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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 901 records · Page 50Linked to original sources

bioRxiv2016

PHARMACOLOGY OF W-18 AND W-15

W-18 (1-(4-Nitrophenylethyl)piperidylidene-2-(4-chlorophenyl)sulfonamide)and W-15 (4-chloro-N-[1-(2-phenylethyl)-2-piperidinylidene]-benzenesulfonamide) represent two emerging drugs of abuse chemically related to the potent opioid agonist fentanyl (N-(1-(2-phenylethyl)-4-piperidinyl)-N-phenylpropanamide). Here we describe the comprehensive pharmacological profiles of W-18 and W-15. Although W-18 and W-15 have been described as having potent anti-nociceptive activity and are presumed to interact with opioid receptors, we found them to be without detectible opioid activity at , {delta}, {kappa} and nociception opioid receptors in a variety of assays. We also tested W-18 and W-15 for activity as allosteric modulators at opioid receptors and found them devoid of significant positive or negative allosteric modulatory activity. Comprehensive profiling at essentially all the druggable G-protein coupled receptors in the human genome using the PRESTO-Tango platform revealed no significant activity. In silico predictions using the Similarity Ensemble Approach suggested activity for W-18 only weakly at H3-histamine receptors, which was not confirmed in radioligand binding studies. Weak activity at the sigma receptors and the peripheral benzodiazepine receptor were found for W-18 (Ki=271 nM); W-15 displayed weak antagonist activity at 5-HT2-family serotonin receptors. W-18 is extensively metabolized, but its metabolites also lack opioid activity. W-18 and W-15 did inhibit hERG binding suggesting possible cardiovascular side-effects with high doses. Thus although W-18 and W-15 have been suggested to be potent opioid agonists, our results reveal no significant activity at these or other known targets for psychoactive drugs.

Neuroscience↗

bioRxiv2016

EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, which is known to affect upper and lower motor neurons. In contrast to the classical tenet that ALS represents the outcome of extensive and progressive impairment of a fixed set of motor connections, recent neuroimaging findings suggest that the disease spreads along vast non-motor connections. Here, we hypothesised that functional network topology is perturbed in ALS, and that this reorganisation is associated with disability. We tested this hypothesis in 21 patients affected by ALS at several stages of impairment using resting-state electroencephalography (EEG) and compared the results to 16 age-matched healthy controls. We estimated functional connectivity using the Phase Lag Index (PLI), and characterized the network topology using the minimum spanning tree (MST). We found a significant difference between groups in terms of MST dissimilarity and MST leaf fraction in the beta band. Moreover, some MST parameters (leaf, hierarchy and kappa) significantly correlated with disability. These findings suggest that the topology of resting-state functional networks in ALS is affected by the disease in relation to disability. EEG network analysis may be of help in monitoring and evaluating the clinical status of ALS patients.

Neuroscience↗

bioRxiv2016

Connectivity map of bipolar cells and photoreceptors in the mouse retina

Visual processing begins at the first synapse of the visual system. In the mouse retina, three different types of photoreceptors provide input to 14 bipolar cell (BC) types. Classically, most BC types are thought to contact all cones within their dendritic field; ON BCs would contact cones exclusively via so-called invaginating synapses, while OFF BCs would form basal synapses. By mining publically available electron microscopy data, we discovered interesting violations of these rules of outer retinal connectivity: ON BC type X contacted only ~20% of the cones in its dendritic field and made mostly atypical non-invaginating contacts. Types 5T, 5O and 8 also contacted fewer cones than expected. In addition, we found that rod BCs received input from cones, providing anatomical evidence that rod and cone pathways are interconnected in both directions. This suggests that the organization of the outer plexiform layer is more complex than classically thought.

