Cholinergic Switch between Two Types of Slow Waves in Cerebral Cortex.

Sleep slow waves are known to participate in memory consolidation, yet slow waves occurring under anesthesia present no positive effects on memory. Here, we shed light onto this paradox, based on a combination of extracellular recordings in vivo, in vitro, and computational models. We find two types of slow waves, based on analyzing the temporal patterns of successive slow-wave events. The first type is consistently observed in natural slow-wave sleep, while the second is shown to be ubiquitous under anesthesia. Network models of spiking neurons predict that the two slow wave types emerge due to a different gain on inhibitory versus excitatory cells and that different levels of spike-frequency adaptation in excitatory cells can account for dynamical distinctions between the two types. This prediction was tested in vitro by varying adaptation strength using an agonist of acetylcholine receptors, which demonstrated a neuromodulatory switch between the two types of slow waves. Finally, we show that the first type of slow-wave dynamics is more sensitive to external stimuli, which can explain how slow waves in sleep and anesthesia differentially affect memory consolidation, as well as provide a link between slow-wave dynamics and memory diseases.

Duplication 2p16 is associated with perisylvian polymicrogyria.

Polymicrogyria (PMG) is a heterogeneous brain malformation that may result from prenatal vascular disruption or infection, or from numerous genetic causes that still remain difficult to identify. We identified three unrelated patients with polymicrogyria and duplications of chromosome 2p, defined the smallest region of overlap, and performed gene pathway analysis using Cytoscape. The smallest region of overlap in all three children involved 2p16.1-p16.3. All three children have bilateral perisylvian polymicrogyria (BPP), intrauterine and postnatal growth deficiency, similar dysmorphic features, and poor feeding. Two of the three children had documented intellectual disability. Gene pathway analysis suggested a number of developmentally relevant genes and gene clusters that were over-represented in the critical region. We narrowed a rare locus for polymicrogyria to a region of 2p16.1-p16.3 that contains 33-34 genes, 23 of which are expressed in cerebral cortex during human fetal development. Using pathway analysis, we showed that several of the duplicated genes contribute to neurodevelopmental pathways including morphogen, cytokine, hormonal and growth factor signaling, regulation of cell cycle progression, cell morphogenesis, axonal guidance, and neuronal migration. These findings strengthen the evidence for a novel locus associated with polymicrogyria on 2p16.1-p16.3, and comprise the first step in defining the underlying genetic etiology.

Dendritic sodium spikes endow neurons with inverse firing rate response to correlated synaptic activity.

Many neurons possess dendrites enriched with sodium channels and are capable of generating action potentials. However, the role of dendritic sodium spikes remain unclear. Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods. However, neurons with active dendrites show the opposite behavior: for a wide range of conditions the firing rate decreases as a function of correlation. We found this property in three types of models of dendritic excitability: a Hodgkin-Huxley model of dendritic spikes, a model with integrate and fire dendrites, and a discrete-state dendritic model. We conclude that fast dendritic spikes confer much broader computational properties to neurons, sometimes opposite to that of point neurons.

Gene networks show associations with seed region connectivity.

Primary patterns in adult brain connectivity are established during development by coordinated networks of transiently expressed genes; however, neural networks remain malleable throughout life. The present study hypothesizes that structural connectivity from key seed regions may induce effects on their connected targets, which are reflected in gene expression at those targeted regions. To test this hypothesis, analyses were performed on data from two brains from the Allen Human Brain Atlas, for which both gene expression and DW-MRI were available. Structural connectivity was estimated from the DW-MRI data and an approach motivated by network topology, that is, weighted gene coexpression network analysis (WGCNA), was used to cluster genes with similar patterns of expression across the brain. Group exponential lasso models were then used to predict gene cluster expression summaries as a function of seed region structural connectivity patterns. In several gene clusters, brain regions located in the brain stem, diencephalon, and hippocampal formation were identified that have significant predictive power for these expression summaries. These connectivity-associated clusters are enriched in genes associated with synaptic signaling and brain plasticity. Furthermore, using seed region based connectivity provides a novel perspective in understanding relationships between gene expression and connectivity. Hum Brain Mapp 38:3126-3140, 2017. © 2017 Wiley Periodicals, Inc.

