A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials.

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies. Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics. To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings.

Probabilistic functional tractography of the human cortex revisited.

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.

Absence of calretinin protein expression in malignant mesotheliomas from asbestos-exposed NF2+/- mice and mouse mesothelioma cell lines from various mouse strains.

BACKGROUND: Calretinin is the most widespread positive marker for the immunohistochemical identification of malignant mesothelioma (MM) and was proposed to serve as a blood-based biomarker. Functionally, evidence has accumulated that calretinin might be implicated in MM tumorigenesis. We aimed to identify calretinin (CR; Calb2) in murine MM and reactive mesothelial cells in granuloma from asbestos-exposed NF2+/- mice, a line heterozygous for the tumor suppressor merlin (NF2), used as a mouse MM model. Additionally, we sought to ascertain the presence of calretinin in MM cell lines from other mouse strains. We also intended to investigate the role of calretinin in mesotheliomagenesis by comparing the survival of asbestos-exposed NF2+/- and NF2+/-CR-/- mice. METHODS: NF2+/- and NF2+/-CR-/- mice, both lines on a C57Bl/6J background, were exposed to asbestos following an established protocol. Tumor histology and asbestos-induced mortality were assessed. MM and granuloma from NF2+/- mice were analyzed with immunohistochemical methods for calretinin expression. Levels of Calb2 mRNA and calretinin expression in tumors and MM cell lines of various mouse strains were determined by RT-qPCR and Western blot analysis, respectively. RESULTS: No expression of calretinin at the protein level was detected, neither in MM from NF2+/- mice, NF2+/- MM-derived cell lines nor immortalized mesothelial cells of mouse origin. At the mRNA level we detected Calb2 expression in MM cell lines from different mouse strains. Survival of NF2+/- and NF2+/-CR-/- mice exposed to asbestos showed no significant difference in a log-rank (Kaplan-Meier) comparison. CONCLUSIONS: The concomitant determination of calretinin and mesothelin blood levels has been proposed for early detection of human MM. Mouse MM models based on asbestos exposure are assumed to yield helpful information on the time course of appearance of mesothelin and calretinin in the blood of asbestos-treated mice determining the earliest time point for interventions. However, the observed absence of calretinin in MM from NF2+/- mice and derived cell lines, as well as from MM cells from Balb/c and C3H mice likely precludes the use of calretinin as a biomarker for mouse MM. The results also indicate possible species differences with respect to an involvement of calretinin in the formation of MM.

Modeling the outcome of structural disconnection on resting-state functional connectivity.

A growing body of experimental evidence suggests that functional connectivity at rest is shaped by the underlying anatomical structure. Furthermore, the organizational properties of resting-state functional networks are thought to serve as the basis for an optimal cognitive integration. A disconnection at the structural level, as occurring in some brain diseases, would then lead to functional and presumably cognitive impairments. In this work, we propose a computational model to investigate the role of a structural disconnection (encompassing putative local/global and axonal/synaptic mechanisms) on the organizational properties of emergent functional networks. The brain's spontaneous neural activity and the corresponding hemodynamic response were simulated using a large-scale network model, consisting of local neural populations coupled through white matter fibers. For a certain coupling strength, simulations reproduced healthy resting-state functional connectivity with graph properties in the range of the ones reported experimentally. When the structural connectivity is decreased, either globally or locally, the resultant simulated functional connectivity exhibited a network reorganization characterized by an increase in hierarchy, efficiency and robustness, a decrease in small-worldness and clustering and a narrower degree distribution, in the same way as recently reported for schizophrenia patients. Theoretical results indicate that most disconnection-related neuropathologies should induce the same qualitative changes in resting-state brain activity.

Neural network mechanisms underlying stimulus driven variability reduction.

