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opencc-by-4.0Mar 2022View details →

Computational analysis of cortical neuronal excitotoxicity in a large animal model of neonatal brain injury

This is the dataset accompanying the manuscript: "Computational Analysis of Cortical Neuronal Excitotoxicity in a Large Animal Model of Neonatal Brain Injury" Panagiotis Kratimenos1,2,5 *, Abhya Vij5, Robinson Vidva6, Ioannis Koutroulis3,4,5, Maria Delivoria-Papadopoulos7**, Vittorio Gallo1,5, and Aaron Sathyanesan1,5* 1Center for Neuroscience Research, Children’s National Research Institute, Children’s National Hospital, Washington DC, USA 2Department of Pediatrics, Division of Neonatology, Children’s National Hospital, Washington DC, USA 3Department of Pediatrics, Division of Emergency Medicine, Children’s National Hospital, Washington, DC, USA 4Center for Genetic Medicine Research, Children’s National Research Institute and Department of Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA 5George Washington University School of Medicine and Health Sciences, Washington DC, USA 6Digirobi Solutions, Bengaluru, Karnataka, India 7Department of Pediatrics, Drexel University College of Medicine, Philadelphia, PA, USA *Corresponding Authors: Panagiotis Kratimenos, MD, PhD: <a href="mailto:panagiotis.kratimenos@childrensnational.org">panagiotis.kratimenos@childrensnational.org</a> Aaron Sathyanesan, PhD: <a href="mailto:asathyanesan@childrensnational.org">asathyanesan@childrensnational.org</a> 111 Michigan Avenue, Washington, DC, 20010, USA

opencc-by-4.0May 2022View details →

Data set of Reversing anterior insular cortex neuronal hypoexcitability attenuates compulsive behavior in juvenile rats

Development of self-regulatory competencies during adolescence is partially dependent on normative brain maturation. Here we report that adolescent rats as compared to adults exhibit impulsive and compulsive-like behavioral traits, the latter being associated with lower expression of mRNA levels of the immediate early gene zif268 in the anterior insula cortex (AIC). This suggests that underdeveloped AIC function in adolescent rats could contribute to an immature pattern of interoceptive cue integration in decision-making and a compulsive phenotype.  In support of this, we report that layer 5 pyramidal neurons in the adolescent rat AIC are hypoexcitable and receive fewer glutamatergic synaptic inputs compared to adults. Chemogenetic activation of the AIC attenuated compulsive traits in adolescent rats supporting the idea that in early stages of AIC maturity there exists a suboptimal integration of sensory and cognitive information that contributes to inflexible behaviors in specific conditions of reward availability.

opencc-by-4.0Dec 2021View details →

Stimulation of medial amygdala GABA neurons with kinetically different channelrhodopsins yields opposite behavioral outcomes