Netrin-1 promotes excitatory synaptogenesis between cortical neurons by initiating synapse assembly.

Netrin-1 is a secreted protein that directs long-range axon guidance during early stages of neural circuit formation and continues to be expressed in the mammalian forebrain during the postnatal period of peak synapse formation. Here we demonstrate a synaptogenic function of netrin-1 in rat and mouse cortical neurons and investigate the underlying mechanism. We report that netrin-1 and its receptor DCC are widely expressed by neurons in the developing mammalian cortex during synapse formation and are enriched at synapses in vivo. We detect DCC protein distributed along the axons and dendrites of cultured cortical neurons and provide evidence that newly translated netrin-1 is selectively transported to dendrites. Using gain and loss of function manipulations, we demonstrate that netrin-1 increases the number and strength of excitatory synapses made between developing cortical neurons. We show that netrin-1 increases the complexity of axon and dendrite arbors, thereby increasing the probability of contact. At sites of contact, netrin-1 promotes adhesion, while locally enriching and reorganizing the underlying actin cytoskeleton through Src family kinase signaling and m-Tor-dependent protein translation to locally cluster presynaptic and postsynaptic proteins. Finally, we demonstrate using whole-cell patch-clamp electrophysiology that netrin-1 increases the frequency and amplitude of mEPSCs recorded from cortical pyramidal neurons. These findings identify netrin-1 as a synapse-enriched protein that promotes synaptogenesis between mammalian cortical neurons.

High-Density Exploration of Activity States in a Multi-Area Brain Model.

To simulate whole brain dynamics with only a few equations, biophysical, mesoscopic models of local neuron populations can be connected using empirical tractography data. The development of mesoscopic mean-field models of neural populations, in particular, the Adaptive Exponential (AdEx mean-field model), has successfully summarized neuron-scale phenomena leading to the emergence of global brain dynamics associated with conscious (asynchronous and rapid dynamics) and unconscious (synchronized slow-waves, with Up-and-Down state dynamics) brain states, based on biophysical mechanisms operating at cellular scales (e.g. neuromodulatory regulation of spike-frequency adaptation during sleep-wake cycles or anesthetics). Using the Virtual Brain (TVB) environment to connect mean-field AdEx models, we have previously simulated the general properties of brain states, playing on spike-frequency adaptation, but have not yet performed detailed analyses of other parameters possibly also regulating transitions in brain-scale dynamics between different brain states. We performed a dense grid parameter exploration of the TVB-AdEx model, making use of High Performance Computing. We report a remarkable robustness of the effect of adaptation to induce synchronized slow-wave activity. Moreover, the occurrence of slow waves is often paralleled with a closer relation between functional and structural connectivity. We find that hyperpolarization can also generate unconscious-like synchronized Up and Down states, which may be a mechanism underlying the action of anesthetics. We conclude that the TVB-AdEx model reveals large-scale properties identified experimentally in sleep and anesthesia.

A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics.

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirically observed spontaneous and stimulus-evoked dynamics in space, time, phase, and frequency domains. Large-scale properties of cortical dynamics are shown to emerge from both microscopic-scale adaptation that control transitions between wake-like to sleep-like activity, and the organization of the human structural connectome; together, they shape the spatial extent of synchrony and phase coherence across brain regions consistent with the propagation of sleep-like spontaneous traveling waves at intermediate scales. Remarkably, the model also reproduces brain-wide, enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.

Bridging Single Neuron Dynamics to Global Brain States.

