Spontaneous brain activity was mapped with functional MRI (fMRI) in a sample of 180 subjects while in a conscious resting-state condition. With the use of independent component analysis (ICA) of each individual fMRI signal and classification of the ICA-defined components across subjects, a set of 23 resting-state networks (RNs) was identified. Functional connectivity between each pair of RNs was assessed using temporal correlation analyses in the 0.01- to 0.1-Hz frequency band, and the corresponding set of correlation coefficients was used to obtain a hierarchical clustering of the 23 RNs. At the highest hierarchical level, we found two anticorrelated systems in charge of intrinsic and extrinsic processing, respectively. At a lower level, the intrinsic system appears to be partitioned in three modules that subserve generation of spontaneous thoughts (M1a; default mode), inner maintenance and manipulation of information (M1b), and cognitive control and switching activity (M1c), respectively. The extrinsic system was found to be made of two distinct modules: one including primary somatosensory and auditory areas and the dorsal attentional network (M2a) and the other encompassing the visual areas (M2b). Functional connectivity analyses revealed that M1b played a central role in the functioning of the intrinsic system, whereas M1c seems to mediate exchange of information between the intrinsic and extrinsic systems.

With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behaviour and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modelling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modelled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.

Alcohol abuse is a major public health problem worldwide, causing a wide range of preventable morbidity and mortality. In this translational study, we show that heavy drinking (HD) (≥6 standard drinks/day) is independently associated with a worse outcome for ischemic stroke patients. To study the underlying mechanisms of this deleterious effect of HD, we performed an extensive analysis of the brain inflammatory responses of mice chronically exposed or not to 10% alcohol before and after ischemic stroke. Inflammatory responses were analyzed at the parenchymal, perivascular, and vascular levels by using transcriptomic, immunohistochemical, in vivo 2-photon microscopy and molecular MRI analyses. Alcohol-exposed mice show, in the absence of any other insult, a neurovascular inflammatory priming (i.e., an abnormal inflammatory status including an increase in brain perivascular macrophages [PVM]) associated with exacerbated inflammatory responses after a secondary insult (ischemic stroke or LPS challenge). Similar to our clinical data, alcohol-exposed mice showed larger ischemic lesions. We show here that PVM are key players on this aggravating effect of alcohol, since their specific depletion blocks the alcohol-induced aggravation of ischemic lesions. This study opens potentially new therapeutic avenues aiming at blocking alcohol-induced exacerbation of the neurovascular inflammatory responses triggered after ischemic stroke.

Molecular magnetic resonance imaging (MRI) holds great promise for diagnosis and therapeutic monitoring in a wide range of diseases. However, the low intrinsic sensitivity of MRI to detect exogenous contrast agents and the lack of biodegradable microprobes have prevented its clinical development. Here, we synthetized a contrast agent for molecular MRI based on a previously unknown mechanism of self-assembly of catechol-coated magnetite nanocrystals into microsized matrix-based particles. The resulting biodegradable microprobes (M3P for microsized matrix-based magnetic particles) carry up to 40,000 times higher amounts of superparamagnetic material than classically used nanoparticles while preserving favorable biocompatibility and excellent water dispersibility. After conjugation to monoclonal antibodies, targeted M3P display high sensitivity and specificity to detect inflammation in vivo in the brain, kidneys, and intestinal mucosa. The high payload of superparamagnetic material, excellent toxicity profile, short circulation half-life, and widespread reactivity of the M3P particles provides a promising platform for clinical translation of immuno-MRI.