This dataset represents the raw data that gave rise to the study by Baleisyte et al., Cell Reports 2022 (DOI: 10.1016/j.celrep.2022.110850), previously published as a preprint at bioRxiv (DOI: 10.1101/2021.06.30.450543). Please refer to the original publication regarding experimental design and methodological details of data acquisition and analysis.  Below we supply information on the provided metadata files which, in turn, refer to individual raw data files. General repository structure: <ul> <li>the raw data is organized in 11 datasets related to the Figures 1, S1, 2, S2, 3A-H, 3I-K, 3L-N, S3A-I, S3J-P, 4A;C-G;H_top;I-J;S4, 4B;C-G;H_bottom;I-J;S4;</li> <li>the metadata listing individual data filenames from the individual dataset are stored in separate “.csv” files, one per dataset. Field separator: comma;</li> <li>the custom script for the reconstruction of the optic fiber placement is described in a separate metadata file “script_metadata.csv”. Field separator: comma;</li> <li>all individual metadata files are summarized in a master metadata file “metadata_master.csv”. Field separator: comma.</li> <li>the data files related to Figures 2, S2, S3A-I are continued in a separate linked repository accessible by the following doi: 10.5281/zenodo.6489354</li> </ul>   Description of the data formats: <ul> <li>video recordings of resident-intruder test experiments (Figures 1, 3, S1) are provided as unmodified “.mpg” files created by the acquisition software EthoVision (Noldus Information Technology). The files were, however, renamed for convenience. Video stream parameters: MPEG-4 (DIVX) codec, color space yuv420p, 1280x512 pixels, 30 fps. Along with each video file, there is an associated text file (“.txt”) containing the metadata of video recording and the timestamps of hardware state changes. In these files, unmodified after creation by the EthoVision software, the status of hardware TTL inputs was logged whenever a change of state of these inputs was detected. Typically, “input 2” was sampling the gating signal from the Master-8 pulse generator, with the "high" signal level indicating the application of train of light pulses. This hardware state, signaling the presence of the light train, is noted in the individual metadata files;    </li> <li>widefield fluorescent images of single coronal sections containing the MeApd (Figures 2, S2) were converted from the proprietary format of Olympus slide scanning microscope into composite TIFF format, readable by FIJI/ImageJ (<a href="https://fiji.sc/">https://fiji.sc/</a> or <a href="https://imagej.net/Fiji/Downloads">https://imagej.net/Fiji/Downloads</a>). The information on pixel resolution and inter-section distance is embedded in the individual image files as TIFF metadata. Attribution of fluorescent probes to the color channels is given in the corresponding metadata files.</li> <li>confocal fluorescent image stacks acquired from single coronal sections containing MeApd (Figure S3) are provided in composite TIFF format after stitching the tiles (originally stored as “lsm” format; Carl Zeiss) using a stitching plugin (Preibisch et al., Bioinformatics 2009) in FIJI. The information on pixel resolution is embedded inside the individual image files as TIFF metadata. Attribution of fluorescent probes to the color channels is given in the corresponding metadata file. For each stack, a region of interest (ROI) highlighting the borders of the MeApd is provided as a separate file in a “.roi“ format (FIJI).</li> <li>patch clamp recordings (Figures 4, S4) are provided as “.dat” files, unmodified from the original version created by the acquisition software PatchMaster (HEKA Elektronik, Germany). Besides by the original PatchMaster software, these files can be imported using one of the following methods: I) via Igor Pro extension bpc_ReadHeka.xop (for 32-bit Igor Pro versions 5.xx - 6.37) by Holger Taschenberger (<a href="https://www.wavemetrics.com/project/bpc_ReadHeka">https://www.wavemetrics.com/project/bpc_ReadHeka</a>); II) via Python script by Luke Campagnola (<a href="https://github.com/campagnola/heka_reader">https://github.com/campagnola/heka_reader</a>); III) via Matlab script HEKA PatchMaster Importer by Christian Keine (<a href="https://github.com/ChristianKeine/HEKA_Patchmaster_Importer">https://github.com/ChristianKeine/HEKA_Patchmaster_Importer</a>).</li> </ul>

opencc-by-4.0Apr 2022View details →

Structural conversion of α-synuclein at the mitochondria induces neuronal toxicity; Image data

Lists of image sets included in "Structural conversion of α-synuclein at the mitochondria induces neuronal toxicity"   Duplex-1 and Duplex-2 Images Amyloid Fibril TIRFM Images (SNCA-A53T ImagesTIRF Images) TEM Fibril Images DLS Images  SMLM Images 1 & 2   CLEM Images <ul> <li>FIB SEM Images (videos)</li> <li>TEM Images </li> </ul>   Live-cell imaging <ul> <li>Superoxide Images</li> <li>MitoTracker® Red Images</li> <li>Membrane Potential (TMRM) Images </li> <li>Ca 2+ Images</li> <li>NADH Autofluorescence Images</li> <li>Cell Death Images</li> <li>Amytracker Images</li> <li>Cardiolipin Images</li> <li>FRET Images</li> </ul>

opencc-zeroMay 2022View details →

Dataset and software to analyze and simulate neuronal morphogenesis in Drosophila class IV da neurons

The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology. Yet, the underlying principles by which molecular interactions generate branched morphologies are not understood. To elucidate these principles, we visualized the growth of dendrites throughout larval development of Drosophila sensory neurons and discovered that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into a novel, agent-based computational model, we showed that the complex and highly variable dendritic morphologies of these cells are a consequence of the stochastic dynamics of their dendrite tips. These principles may generalize to branching of other neuronal cell-types, as well as to branching at the subcellular and tissue levels.

opencc-zeroJun 2022View details →

Intrinsic excitability mechanisms of neuronal ensemble formation

Neuronal ensembles are coactive groups of cortical neurons, found in spontaneous and evoked activity, that can mediate perception and behavior. To understand the mechanisms that lead to the formation of ensembles, we co-activated optogenetically and electrically layer 2/3 pyramidal neurons in brain slices from mouse visual cortex, in animals from both sexes, replicating in vitro an optogenetic protocol to generate ensembles in vivo. Using whole-cell and perforated patch-clamp pair recordings we find that, after optogenetic or electrical stimulation, coactivated neurons increase their correlation in spontaneous activity, a hallmark of ensemble formation. Coactivated neurons showed small biphasic changes in presynaptic plasticity, with an initial depression followed by a potentiation after a recovery period. Unexpectedly, optogenetic and electrical stimulation-induced significant increases in frequency and amplitude of spontaneous EPSPs, even after single-cell stimulation. In addition, we observed strong and persistent increases in neuronal excitability after stimulation, with increases in membrane resistance and reduction in spike threshold. A pharmacological agent that blocks changes in membrane resistance can revert this effect. These significant increases in excitability may partly explain the observed biphasic synaptic plasticity. We propose that cell-intrinsic changes in excitability are involved in the formation of neuronal ensembles. We propose an "iceberg" model, by which increased neuronal excitability makes subthreshold connections suprathreshold, enhancing the effect of already existing synapses, and generating a new neuronal ensemble.

