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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 973 records · Page 54Linked to original sources

bioRxiv2016

Intrinsic brain connectivity after partial sleep deprivation in young and older adults: results from the Stockholm Sleepy Brain study

Sleep deprivation has been reported to affect intrinsic brain connectivity, notably reducing connectivity in the default mode network. Studies to date have however shown inconsistent effects, in many cases lacked monitoring of wakefulness, and largely included young participants. We investigated effects of sleep deprivation on intrinsic brain connectivity in young and older participants. Participants aged 20-30 (n=30) and 65-75 (n=23) years underwent partial sleep deprivation (3h sleep) in a cross-over design, with two 8-minutes eyes-open resting state functional magnetic resonance imaging (fMRI) runs in each session, monitored by eye-tracking. We assessed intrinsic brain connectivity using independent components analysis (ICA) as well as seed-region analyses of functional connectivity, and also analysed global signal variability, regional homogeneity, and the amplitude of low-frequency fluctuations. Sleep deprivation caused increased global signal variability. In our study, changes in investigated resting state networks and in regional homogeneity were not statistically significant. Younger participants had higher connectivity in most examined networks, as well as higher regional homogeneity in areas including anterior and posterior cingulate cortex. In conclusion, we found that sleep deprivation caused increased global signal variability. We speculate that this may be caused by wake-state instability.

Neuroscience↗

bioRxiv2016

Investigating resting-state functional connectivity in the cervical spinal cord at 3T

The study of spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord. Two ultra-high field functional magnetic resonance imaging (fMRI) studies in humans have provided evidence for reproducible resting-state connectivity between the dorsal horns as well as between the ventral horns, and a study in non-human primates has shown that these resting-state signals are impacted by spinal cord injury. As these studies were carried out at ultra-high field strengths using region-of-interest (ROI) based analyses, we investigated whether such resting-state signals could also be observed at the clinically more prevalent field strength of 3T. In a reanalysis of a sample of 20 healthy human participants who underwent a resting-state fMRI acquisition of the cervical spinal cord, we were able to observe significant dorsal horn connectivity as well as ventral horn connectivity, but no consistent effects for connectivity between dorsal and ventral horns, thus replicating the human 7T results. These effects were not only observable when averaging along the acquired length of the spinal cord, but also when we examined each of the acquired spinal segments separately, which showed similar patterns of connectivity. Finally, we investigated the robustness of these resting-state signals against variations in the analysis pipeline by varying the type of ROI creation, temporal filtering, nuisance regression and connectivity metric. We observed that - apart from the effects of band-pass filtering - ventral horn connectivity showed excellent robustness, whereas dorsal horn connectivity showed moderate robustness. Together, our results provide evidence that spinal cord resting-state connectivity is a robust and spatially consistent phenomenon that could be a valuable tool for investigating the effects of pathology, disease progression, and treatment response in neurological conditions with a spinal component, such as spinal cord injury.

Neuroscience↗

bioRxiv2016

Measuring MEG closer to the brain: Performance of on-scalp sensor arrays

Optically-pumped magnetometers (OPMs) have recently reached sensitivity levels required for magnetoencephalogra-phy (MEG). OPMs do not need cryogenics and can thus be placed within millimetres from the scalp into an array that adapts to the invidual head size and shape, thereby reducing the distance from cortical sources to the sensors. Here, we quantified the improvement in recording MEG with hypothetical on-scalp OPM arrays compared to a 306-channel state-of-the-art SQUID array (102 magnetometers and 204 planar gradiometers).\n\nWe simulated OPM arrays that measured either normal (nOPM; 102 sensors), tangential (tOPM; 204 sensors), or all components (aOPM; 306 sensors) of the magnetic field. We built forward models based on magnetic resonance images of 10 adult heads; we employed a three-compartment boundary element model and distributed current dipoles evenly across the cortical mantle.\n\nCompared to the SQUID magnetometers, nOPM and tOPM yielded 7.5 and 5.3 times higher signal power, while the correlations between the field patterns of source dipoles were reduced by factors of 2.8 and 3.6, respectively. Values of the field-pattern correlations were similar across nOPM, tOPM and SQUID gradiometers. Volume currents reduced the signals of primary currents on average by 10%, 72% and 15% in nOPM, tOPM and SQUID magnetometers, respectively. The information capacities of the OPM arrays were clearly higher than that of the SQUID array. The dipole-localization accuracies of the arrays were similar while the minimum-norm-based point-spread functions were on average 2.4 and 2.5 times more spread for the SQUID array compared to nOPM and tOPM arrays, respectively.

