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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 919 records · Page 51Linked to original sources

bioRxiv2016

Bayesian Inference of Two-Dimensional Contrast Sensitivity Functions

The contrast sensitivity function (CSF) is crucial in predicting functional vision both in research and clinical areas. Recently, a group of novel strategies, multi-dimensional adaptive methods, were proposed and allowed more rapid measurements when compared to usual methods such as {Psi} or staircase. Our study further presents a multi-dimensional Bayesian framework to estimate parameters of the CSF from experimental data obtained by classical sampling. We extensively simulated the frameworks performance as well as validated the results in a psychophysical experiment. The results showed that the Bayesian framework significantly improves the accuracy and precision of parameter estimates from usual strategies, and requires about the same number of observations as the novel methods to obtain reliable inferences. Additionally, the improvement with the Bayesian framework was maintained when the prior poorly matched the observers CSFs. The results indicated that the Bayesian framework is flexible and sufficiently precise for estimating CSFs.

Neuroscience↗

bioRxiv2016

Aging increases lateral but not local inhibition of orientation processing in primary visual cortex

Aging-related declines in vision can decrease well-being of the elder. Concerning early sensory changes as in the primary visual cortex, physiological and behavioral reports seem contradictory. Neurophysiological studies on orientation tuning properties suggested that neuronal changes might come from decreased cortical local inhibition. However, behavioral results either showed no clear deficits in orientation processing in the elder, or proposed stronger surround suppression. Through psychophysical experiments conducted on old and young human subjects combined with computational modeling, we resolved these discrepancies by demonstrating stronger lateral inhibition in the elder while neuronal orientation tuning widths, related to local inhibition, stayed globally intact across age. We confirmed this later finding by re-analyzing published neurophysiological data from rhesus monkeys, which showed no systematic tuning width changes, but instead a higher neuronal noise with aging. These results suggest a stronger lateral inhibition and mixed effects on local inhibition during aging, revealing a more complex picture of age-related effects in the central visual system than previously thought.\n\nSignificance StatementVisual functions decline during aging, adversely affecting quality of life. Much of this dysfunction is probably mediated by disturbances in the balance between inhibition and excitation in the central visual system. It was proposed that the inhibitory function within the aging visual cortex might be modified, but huge discrepancies exist among different reports. Here we identify the specific inhibitory circuit change, which has not been clearly evaluated, by using behavioral measures, neural modeling and re-analysis of non-human primate electrophysiological data. We provide evidence that a stronger lateral inhibition and mixed effects on local inhibition during aging, revealing a more complex picture of age-related effects in the central visual system than previously thought.\n\nAuthor contributionsZCW and TT designed and analyzed behavioral experiments; ZCW performed experiments; ZCW and TT performed data analysis and neurophysiological re-analysis; YS and FY provided physiological data and discussed its re-analysis; TT performed modeling; ZCW, YFZ and TT provided project supervision and funds; ZCW and TT wrote the paper; all authors discussed and commented on the manuscript.

Neuroscience↗

bioRxiv2016

Dendrites of DG granule cells contribute to pattern separation by controlling sparsity

The hippocampus plays a key role in pattern separation, the process of transforming similar incoming information to highly dissimilar, non-overlapping representations. Sparse firing granule cells (GCs) in the dentate gyrus (DG) have been proposed to undertake this computation, but little is known about which of their properties influence pattern separation. Dendritic atrophy has been reported in diseases associated with pattern separation deficits, suggesting a possible role for dendrites in this phenomenon. To investigate whether and how the dendrites of GCs contribute to pattern separation, we build a simplified, biologically relevant, computational model of the DG. Our model suggests that the presence of GC dendrites is associated with high pattern separation efficiency while their atrophy leads to increased excitability and performance impairments. These impairments can be rescued by restoring GC sparsity to control levels through various manipulations. We predict that dendrites contribute to pattern separation as a mechanism for controlling sparsity.

Neuroscience↗

bioRxiv2016

Influence of cognitive control on semantic representation

The neural representation of an object can change depending on its context. For instance, a horse may be more similar to a bear than to a dog in terms of size, but more similar to a dog in terms of domesticity. We used behavioral measures of similarity together with representational similarity analysis and functional connectivity of fMRI data in humans to reveal how the neural representation of semantic knowledge can change to match the current goal demand. Here we present evidence that objects similar to each other in a given context are also represented more similarly in the brain and that these similarity relationships are modulated by context specific activations in frontal areas.\n\nSignificance statementThe judgment of similarity between two objects can differ in different contexts. Here we report a study that tested the hypothesis that brain areas associated with task context and cognitive control modulate semantic representations of objects in a task-specific way.\n\nWe first demonstrate that task instructions impact how objects are represented in the brain. We then show that the expression of these representations is correlated with activity in regions of frontal cortex widely thought to represent context, attention and control.\n\nIn addition, we introduce spatial variance as a novel index of representational expression and attentional modulation. This promises to lay the groundwork for more exacting studies of the neural basis of semantics, as well as the dynamics of attentional modulation.

