Dividing the Preplate: Characterization of Neuronal Subpopulations in the Early Murine Cerebral Cortex

The preplate is a transient layer of the developing cerebral cortex which is comprised of the earliest generated cortical neurons. Preplate neurons are a heterogenous population of future Cajal-Retzius neurons and future subplate neurons, which are derived from multiple sources of progenitors. During the formation of the cortical layers, the preplate is split into an upper marginal zone and the lower subplate layer by the radial migration of projection neurons from the cortical ventricular zone. Cajal-Retzius and subplate neurons have important developmental functions in regulating radial migration and in pioneering corticofugal projections. The genetic mechanisms of preplate neuron specification are not well understood, and few markers exist to identify subpopulations of the preplate. The aim of this thesis is to functionally and molecularly characterize neuronal subpopulations of the mouse preplate. Using transgenic mice expressing EGFP in distinct preplate subpopulations, I applied birthdating analyses and live imaging to describe the proliferative and migratory characteristics of subpopulations of Cajal-Retzius and subplate neurons. Purified subpopulations were used in a gene expression array analysis to define mRNAs differentially expressed between subpopulations. New markers for a subpopulation of Cajal-Retzius neurons were identified, as well as novel markers for future subplate neurons, which will be of use in the study of these cells. These data may yield insight into genetic and cellular mechanisms of preplate differentiation and development, and identify novel genes with potential roles in preplate neuron functions

From microscopy data to in silico environments for in vivo-oriented simulations

In our previous study, we introduced a combination methodology of Fluorescence Correlation Spectroscopy (FCS) and Transmission Electron Microscopy (TEM), which is powerful to investigate the effect of intracellular environment to biochemical reaction processes. Now, we developed a reconstruction method of realistic simulation spaces based on our TEM images. Interactive raytracing visualization of this space allows the perception of the overall 3D structure, which is not directly accessible from 2D TEM images. Simulation results show that the diffusion in such generated structures strongly depends on image post-processing. Frayed structures corresponding to noisy images hinder the diffusion much stronger than smooth surfaces from denoised images. This means that the correct identification of noise or structure is significant to reconstruct appropriate reaction environment in silico in order to estimate realistic behaviors of reactants in vivo. Static structures lead to anomalous diffusion due to the partial confinement. In contrast, mobile crowding agents do not lead to anomalous diffusion at moderate crowding levels. By varying the mobility of these non-reactive obstacles (NRO), we estimated the relationship between NRO diffusion coefficient (Dnro) and the anomaly in the tracer diffusion (α). For Dnro=21.96 to 44.49 μ m2/s, the simulation results match the anomaly obtained from FCS measurements. This range of the diffusion coefficient from simulations is compatible with the range of the diffusion coefficient of structural proteins in the cytoplasm. In addition, we investigated the relationship between the radius of NRO and anomalous diffusion coefficient of tracers by the comparison between different simulations. The radius of NRO has to be 58 nm when the polymer moves with the same diffusion speed as a reactant, which is close to the radius of functional protein complexes in a cell.ISSN:1687-4145ISSN:1687-415

Closed-loop modulation of local slow oscillations in human NREM sleep.

Slow-wave sleep is the deep non-rapid eye-movement (NREM) sleep stage that is most relevant for the recuperative function of sleep. Its defining property is the presence of slow oscillations (<2 Hz) in the scalp electroencephalogram (EEG). Slow oscillations are generated by a synchronous back and forth between highly active UP-states and silent DOWN-states in neocortical neurons. Growing evidence suggests that closed-loop sensory stimulation targeted at UP-states of EEG-defined slow oscillations can enhance the slow oscillatory activity, increase sleep depth, and boost sleep's recuperative functions. However, several studies failed to replicate such findings. Failed replications might be due to the use of conventional closed-loop stimulation algorithms that analyze the signal from one single electrode and thereby neglect the fact that slow oscillations vary with respect to their origins, distributions, and trajectories on the scalp. In particular, conventional algorithms nonspecifically target functionally heterogeneous UP-states of distinct origins. After all, slow oscillations at distinct sites of the scalp have been associated with distinct functions. Here we present a novel EEG-based closed-loop stimulation algorithm that allows targeting UP- and DOWN-states of distinct cerebral origins based on topographic analyses of the EEG: the topographic targeting of slow oscillations (TOPOSO) algorithm. We present evidence that the TOPOSO algorithm can detect and target local slow oscillations with specific, predefined voltage maps on the scalp in real-time. When compared to a more conventional, single-channel-based approach, TOPOSO leads to fewer but locally more specific stimulations in a simulation study. In a validation study with napping participants, TOPOSO targets auditory stimulation reliably at local UP-states over frontal, sensorimotor, and centro-parietal regions. Importantly, auditory stimulation temporarily enhanced the targeted local state. However, stimulation then elicited a standard frontal slow oscillation rather than local slow oscillations. The TOPOSO algorithm is suitable for the modulation and the study of the functions of local slow oscillations

