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Results for “Neuroscience”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,153 records · Page 64Linked to original sources

bioRxiv2016

CRISPR-Cas9 targeted deletion of the C9orf72 repeat expansion mutation corrects cellular phenotypes in patient-derived iPS cells

The large hexanucleotide (GGGGCC) repeat expansion in the non-coding promoter region of C9orf72 is the leading cause of Frontotemporal Dementia (FTD) and Amyotrophic Lateral Sclerosis (ALS). Mechanisms underlying neurodegeneration are not clear, and both a C9orf72 loss of function and a gain of toxicity, in the form of RNA foci or dipeptide repeat deposition, are implicated. CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9-mediated genome editing is an attractive strategy for disease modeling and therapeutic intervention. Here we show that this system can be utilized to completely remove the large repeat expansion mutation within C9orf72 in patient-derived induced pluripotent stem (iPS) cells. Removal of the mutation prevented RNA foci formation and promoter hypermethylation, two phenotypes of the C9orf72 mutation. Interestingly, these changes did not significantly alter C9orf72 expression at the mRNA or protein level. This work provides a proof-of-principle for the use of CRISPR-Cas9-mediated excision of the pathogenic C9orf72 repeat expansion as a therapeutic strategy in FTD/ALS.\n\nOne Sentence SummaryCRISPR-Cas9-mediated excision of the large C9orf72 repeat expansion mutation rescues RNA foci formation and promoter hypermethylation without altering C9orf72 transcript and protein expression.

Neuroscience↗

bioRxiv2016

Dendritic spine taxonomy and shape transition analysis

The common approach in morphological analysis of dendritic spines is to categorize spines into subpopulations based on whether they are stubby, mushroom, thin, or filopodia. Corresponding cellular models of synaptic plasticity, long-term potentiation, and long-term depression associate synaptic strength with either spine enlargement or spine shrinkage. Although a variety of automatic spine segmentation and feature extraction methods were developed recently, no approaches allowing for an automatic and unbiased distinction between dendritic spine subpopulations and detailed computational models of spine behavior exist.\n\nWe propose an automatic and statistically based method for the unsupervised construction of spine shape taxonomy based on arbitrary features. The taxonomy is then utilized in the newly introduced computational model of behavior, which relies on transitions between shapes. Models of different populations are compared using supplied bootstrap-based statistical tests.\n\nWe compared two populations of spines at two time points. The first population was stimulated with long-term potentiation, and the other in the resting state was used as a control. The comparison of shape transition characteristics allowed us to identify differences between population behaviors. Although some extreme changes were observed in the stimulated population, statistically significant differences were found only when whole models were compared. Therefore, we hypothesize that the learning process is related to the subtle changes in the whole ensemble of different dendritic spine structures, but not at the level of single shape classes.\n\nThe source code of our software is freely available for non-commercial use1.

Neuroscience↗

bioRxiv2016

Systematic exploration of unsupervised methods for mapping behavior

To fully understand the mechanisms giving rise to behavior, we need to be able to precisely measure it. When coupled with large behavioral data sets, unsupervised clustering methods offer the potential of unbiased mapping of behavioral spaces. However, unsupervised techniques to map behavioral spaces are in their infancy, and there have been few systematic considerations of all the methodological options. We compared the performance of seven distinct mapping methods in clustering a data set consisting of the x-and y-positions of the six legs of individual flies. Legs were automatically tracked by small pieces of fluorescent dye, while the fly was tethered and walking on an air-suspended ball. We find that there is considerable variation in the performance of these mapping methods, and that better performance is attained when clustering is done in higher dimensional spaces (which are otherwise less preferable because they are hard to visualize). High dimensionality means that some algorithms, including the non-parametric watershed cluster assignment algorithm, cannot be used. We developed an alternative watershed algorithm which can be used in high-dimensional spaces when the probability density estimate can be computed directly. With these tools in hand, we examined the behavioral space of fly leg postural dynamics and locomotion. We find a striking division of behavior into modes involving the fore legs and modes involving the hind legs, with few direct transitions between them. By computing behavioral clusters using the data from all flies simultaneously, we show that this division appears to be common to all flies. We also identify individual-to-individual differences in behavior and behavioral transitions. Lastly, we suggest a computational pipeline that can achieve satisfactory levels of performance without the taxing computational demands of a systematic combinatorial approach.\n\nAbbreviationsGMM: Gaussian mixture model; PCA: principal components analysis; SW: sparse watershed; t-SNE: t-distributed stochastic neighbor embedding