Neuroscience↗

bioRxiv2016

Volumetric Two-photon Imaging of Neurons Using Stereoscopy (vTwINS)

Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a widely used imaging method for large scale recording of neural activity in vivo. Here we introduce volumetric Two-photon Imaging of Neurons using Stereoscopy (vTwINS), a volumetric calcium imaging method that employs an elongated, V-shaped point spread function to image a 3D brain volume. Single neurons project to spatially displaced image pairs in the resulting 2D image, and the separation distance between images is proportional to depth in the volume. To demix the fluorescence time series of individual neurons, we introduce a novel orthogonal matching pursuit algorithm that also infers source locations within the 3D volume. We illustrate vTwINS by imaging neural population activity in mouse primary visual cortex and hippocampus. Our results demonstrate that vTwINS provides an effective method for volumetric two-photon calcium imaging that increases the number of neurons recorded while maintaining a high frame-rate.

Neuroscience↗

bioRxiv2016

Functional connectivity models for brain state identification: application to decoding of spatial representations from hippocampal CA1 and CA3 recordings

Hippocampus stores spatial representations, or maps, which are recalled each time a subject is placed in the corresponding environment. Across different environments of similar geometry, these representations show strong orthogonality in CA3 of hippocampus, whereas in the CA1 subfield a considerable overlap between the maps can be seen. The lower orthogonality decreases reliability of various decoders developed in an attempt to identify which of the stored maps is active at the mo-ment. Especially, the problem with decoding emerges with a need to analyze data at high temporal resolution. Here, we introduce a functional-connectivity-based de-coder, which accounts for the pairwise correlations between the spiking activities of neurons in each map and does not require any positional information, i.e. any knowledge about place fields. We first show, on recordings of hippocampal activity in constant environmental conditions, that our decoder outperforms existing decoding methods in CA1. Our decoder is then applied to data from teleportation experiments, in which an instantaneous switch between the environment identity triggers a recall of the corresponding spatial representation. We test the sensitivity of our approach on the transition dynamics between the respective memory states (maps). We find that the rate of spontaneous state shifts (flickering) after a teleportation event is increased not only within the first few seconds as already reported, but this instability is sustained across much longer (> 1 min.) periods.

Neuroscience↗

bioRxiv2016

A self-initiated two-alternative forced choice paradigm for head-fixed mice

Psychophysical tasks for non-human primates have been instrumental in studying circuits underlying perceptual decision-making. To obtain greater experimental flexibility, these tasks have subsequently been adapted for use in freely moving rodents. However, advances in functional imaging and genetic targeting of neuronal populations have made it critical to develop similar tasks for head-fixed mice. Although head-fixed mice have been trained in two-alternative forced choice tasks before, these tasks were not self-initiated, making it difficult to attribute error trials to perceptual or decision errors as opposed to mere lapses in task engagement. Here, we describe a paradigm for head-fixed mice with three lick spouts, analogous to the well-established 3-port paradigm for freely moving rodents. Mice readily learned to initiate trials on the center spout and performed around 200 self-initiated trials per session, reaching good psychometric performance within two weeks of training. We expect this paradigm will be useful to study the role of defined neural populations in sensory processing and decision-making.

Neuroscience↗

bioRxiv2016

Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations

The network oscillations are ubiquitous across many brain regions. In the basal ganglia, oscillations are also present at many levels and a wide range of characteristic frequencies have been reported to occur during both health and disease.The striatum is the input nucleus of the basal ganglia that receives massive glutamatergic inputs from the cortex and is highly susceptible to cortical oscillations. However, there is limited knowledge about the exact nature of this routing process and therefore, it is of key importance to understand how time-dependent, periodic external stimuli propagate through the striatal circuitry. Using a large-scale network model of the striatum and corticostriatal projections, here we try to elucidate the importance of specific GABAergic neurons and their interactions in shaping striatal oscillatory activity. Our results show that fast-spiking interneurons, despite their uncorrelated firing, might have a crucial role in the emergence of high-frequency oscillations in the medium spiny neuron population, even if their activity is kept low. Rather, what matters is the firing time relative to just a few other neurons within an oscillation cycle. Finally, we show how the state of ongoing activity, the strengths of different types of inhibitions, the density of outgoing projections, and the overall activity of striatal cells influence network activity. These results suggest that the propagation of oscillatory inputs into the medium spiny neuron population is efficient, if indirect, through fast-spiking interneurons. Therefore, pharmaceuticals that target fast-spiking interneurons may provide a novel treatment for regaining the spectral characteristics of striatal activity that correspond to the healthy state.\n\nAuthor SummaryThe striatum is the largest and primary gateway component of the BG, receiving glutamatergic inputs from all cortical areas and is highly susceptible to cortical oscillations. However, there is limited knowledge about the exact nature of this routing process and therefore, it is of key importance to understand how time-dependent, external stimuli propagate through the striatal circuitry. The vast majority of striatal neurons, at least 95% of them, are medium spiny neurons (MSNs) that are also the only source of output from the nucleus. Two of the most examined sources of GABAergic inhibition into MSNs are the feedback inhibition (FB) from the axon collaterals of the MSNs themselves, and the feedforward inhibition (FF) via the small population of fast-spiking interneurons (FSIs) comprising roughly 1-2% of striatal neurons. Using a large-scale network model of the striatum we systematically investigate the propagation of asynchronous periodic cortical inputs, throughout the physiologically relevant range, onto the striatal neurons and their influence on striatal network dynamics. Our results show that FSIs, despite their firing being uncorrelated, may play a crucial role in the efficient propagation of external oscillations onto MSNs. Finally, we show how the state of ongoing activity, the strengths of different types of inhibitions, the density of outgoing projections, and the overall activity of striatal cells influence network activity.