It is well established that the variability of the neural activity across trials, as measured by the Fano factor, is elevated. This fact poses limits on information encoding by the neural activity. However, a series of recent neurophysiological experiments have changed this traditional view. Single cell recordings across a variety of species, brain areas, brain states and stimulus conditions demonstrate a remarkable reduction of the neural variability when an external stimulation is applied and when attention is allocated towards a stimulus within a neuron's receptive field, suggesting an enhancement of information encoding. Using an heterogeneously connected neural network model whose dynamics exhibits multiple attractors, we demonstrate here how this variability reduction can arise from a network effect. In the spontaneous state, we show that the high degree of neural variability is mainly due to fluctuation-driven excursions from attractor to attractor. This occurs when, in the parameter space, the network working point is around the bifurcation allowing multistable attractors. The application of an external excitatory drive by stimulation or attention stabilizes one specific attractor, eliminating in this way the transitions between the different attractors and resulting in a net decrease in neural variability over trials. Importantly, non-responsive neurons also exhibit a reduction of variability. Finally, this reduced variability is found to arise from an increased regularity of the neural spike trains. In conclusion, these results suggest that the variability reduction under stimulation and attention is a property of neural circuits.

Balanced input allows optimal encoding in a stochastic binary neural network model: an analytical study.

Recent neurophysiological experiments have demonstrated a remarkable effect of attention on the underlying neural activity that suggests for the first time that information encoding is indeed actively influenced by attention. Single cell recordings show that attention reduces both the neural variability and correlations in the attended condition with respect to the non-attended one. This reduction of variability and redundancy enhances the information associated with the detection and further processing of the attended stimulus. Beyond the attentional paradigm, the local activity in a neural circuit can be modulated in a number of ways, leading to the general question of understanding how the activity of such circuits is sensitive to these relatively small modulations. Here, using an analytically tractable neural network model, we demonstrate how this enhancement of information emerges when excitatory and inhibitory synaptic currents are balanced. In particular, we show that the network encoding sensitivity--as measured by the Fisher information--is maximized at the exact balance. Furthermore, we find a similar result for a more realistic spiking neural network model. As the regime of balanced inputs has been experimentally observed, these results suggest that this regime is functionally important from an information encoding standpoint.

STDP allows fast rate-modulated coding with Poisson-like spike trains.

Spike timing-dependent plasticity (STDP) has been shown to enable single neurons to detect repeatedly presented spatiotemporal spike patterns. This holds even when such patterns are embedded in equally dense random spiking activity, that is, in the absence of external reference times such as a stimulus onset. Here we demonstrate, both analytically and numerically, that STDP can also learn repeating rate-modulated patterns, which have received more experimental evidence, for example, through post-stimulus time histograms (PSTHs). Each input spike train is generated from a rate function using a stochastic sampling mechanism, chosen to be an inhomogeneous Poisson process here. Learning is feasible provided significant covarying rate modulations occur within the typical timescale of STDP (~10-20 ms) for sufficiently many inputs (~100 among 1000 in our simulations), a condition that is met by many experimental PSTHs. Repeated pattern presentations induce spike-time correlations that are captured by STDP. Despite imprecise input spike times and even variable spike counts, a single trained neuron robustly detects the pattern just a few milliseconds after its presentation. Therefore, temporal imprecision and Poisson-like firing variability are not an obstacle to fast temporal coding. STDP provides an appealing mechanism to learn such rate patterns, which, beyond sensory processing, may also be involved in many cognitive tasks.

Role of local network oscillations in resting-state functional connectivity.

Spatio-temporally organized low-frequency fluctuations (<0.1 Hz), observed in BOLD fMRI signal during rest, suggest the existence of underlying network dynamics that emerge spontaneously from intrinsic brain processes. Furthermore, significant correlations between distinct anatomical regions-or functional connectivity (FC)-have led to the identification of several widely distributed resting-state networks (RSNs). This slow dynamics seems to be highly structured by anatomical connectivity but the mechanism behind it and its relationship with neural activity, particularly in the gamma frequency range, remains largely unknown. Indeed, direct measurements of neuronal activity have revealed similar large-scale correlations, particularly in slow power fluctuations of local field potential gamma frequency range oscillations. To address these questions, we investigated neural dynamics in a large-scale model of the human brain's neural activity. A key ingredient of the model was a structural brain network defined by empirically derived long-range brain connectivity together with the corresponding conduction delays. A neural population, assumed to spontaneously oscillate in the gamma frequency range, was placed at each network node. When these oscillatory units are integrated in the network, they behave as weakly coupled oscillators. The time-delayed interaction between nodes is described by the Kuramoto model of phase oscillators, a biologically-based model of coupled oscillatory systems. For a realistic setting of axonal conduction speed, we show that time-delayed network interaction leads to the emergence of slow neural activity fluctuations, whose patterns correlate significantly with the empirically measured FC. The best agreement of the simulated FC with the empirically measured FC is found for a set of parameters where subsets of nodes tend to synchronize although the network is not globally synchronized. Inside such clusters, the simulated BOLD signal between nodes is found to be correlated, instantiating the empirically observed RSNs. Between clusters, patterns of positive and negative correlations are observed, as described in experimental studies. These results are found to be robust with respect to a biologically plausible range of model parameters. In conclusion, our model suggests how resting-state neural activity can originate from the interplay between the local neural dynamics and the large-scale structure of the brain.