opencc-zeroJun 2022View details →

Molecular and electrophysiological features of GABAergic neurons in the dentate gyrus reveal limited homology with cortical interneurons

GABAergic interneurons tend to diversify into similar classes across telencephalic regions. However, it remains unclear whether the electrophysiological and molecular properties commonly used to define these classes are discriminant in the hilus of the dentate gyrus. Here, using patch-clamp combined with single cell RT-PCR, we compare the relevance of commonly used electrophysiological and molecular features for the clustering of GABAergic interneurons sampled from the mouse hilus and primary sensory cortex. While unsupervised clustering groups cortical interneurons into well-established classes, it fails to provide a convincing partition of hilar interneurons. Statistical analysis based on resampling indicates that hilar and cortical GABAergic interneurons share limited homology. While our results do not invalidate the use of classical molecular marker in the hilus, they indicate that classes of hilar interneurons defined by the expression of molecular markers do not exhibit strongly discriminating electrophysiological properties.

opencc-by-4.0Jul 2022View details →

Molecular and Anatomical Characterization of Parabrachial Neurons and Their Axonal Projections

The parabrachial nucleus (PBN) predominantly relays threatening signals and learned sensory cues that predict threats to forebrain regions. Viral-mediated expression of Cre-dependent effector genes in Cre-driver lines of mice has demonstrated that specific populations of PBN neurons are necessary and sufficient for learning about threats. However, these neurons account for only a fraction of the total population. To further our understanding of the complexity of the PBN neuronal populations, we used single-cell RNA-sequencing technologies, which revealed 21 clusters of neurons (19 glutamatergic, 2 GABAergic) in the PBN and neighboring regions. RNAscope HiPlex in situ hybridization located 12 of these clusters within subregions of the PBN. Viral expression of fluorescent proteins in 21 Cre-driver lines of mice was used to map their axonal projections throughout the brain; they constitute two pathways innervating distinct brain regions. These results are a resource for further interrogation of PBN functions.

opencc-by-4.0Sep 2022View details →

Expression of immunoglobulin constant domain genes in neurons of the mouse central nervous system

Data related to the publication "Expression of immunoglobulin constant domain genes in neurons of the mouse central nervous system".  The fasta file (.fa) contains the sequence of neuronal FC-Ighm The .pdb files contain the model of the two different protein versions of Ighm The excel file contains the data of the quantification of co-expression and the ATG prediction results.

opencc-zeroSep 2022View details →

Microtubule retrograde flow retains neuronal polarization in a fluctuating state

In developing vertebrate neurons, a neurite is formed by more than a hundred microtubules. While individual microtubules are dynamic, the microtubule array has been regarded as stationary. Using live-cell imaging of neurons in culture or in brain slices, combined with photoconversion techniques and pharmacological manipulations, we uncovered that the microtubule array flows retrogradely within neurites to the soma. This flow drives cycles of microtubule density, a hallmark of the fluctuating state before axon formation, thereby inhibiting neurite growth. The motor protein dynein fuels this process. Shortly after axon formation, microtubule retrograde flow slows down in the axon, reducing microtubule density cycles and enabling axon extension. Thus, keeping neurites short is an active process. Microtubule retrograde flow is a novel type of cytoskeletal dynamics, which changes the hitherto axon-centric view of neuronal polarization.

opencc-by-4.0Sep 2022View details →

Inhibitory Kcnip2 neurons of the spinal dorsal horn control behavioral sensitivity to environmental cold

Excel file containing datasets for all Figures published in the article "Inhibitory Kcnip2 neurons of the spinal dorsal horn control behavioral sensitivity to environmental cold" by Albisetti et al.

opencc-by-4.0Oct 2022View details →

STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale (validation data)

Raw and refined data for the validation tests performed in the paper "STEPS 4.0: Fast and memory-efficient molecular simulations of neurons at the nanoscale": - rallpack 1, analytical, STEPS3, STEPS4, mesh scaling with STEPS4. It is so simple that I provide only the raw data. The space saving using refined data is minimal - rallpack 3, STEPS 3, STEPS 4. Both raw and refined data are provided. - caburst, STEPS 3, STEPS 4. Both raw and refined data are provided.

opencc-by-4.0Oct 2010View details →

Figure 3: The microscopy images fo neuronal cells generated by SWCNT (a) and MWCNT (b)-COMPARATIVE STUDY OF SINGLE- AND MULTI-WALL CARBON NANOTUBES WITH APPLICATION IN CEREBRAL ANEURYSM