Neuroscience↗

bioRxiv2016

AFNI and Clustering: False Positive Rates Redux

In response to reports of inflated false positive rate (FPR) in FMRI group analysis tools, a series of replications, investigations, and software modifications were made to address this issue. While these investigations continue, significant progress has been made to adapt AFNI to fix such problems. Two separate lines of changes have been made. First, a long-tailed model for the spatial correlation of the FMRI noise characterized by autocorrelation function (ACF) was developed and implemented into the 3dClustSim tool for determining the cluster-size threshold to use for a given voxel-wise threshold. Second, the 3dttest++ program was modified to do randomization of the voxel-wise t-tests and then to feed those randomized t-statistic maps into 3dClustSim directly for cluster-size threshold determination-without any spatial model for the ACF. These approaches were tested with the Beijing subset of the FCON-1000 data collection. The first approach shows markedly improved (reduced) FPR, but in many cases is still above the nominal 5%. The second approach shows FPRs clustered tightly about 5% across all per-voxel p-value thresholds [≤] 0.01. If t-tests from a univariate GLM are adequate for the group analysis in question, the second approach is what the AFNI group currently recommends for thresholding. If more complex per-voxel statistical analyses are required (where permutation/randomization is impracticable), then our current recommendation is to use the new ACF modeling approach coupled with a per-voxel p-threshold of 0.001 or below. Simulations were also repeated with the now infamously \"buggy\" version of 3dClustSim: the effect of the bug on FPRs was minimal (of order a few percent).

Neuroscience↗

bioRxiv2016

The role of brain oscillations in predicting the sensory consequences of your actions

Being able to predict self-generated sensory consequences is an important feature of normal brain functioning. In the auditory domain, self-generated sounds lead to smaller brain responses compared to externally generated sounds. Here we investigated the role of brain oscillations underlying this effect. With magnetoencephalography, we show that self-generated sounds are associated with increased pre-stimulus alpha power and decreased post-stimulus gamma power and alpha/beta phase locking in auditory cortex. All these oscillatory changes are correlated with changes in evoked responses. Furthermore, they correlate with each other across participants, supporting the idea that they constitute a neural information processing sequence for self-generated sounds, with pre-stimulus alpha power representing prediction and post-stimulus gamma power representing prediction error, which is further processed with post-stimulus alpha/beta phase resetting. Additional cross-trial analysis provides further support for the proposed sequence that might reflect a general mechanism for the prediction of self-generated sensory input.

Neuroscience↗

bioRxiv2016

Axonal transcriptome of human stem cell derived neurons

The identification of axonal mRNAs in model organisms has led to the discovery of multiple proteins synthesized within axons for functional roles such as axon guidance and injury response. The extent to which protein synthesis within the axon is conserved in humans is unknown. Here we used axon-isolating microfluidic chambers to characterize the axonal transcriptome of human embryonic stem cells (hESC-neurons) differentiated using a protocol for glutamatergic neurons. Using gene expression analysis, we identified mRNAs proportionally enriched in axons, representing a functionally unique local transcriptome as compared to the human neuronal transcriptome inclusive of somata and dendrites. Further, we found that the most abundant mRNAs within hESC-neuron axons were functionally similar to the axonal transcriptome of rat cortical neurons. We confirmed the presence of two well characterized axonal mRNAs in model organisms, {beta}-actin and GAP43, within hESC-neuron axons using multiplexed single molecule RNA-FISH. Additionally, we report the novel finding that oxytocin mRNA localized to these human axons and confirmed its localization using RNA-FISH. This new evaluation of mRNA within human axons provides an important resource for studying local mRNA translation and has the potential to reveal both conserved and unique axonal mechanisms across species and neuronal types.

Neuroscience↗

bioRxiv2016

A common perceptual inference for cross-modally induced illusions of body schema

Body-schema, or the multimodal representation of ones own body attributes, has been demonstrated previously to be malleable. In the rubber-hand illusion (Botvinick & Cohen, 1998), synchronous visual and tactile stimulation cause a fake hand to be perceived as ones own. Similarly, if a strangers voice is heard synchronously with ones own vocal production, that voice comes to be attributed to oneself (Zheng et al., 2011). Multimodal illusions like these involve distorting body schema based on correlated input, yet the degree to which different instances of distortion are perceived within the same individuals has never been examined. Here we show that participants embraced the ownership of a fake hand and a strangers voice to a similar degree, controlling both for individual suggestibility and for general susceptibility to illusion of body schema. Our findings suggest that the perceptual inference that leads to the distortion of body schema is a stable trait.