Dataset of cortical activity recorded with high spatial resolution from anesthetized rats

Publicly available neural recordings obtained with high spatial resolution are scarce. Here, we present an electrophysiological dataset recorded from the neocortex of twenty rats anesthetized with ketamine/xylazine. The wideband, spontaneous recordings were acquired with a single-shank silicon-based probe having 128 densely-packed recording sites arranged in a 32 × 4 array. The dataset contains the activity of a total of 7126 sorted single units extracted from all layers of the cortex. Here, we share raw neural recordings, as well as spike times, extracellular spike waveforms and several properties of units packaged in a standardized electrophysiological data format. For technical validation of our dataset, we provide the distributions of derived single unit properties along with various spike sorting quality metrics. This large collection of in vivo data enables the investigation of the high-resolution electrical footprint of cortical neurons which in turn may aid their electrophysiology-based classification. Furthermore, the dataset might be used to study the laminar-specific neuronal activity during slow oscillation, a brain rhythm strongly involved in neural mechanisms underlying memory consolidation and sleep

OVERSEE: Identification Of Anomalous Vessel Behaviour

This project revolves around the detection of anomalous behaviors, more specifically on maritime vessels and using the Automatic Identification System (AIS) data. To accomplish the intended goal while having several restrictions like the large amounts of data which imply performance issues or the lack of labelled data and restrictions on creating one since anomalous behavior should be detected even without a priori characterization. To achieve the detection of anomalies Hierarchical Temporal Memory (HTM) based algorithms were used. HTM theory core is based on cortical research, the algorithms used on this project have as focus the ability to learn temporal sequences, since the behavior of maritime vessels is described by a data stream of AIS data which includes fields like the geolocation, speed over ground (SOG), course over ground (COG), vessel type or identification. This project starts with the expectation that an algorithm that can use the sequences of behavior instead of the behavior at any point in time will present better results.The first step of the project was to understand how to use HTM algorithms to detect anomalous behavior. The basic model used to process the data is composed of several components. The first is a Geospatial Encoder which transforms coordinates data into Sparse Distributed Representations (SDRs) used on all other steps of the process. SDRs are basically a list of bits, each being a 0 or 1 depending on the input data but with semantic meaning which produces differences from simple binary data. The second being a Spatial Pooler algorithm able to make use of the SDRs semantics and able to normalize the data while maintaining encoded information and presenting the first step on the general learning capabilities, being able to learn and predict the next step based only on the current information. The last component is the Temporal Memory, it extends the capability of the learning algorithm, it enables the prediction of the future data based on the sequence of data previously learned. All these components allow the ability to learn complex sequences of data, in this project the ability to learn sequences was first applied to learn sequences of positions. The ability to learn sequences and later perform predictions allows a simple way of detecting anomalous behavior. After the creation of a model capable of having as input the current position and making predictions, an anomalous behavior should be detected when a prediction is very different from the actual next value. In this case if there's no close prediction to a position after the sequence of positions being previously assessed then an abnormal behavior is detected.The next phase of the project was about improving the characterization of the sequences, the vessel trajectories are not only characterized by positions but also by other information like speed or the timestamp at each new data point. This data could be used directly or by derivation of new information, e.g. timestamp can be converted into time from sequence start. To use these data as input was also important to choose the right encoder and pertinent parameters. To accomplish these a better understanding of the vessels' trajectories is fundamental to which an analysis of the data was conducted. Still, the main concern on this stage was to try different data with different HTM algorithm parameters to understand the impact of different information on the ability to detect anomalies while trying to improve the performance both in terms of learning sequences, reducing false anomalies, and improving the amount of information describing a sequence, boosting the ability to find true anomalies. This is the main phase and involves a multitude of experiments and the definition of some metrics and data samples to allow the quantification of the results obtained in order to compare different models.With an increasingly number of ships equipped with an Automatic Identification System more and more data is being generated and creating an opportunity for new studies of the maritime vessel behaviors.The problem of securing and protecting vast expanses of the maritime zone could use the help of an automatic vessel anomalous behavior system. With that objective in mind the development of this project took the capabilities of Hierarchical Temporal Memory theory and respective algorithms to help identify anomalous behaviors on vessel trajectories improving sea monitoring capabilities and the possibility of more opportune warnings and subsequent action plans from simple contact or vessel identification to arrest or rescue missions

