooth Loss Repair

INTRODUCTION. Recent neuroimaging studies suggest that dental loss replacements induce changes in neuroplasticity as well as in correlated connectivity between brain networks. However, as the typical temporal delay in detecting brain activity by neuroimaging cannot account for the influence one neural system exerts over another in a context of real activation (“effective” connectivity), it seems of interest to approach this dynamic aspect of brain networking in the time frame of milliseconds by exploiting electroencephalographic (EEG) data. MATERIAL AND METHODS. The present study describes one subject who received a new prosthodontic provisional implant in substitution for previous dental repairs. Two EEG sessions led with a portable device were recorded before and after positioning the new dental implant. By following MATLAB-EEGLAB processing supported by the plugins FIELDTRIP and SIFT, the independent component analysis (ICA) derived from EEG raw signals was rendered as current density fields and interpolated with the dipoles generated by each electrode for a dynamic study of the effective connectivity. One more recording session was undertaken six months after the placement of the final implant. RESULTS. Compared to the baseline, the new prosthodontic implant induced a novel modulation of the neuroplasticity in sensory-motor areas which was maintained following the definitive implant after six months, as revealed by changes in the effective connectivity from the basal strong enslavement of a single brain area over the others, to an equilibrate inter-related connectivity evenly distributed along the frontotemporal regions of both hemispheres. CONCLUSIONS. The rapid shift of the effective connectivity after positioning the new prosthodontic implant and its substantial stability after six months suggest the possibility that synaptic modifications, induced by novel sensory motor conditions, modulate the neuroplasticity and reshape the final dynamic frame of the interarea connectivity. Moreover, given the viability of the EEG practice, this approach could be of some interest in assessing the association between oral pathophysiology and neuronal networking.

Power shift and connectivity changes in healthy aging during resting-state EEG

The neural activity of human brain changes in healthy individuals during aging. The most frequent variation in patterns of neural activity are a shift from posterior to anterior areas and a reduced asymmetry between hemispheres. These patterns are typically observed during task execution and by using functional magnetic resonance imaging data. In the present study we investigated whether analogous effects can also be detected during rest and by means of source-space time series reconstructed from electroencephalographic recordings. By analyzing oscillatory power distribution across the brain we indeed found a shift from posterior to anterior areas in older adults. We additionally examined this shift by evaluating connectivity and its changes with age. The findings indicated that inter-area connections among frontal, parietal and temporal areas were strengthened in older individuals. A more complex pattern was shown in intra-area connections, where age-related activity was enhanced in parietal and temporal areas, and reduced in frontal areas. Finally, the resulting network exhibits a loss of modularity with age. Overall, the results extend to resting-state condition the evidence of an age-related shift of brain activity from posterior to anterior areas, thus suggesting that this shift is a general feature of the aging brain rather than being task-specific. In addition, the connectivity results provide new information on the reorganization of resting-state brain activity in aging.

LFP Analysis of Brain Injured Anesthetized Animals Undergoing Closed-Loop Intracortical Stimulation.

Activity dependent stimulation (ADS) is a closed loop stimulation technique whose neurophysiological effects have not been deeply investigated. Here we explored how Local field Potentials (LFP) are impacted by a focal ischemic lesion and, subsequently, by ADS treatment. Intracortical microelectrode arrays were implanted in the rostral forelimb area (RFA) and in the primary somatosensory area (S1) of anaesthetized rats. An ischemic injury was induced in the caudal forelimb area through microinjections of Endothelin-1. The lesion induced an acute depressive trend in LFP power in RFA (evaluated in 6 bands of interest: Delta (1–4Hz), Theta (4–8Hz), Alpha (8–11Hz), Beta (11–30Hz), LowGamma (30–55Hz) and HighGamma (55–80)) followed by a noticeable significant rebound in both areas. Applying ADS induced an overall decrease of power. The lesion impacted the connectivity in a frequency specific manner, resulting in widespread increase in connectivity in Delta both between and within areas. Two hours after the lesion, without stimulation, correlated activity between areas increased in Beta and Gamma. After stimulation, inter-area connectivity increased in Delta, Theta and Alpha, while considerably dropping within RFA in highGamma. By computing phase-amplitude coupling, we found that the lesion produced an incremental increase in the coupling between (Theta) Alpha phase and (lowGamma) highGamma amplitude within RFA, while S1 had a more generalized increase. Likewise, coupling between Theta phase and lowGamma/highGamma amplitudes increased between areas after lesion. ADS induced a similar increase, but greater in magnitude both within and between RFA and S1. These results have important implications on the emerging field of closed-loop adaptive stimulation promoting ADS as an innovative tool for the treatment of neurological disorders.