Neuroscience↗

bioRxiv2016

Local expectation violations result in global activity gain in primary visual cortex

During natural perception, we often form expectations about upcoming input. These expectations are usually multifaceted - we expect a particular object at a particular location. However, expectations about spatial location and stimulus features have mostly been studied in isolation, and it is unclear whether feature-based expectation can be spatially specific. Interestingly, feature-based attention automatically spreads to unattended locations. It is still an open question whether the neural mechanisms underlying feature-based expectation differ from those underlying feature-based attention. Therefore, establishing whether the effects of feature-based expectation are spatially specific may inform this debate. Here, we investigated this by inducing expectations of a specific stimulus feature at a specific location, and probing the effects on sensory processing across the visual field using fMRI. We found an enhanced sensory response for unexpected stimuli, which was elicited only when there was a violation of expectation at the specific location where participants formed a stimulus expectation. The neural consequences of this expectation violation, however, spread to cortical locations processing the stimulus in the opposite hemifield. This suggests that an expectation violation at one location in the visual world can lead to a spatially non-specific gain increase across the visual field.

Neuroscience↗

bioRxiv2016

Fast and accurate spike sorting in vitro and in vivo for up to thousands of electrodes

Understanding how assemblies of neurons encode information requires recording large populations of cells in the brain. In recent years, multi-electrode arrays and large silicon probes have been developed to record simultaneously from hundreds or thousands of electrodes packed with a high density. However, these new devices challenge the classical way to do spike sorting. Here we developed a new method to solve these issues, based on a highly automated algorithm to extract spikes from extracellular data, and show that this algorithm reached near optimal performance both in vitro and in vivo. The algorithm is composed of two main steps: 1) a \"template-finding\" phase to extract the cell templates, i.e. the pattern of activity evoked over many electrodes when one neuron fires an action potential; 2) a \"template-matching\" phase where the templates were matched to the raw data to find the location of the spikes. The manual intervention by the user was reduced to the minimal, and the time spent on manual curation did not scale with the number of electrodes. We tested our algorithm with large-scale data from in vitro and in vivo recordings, from 32 to 4225 electrodes. We performed simultaneous extracellular and patch recordings to obtain \"ground truth\" data, i.e. cases where the solution to the sorting problem is at least partially known. The performance of our algorithm was always close to the best expected performance. We thus provide a general solution to sort spikes from large-scale extracellular recordings.

Neuroscience↗

bioRxiv2016

Low activity microstates during sleep

A better understanding of sleep requires evaluating the distinct activity patterns of the brain during sleep. We performed extracellular recordings of large populations of hippocampal region CA1 neurons in freely moving rats across sleep and waking states. Throughout non-REM (non-rapid eye movement) sleep, we observed periods of diminished oscillatory and population spiking activity lasting on the order of seconds, which we refer to as \"LOW\" activity sleep states. LOW states featured enhanced firing in a subset of \"LOW-active\" cells, and greater firing in putative interneurons compared to DOWN/OFF states. LOW activity sleep was preceded and followed by increased sharp-wave ripple (SWR) activity. We also observed decreased slow-wave activity (SWA) and sleep spindles in the hippocampus local-field potential (LFP) and neocortical electroencephalogram (EEG) upon LOW onset, but only a partial rebound immediately after LOW. LOW states demonstrated LFP, EEG, and EMG patterns consistent with sleep, but frequently transitioned into microarousals (MAs) and showed EMG and LFP spectral differences from previously described small-amplitude irregular activity (SIA) during quiet waking. Their likelihood increased over the course of sleep, particularly following REM sleep. To confirm that LOW is a brain-wide phenomenon, we analyzed data from the entorhinal cortex of rats, medial prefrontal cortex, and anterior thalamus of mice, obtained from crcns.org and found that LOW states corresponded to markedly diminished activity simultaneously in all of these regions. We propose that LOW states are an important microstate within non-REM sleep that provide respite from high-activity sleep, and may serve a restorative function.