Mol Psychiatry

Autism spectrum disorders (ASD) are common, complex and heterogeneous neurodevelopmental disorders. Cellular and molecular mechanisms responsible for ASD pathogenesis have been proposed based on genetic studies, brain pathology and imaging, but a major impediment to testing ASD hypotheses is the lack of human cell models. Here, we reprogrammed fibroblasts to generate induced pluripotent stem cells, neural progenitor cells (NPCs) and neurons from ASD individuals with early brain overgrowth and non-ASD controls with normal brain size. ASD-derived NPCs display increased cell proliferation because of dysregulation of a \u3b2-catenin/BRN2 transcriptional cascade. ASD-derived neurons display abnormal neurogenesis and reduced synaptogenesis leading to functional defects in neuronal networks. Interestingly, defects in neuronal networks could be rescued by insulin growth factor 1 (IGF-1), a drug that is currently in clinical trials for ASD. This work demonstrates that selection of ASD subjects based on endophenotypes unraveled biologically relevant pathway disruption and revealed a potential cellular mechanism for the therapeutic effect of IGF-1.KL2 TR000099/NCATS NIH HHS/National Center for Advancing Translational Sciences/United StatesDP2 OD006495/ODCDC CDC HHS/Office of the Director/United StatesR01 MH113924/NIMH NIH HHS/National Institute of Mental Health/United StatesR01 MH100175/NIMH NIH HHS/National Institute of Mental Health/United StatesR01 MH094753/NIMH NIH HHS/National Institute of Mental Health/United StatesR00 MH101634/NIMH NIH HHS/National Institute of Mental Health/United StatesU19 MH107367/NIMH NIH HHS/National Institute of Mental Health/United States2017-05-23T00:00:00Z27378147PMC52159916525vault:3354

Predicting learning and achievement using GABA and glutamate concentrations in human development

Previous research has highlighted the role of glutamate and gamma-aminobutyric acid (GABA) in learning and plasticity. What is currently unknown is how this knowledge translates to real-life complex cognitive abilities that emerge slowly and how the link between these neurotransmitters and human learning and plasticity is shaped by development. While some have suggested a generic role of glutamate and GABA in learning and plasticity, others have hypothesized that their involvement shapes sensitive periods during development. Here we used a cross-sectional longitudinal design with 255 individuals (spanning primary school to university) to show that glutamate and GABA in the intraparietal sulcus explain unique variance both in current and future mathematical achievement (approximately 1.5 years). Furthermore, our findings reveal a dynamic and dissociable role of GABA and glutamate in predicting learning, which is reversed during development, and therefore provide novel implications for models of learning and plasticity during childhood and adulthood

Materials and neuroscience: validating tools for large-scale, high-density neural recording

Extracellular recording remains the only technique capable of measuring the activity of many neurons simultaneously with a sub-millisecond precision, in multiple brain areas, including deep structures. Nevertheless, many questions about the nature of the detected signal and the limitations/capabilities of this technique remain unanswered. The general goal of this work is to apply the methodology and concepts of materials science to answer some of the major questions surrounding extracellular recording, and thus take full advantage of this seminal technique. We start out by quantifying the effect of electrode impedance on the amplitude of measured extracellular spikes and background noise. Can we improve data quality by lowering electrode impedance? We demonstrate that if the proper recording system is used, then the impedance of a microelectrode, within the range typical of standard polytrodes (~ 0.1 to 2 MΩ), does not significantly affect a neural spike amplitude or the background noise, and therefore spike sorting. In addition to improving the performance of each electrode, increasing the number of electrodes in a single neural probe has also proven advantageous for simultaneously monitoring the activity of more neurons with better spatiotemporal resolution. How can we achieve large-scale, highdensity extracellular recordings without compromising brain tissue? Here we report the design and in vivo validation of a complementary metal–oxide–semiconductor (CMOS)-based scanning probe with 1356 electrodes arranged along approximately 8 mm of a thin shaft (50 μm thick and 100 μm wide). Additionally, given the ever-shrinking dimensions of CMOS technology, there is a drive to fabricate sub-cellular electrodes (< 10 μm). Therefore, to evaluate electrode configurations for future probe designs, several recordings from many different brain regions were performed with an ultra-dense probe containing 255 electrodes, each with a geometric area of 5 x 5 μm and a pitch of 6 μm. How can we validate neural probes with different electrode materials/configurations and different sorting algorithms? We describe a new procedure for precisely aligning two probes for in vivo “paired-recordings” such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micro-pipette. We gathered a dataset of paired-recordings, which is available online. The “ground truth” data, for which one knows exactly when a neuron in the vicinity of an extracellular probe generates an action potential, has been used for several groups to validate and quantify the performance of new algorithms to automatically detect/sort single-units