Neuroscience↗

bioRxiv2016

Mind over matter: A perceptual decision bias toward filled-in stimuli in the blind spot

Humans often evaluate sensory signals according to their reliability for optimal decision-making. However, how do we evaluate percepts generated in the absence of direct input that are, therefore, completely unreliable? Here, we utilize the phenomenon of filling-in occurring at the physiological blind-spots to compare partially inferred and veridical percepts. Subjects chose between stimuli that elicit filling-in, and perceptually equivalent ones presented outside the blind-spots, looking for a Gabor stimulus without a small orthogonal inset. In ambiguous conditions, when the stimuli were physically identical and the inset was absent in both, subjects behaved opposite to optimal, preferring the blind-spot stimulus as the better example of a collinear stimulus, even though no relevant veridical information was available. Thus, a percept that is partially inferred is paradoxically considered more reliable than a percept based on external input. In other words: Humans treat filled-in inferred percepts as more real than veridical ones.

Neuroscience↗

bioRxiv2016

fMRI Dependent Components Analysis Reveals Dynamic Relations Between Functional Large Scale Cortical Networks

One of the major advantages of whole brain fMRI is the detection of large scale cortical networks. Dependent Components Analysis (DCA) is a novel approach designed to extract both cortical networks and their dependency structure. DCA is fundamentally different from prevalent data driven approaches, i.e. spatial ICA, in that instead of maximizing the independence of components it optimizes their dependency (in a tree graph structure, tDCA) depicting cortical areas as part of multiple cortical networks. Here tDCA was shown to reliably detect large scale functional networks in single subjects and in group analysis, by clustering non-noisy components on one branch of the tree structure. We used tDCA in three fMRI experiments in which identical auditory and visual stimuli were presented, but novelty information and task relevance were modified. tDCA components tended to include two anticorrelated networks, which were detected in two separate ICA components, or belonged in one component in seed functional connectivity. Although sensory components remained the same across experiments, other components changed as a function of the experimental conditions. These changes were either within component, where it encompassed other cortical areas, or between components, where the pattern of anticorrelated networks and their statistical dependency changed. Thus tDCA may prove to be a useful, robust tool that provides a rich description of the statistical structure underlying brain activity and its relationships to changes in experimental conditions. This tool may prove effective in detection and description of mental states, neural disorders and their dynamics.

Neuroscience↗

bioRxiv2016

Evidence accumulation and change rate inference in dynamic environments

In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is updating the posterior probability of all possible changepoint counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation based plasticity rule. We thus show how optimal observers accumulates evidence in changing environments, and map this computation to reduced models which perform inference using plausible neural mechanisms.

Neuroscience↗

bioRxiv2016

Receptive field formation by interacting excitatory and inhibitory synaptic plasticity

The stimulus selectivity of synaptic currents in cortical neurons often shows a co-tuning of excitation and inhibition, but the mechanisms that underlie the emergence and plasticity of this co-tuning are not fully understood. Using a computational model, we show that an interaction of excitatory and inhibitory synaptic plasticity reproduces both the developmental and - when combined with a disinhibitory gate - the adult plasticity of excitatory and inhibitory receptive fields in auditory cortex. The co-tuning arises from inhibitory plasticity that balances excitation and inhibition, while excitatory stimulus selectivity can result from two different mechanisms. Inhibitory inputs with a broad stimulus tuning introduce a sliding threshold as in Bienenstock-Cooper-Munro rules, introducing an excitatory stimulus selectivity at the cost of a broader inhibitory receptive field. Alternatively, input asymmetries can be amplified by synaptic competition. The latter leaves any receptive field plasticity transient, a prediction we verify in recordings in auditory cortex.

