Brain network modules of meaningful and meaningless objects

Network modularity is a key feature for efficient information processing in the human brain. This information processing is however dynamic and networks can reconfigure at very short time period, few hundreds of millisecond. This requires neuroimaging techniques with sufficient time resolution. Here we use the dense electroencephalography, EEG, source connectivity methods to identify cortical networks with excellent time resolution, in the order of millisecond. We identify functional networks during picture naming task. Two categories of visual stimuli were presented, meaningful (tools, animals) and meaningless (scrambled) objects. In this paper, we report the reconfiguration of brain network modularity for meaningful and meaningless objects. Results showed mainly that networks of meaningful objects were more modular than those of meaningless objects. Networks of the ventral visual pathway were activated in both cases. However a strong occipitotemporal functional connectivity appeared for meaningful object but not for meaningless object. We believe that this approach will give new insights into the dynamic behavior of the brain networks during fast information processing.Comment: The 3rd Middle East Conference on Biomedical Engineering (MECBME'16

Is the functional interaction between adenosine A2A receptors and metabotropic glutamate 5 receptors a general mechanism in the brain? Differences and similarities between the striatum and the hippocampus

The aim of the present paper was to examine, in a comparative way, the occurrence and the mechanisms of the interactions between adenosine A2A receptors (A2ARs) and metabotropic glutamate 5 receptors (mGlu5Rs) in the hippocampus and the striatum. In rat hippocampal and corticostriatal slices, combined ineffective doses of the mGlu5R agonist 2-chloro-5-hydroxyphenylglycine (CHPG) and the A2AR agonist CGS 21680 synergistically reduced the slope of excitatory postsynaptic field potentials (fEPSPs) recorded in CA1 and the amplitude of field potentials (FPs) recorded in the dorsomedial striatum. The cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) pathway appeared to be involved in the effects of CGS 21680 in corticostriatal but not in hippocampal slices. In both areas, a postsynaptic locus of interaction appeared more likely. N-methyl-D-aspartate (NMDA) reduced the fEPSP slope and FP amplitude in hippocampal and corticostriatal slices, respectively. Such an effect was significantly potentiated by CHPG in both areas. Interestingly, the A2AR antagonist ZM 241385 significantly reduced the NMDA-potentiating effect of CHPG. In primary cultures of rat hippocampal and striatal neurons (ED 17, DIV 14), CHPG significantly potentiated NMDA-induced lactate dehydrogenase (LDH) release. Again, such an effect was prevented by ZM 241385. Our results show that A2A and mGlu5 receptors functionally interact both in the hippocampus and in the striatum, even though different mechanisms seem to be involved in the two areas. The ability of A2ARs to control mGlu5R-dependent effects may thus be a general feature of A2ARs in different brain regions (irrespective of their density) and may represent an additional target for the development of therapeutic strategies against neurological disorders

Neuron to Astrocyte Communication via Cannabinoid Receptors Is Necessary for Sustained Epileptiform Activity in Rat Hippocampus

Astrocytes are integral functional components of synapses, regulating transmission and plasticity. They have also been implicated in the pathogenesis of epilepsy, although their precise roles have not been comprehensively characterized. Astrocytes integrate activity from neighboring synapses by responding to neuronally released neurotransmitters such as glutamate and ATP. Strong activation of astrocytes mediated by these neurotransmitters can promote seizure-like activity by initiating a positive feedback loop that induces excessive neuronal discharge. Recent work has demonstrated that astrocytes express cannabinoid 1 (CB1) receptors, which are sensitive to endocannabinoids released by nearby pyramidal cells. In this study, we tested whether this mechanism also contributes to epileptiform activity. In a model of 4-aminopyridine induced epileptic-like activity in hippocampal slice cultures, we show that pharmacological blockade of astrocyte CB1 receptors did not modify the initiation, but significantly reduced the maintenance of epileptiform discharge. When communication in astrocytic networks was disrupted by chelating astrocytic calcium, this CB1 receptor-mediated modulation of epileptiform activity was no longer observed. Thus, endocannabinoid signaling from neurons to astrocytes represents an additional significant factor in the maintenance of epileptiform activity in the hippocampus

Deleterious GRM1 Mutations in Schizophrenia

We analysed a phenotypically well-characterised sample of 450 schziophrenia patients and 605 controls for rare non-synonymous single nucleotide polymorphisms (nsSNPs) in the GRM1 gene, their functional effects and family segregation. GRM1 encodes the metabotropic glutamate receptor 1 (mGluR1), whose documented role as a modulator of neuronal signalling and synaptic plasticity makes it a plausible schizophrenia candidate. In a recent study, this gene was shown to harbour a cluster of deleterious nsSNPs within a functionally important domain of the receptor, in patients with schizophrenia and bipolar disorder. Our Sanger sequencing of the GRM1 coding regions detected equal numbers of nsSNPs in cases and controls, however the two groups differed in terms of the potential effects of the variants on receptor function: 6/6 case-specific and only 1/6 control-specific nsSNPs were predicted to be deleterious. Our in-vitro experimental follow-up of the case-specific mutants showed that 4/6 led to significantly reduced inositol phosphate production, indicating impaired function of the major mGluR1signalling pathway; 1/6 had reduced cell membrane expression; inconclusive results were obtained in 1/6. Family segregation analysis indicated that these deleterious nsSNPs were inherited. Interestingly, four of the families were affected by multiple neuropsychiatric conditions, not limited to schizophrenia, and the mutations were detected in relatives with schizophrenia, depression and anxiety, drug and alcohol dependence, and epilepsy. Our findings suggest a possible mGluR1 contribution to diverse psychiatric conditions, supporting the modulatory role of the receptor in such conditions as proposed previously on the basis of in vitro experiments and animal studies