Neuroscience↗

bioRxiv2016

Large-scale multivariate activation states of the human brain

Much of our lives are spent in unconstrained rest states, yet cognitive brain processes are primarily investigated using task-constrained states. It may be possible to utilize the insights gained from experimental control of task processes as reference points for investigating unconstrained rest. To facilitate comparison of rest and task functional MRI (fMRI) data we focused on activation amplitude patterns, commonly used for task but not rest analyses. During rest, we identified spontaneous changes in temporally extended whole-brain activation pattern states. This revealed a hierarchical organization of rest states. The top consisted of two competing states consistent with previously identified \"task-positive\" and \"task-negative\" activation patterns. These states were composed of more specific states that repeated over time and across individuals. Contrasting with the view that rest consists of only task-negative states, task-positive states occurred 40% of the time while individuals \"rested,\" suggesting task-focused activity occurs during rest. Further, analysis of task data revealed a similar hierarchical structure of brain states. Together these results suggest brain activation dynamics form a general hierarchy across task and rest, with a small number of dominant general states reflecting basic functional modes and a variety of specific states likely reflecting a rich variety of cognitive processes.

Neuroscience↗

bioRxiv2016

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

Investigations of the human brains connectomic architecture have produced two alternative models: one describes the brains spatial structure in terms of localized networks, and the other describes the brains temporal structure in terms of whole-brain states. Here, we used tools from connectivity dynamics to develop a synthesis that bridges these models. Using task-free fMRI data, we investigated the assumptions undergirding current models of the connectome. Consistent with state-based models, our results suggest that localized networks are superordinate approximations of underlying dynamic states. Furthermore, each of these localized, moment-to-moment connectivity states is associated with global changes in the whole-brain functional connectome. By nesting localized connectivity states within their whole-brain contexts, we demonstrate the relative temporal independence of brain networks. Our assay for functional autonomy of coordinated neural systems is broadly applicable across populations, and our findings provide evidence of structure in temporal dynamics that complements the well-described spatial organization of the brain.

Neuroscience↗

bioRxiv2016

Lingering representations of stimulus history at encoding influence recall organization

Several prominent theories posit that information about recent experiences lingers in the brain and organizes memories for current experiences, by forming a temporal context that is linked to those memories at encoding. According to these theories, if the thoughts preceding an experience X resemble the thoughts preceding an experience Y, then X and Y should show an elevated probability of being recalled together. We tested this prediction by using multi-voxel pattern analysis (MVPA) of fMRI data to measure neural evidence for lingering processing of preceding stimuli. As predicted, memories encoded with similar lingering thoughts (about the category of preceding stimuli) were more likely to be recalled together, thereby showing that the \"fading embers\" of previous stimuli help to organize recall.

Neuroscience↗

bioRxiv2016

Oxytocin enhances intrinsic corticostriatal functional connectivity in women

BackgroundOxytocin may influence various human behaviors and the connectivity across subcortical and cortical networks. Previous oxytocin studies are male-biased and often constrained by task-based inferences. Here we investigate the impact of oxytocin on resting state connectivity between subcortical and cortical networks women.\n\nMethodsWe collected resting state fMRI data on 26 typically-developing women following intranasal oxytocin administration using a double-blind placebo-controlled crossover design. Independent components analysis (ICA) was applied to examine connectivity between networks. An independent analysis of oxytocin receptor (OXTR) gene expression in human subcortical and cortical areas was carried out to determine plausibility of direct oxytocin effects on OXTR.\n\nResultsIn women, OXTR was highly expressed in striatal and other subcortical regions, but showed modest expression in cortical areas. Oxytocin increased connectivity between corticostriatal circuitry typically involved in reward, emotion, social-communication, language, and pain processing. This effect was 1.39 standard deviations above the null effect of no difference between oxytocin and placebo. This oxytocin-related effect on corticostriatal connectivity covaried with autistic traits, such that oxytocin-related increase in connectivity was stronger in individuals with higher autistic traits.\n\nDiscussionOxytocin strengthens corticostriatal connectivity in women, particularly with cortical networks that are involved in social-communicative, motivational, and affective processes. This effect may be important for future work on neurological and psychiatric conditions (e.g., autism, chronic pain), particularly through highlighting how oxytocin may operate differently for subsets of individuals.\n\nSignificance statementThis is the first study to examine how oxytocin affects corticostriatal connectivity in humans, and specifically in women. This is important given oxytocin could impact neural circuitry differently in males and females. Here, we show that oxytocin increases connectivity between corticostriatal circuitry involved in a number of social-communicative, motivational, and affective processes that are implicated in certain neuropsychiatric conditions (e.g., autism). This effect is bolstered by independent evidence that oxytocin receptor gene expression is high in striatal regions and modest in cortical areas. These results may have potential implications for personalized treatment, as oxytocin-related effects on connectivity may vary substantially between individuals.