The role of rhythmic neural synchronization in rest and task conditions.

Rhythmic neural synchronization is found throughout the brain during many different tasks and even at rest. Beyond their underlying mechanisms, the question of their role is still controversial. Modeling can bring insight on this difficult question. We review here our recent modeling results concerning this issue in different situations. During rest, we show how local rhythmic synchrony can induce a spatiotemporally organized spontaneous activity at the brain level. Then, we show how rhythmic synchrony decreases reaction time in attention and enhances the strength and speed of information transfer between different groups of neurons. Finally, we show that when rhythmic synchrony creates firing phases, the learning with spike timing-dependent plasticity of repeatedly presented input patterns is greatly enhanced.

A unified and quantitative network model for spatial attention in area V4.

Electrophysiological experiments in visual area V4 have shown that spatial attention induces a number of neural activity modulations. Depending on the stimulus characteristics, neuronal firing rates either increase or decrease. At the network level, the oscillatory activity in the gamma frequency range (30-70Hz) is enhanced by attention. Recently, pyramidal neurons and interneurons have been surmised to respond differently, but have been shown to have both a high firing variability. These results raise the question of the nature of the modulatory attentional input to V4 and of the network mechanisms that lead to the emergence of these different modulations. Here, we propose a biophysical network model of V4. We first reproduce the neural activity observed in response to different stimulus configurations. We found that different forms of the attentional input are possible, and that this fact could explain the observed multiplicity of modulations when stimulus contrast is varied. Our model offers a unified and quantitative picture, from which the cognitive roles played by these attentional modulations can be investigated.

Specific entrainment of mitral cells during gamma oscillation in the rat olfactory bulb.

Local field potential (LFP) oscillations are often accompanied by synchronization of activity within a widespread cerebral area. Thus, the LFP and neuronal coherence appear to be the result of a common mechanism that underlies neuronal assembly formation. We used the olfactory bulb as a model to investigate: (1) the extent to which unitary dynamics and LFP oscillations can be correlated and (2) the precision with which a model of the hypothesized underlying mechanisms can accurately explain the experimental data. For this purpose, we analyzed simultaneous recordings of mitral cell (MC) activity and LFPs in anesthetized and freely breathing rats in response to odorant stimulation. Spike trains were found to be phase-locked to the gamma oscillation at specific firing rates and to form odor-specific temporal patterns. The use of a conductance-based MC model driven by an approximately balanced excitatory-inhibitory input conductance and a relatively small inhibitory conductance that oscillated at the gamma frequency allowed us to provide one explanation of the experimental data via a mode-locking mechanism. This work sheds light on the way network and intrinsic MC properties participate in the locking of MCs to the gamma oscillation in a realistic physiological context and may result in a particular time-locked assembly. Finally, we discuss how a self-synchronization process with such entrainment properties can explain, under experimental conditions: (1) why the gamma bursts emerge transiently with a maximal amplitude position relative to the stimulus time course; (2) why the oscillations are prominent at a specific gamma frequency; and (3) why the oscillation amplitude depends on specific stimulus properties. We also discuss information processing and functional consequences derived from this mechanism.

Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme.

Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential-a phenomenon referred to as "phase-of-firing coding" (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions-only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents ( approximately 10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.

Learning modulation of odor-induced oscillatory responses in the rat olfactory bulb: a correlate of odor recognition?