Neuroscience↗

bioRxiv2016

Generating an Executable Model of the Drosophila Central Complex

The central complex (CX) is a set of neuropils in the center of the fly brain that have been implicated as playing an important role in vision-mediated behavior and integration of spatial information for locomotor control. In contrast to currently available data regarding the neural circuitry of neuropils in the fly's vision and olfactory systems, comparable data for the CX neuropils is relatively incomplete; many categories of neurons remain only partly characterized, and the synaptic connectivity between CX neurons has yet to be experimentally determined. Successful modeling of the information processing functions of the CX neuropils therefore requires a means of easily constructing and testing a range of hypotheses regarding both the high-level structure of their neural circuitry and the properties of their constituent neurons and synapses. This document demonstrates how NeuroArch and Neurokernel may be used to algorithmically construct and evaluate executable neural circuit models of the CX neuropils and their interconnects based upon currently available information regarding the geometry and polarity of the arborizations of identified local and projection neurons in the CX.

Neuroscience↗

bioRxiv2016

A signature of neural coding at human perceptual limits

Simple visual features, such as orientation, are thought to be represented in the spiking of visual neurons using population codes. I show that optimal decoding of such activity predicts characteristic deviations from the normal distribution of errors at low gains. Examining human perception of orientation stimuli, I show that these predicted deviations are present at near-threshold levels of contrast. The findings may provide a neural-level explanation for the appearance of a threshold in perceptual awareness, whereby stimuli are categorized as seen or unseen. As well as varying in error magnitude, perceptual judgments differ in certainty about what was observed. I demonstrate that variations in the total spiking activity of a neural population can account for the empirical relationship between subjective confidence and precision. These results establish population coding and decoding as the neural basis of perception and perceptual confidence.

Neuroscience↗

bioRxiv2016

Calcium imaging reveals coordinated simple spike pauses in populations of Cerebellar Purkinje cells

The brains control of movement is thought to involve coordinated activity between cerebellar Purkinje cells. The results reported here demonstrate that somatic Ca2+ imaging is a faithful reporter of Na+-dependent \"simple spike\" pauses and enables us to optically record changes in firing rates in populations of Purkinje cells. This simultaneous calcium imaging of populations of Purkinje cells reveals a striking spatial organization of pauses in Purkinje cell activity between neighboring cells. The source of this organization is shown to be the presynaptic GABAergic network and blocking GABAARs abolishes the synchrony. These data suggest that presynaptic interneurons synchronize (in)activity between neighboring Purkinje cells and thereby maximize their effect on downstream targets in the deep cerebellar nuclei.

Neuroscience↗

bioRxiv2016

Age differences in brain white matter microstructure in UK Biobank (N = 3,513)

Quantifying the microstructural properties of the human brains connections is necessary for understanding normal ageing and disease states. We examined brain white matter MRI data in 3,513 generally healthy people aged 45-75 years from the UK Biobank sample. Using conventional water diffusion measures and newer, as-yet rarely-studied indices from neurite orientation dispersion and density imaging (NODDI), we document large age differences in white matter microstructure. Mean diffusivity was the most age-sensitive diffusion measure, with negative age associations strongest in the thalamic radiation and association fibres. Inter-individual differences in white matter microstructure across brain tracts become increasingly correlated in older age. This connectivity de-differentiation may reflect an age-related aggregation of systemic detrimental effects on the brain. We report several other novel results, including comparative age associations with volumetric indices and associations with hemisphere and sex. Results from this unusually large, single-scanner sample provide one of the most definitive characterisations to date of age differences in major white matter tracts in the human brain.\n\nAbbreviations