Integrating multi-scale data for a network model of macaque visual cortex

Models of cortical dynamics usually either cover small cortical circuits in detail, or represent large patches in a highly simplified manner, for instance using a few differential equations for each area. This is due partly to limited computational resources, and partly to sparsity of large-scale structural connectivity data. These two barriers are now being lifted. Recent developments in the simulation technology NEST [1] and the availability of supercomputers like JUQUEEN in Julich and the K computer in Japan make it possible to represent substantial parts of primate cortical tissue at cellular and synaptic resolution. At the same time, gaps in experimental knowledge on large-scale connectivity are gradually being closed [2]. Making use of these developments, we construct a model comprising the 32 areas of the macaque cortex associated with visual processing, where the individual areas are based on our recent layered cortical microcircuit model [3]. While this model accounts for the distribution of spike rates across layers and populations, it is heavily underconstrained because only about half the synapses onto the cells in the microcircuit have a local origin. The extension to multiple areas makes it possible to account for a large majority of the synapses and thereby enhances the self-consistency of the model. We compile a realistic structural connectivity map by incorporating multiple datasets from both electro-physiological [4] and anatomical studies [2,5] on different species. The map is adapted to the macaque using realistic area- and layer-specific neuron densities and in-degrees. Both local and inter-area connectivity are layer-specific. The CoCoMac database [5] provides the basis for the inter-area connectivity, and is supplemented with quantitative information from further tracing studies [2]. We complete these data sets by applying empirical connectivity rules, for instance exploiting the dependence of connection strengths on inter-area distances, and of laminar connection patterns on architectural type differences [6]. Combining a simple neuron model with complex connectivity enables us to study the influence of the structural connectivity itself on cortical dynamics. We present preliminary results on basic dynamical features like area- and population-specific firing rates and degrees of irregularity. In addition, we examine the influence of the cortico-cortical connections by comparing dynamical features like power spectra of the isolated primary visual cortex (V1) with Poisson input to its dynamics when embedded in the global network. This model bridges the gap between abstract large-scale models and small-scale microcircuit models. It therefore provides a link between global activity patterns and local spiking activity.

P02-154 - Intracebral functional (LORETA) connectivity during attention to bodily and mental processes and resting

ObjectivesAttention is hypothesized to increase the brain's functional connectivity measured as EEG coherence between brain areas. Is this true for different types of attention compared to no-task resting?MethodsIn 25 healthy, meditation-naïve, right-handed, male students 58-channel EEG was recorded during three conditions of 5 minutes each with closed eyes in randomized order: (1) resting [3 runs], (2) mental arithmetic [2 runs], and (3) breath counting, a meditation initiation technique [2 runs]. For the 8 EEG frequency bands, the artifacted EEG was recomputed into sLORETA intracerebral current densities. To avoid localization ambiguities, we computed EEG coherence between sLORETA areas. To avoid effects of volume conduction, we computed intracerebral `lagged coherence' connectivity between sLORETA current densities in 19 areas. Averaged resting runs were compared to those breath counting and arithmetic runs of which participants post-hoc reported that their concentration was best.ResultsPaired t-tests between conditions yielded differences of coherence at p< 0.1 (corrected for multiple testing) for 5 EEG frequency bands (delta, theta, alpha1, alpha2, beta3) [number of connections in brackets]: coherence was lower in breath counting than arithmetic (delta, beta3 [1,1]). Coherence was lower in resting than arithmetic (alpha2 [1]), but higher in resting than breath counting (theta, alpha1, alpha2, beta3 [2,1,1,1]).ConclusionsAttention to breath counting showed lowest, to arithmetic highest demands on intracerebral functional connectivity. Breath counting and mental arithmetic induce mental states whose inter-area connectivity differs in opposite directions from resting. The results do not support a global hypothesis of increased coherence during attention.