Biological neural networks produce information backgrounds of multi-scale spontaneous activity that become more complex in brain states displaying higher capacities for cognition, for instance, attentive awake versus asleep or anesthetized states. Here, we review brain state-dependent mechanisms spanning ion channel currents (microscale) to the dynamics of brain-wide, distributed, transient functional assemblies (macroscale). Not unlike how microscopic interactions between molecules underlie structures formed in macroscopic states of matter, using statistical physics, the dynamics of microscopic neural phenomena can be linked to macroscopic brain dynamics through mesoscopic scales. Beyond spontaneous dynamics, it is observed that stimuli evoke collapses of complexity, most remarkable over high dimensional, asynchronous, irregular background dynamics during consciousness. In contrast, complexity may not be further collapsed beyond synchrony and regularity characteristic of unconscious spontaneous activity. We propose that increased dimensionality of spontaneous dynamics during conscious states supports responsiveness, enhancing neural networks' emergent capacity to robustly encode information over multiple scales.

Quantitative and Qualitative Analysis of Transient Fetal Compartments during Prenatal Human Brain Development.

The cerebral wall of the human fetal brain is composed of transient cellular compartments, which show characteristic spatiotemporal relationships with intensity of major neurogenic events (cell proliferation, migration, axonal growth, dendritic differentiation, synaptogenesis, cell death, and myelination). The aim of the present study was to obtain new quantitative data describing volume, surface area, and thickness of transient compartments in the human fetal cerebrum. Forty-four postmortem fetal brains aged 13-40 postconceptional weeks (PCW) were included in this study. High-resolution T1 weighted MR images were acquired on 19 fetal brain hemispheres. MR images were processed using in-house software (MNI-ACE toolbox). Delineation of fetal compartments was performed semi-automatically by co-registration of MRI with histological sections of the same brains, or with the age-matched brains from Zagreb Neuroembryological Collection. Growth trajectories of transient fetal compartments were reconstructed. The composition of telencephalic wall was quantitatively assessed. Between 13 and 25 PCW, when the intensity of neuronal proliferation decreases drastically, the relative volume of proliferative (ventricular and subventricular) compartments showed pronounced decline. In contrast, synapse- and extracellular matrix-rich subplate compartment continued to grow during the first two trimesters, occupying up to 45% of telencephalon and reaching its maximum volume and thickness around 30 PCW. This developmental maximum coincides with a period of intensive growth of long cortico-cortical fibers, which enter and wait in subplate before approaching the cortical plate. Although we did not find significant age related changes in mean thickness of the cortical plate, the volume, gyrification index, and surface area of the cortical plate continued to exponentially grow during the last phases of prenatal development. This cortical expansion coincides developmentally with the transformation of embryonic cortical columns, dendritic differentiation, and ingrowth of axons. These results provide a quantitative description of transient human fetal brain compartments observable with MRI. Moreover, they will improve understanding of structural-functional relationships during brain development, will enable correlation between in vitro/in vivo imaging and fine structural histological studies, and will serve as a reference for study of perinatal brain injuries.

Generation of microisland cultures using microcontact printing to pattern protein substrates.

The capacity to isolate small numbers of neurons in vitro is an essential tool to study the cell biology of synapses and the development of neuronal networks by specific cell types. Microisland culture assays allow for single neurons, or simple neural networks, to be isolated on islands of glial cells; however, the techniques commonly used to produce microisland substrates are expensive, challenging to control, and typically result in many discarded substrates. Here, we used microcontact printing to pattern a glass surface with islands of extracellular matrix proteins known to support neural cell growth and differentiation. To promote segregation of the cells to the islands, the substrate surrounding the islands was backfilled with polyethylene glycol (PEG), forming a relatively non-permissive surface on which cell attachment is limited. Astrocytes, and subsequently hippocampal neurons, were then seeded onto the islands of patterned protein. Using this method, readily reproducible patterns of protein islands were produced that permit cell attachment, differentiation, and growth. The technique is a rapid, inexpensive, and reliable means to generate patterned substrates appropriate for microisland cultures.