Neuroscience↗

bioRxiv2016

Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels

BackgroundData-driven approaches can capture behavioral and biological variation currently unaccounted for by contemporary diagnostic categories, thereby enhancing the ability of neurobiological studies to characterize brain-behavior relationships.\n\nMethodsA community-ascertained sample of individuals (N=347, ages 18-59) completed a battery of behavioral measures, psychiatric assessment, and resting state functional magnetic resonance imaging (R-fMRI) in a cross-sectional design. Bootstrap-based exploratory factor analysis was applied to 49 phenotypic subscales from 10 measures. Hybrid Hierarchical Clustering was applied to resultant factor scores to identify nested groups. Adjacent groups were compared via independent samples t-tests and chi-square tests of factor scores, syndrome scores, and psychiatric prevalence. Multivariate Distance Matrix Regression examined functional connectome differences between adjacent groups.\n\nResultsReduction yielded six factors, which explained 77.8% and 65.4% of the variance in exploratory and constrained exploratory models, respectively. Hybrid Hierarchical Clustering of these 6 factors identified 2, 4, and 8 nested groups (i.e., phenotypic communities). At the highest clustering level, the algorithm differentiated functionally adaptive and maladaptive groups. At the middle clustering level, groups were separated by problem type (maladaptive groups; internalizing vs. externalizing problems) and behavioral type (adaptive groups; sensation-seeking vs. extraverted/emotionally stable). Unique phenotypic profiles were also evident at the lowest clustering level. Group comparisons exhibited significant differences in intrinsic functional connectivity at the highest clustering level in somatomotor, thalamic, basal ganglia, and limbic networks.\n\nConclusionsData-driven approaches for identifying homogenous subgroups, spanning typical function to dysfunction not only yielded clinically meaningful groups, but captured behavioral and neurobiological variation among healthy individuals as well.

Neuroscience↗

bioRxiv2016

Complementary learning systems within the hippocampus: A neural network modeling approach to reconciling episodic memory with statistical learning

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway -- the pathway connecting entorhinal cortex directly to region CA1 -- was able to support statistical learning, while the trisynaptic pathway -- connecting entorhinal cortex to CA1 through dentate gyrus and CA3 -- learned only individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself.

Neuroscience↗

bioRxiv2016

Refined biomimetic model of chronic pain healed with erythropoietin

Humans suffering with chronic pain may have no evidence of a lesion or disease. They are managed with a morass of drugs and invasive procedures. In many, their persistent pain occurs after the healing of a soft tissue injury, with a neural source hypothesized. Opiates, commonly used to mitigate their symptoms, can cause an increase in neuropathic pain over time. Current animal models of neuropathic pain commonly create direct neural damage with open surgeries using ligatures, neurectomies, chemicals or other forms of intentional trauma. However, we have observed clinically that after an injury in humans, the naturally occurring process of tissue repair can cause chronic neural pain. We show here how the refined biomimetic NeuroDigm GELTM Model, in the mature male rat, gradually induces neuropathic pain behavior with a nonsurgical percutaneous injection of tissue-derived hydrogel in the tunnel of the distal tibial nerve. This perineural model creates a mononeuritis with the biogenic matrix induction of tissue remodeling, the last stage of tissue repair. Repeated behavioral analgesic testing over 5 months in the model implied a unique predictive validity for all analgesics tested. Morphine, initially effective, had an increase in pain behavior over time, suggesting an opioid-induced hyperalgesia, as seen in humans. Celecoxib produced no analgesia, while gabapentin and duloxetine at low doses had profound analgesia. Histology reveals focal neural remodeling, with neural regeneration, as in human biopsies. For the first time, targeted erythropoietin appears to heal neural pain, by extinguishing bilateral pain behavior present for over 4 months.\n\ntranslational model, neuropathic pain, erythropoietin, neural regeneration, soft tissue injuries, neuritis, tissue repair, hydrogel, animal model of disease, neural remodeling, age, opioid-induced hyperalgesia, morphine resistance, analgesics, refined pain model, matrix remodeling, neuroinflammation, predictive validity, habituation, estrogen

Neuroscience↗

bioRxiv2016

GEFs and Rac GTPases control directional specificity of neurite extension along the anterior-posterior axis

Although previous studies have identified many extracellular guidance molecules and intracellular signaling proteins that regulate axonal outgrowth and extension, most were conducted in the context of unidirectional neurite growth, in which the guidance cues either attract or repel growth cones. Very few studies addressed how intracellular signaling molecules differentially specify bidirectional outgrowth. Here, using the bipolar PLM neurons in C. elegans, we show that the guanine nucleotide exchange factors (GEFs) UNC-73/Trio and TIAM-1 promote anterior and posterior neurite extension, respectively. The Rac subfamily GTPases act downstream of the GEFs; CED-10/Rac1 is activated by TIAM-1, whereas CED-10 and MIG-2/RhoG act redundantly downstream of UNC-73. Moreover, these two pathways antagonize each other and, thus, regulate the directional bias of neuritogenesis. Our study suggests that directional specificity of neurite extension is conferred through the intracellular activation of distinct GEFs and Rac GTPases.\n\nSignificance StatementMost previous studies on intracellular signaling during neurite guidance were performed in the context of unidirectional neurite growth. They could not address the molecular basis of directional outgrowth of multiple neurites mainly because of the lack of a good model system. Using a pair of bipolar neurons in the nematode Caenorhabditis elegans, we found that distinct sets of intracellular molecules are required for neurite extension towards the anterior and the posterior. Moreover, signaling pathways that promote neurite extension in different directions antagonize each other to achieve balanced growth. Therefore, our study offers an in vivo example for a long-standing concept that spatially selective activation of intracellular signaling molecules could enable a diverse range of neuronal growth patterns.