Neuroscience↗

bioRxiv2016

Preserved position information in high-level visual cortex with large receptive fields

Neurons in high-level visual areas respond to more complex visual features with broader receptive fields (RFs) compared to those in low-level visual areas. Thus, high-level visual areas are generally considered to carry less information regarding the position of seen objects in the visual field. However, larger RFs may not imply loss of position information at the population level. Here, we evaluated how accurately the position of a seen object could be predicted (decoded) from activity patterns in each of six representative visual areas with different RF sizes (V1-V4, LOC, and FFA). We collected fMRI responses while human subjects viewed a ball randomly moving in a two-dimensional field. To estimate population RF sizes of individual fMRI voxels, RF models were fitted for individual voxels in each brain area. The voxels in higher visual areas showed larger estimated RFs than those in lower visual areas. Then, the balls position in a separate session was predicted by maximum likelihood regression (support vector regression, SVR) to predict the position. We found that regardless of the difference in RF size, all visual areas showed similar prediction accuracies, especially on the horizontal dimension. Higher areas showed slightly lower accuracies on the vertical dimension, which appears to be attributed to the narrower spatial distributions of the RFs centers. The results suggest that much of position information is preserved in population activity through the hierarchical visual pathway regardless of RF sizes, and is potentially available in later processing for recognition and behavior.\n\nSignificance statementHigh-level ventral visual areas are thought to achieve position invariance with larger receptive fields at the cost of the loss of precise position information. However, larger receptive fields may not imply loss of position information at the population level. Here, multivoxel fMRI decoding reveals that high-level visual areas are predictive of an objects position with similar accuracies to low-level visual areas, especially on the horizontal dimension, preserving the information potentially available for later processing.

Neuroscience↗

bioRxiv2016

A spatially resolved network spike in model neuronal cultures reveals nucleation centers, circular traveling waves and drifting spiral waves

We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exists a small number of spatial nucleation centers of a network spike, from where the synchronous spiking activity starts propagating in the network typically in the form of circular traveling waves. The number of nucleation centers and their spatial locations are unique and unchanged for a given realization of neuronal network but are different for different networks. In contrast, if the probability of interneuronal connection formation is independent of the distance between neurons, then the nucleation centers do not arise and the synchronization of spiking activity during a network spike occurs spatially uniform throughout the network. Therefore one can conclude that spatial proximity of connections between neurons is important for the formation of nucleation centers. It is also shown that fluctuations of the spatial density of neurons at their random homogeneous distribution typical for the experiments in vitro do not determine the locations of the nucleation centers. The simulation results are qualitatively consistent with the experimental observations.

Neuroscience↗

bioRxiv2016

Directed Network Discovery with Dynamic Network Modeling

Cognitive tasks recruit multiple brain regions. Understanding how these regions influence each other (the network structure) is an important step to characterize the neural basis of cognitive processes. Often, limited evidence is available to restrict the range of hypotheses a priori, and techniques that sift efficiently through a large number of possible network structures are needed (network discovery). This article introduces a novel modeling technique for network discovery (Dynamic Network Modeling or DNM) that builds on ideas from Granger Causality and Dynamic Causal Modeling introducing three key changes: 1) regularization is exploited for efficient network discovery, 2) the magnitude and sign of each influence are tested with a random effects model across participants, and 3) variance explained in independent data is used as an absolute (rather than relative) measure of the quality of the network model. In this article, we outline the functioning of DNM and we report an example of its application to the investigation of influences between regions during emotion recognition. Across two experiments, DNM individuates a stable set of influences between face-selective regions during emotion recognition.\n\nNew and NoteworthyIn this article we introduce a new analysis method (Dynamic Network Mod- elling or DNM) which exploits{ell} 1 regularization to perform efficient for network discovery. DNM provides information about the direction and sign (inhibitory vs excitatory) of influences between brain regions, and generates measures of variance explained in independent data to evaluate quality of fit. The method is applied to brain regions engaged in emotion recognition, individuating a similar network structure across two separate experiments.