Neuroscience↗

bioRxiv2016

Contextual Fear Conditioning in Zebrafish

Zebrafish are a genetically tractable vertebrate that hold considerable promise for elucidating the molecular basis of behavior. Although numerous recent advances have been made in the ability to precisely manipulate the zebrafish genome, much less is known about many aspects learning and memory in adult fish. Here, we develop a contextual fear conditioning paradigm using an electric shock as the aversive stimulus. We find that contextual fear conditioning is modulated by shock intensity, prevented by inhibition of (N-methyl-D-aspartate) NMDA receptors, lasts at least 14 days, and exhibits extinction. Furthermore, fish of various background strains (AB, Tu, and TL) are able to acquire fear conditioning, but differ in fear extinction rates. Taken together, we find that contextual fear conditioning in zebrafish shares many similarities with the widely used contextual fear conditioning paradigm in rodents. Combined with the amenability of genetic manipulation in zebrafish, we anticipate that our paradigm will prove to be a useful complementary system in which to examine the molecular basis of vertebrate learning and memory.

Neuroscience↗

bioRxiv2016

Dopaminergic drugs decrease loss aversion in Parkinson’s disease with but not without depression

Depression, a common non-motor symptom of Parkinsons disease (PD), is accompanied by impaired decision making and an enhanced response to aversive outcomes. Current strategies to treat depression in PD include dopaminergic medication. However, their use can be accompanied by detrimental side effects, such as enhanced risky choice. The mechanisms underlying dopamine-induced increases in risky choice are unclear. In the current study we adopt a clinical-neuroeconomic approach to investigate the effects of dopaminergic medication on loss aversion during risky choice in depressed and non-depressed PD. Twenty-three healthy controls, 21 depressed and 22 non-depressed PD patients were assessed using a well-established gambling task measuring loss aversion during risky choice. Patients were tested on two occasions, after taking their normal dopaminergic medication (ON) and after withdrawal of their medication (OFF). Dopaminergic medication decreased loss aversion to a greater extent in depressed than non-depressed PD patients. Moreover, we show that the degree to which dopaminergic medication decreases loss aversion correlated with current depression severity and with drug effects on depression scores. These findings demonstrate that dopamine-induced changes in loss aversion depend on the presence of depressive symptoms in PD.\n\nSignificance statementDopaminergic medication that is used to treat motor and non-motor symptoms in patients with Parkinsons disease is known to contribute to risky decision-making. The underlying mechanisms are unclear. The present study demonstrates that dopaminergic medication in Parkinsons disease decreases loss aversion during risky choice, but only in depressed and not in non-depressed patients with Parkinsons disease. These results advance our understanding of the mechanisms underlying dopamine-induced risky choice, while also identifying depression as an important factor that confers vulnerability to such dopamine-induced risky choice.\n\nConflict of InterestThe authors declare no competing financial interests.

Neuroscience↗

bioRxiv2016

Reward learning deficits in Parkinson’s disease depend on depression

BackgroundDepression is one of the most common and debilitating non-motor symptoms of Parkinsons disease (PD). The neurocognitive mechanisms underlying depression in PD are unclear and treatment is often suboptimal.\n\nMethodsWe investigated the role of striatal dopamine in reversal learning from reward and punishment by combining a controlled medication withdrawal procedure with functional magnetic resonance imaging (fMRI) in 22 non-depressed PD patients and 19 PD patients with past or present PD-related depression.\n\nResultsPD patients with a PD-related depression (history) exhibited impaired reward versus punishment reversal learning as well as reduced reward versus punishment-related BOLD signal in the striatum (putamen) compared with non-depressed PD patients. No effects of dopaminergic medication were observed.\n\nConclusionsThe present findings demonstrate that impairments in reversal learning from reward versus punishment and associated reward-related striatal signalling depend on the presence of (a history of) depression in PD.