Neuroscience↗

bioRxiv2016

Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex

The local field potential (LFP) is generated by large populations of neurons, but unitary contribution of spiking neurons to LFP is not well characterised. We investigated this contribution in multi-electrode array recordings from human and monkey neocortex by examining the spike-triggered LFP average (st-LFP). The resulting st-LFPs were dominated by broad spatio-temporal components due to ongoing activity, synaptic inputs and recurrent connectivity. To reduce the spatial reach of the st-LFP and observe the local field related to a single spike we applied a spatial filter, whose weights were adapted to the covariance of ongoing LFP. The filtered st-LFPs were limited to the perimeter of 800 m around the neuron, and propagated at axonal speed, which is consistent with their unitary nature. In addition, we discriminated between putative inhibitory and excitatory neurons and found that the inhibitory st-LFP peaked at shorter latencies, consistently with previous findings in hippocampal slices. Thus, in human and monkey neocortex, the LFP reflects primarily inhibitory neuron activity.

Neuroscience↗

bioRxiv2016

A cellular and regulatory map of the GABAergic nervous system of C. elegans

Neurotransmitter maps are important complements to anatomical maps and represent an invaluable resource to understand how signals are transmitted throughout the nervous system and how a nervous system is developmentally patterned. We report here a comprehensive map of neurons in the C. elegans nervous system that contain the neurotransmitter GABA, revealing twice as many GABA-positive neuron classes as previously reported. We define previously unknown glia-like cells that reuptake GABA, as well as \"GABA reuptake neurons\" which do not synthesize GABA but take it up from the extracellular environment. We used the map of GABA-positive neurons for a comprehensive analysis of transcriptional regulators that define the GABA phenotype. We synthesize our findings of specification of GABAergic neurons with previous reports on the specification of glutamatergic and cholinergic neurons into a nervous system-wide regulatory map which defines neurotransmitter specification mechanisms for more than half of all neuron classes in C. elegans.

Neuroscience↗

bioRxiv2016

The effect of local inter-inhibitory connectivity on the dynamics of an activity-dependent neuronal network growth model

The balance between excitation and inhibition in a neuronal network is considered to be an important predictor of neural excitability. Various processes are thought to maintain this balance across a range of stimuli/conditions. However, the developmental formation of this balance remains an open question, especially regarding the interplay between network blue-print (the spatial arrangement of excitatory and inhibitory nodes) and homeostatic processes. In this paper, we use a published model of activity-dependent growth to show that the E/I ratio alone cannot accurately predict system behaviour but rather it is the combination of this ratio and the underlying spatial arrangement of neurones that predict both activity in, and structure of, the resulting network. In particular, we highlight the particular role of clustered inter-inhibitory connectivity. We develop a measure that allows us to determine the relationship between inter-inhibitory connectivity clustering and system behaviour in an exhaustive list of spatial arrangements with a given fixed number of excitatory and inhibitory neurones. Our results reveal that, for a given E/I ratio, networks with high levels of inter-inhibitory clustering are more likely to experience oscillatory behaviour than networks with low levels, and we investigate the network attributes which characterise each global behaviour type produced by the model. We identify possible approaches for extensions of the current work, and discuss the implications these results may have on future modelling studies in this field.

Neuroscience↗

bioRxiv2016

Spatial mnemonic encoding: Theta power decreases co-occur with medial temporal lobe BOLD increases during the usage of the Method of Loci