Neuroscience↗

bioRxiv2016

Shared roles of dorsal and subgenual anterior cingulate cortices in economic decisions

Theories of dorsal anterior cingulate cortex (dACC) function generally emphasize its cognitive and regulatory functions while theories of subgenual ACC (sgACC) emphasize its emotional, limbic, and arousal-related roles. But how different are these areas when compared in the same task? We recorded neuronal responses in both regions in macaques in a task with cognitive and limbic aspects, a token gambling task. Using tokens allowed us to compare responses to wins and losses. Both regions phasically encoded several important economic variables in similar ways; these included offered values, remembered values, attended values, and obtained values, and number of current tokens. Signal-to-noise ratio in sgACC was substantially lower than in dACC, and sgACC neurons responded more strongly to losses and in anticipation of large rewards. These results highlight the common economic functions of the anterior cingulum and suggest different functional emphases between regions, rather than a strict cognitive vs. emotional division.

Neuroscience↗

bioRxiv2016

A note on detecting statistical outliers in psychophysical data

This paper considers how best to identify statistical outliers in psychophysical datasets, where the underlying sampling distributions are unknown. Eight methods are described, and each is evaluated using Monte Carlo simulations of a typical psychophysical experiment. The best method is shown to be one based on a measure of absolute-deviation known as Sn. This method is shown to be more accurate than popular heuristics based on standard deviations from the mean, and more robust than non-parametric methods based on interquartile range. Matlab code for computing Sn is included.

Neuroscience↗

bioRxiv2016

Top-Down Beta Oscillatory Signaling Conveys Behavioral Context to Primary Visual Cortex

Top-down modulation of sensory processing is a critical neural mechanism subserving a number of important cognitive roles. Principally, top-down influences appear to inform lower-order sensory systems of the current task at hand, and thus may convey behavioral context to these systems. Accumulating evidence indicates that top-down cortical influences are carried by directed interareal synchronization of oscillatory neuronal populations. An important question currently under investigation by a number of laboratories is whether the information conveyed by directed interareal synchronization depends on the frequency band in which it is conveyed. Recent results point to the beta frequency band as being particularly important for conveying task-related information. However, little is known about the nature of the information conveyed by top-down directed influences. To investigate the information content of top-down directed beta-frequency influences, we measured spectral Granger Causality using local field potentials recorded from microelectrodes chronically implanted in visual cortical areas V1, V4, and TEO, and then applied multivariate pattern analysis to the spatial patterns of top-down spectral Granger Causality in the visual cortex. We decoded behavioral context by discriminating patterns of top-down (V4/TEO [->] V1) beta-peak spectral Granger Causality for two different task rules governing the correct responses to visual stimuli. The results indicate that top-down directed influences in visual cortex are carried by beta oscillations, and differentiate current task demands even before visual stimulus processing. They suggest that top-down beta-frequency oscillatory processes may coordinate the processing of sensory information by conveying global knowledge states to early levels of the sensory cortical hierarchy independently of bottom-up stimulus-driven processing.

Neuroscience↗

bioRxiv2016

Global Reduction of Information Exchange during Anesthetic-Induced Unconsciousness

During anesthetic-induced unconsciousness (AIU), the brain undergoes a dramatic change in its capacity to exchange information between regions. However, the spatial distribution of information exchange loss/gain across the entire brain remains elusive. In the present study, we acquired and analyzed resting-state functional magnetic resonance imaging (rsfMRI) data in rats during wakefulness and graded levels of consciousness induced by incrementally increasing the concentration of isoflurane. We found that, regardless of spatial scale, the absolute functional connectivity (FC) change was significantly dependent on the FC strength at the awake state across all connections. This dependency became stronger at higher doses of isoflurane. In addition, the relative FC change (i.e. the FC change normalized to the corresponding FC strength at the awake state) exhibited a spatially homogenous reduction across the whole brain particularly after animals lost consciousness, indicating a globally uniform disruption of meaningful information exchange. To further support this notion, we showed that during unconsciousness, the entropy of rsfMRI signal increased to a value comparable to random noise while the mutual information decreased appreciably. Importantly, consistent results were obtained when unconsciousness was induced by dexmedetomidine, an anesthetic agent with a distinct molecular action than isoflurane. This result indicates that the observed global reduction in information exchange may be agent invariant. Taken together, these findings provide compelling neuroimaging evidence suggesting that the brain undergoes a widespread, uniform disruption in the exchange of meaningful information during AIU, and that this change may represent a common systems-level neural mechanism of AIU.