Neuroscience↗

bioRxiv2016

Differential state-dependence of low and high firing neurons in the hippocampus

Recent evidence suggests that low and high firing neurons display different plasticity and dynamics. Here, we assess how neuronal firing changes across brain states in both the hippocampus and frontal cortices of rats. To do so, we rank-ordered rat hippocampal CA1 and frontal cortical units by their firing rates and implemented corrections to account for regression-to-the-mean. We observed remarkably consistent effects across these regions. In both regions firing rates increased during rapid eye movement (REM) sleep, but with relatively larger increases in high-firing versus low-firing cells accompanied by increased interneuron spiking, indicative of competitive interactions. In contrast, upon non-REM (NREM) sleep, inhibitory firing decreased, and low-firing rate neurons showed larger increases in firing compared to high-firing neurons. These state changes resulted in narrowing of firing rate distributions during each NREM episode with subsequent dispersion of distributions during REM. Following these undulations in variance over the course of sleep, the net effect of sleep was a decrease in firing-rates across all cells, which had a stronger relative effect on lower-firing neurons. Our results suggest greater plasticity in low-firing neurons and indicate that NREM sleep plays a uniquely normalizing role in sleep, while REM sleep disperses firing rates through competitive interactions.\n\nSignificance StatementMiyawaki and colleagues analyze firing patterns separately in the hippocampus and the neocortex within and across sleep. They find that in both regions REM sleep activity is dominated by high-firing neurons and increased inhibition, resulting in a wider distribution of firing rates. On the other hand, NREM sleep produces lower inhibition, and results in a more homogenous distribution of firing rates. These findings provide insights into the effects and functions of different sleep stages on cortical neurons.

Neuroscience↗

bioRxiv2016

3-5 Hz membrane potential oscillations decrease the gain of neurons in visual cortex

Gain modulation is a computational mechanism critical for sensory processing. Yet, the cellular mechanisms that decrease the gain of cortical neurons are unclear. To test if low frequency subthreshold oscillations could reduce neuronal gain during wakefulness, we measured the membrane potential of primary visual cortex (V1) layer 2/3 excitatory, parvalbumin-positive (PV+), and somatostatin-positive (SOM+) neurons in awake mice during passive visual stimulation and sensory discrimination tasks. We found prominent 3-5 Hz membrane potential oscillations that reduced the gain of excitatory neurons but not the gain of PV+ and SOM+ interneurons, which oscillated synchronously with excitatory neurons and fired strongly at the peak of de polarizations. 3-5 Hz oscillation prevalence and timing were strongly modulated by visual input and the animals behavior al response, suggesting that these oscillations are triggered to adjust sensory responses for specific behavioral contexts. Therefore, these findings reveal a novel gain reduction mechanism that adapts sensory processing to behavior.

Neuroscience↗

bioRxiv2016

Dynamic representation of time in brain states

The ability to process time on the scale of milliseconds and seconds is essential for behaviour. A growing number of studies have started to focus on brain dynamics as a mechanism for temporal encoding. Although there is growing evidence in favour of this view from computational and in vitro studies, there is still a lack of results from experiments in humans. We show that high-dimensional brain states revealed by multivariate pattern analysis of human EEG are correlated to temporal judgements. First, we show that, as participants estimate temporal intervals, the spatiotemporal dynamics of their brain activity are consistent across trials. Second, we present evidence that these dynamics exhibit properties of temporal perception, such as the scalar property. Lastly, we show that it is possible to predict temporal judgements based on brain states. These results show how scalp recordings can reveal the spatiotemporal dynamics of human brain activity related to temporal processing.

Neuroscience↗

bioRxiv2016

The field of view available to the cortical reading circuitry

Skilled reading requires rapidly recognizing letters and word forms; people learn this skill best for words presented in the central visual field. Measurements over the last decade have shown that when children learn to read, responses within ventral occipito-temporal cortex (VOT) become increasingly selective to word forms. We call these regions the VOT reading circuitry (VOTRC). The portion of the visual field that evokes a response in the VOTRC is called the field of view (FOV). We measured the FOV of the VOTRC and found that it is a small subset of the entire field of view available to the human visual system. For the typical subject, the FOV of the VOTRC in each hemisphere is contralaterally and foveally biased. The FOV of the left VOTRC extends ~9{degrees} into the right visual field and ~4{degrees} into the left visual field along the horizontal meridian. The FOV of the right VOTRC is roughly mirror symmetric to that of the left VOTRC. The size and shape of the FOV covers the region of the visual field that contains relevant information for reading English. It may be that the size and shape of the FOV, which varies between subjects, will prove useful in predicting behavioral aspects of reading.

Neuroscience↗