Carbon nanotubes (CNTs) are nanometer-scale cylindrical graphitic struc- tures that exhibit extraordinary physical properties as determined by their structure [6]. Developing neural implants and the process of neuron regener- ation are extremely di±cult. Nerve cells require the right environment and the right growth factors at the right time to grow and proliferate. The elec- trical conductive properties of these nanotubes o®er the possibility of using it as a replacement to transmit and receive signals. The resulting 'hair like' conductive wires that incorporate the properties of electrodes, permeable mi- cro°uidic conduits and the porosity of the CNTs was found to promote cell growth, migration and proliferation. The bridging consists either of an axon or bundles of axons and dendrites. In some cases the bridge is covered with clusters of cells [7]. These bridges form very e±ciently over quartz surfaces which are apparently very poor surfaces for cell attachment. Fig. 2 shows the evolution of a network generated by SWCNT and MWCNT. The data show that cells ¯rst aggregate at the NT islands. As they complete this step axons and dendrites begin to form and to build connections. Also, has been observed for MWCNT higher connections than for SWCNT, Figure 3.

opencc-by-4.0Jan 2018View details →

Figure 2: The microscopy images fo neuronal cells control (a) generated by MWCNT (b) and SWCNT (c)-COMPARATIVE STUDY OF SINGLE- AND MULTI-WALL CARBON NANOTUBES WITH APPLICATION IN CEREBRAL ANEURYSM

Fig. 2 shows the evolution of a network generated by SWCNT and MWCNT. The data show that cells ¯rst aggregate at the NT islands. As they complete this step axons and dendrites begin to form and to build connections.

BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 10: A typical neuron and postsynaptic connections with weights and delays

First, an initial random population of creatures is generated where the neural networks of the creatures are coded as chromosomes, as shown in Figure 10a and Figure 10b. Each chromosome consists of four parts: A1, A2, A3 and A4. Each part consists of N segments for N neurons of a typical neural network structure. The first part, A1, denotes a, b, c and d parameters of neurons Izhikevich model (discussed in (1) and (2)). Each segment of A2 shows postsynaptic weights and connections for corresponding neuron and each segment of A3 indicates postsynaptic delays of theconnections. Segment A4 shows postsynaptic neurons that are connected to corresponding neuron, as shown in Figure 10b.

BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 2. Example of different axonal conduction delay between presynaptic neuron A0 and postsynaptic neuron A1 and A2

Different axonal conduction delays between every two neurons are applied as follows: For instance, consider presynaptic neuron A0 and postsynaptic neurons A1 and A2 in Figure 2. If neuron A0 fires spike S0 in t = 9 and S1 in t = 11 and with a time step equal to 0.5 ms, S0 arrives to A1 in t = 13 and to A2 in t = 14 where S1 arrives to A1 in t = 15 and to A2 in t = 16. This is implemented using counters assigned to each spike which increases with each time step in the time window. If counters arrive to axonal delay values, the effect of spikes from presynaptic neuron applies to the post synaptic neurons.

BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 1. Typical feedforward artificial neural network processing unit

In Figure 1, each input X is weighted by a separate weight value w. These are then summed together to give a total input value for the neuron. This total can then be passed through a function, to transform it into the desired output value. This is then compared to the actual output value d; where the error or differences between the two sets is measured and used to correct the weight values, to bring the two sets of values closer. One way to update the weights is after each individual pattern is presented and processed. Another option is to process the error after the presentation of the whole dataset, as part of a batch update.

BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 2. New neuronal model, based on matching a wave-like representation of the output.

For the new model, shown in Figure 2, the ‘differences’ between the outputs is measured and combined, to produce a kind of wave-like shape. If output point 1 has a value of 10, and output point 2 has a value of 5, for example, then this results in the shape moving down 5 points on the wave shape. This shape will, of course, change depending on the order that the dataset values are presented in. Also, if new data is presented, then that will produce a different shape. However, there is still an association between the input values and the output values and it is still this relation that is being learnt. The neural network needs to be able to learn a function that can generalise over the presented datasets, so that it can recognise the relation in previously unseen data as well. Generalising over trying to learn a wave-like shape, or trying to match the output values exactly looks quite similar, suggesting that the process is at least valid. In Figure 2, note that a transposition to move the learned shape up or down first is possible, before a weight value would try to scale it. If the initial match of the combined inputs can be as close as possible, then these adjustments will become less and will be more for fine tuning.

BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 4: Pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons

In each time step, axonal conduction delays between presynaptic neurons and postsynaptic neurons are examined whether they are equal to the elements of array I_S, to apply the respective spikes. The pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons are shown in Figure 4.