Realistic mean field forward predictions for the integration of co-registered EEG/fMRI

Brain activity can be measured non-invasively with functional imaging techniques. Each pixel in such an image represents a neural mass of about 105 to 107 neurons. Mean field models (MFMs) approximate their activity by averaging out neural variability while retaining salient underlying features, like neurotransmitter kinetics. However, MFMs incorporating the regional variability, realistic geometry and connectivity of cortex have so far appeared intractable. This lack of biological realism has led to a focus on gross temporal features of the EEG. We address these impediments and showcase a proof of principle forward prediction of co-registered EEG/fMRI for a full-size human cortex in a realistic head model with anatomical connectivity, see figure 1. MFMs usually assume homogeneous neural masses, isotropic long-range connectivity and simplistic signal expression to allow rapid computation with partial differential equations. But these approximations are insufficient in particular for the high spatial resolution obtained with fMRI, since different cortical areas vary in their architectonic and dynamical properties, have complex connectivity, and can contribute non-trivially to the measured signal. Our code instead supports the local variation of model parameters and freely chosen connectivity for many thousand triangulation nodes spanning a cortical surface extracted from structural MRI. This allows the introduction of realistic anatomical and physiological parameters for cortical areas and their connectivity, including both intra- and inter-area connections. Proper cortical folding and conduction through a realistic head model is then added to obtain accurate signal expression for a comparison to experimental data. To showcase the synergy of these computational developments, we predict simultaneously EEG and fMRI BOLD responses by adding an established model for neurovascular coupling and convolving Balloon-Windkessel hemodynamics. We also incorporate regional connectivity extracted from the CoCoMac database [1]. Importantly, these extensions can be easily adapted according to future insights and data. Furthermore, while our own simulation is based on one specific MFM [2], the computational framework is general and can be applied to models favored by the user. Finally, we provide a brief outlook on improving the integration of multi-modal imaging data through iterative fits of a single underlying MFM in this realistic simulation framework.

On the importance of coastal areas in the survival of Neanderthal populations during the Late Pleistocene

This paper examines the distribution of Neanderthal populations across Europe and the Middle East. Key geographical variables are used to identify major population strongholds. Four are identified: southern Iberia, Atlantic Europe, Black Sea-Aegean and coastal Italy. Neanderthal site density in each stronghold was found to correspond closely with the predicted suitability of each area. A strong correlation was found between area suitability and last Neanderthal dates and the process of population fragmentation and extinction was found to affect continental areas first and coastal ones last. Oceanic influence, inter-area connectivity and proximity to coasts were found to be key variables in the Neanderthal extinction process. The functional ecological significance of coastal areas to Neanderthals is discussed.

Biomedical knowledge engineering approaches driven by processing the primary experimental literature.