Neuroscience↗

bioRxiv2016

Posterior parietal cortex guides visual decisions in rats

Neurons in putative decision-making structures can reflect both sensory and decision signals, making their causal role in decisions unclear. Here, we tested whether rat posterior parietal cortex (PPC) is causal for processing visual sensory signals or instead for accumulating evidence for decision alternatives. We optogenetically disrupted PPC activity during decision-making and compared effects on decisions guided by auditory vs. visual evidence. Deficits were largely restricted to visual decisions. To further test for visual dominance in PPC, we evaluated electrophysiological responses following individual sensory events and observed much larger responses following visual stimuli than auditory stimuli. Finally, we measured spike count variability during stimulus presentation and decision formation. This sharply decreased, suggesting the network is stabilized by inputs, unlike what would be expected if sensory signals were locally accumulated. Our findings argue that PPC plays a causal role in discriminating visual signals that are accumulated elsewhere.

Neuroscience↗

bioRxiv2016

The effect of crack cocaine addiction on the microstructure and morphology of the human striatum and thalamus using novel shape analysis and fast diffusion kurtosis imaging.

The striatum and thalamus are subcortical structures intimately involved in addiction. The morphology and microstructure of these have been studied in murine models of cocaine addiction, showing an effect of drug use, but also chronological age in morphology. Human studies using non-invasive MRI have shown inconsistencies in volume changes, and have also shown an age effect. In this exploratory study, we used MRI-based volumetric and novel shape analysis, as well as a novel fast diffusion kurtosis imaging sequence to study the morphology and microstructure of striatum and thalamus in crack cocaine addiction (CA) compared to matched healthy controls (HC), while investigating the effect of age and years of cocaine consumption. We did not find significant differences in volume and mean kurtosis (MKT) between groups. However, we found significant contraction of nucleus accumbens in CA compared to HC. We also found significant age related changes in volume and MKT of CA in striatum and thalamus that are different to those seen in normal aging. Interestingly, we found different effects and contributions of age and years of consumption in volume, displacement and MKT changes, suggesting each measure provides different but complementing information about morphological brain changes and that not all changes are related to the toxicity or the addiction to the drug. Our findings suggest that the use of finer methods and sequences provide complementing information about morphological and microstructural changes in cocaine addiction, and that brain alterations in cocaine addiction are related cocaine use and age differently.

Neuroscience↗

bioRxiv2016

Optimizing for generalization in the decoding of internally generated activity in the hippocampus

The decoding of a sensory or motor variable from neural activity benefits from a known ground truth against which decoding performance can be compared. In contrast, the decoding of covert, cognitive neural activity, such as occurs in memory recall or planning, typically cannot be compared to a known ground truth. As a result, it is unclear how decoders of such internally generated activity should be configured in practice. We suggest that if the true code for covert activity is unknown, decoders should be optimized for generalization performance using cross-validation. Using ensemble recording data from hippocampal place cells, we show that this cross-validation approach results in different decoding error, different optimal decoding parameters, and different distributions of error across the decoded variable space. In addition, we show that a minor modification to the commonly used Bayesian decoding procedure, which enables the use of spike density functions, results in substantially lower decoding errors. These results have implications for the interpretation of covert neural activity, and suggest easy-to-implement changes to commonly used procedures across domains, with applications to hippocampal place cells in particular.

Neuroscience↗

bioRxiv2016

Inconsistencies in C. elegans behavioural annotation

High quality behavioural annotation is a key component to link genes to behaviour, yet relatively little attention has been paid to check the consistency of various automated methods and expert judgement. In this paper we investigate the consistency of annotation for the Omega turn of C. elegans, which is a frequently used behavioural assay for this animal. First the output of four Omega detection algorithms are examined for the same data set, and shown to have relative low consistency, with F-scores around 0.5. Consistency of expert annotation is then analysed, based on an online survey combining two methods: participants judged a fixed set of predetermined clips; and an adaptive psychophysical procedure was used to estimate individuals threshold for Omega turn detection. This survey also revealed a substantial lack of consistency in decisions and thresholds. Such inconsistency makes cross-publication comparison difficult and raises issues of reproducibility.