The Method of Loci is one, if not the most, efficient mnemonic encoding strategy. This strategy combines the core cognitive processes commonly linked to medial temporal lobe (MTL) activity: spatial processing and associative memory processes. During such tasks fMRI studies consistently demonstrate increases in medial temporal lobe (MTL) activity, while electrophysiological studies have emphasized the important role of theta oscillations (3-8 Hz) in the MTL. How MTL activity is linked to theta oscillatory EEG power, however, is unknown. Specifically, it is not known whether increases or decreases in MTL theta power are associated with increased BOLD signal. To investigate this question we recorded EEG and fMRI while participants used the spatial Method of Loci contrasted to the Pegword Method, a similarly associative but non spatial mnemonic. Surprisingly, the more effective spatial mnemonic induced a pronounced theta power decrease in the left MTL compared to the non-spatial associative mnemonic strategy. This effect was mirrored by BOLD signal increases in the MTL. This pattern of results suggests that theta oscillations in the MTL are negatively related to BOLD signal increases. These findings extend the well-known negative relation of alpha/beta oscillations and BOLD signals in the cortex to theta oscillations in the MTL. The results also demonstrate that decreases in theta power can index MTL involvement during encoding.\n\nSignificance StatementStudies investigating the oscillatory correlates of memory encoding largely focus on activity in the theta frequency and often implicitly assume that increases in theta activity reflect similar processes as typically reported increases MTL activity changes in fMRI studies. The presented study found decreases in theta power, not increases, closely mapping to MTL BOLD signal increases during the same paradigm. This finding is in line with studies showing a negative relationship between low frequency power and BOLD changes in the cortex, but challenges the assumption that theta power increases reflect MTL activity. The reported findings importantly contribute to answer the question of how and which oscillatory activity indexes MTL memory processes.

Neuroscience↗

bioRxiv2016

Goggatomy: a Method for Opening Small Cuticular Compartments in Arthropods for Physiological Experiments

Most sense organs of arthropods are ensconced in small exoskeletal compartments that hinder direct access to plasma membranes. We have developed a method for exposing live sensory and supporting cells in such structures. The technique uses a viscous light cured resin to embed and support the structure, which is then sliced with a sharp blade. We term the procedure a goggatomy, from the Khoisan word for a bug, gogga. To demonstrate the utility of the method we show that it can be used to expose the auditory chordotonal organs in the second antennal segment and the olfactory receptor neurons in the third antennal segment of Drosophila melanogaster, preserving the transduction machinery. The procedure can also be used on other small arthropods, like mites, Daphnia, mosquitoes, wasps and ants to expose a variety of cells.

Neuroscience↗

bioRxiv2016

Nociceptor-enriched genes required for normal thermal nociception.

Here, we describe a targeted reverse genetic screen for thermal nociception genes of Drosophila larvae. Using laser capture microdissection and microarray analyses of nociceptive and non-nociceptive neurons we identified 275 nociceptor-enriched genes. We then tested the function of the enriched genes with nociceptor-specific RNAi and thermal nociception assays. Tissue specific RNAi targeted against 14 genes caused insensitive thermal nociception while targeting of 22 genes caused hypersensitive thermal nociception. Previously uncategorized genes were named for heat resistance (ie. boilerman, fire dancer, oven mitt, trivet, thawb and bunker gear) or heat sensitivity (firelighter, black match, eucalyptus, primacord, jet fuel, detonator, gasoline, smoke alarm, and jetboil). Insensitive nociception phenotypes were often associated with severely reduced branching of nociceptor neurites and hyperbranched dendrites were seen in two of the hypersensitive cases. Many genes that we identified were not isolated in a prior genome-wide screen, and are evolutionarily conserved in mammals.

Neuroscience↗

bioRxiv2016

Figure-ground organization in visual cortex for natural scenes

Figure-ground organization and border-ownership assignment are essential for understanding natural scenes. It has been shown that many neurons in the macaque visual cortex signal border-ownership in displays of simple geometric shapes such as squares, but how well these neurons resolve border-ownership in natural scenes is not known. We studied area V2 neurons in behaving macaques with static images of complex natural scenes. We found that about half of the neurons were border-ownership selective for contours in natural scenes and this selectivity originated from the image context. The border-ownership signals emerged within 70 ms after stimulus onset, only ~30 ms after response onset. A substantial fraction of neurons were highly consistent across scenes. Thus, the cortical mechanisms of figure-ground organization are fast and efficient even in images of complex natural scenes. Understanding how the brain performs this task so fast remains a challenge.\n\nSignificance StatementHere we show, for the first time, that neurons in primate visual area V2 signal border-ownership for objects in complex natural scenes. Surprisingly, these signals appear as early as the border-ownership signals for simple figure displays. In fact, they emerge well before object selective activity appears in infero-temporal cortex, which rules out feedback from that region as an explanation. Thus, \"objectness\" is detected by extremely fast mechanisms that do not depend on feedback from the known object-recognition centers.

Neuroscience↗