Neuroscience↗

bioRxiv2016

Joint analysis of area and thickness of the cerebral cortex replaces cortical volume measurements

A detailed investigation of the surface-based methods for assessment of cortical volume and area from magnetic resonance images shows that volume mostly mirrors variability in surface area, while having little sensitivity to cortical thickness, and this remains the case even when volume is assessed using an improved analytic method. Using data from young adults born with very low birth weight and coetaneous controls, it is demonstrated that the permutation-based non-parametric combination (NPC) of thickness and area is more sensitive than volume for studying joint effects on these two quantities, giving equal weight to variation in both. NPC can replace current analyses of cortical volume altogether, allowing a better characterisation of processes that can concomitantly influence brain anatomy, even when their effects are in opposite directions. The method is remarkably general and can be extended to other indices of brain structure and function.

Neuroscience↗

bioRxiv2016

Valuation in major depression is intact and stable in a non-learning environment

The clinical diagnosis and symptoms of major depressive disorder (MDD) have been closely associated with impairments in reward processing. In particular, various studies have shown blunted neural and behavioral responses to the experience of reward in depression. However, little is known about whether depression affects individuals valuation of potential rewards during decision-making, independent from reward experience. To address this question, we used a gambling task and a model-based analytic approach to measure two types of individual sensitivity to reward values in participants with MDD: risk preference, indicating how objective values are subjectively perceived and inverse temperature, determining the degree to which subjective value differences between options influences participants choices. On both of these measures of value sensitivity, participants with MDD were comparable to non-psychiatric controls. Both risk preference and inverse temperature were also stable over four laboratory visits and comparable between the groups at each visit. Moreover, neither value sensitivity measure varied with severity of clinical symptoms in MDD. These data suggest intact and stable value processing in MDD during risky decision-making.

Neuroscience↗

bioRxiv2016

Relating dynamic brain states to dynamic machine states: human and machine solutions to the speech recognition problem

There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental 'machine states', generated as the ASR analysis progresses over time, to the incremental 'brain states', measured using combined electro-and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain.\n\nAuthor SummaryThe ability to understand spoken language is a defining human capacity. But despite decades of research, there is still no well-specified account of how sound entering the ear is neurally interpreted as a sequence of meaningful words. At the same time, modern computer-based Automatic Speech Recognition (ASR) systems are capable of nearhuman levels of performance, especially where word-identification is concerned. In this research we aim to bridge the gap between human and machine solutions to speech recognition. We use a novel combination of neuroimaging and statistical methods to relate human and machine internal states that are dynamically generated as spoken words are heard by human listeners and analysed by ASR systems. We find that the stable regularities discovered by the ASR process, linking speech input to phonetic labels, can be significantly related to the regularities extracted in the human brain. Both systems may have in common a representation of these regularities in terms of articulatory phonetic features, consistent with an analysis process which recovers the articulatory gestures that generated the speech. These results suggest a possible partnership between human-and machine-based research which may deliver both a better understanding of how the human brain provides such a robust solution to speech understanding, and generate insights that enhance the performance of future ASR systems.

Neuroscience↗

bioRxiv2016

Measuring and Modeling Transformations of Information Between Brain Regions with fMRI

In the study of connectivity in large-scale networks of brain regions, a standard assumption is made that the statistical dependence between regions is univariate and linear. However, brain regions encode information in multivariate responses, and neural computations are nonlinear. Multivariate and nonlinear statistical dependence between regions is likely ubiquitous, but it is not captured by current methods. To fill this gap, we introduce a novel analysis framework: fMRI responses are characterized as points in multidimensional spaces, and nonlinear dependence is modeled using artificial neural networks. Converging evidence from multiple experiments shows that nonlinear dependence 1) models mappings between brain regions more accurately than linear dependence, explaining more variance in left-out data; 2) reveals functional subdivisions within cortical networks, and 3) is modulated by the task participants are performing.

neuroscience↗