Neuroscience↗

bioRxiv2016

Training and spontaneous reinforcement of neuronal assemblies by spike timing

1The synaptic connectivity of cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing-dependent plasticity (STDP) have focused on either just pairs of neurons or large-scale simulations where analytic insight is lacking. A simple account for how fast spike time correlations affect both micro- and macroscopic network structure remains lacking. We develop a low-dimensional mean field theory showing how STDP gives rise to strongly coupled assemblies of neurons with shared stimulus preferences, with the connectivity actively reinforced by spike train correlations during spontaneous dynamics. Furthermore, the stimulus coding by cell assemblies is actively maintained by these internally generated spiking correlations, suggesting a new role for noise correlations in neural coding. Assembly formation has been often associated with firing rate-based plasticity schemes; our theory provides an alternative and complementary framework, where temporal correlations and STDP form and actively maintain learned structure in cortical networks.

Neuroscience↗

bioRxiv2016

A rodent model of social rejection

Rats help conspecifics by releasing them from a restrainer only when they have previous social experience with the strain of the restrained rat. When rats have been cross-fostered with rats of a different strain than their own since birth and have never interacted with rats of their own strain, they do not help rats of their own strain when tested as adults. Here we interrogated whether a cross-fostered rat expressed his lack of motivation to help through any behaviors beyond not-helping. Accordingly, a cross-fostered rat was placed in an arena with a trapped rat and the door to the centrally located restrainer was taped shut. We found that cross-fostered rats moved more slowly and approached the trapped rat less than did control, regularly-raised rats tested under the same conditions. We then asked whether the behavior of the cross-fostered rats influenced the trapped rat. After being restrained with cross-fostered rats, trapped rats showed a decrease in exploratory behavior in an open field test compared to trapped rats who were raised normally. The same decrease in movement was observed after subject rats were allowed to freely interact with cross-fostered rats. These results suggest that rats that do not help demonstrate their disinterest to a trapped rat and that trapped rats exposed to apathetic rats show behavior suggestive of an increase in anxiety. In sum, the paradigm introduced here could serve as a rodent model for social rejection.

Neuroscience↗

bioRxiv2016

The Preprocessed Connectomes Project Repository of Manually Corrected Skull-stripped T1-weighted Anatomical MRI Data.

BackgroundSkull-stripping is the procedure of removing non-brain tissue from anatomical MRI data. This procedure is necessary for calculating brain volume and for improving the quality of other image processing steps. Developing new skull-stripping algorithms and evaluating their performance requires gold standard data from a variety of different scanners and acquisition methods. We complement existing repositories with manually-corrected brain masks for 125 T1-weighted anatomical scans from the Nathan Kline Institute Enhanced Rockland Sample Neurofeedback Study.\n\nFindingsSkull-stripped images were obtained using a semi-automated procedure that involved skull-stripping the data using the brain extraction based on non local segmentation technique (BEaST) software and manually correcting the worst results. Corrected brain masks were added into the BEaST library and the procedure was reiterated until acceptable brain masks were available for all images. In total, 85 of the skull-stripped images were hand-edited and 40 were deemed to not need editing. The results are brain masks for the 125 images along with a BEaST library for automatically skull-stripping other data.\n\nConclusionSkull-stripped anatomical images from the Neurofeedback sample are available for download from the Preprocessed Connectomes Project. The resulting brain masks can be used by researchers to improve their preprocessing of the Neurofeedback data, and as training and testing data for developing new skull-stripping algorithms and evaluating the impact on other aspects of MRI preprocessing. We have illustrated the utility of these data as a reference for comparing various automatic methods and evaluated the performance of the newly created library on independent data.

Neuroscience↗