In the first relay of information processing, the olfactory bulb (OB), odors are known to generate specific spatial patterns of activity. Recently, in freely behaving rats, we demonstrated that learning modulated oscillatory activity in local field potential (LFP), in response to odors, in both beta (15-40 Hz) and gamma (60-90 Hz) bands. The present study further characterized this odor-induced oscillatory activity with emphasis on its spatiotemporal distribution over the olfactory bulb and on its relationship with improvement of behavioral performances along training. For that purpose, LFPs were simultaneously recorded from four locations in the OB in freely moving rats performing an olfactory discrimination task. Electrodes were chronically implanted near relay neurons in the mitral cell body layer. Time-frequency methods were used to extract signal characteristics (amplitude, frequency, and time course) in the two frequency bands. Before training, odor presentation produced, on each site, a power decrease in gamma oscillations and a weak but significant increase in power of beta oscillations (approximately 25 Hz). When the training was achieved, these two phenomena were amplified. Interestingly, the beta oscillatory response showed several significant differences between the anterodorsal and posteroventral regions of the OB. In addition, clear-cut beta responses occurred in the signal as soon as animals began to master the task. As a whole, our results point to the possible functional importance of beta oscillatory activity in the mammalian OB, particularly in the context of olfactory learning.

Augmentation of induced visual gamma activity by increased task complexity.

Recently the study of induced gamma band oscillations has focused on their modulation by top-down processes, mainly attention. Numerous studies have observed an increase in induced gamma band energy with increases in covert selective attention and visual perception. The current study investigated the modulation of visually induced gamma band oscillations by top-down processes associated with task complexity. Fourteen human subjects performed a reaction time task under two experimental conditions that differed in task complexity. In one, subjects simply had to press one of four buttons that corresponded to a colour stimulus shown to the subject. In the second, the stimulus response mapping was altered by the implementation of a rule, thus increasing task complexity. Cortical electrical activity was recorded using a 65 electrode whole scalp electroencephalographic (EEG) net. The EEG activity was analysed using Morlet wavelets to produce time-frequency maps. Although induced gamma band activity was observed in both conditions, there was significantly greater energy during the rule-operation condition at approximately 276 ms after the appearance of the stimulus. This increase was localized to electrodes overlying the right-central parietal scalp. The results of this study show that top-down processes modulate the level of induced gamma band activity. We discuss these findings in terms of the role of gamma oscillations in the construction of a sensory representation useful for a correct motor response.

Olfactory learning modifies the expression of odour-induced oscillatory responses in the gamma (60-90 Hz) and beta (15-40 Hz) bands in the rat olfactory bulb.

This study addressed the question of the possible functional relevance of two different oscillatory activities, beta and gamma (15-40 and 60-90 Hz, respectively) for perception and memory processes in olfactory areas of mammals. Local field potentials were recorded near relay olfactory bulb neurons while rats performed an olfactory discrimination task. Signals reflected the mass activity from this region and characteristics of oscillatory activities were used as an index of local synchrony. Beta and gamma oscillatory activities were quantified by time-frequency methods before during and after odour sampling. In rats early in their training, olfactory sampling was associated with a significant decrease in power in the gamma band in parallel with a weak but significant increase in the beta band (centred on 27 Hz). Several days later, in well-trained rats, the gamma oscillatory depression was significantly enhanced both in duration and amplitude. It appeared within the 500 ms time period preceding odour onset and was further reduced during the odour period. Concurrently the beta oscillatory response (now centred on 24 Hz) during odour sampling was amplified by a twofold factor. The beta band response was modulated according to the chemical nature of the stimuli and rat's behavioural response. This study showed for the first time that odour sampling in behaving animals is associated with a clear shift in the olfactory bulb neuronal activity from a gamma to a beta oscillatory regime. Moreover, the data stress the importance of studying the odour-induced beta activity and its relation to perception and memory.

Reduced P300 amplitude in a visual recognition task in patients with schizophrenia.

We studied top-down visual processes in schizophrenia by analyzing visual event-related potentials (ERPs) during a gestalt recognition task, after subjects (patients with schizophrenia, n = 10; controls, n = 14) were trained to perceive three different geometrical shapes. Recognition performance of patients was poorer under both the figure and the nonfigure conditions then that of normal controls. ERPs analysis indicated that P300 amplitudes of the patients were significantly smaller than those of controls during correct figure detection trials. Moreover, topographical analysis of the differences in ERPs during the figure vs the nonfigure condition showed an earlier, more positive and more widely distributed P300 in controls than in patients with schizophrenia. Our study supports the misconnection hypothesis of schizophrenia and highlights the difficulty of the patients to refer to previous experience in order to filter incoming information. In a visual recognition task, this misconnection syndrome might induce a failure to integrate information stored in the frontal and prefrontal sites with incoming stimuli.