Event Abstract Back to Event Biomedical knowledge engineering approaches driven by processing the primary experimental literature. Gully Burns1*, Eduard Hovy1 and Tommy Ingulfsen1 1 USC Information Sciences Institute, United States In general, there is a difference of scale between neuroinformatics and bioinformatics databases. For example, the CoCoMac system (a mature solution concerned with inter-area connections in the cerebral cortex of the Macaque) contained roughly 39,748 connection reports from 413 papers. By way of contrast, the Mouse Genome Informatics system contains 2,237,293 Mouse nucleotide sequences from 128,274 references. Why is this? Why are neuroscience databases not larger? It is not because of paucity of available data: the number of full-text articles available online is of the scale of millions. The absence of two key components prevents us from leveraging this abundance into large-scale, computationally-tractable resources. (A) Much of the information occurs as natural language text and (B) no general-purpose Knowledge Representation (KR) appropriately captures the semantics of experimental observations appropriately (i.e., in a mathematically tractable format that is also understandable to biologists). Bioinformatics systems use high-throughput methods of data acquisition and require only simple structures for data modeling. Here we present a strategy for the construction of knowledge bases from the biomedical literature based on a relatively-simple, generally-applicable knowledge representation for scientific observations called €˜Knowledge Engineering from Experimental Design€™ (€˜KE-f-ED€™). This approach is based on the experimental variables being studied and provides a way to represent data, significance relations and correlations in a generalized informatics framework. We describe the basic theory behind the model, and demonstrate a sample implementation in the domain of neuroendocrinology. We also illustrate how this approach can provide a substrate for semi-automated knowledge acquisition through text-mining. We performed information extraction work over the primary experimental research literature to locate and identify named brain regions. In collaboration with Elsevier Science, we downloaded 39,643 full-text articles as XML documents (and 117,602 as PDFs) multiple neuroanatomically-focused journals. We used Conditional Random Fields to analyze these texts and to annotate individual mentions of variables derived from generic tract-tracing experiments. Finally, we generalize these results ontologically to apply to other variables used in other experimental types. Conference: Neuroinformatics 2008, Stockholm, Sweden, 7 Sep - 9 Sep, 2008. Presentation Type: Poster Presentation Topic: General Neuroinformatics Citation: Burns G, Hovy E and Ingulfsen T (2008). Biomedical knowledge engineering approaches driven by processing the primary experimental literature.. Front. Neuroinform. Conference Abstract: Neuroinformatics 2008. doi: 10.3389/conf.neuro.11.2008.01.011 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 28 Jul 2008; Published Online: 28 Jul 2008. * Correspondence: Gully Burns, USC Information Sciences Institute, Marina del Rey, United States, burns@isi.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Gully Burns Eduard Hovy Tommy Ingulfsen Google Gully Burns Eduard Hovy Tommy Ingulfsen Google Scholar Gully Burns Eduard Hovy Tommy Ingulfsen PubMed Gully Burns Eduard Hovy Tommy Ingulfsen Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules

Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the γ-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson–Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a “Mexican-hat”. Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical framework for the analysis of the relationships between object representation, memory, learning, and γ-band activity. In particular, it extends previous studies on autoassociative memory since it exploits not only oscillatory dynamics, but also a topological organization of features.

Modelling event-related responses in the brain

The aim of this work was to investigate the mechanisms that shape evoked electroencephalographic (EEG) and magneto-encephalographic (MEG) responses. We used a neuronally plausible model to characterise the dependency of response components on the models parameters. This generative model was a neural mass model of hierarchically arranged areas using three kinds of inter-area connections (forward, backward and lateral). We investigated how responses, at each level of a cortical hierarchy, depended on the strength of connections or coupling. Our strategy was to systematically add connections and examine the responses of each successive architecture. We did this in the context of deterministic responses and then with stochastic spontaneous activity. Our aim was to show, in a simple way, how event-related dynamics depend on extrinsic connectivity. To emphasise the importance of nonlinear interactions, we tried to disambiguate the components of event-related potentials (ERPs) or event-related fields (ERFs) that can be explained by a linear superposition of trial-specific responses and those engendered nonlinearly (e.g., by phase-resetting). Our key conclusions were; (i) when forward connections, mediating bottom-up or extrinsic inputs, are sufficiently strong, nonlinear mechanisms cause a saturation of excitatory interneuron responses. This endows the system with an inherent stability that precludes nondissipative population dynamics. (ii) The duration of evoked transients increases with the hierarchical depth or level of processing. (iii) When backward connections are added, evoked transients become more protracted, exhibiting damped oscillations. These are formally identical to late or endogenous components seen empirically. This suggests that late components are mediated by reentrant dynamics within cortical hierarchies. (iv) Bilateral connections produce similar effects to backward connections but can also mediate zero-lag phase-locking among areas. (v) Finally, with spontaneous activity, ERPs/ERFs can arise from two distinct mechanisms: For low levels of (stimulus related and ongoing) activity, the systems response conforms to a quasi-linear superposition of separable responses to the fixed and stochastic inputs. This is consistent with classical assumptions that motivate trial averaging to suppress spontaneous activity and disclose the ERP/ERF. However, when activity is sufficiently high, there are nonlinear interactions between the fixed and stochastic inputs. This interaction is expressed as a phase-resetting and represents a qualitatively different explanation for the ERP/ERF.