Detecting transient brain states of functional connectivity A comparative study

International audienceEstimating functional connectivity (FC) has become an increasingly powerful tool for understanding our brain and investigating healthy and abnormal brain functions. Most previous studies assume temporal stationarity, such as correlation and data-driven decompositions computed across the whole duration of the acquisition. However, emerging evidence revealed the presence of temporal variability of FC, leading to increasing interest in estimating the dynamic Functional Connectivity (dFC). In this context, several approaches have been proposed in order to extract the relevant brain networks fluctuating over time. Still, a clear comparative study among the existing methods is needed. Thus, we aimed here to compare three dimensionality reduction techniques, specifically Independent Component Analysis (ICA), Principle Component Analysis (PCA) and generalized Canonical Correlation Analysis (gCCA), on two Magnetoencephalography (MEG) datasets recorded during motor and memory tasks. First, source connectivity combined with a sliding window approach was used in order to reconstruct the dynamic brain networks at the cortical level. Then, for each algorithm, we extracted the significant patterns of brain network connections with their associated time variation. Results show characteristic properties of each method in terms of computation time, reproducibility and potentiality in extracting the top dominant networks. © 2019 IEEE

Decoding the circuitry of consciousness From local microcircuits to brain-scale networks

International audienceIdentifying the physiological processes underlying the emergence and maintenance of consciousness is one of the most fundamental problems of neuroscience, with implications ranging from fundamental neuroscience to the treatment of patients with disorders of consciousness (DOCs). One major challenge is to understand how cortical circuits at drastically different spatial scales, from local networks to brain-scale networks, operate in concert to enable consciousness, and how those processes are impaired in DOC patients. In this review, we attempt to relate available neurophysiological and clinical data with existing theoretical models of consciousness, while linking the micro-and macrocircuit levels. First, we address the relationships between awareness and wakefulness on the one hand, and cortico-cortical and thalamo-cortical connectivity on the other hand. Second, we discuss the role of three main types of GABAergic interneurons in specific circuits responsible for the dynamical reorganization of functional networks. Third, we explore advances in the functional role of nested oscillations for neural synchronization and communication, emphasizing the importance of the balance between local (high-frequency) and distant (low-frequency) activity for efficient information processing. The clinical implications of these theoretical considerations are presented. We propose that such cellular-scale mechanisms could extend current theories of consciousness. © 2019 Massachusetts Institute of Technology

eCOALIA: Neocortical Neural Mass Model for simulating electroencephalographic signals

This paper introduces eCOALIA, a Python-based environment for simulating intracranial local field potentials and scalp electroencephalography (EEG) signals with neural mass models. The source activity is modeled by a novel neural mass model respecting the layered structure of the neocortex. The whole-brain model is composed of coupled neural masses, each representing a brain region at the mesoscale and connected through the human connectome matrix. The forward solution on the electrode contracts is computed using biophysical modeling. eCOALIA allows parameter evolution during a simulation time course and visualizes the local field potential at the level of cortex and EEG electrodes. Advantaged with the neurophysiological modeling, eCOALIA advances the in silico modeling of physiological and pathological brain activity

eCOALIA: Neocortical Neural Mass Model for simulating electroencephalographic signals

This paper introduces eCOALIA, a Python-based environment for simulating intracranial local field potentials and scalp electroencephalography (EEG) signals with neural mass models. The source activity is modeled by a novel neural mass model respecting the layered structure of the neocortex. The whole-brain model is composed of coupled neural masses, each representing a brain region at the mesoscale and connected through the human connectome matrix. The forward solution on the electrode contracts is computed using biophysical modeling. eCOALIA allows parameter evolution during a simulation time course and visualizes the local field potential at the level of cortex and EEG electrodes. Advantaged with the neurophysiological modeling, eCOALIA advances the in silico modeling of physiological and pathological brain activity

Ictal EEG quantification in epilepsy of infancy with migrating focal seizures (EIMFS): from seizure dynamics to EEG-based markers

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Computational modeling of high frequency oscillations recorded with clinical intracranial macroelectrodes

International audienceHigh Frequency Oscillations (HFOs) are a potential biomarker of epileptogenic regions. They have been extensively investigated in terms of automatic detection, classification and feature extraction. However, the mechanisms governing the generation of HFOs as well as the observability conditions on clinical intracranial macroelectrodes remain elusive. In this paper, we propose a novel physiologically-relevant macroscopic model for accurate simulation of HFOs as invasively recorded in epileptic patients. This model accounts for both the temporal and spatial properties of the cortical patch at the origin of epileptiform activity. Indeed, neuronal populations are combined with a 3D geometrical representation to simulate an extended epileptic source. Then, by solving the forward problem, the contributions of neuronal population signals are projected onto intracerebral electrode contacts. The obtained signals are qualitatively and quantitatively compared to real HFOs, and a relationship is drawn between macroscopic model parameters such as synchronization and spatial extent on the one hand, and HFO features such as the wave and fast ripple (200-600 Hz) components, on the other hand. © 2016 IEEE