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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Red Bull Gives You Incentive Motivation: Understanding Placebo Effects of Energy Drinks on Human Cognitive Performance

The consumption of cognitive enhancers like energy drinks (EnD) is on the rise, but do they really improve cognitive performance, and, if yes, why? We examined two novel psychological mechanisms. First, we dissociated the role of expectations and actual consumption by crossing what people consumed--Red Bull Silver Edition or a similar-tasting Sprite soda--and what they thought they consumed. We found that participants performed better in a numerical Stroop task when they believed that they had consumed an EnD, irrespective of what they had actually consume, Second, we investigated the role of motivation for such a placebo effect of EnD. We found that expected, but not actual, consumption of EnD increased the effects of incentives on cognitive performance. Our results suggest that believing that one consumes an EnD increased participants motivation to perform and thus enhanced their performance.\n\nSignificance statementAcademic doping has become increasingly popular on campuses around the globe over the last years. However, it remains unknown if and how commercially available cognitive enhancers such as energy drinks (EnD) impact cognitive performance. We varied actual and believed consumption of an EnD and measured cognitive performance and its allocation according to the magnitude of points to earn. We found that the belief that one has consumed an EnD but not its actual consumption, increased cognitive performance specifically in high point trials compared to low point trials. These findings provide novel insights into motivational processes underpinning marketing-led expectancy effects, and contribute to the current public policy debate on the use and abuse of increased alertness and concentration claims for EnD.

neuroscience↗
bioRxiv2017

Your favorite color makes learning more adaptable and precise

Learning from reward feedback is essential for survival but can become extremely challenging with myriad choice options. Here, we propose that learning reward values of individual features can provide a heuristic for estimating reward values of choice options in dynamic, multidimensional environments. We hypothesized that this feature-based learning occurs not just because it can reduce dimensionality, but more importantly because it can increase adaptability without compromising precision of learning. We experimentally tested this hypothesis and found that in dynamic environments, human subjects adopted feature-based learning even when this approach does not reduce dimensionality. Even in static, low-dimensional environments, subjects initially adopted feature-based learning and gradually switched to learning reward values of individual options, depending on how accurately objects values can be predicted by combining feature values. Our computational models reproduced these results and highlight the importance of neurons coding feature values for parallel learning of values for features and objects.

neuroscience↗
bioRxiv2017

Adaptive Cortical Parcellations for Source Reconstructed EEG/MEG Connectomes

There is growing interest in the rich temporal and spectral properties of the brain's functional connectome that are provided by Electro- and Magnetoencephalography (EEG/MEG). However, the problem of leakage between brain sources that arises when reconstructing brain activity from EEG/MEG recordings outside the head makes it difficult to distinguish true connections from spurious connections, even when connections are based on measures that ignore zero-lag dependencies. In particular, standard anatomical parcellations for potential cortical sources tend to over- or under-sample the real spatial resolution of EEG/MEG. By using information from cross-talk functions (CTFs) that objectively describe leakage for a given sensor configuration and distributed source reconstruction method, we introduce methods for optimising the number of parcels while simultaneously minimising the leakage between them. More specifically, we compare two image segmentation algorithms: 1) a split-and-merge (SaM) algorithm based on standard anatomical parcellations and 2) a region growing (RG) algorithm based on all the brain vertices with no prior parcellation. Interestingly, when applied to minimum-norm reconstructions for EEG/MEG configurations from real data, both algorithms yielded approximately 70 parcels despite their different starting points, suggesting that this reflects the resolution limit of this particular sensor configuration and reconstruction method. Importantly, when compared against standard anatomical parcellations, resolution matrices of adaptive parcellations showed notably higher sensitivity and distinguishability of parcels. Furthermore, extensive simulations of realistic networks under various circumstances revealed significant improvements in network reconstruction accuracies, particularly in reducing false leakage-induced connections. Adaptive parcellations therefore allow a more accurate reconstruction of functional EEG/MEG connectomes.\n\nHighlightsO_LIWe introduce adaptive cortical parcellation algorithms for E/MEG source estimation.\nC_LIO_LIAlgorithms are based on cross-talk functions and image segmentation methods.\nC_LIO_LIThe resulting parcellations yielded ~70 parcels regardless of starting point.\nC_LIO_LISensitivity and distinguishability improved compared to anatomical parcellations.\nC_LIO_LIAccuracy of realistic whole-brain network reconstruction improved significantly.\nC_LI

neuroscience↗
bioRxiv2017

Masking of a circadian behavior in larval zebrafish involves the thalamo-habenula pathway

Light has the ability to disrupt or mask behavior that is normally controlled by the circadian clock. In mammals, masking requires melanopsin-expressing retinal ganglion cells that detect blue light and project to the thalamus. It is not known whether masking is wavelength-dependent in other vertebrates, nor is it clear what higher circuits are involved. Here, we address these questions in zebrafish. We find that diel vertical migration, a circadian behavior in larval zebrafish, is effectively masked by blue, but not by red light. Two-photon calcium imaging reveals that a retino-recipient thalamic nucleus and a downstream structure, the habenula, are tuned to blue light. Lesioning the habenula inhibits light-evoked climbing. These data suggest that a thalamo-habenula pathway may be involved in the ability of blue light to mask circadian behavior.

neuroscience↗
bioRxiv2017

Individual movement variability magnitudes are predicted by cortical neural variability

Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction, which are thought to be governed by distinct neural processes. Here, we report that individual subjects exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior-parietal cortex of individual subjects predicted their movement-extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We, therefore, propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities.

neuroscience↗
bioRxiv2017

Common object representations for visual production and recognition

Production and comprehension have long been viewed as inseparable components of language. The study of vision, by contrast, has centered almost exclusively on comprehension. Here we investigate drawing -- the most basic form of visual production. How do we convey concepts in visual form, and how does refining this skill, in turn, affect recognition? We developed an online platform for collecting large amounts of drawing and recognition data, and applied a deep convolutional neural network model of visual cortex trained only on natural images to explore the hypothesis that drawing recruits the same abstract feature representations that support natural visual object recognition. Consistent with this hypothesis, higher layers of this model captured the abstract features of both drawings and natural images most important for recognition, and people learning to produce more recognizable drawings of objects exhibited enhanced recognition of those objects. These findings could explain why drawing is so effective for communicating visual concepts, they suggest novel approaches for evaluating and refining conceptual knowledge, and they highlight the potential of deep networks for understanding human learning.

neuroscience↗
bioRxiv2017

The contribution of striatal pseudo-reward prediction errors to value-based decision-making

Most studies that have investigated the brain mechanisms underlying learning have focused on the ability to learn simple stimulus-response associations. However, in everyday life, outcomes are often obtained through complex behavioral patterns involving a series of actions. In such scenarios, parallel learning systems are important to reduce the complexity of the learning problem, as proposed in the framework of hierarchical reinforcement learning (HRL). One of the key features of HRL is the computation of pseudo-reward prediction errors (PRPEs) which allow the reinforcement of actions that led to a sub-goal before the final goal itself is achieved. Here we wanted to test the hypothesis that, despite not carrying any rewarding value per se, pseudo-rewards might generate a bias in choice behavior when reward contingencies are not well-known or uncertain. Second, we also hypothesized that this bias might be related to the strength of PRPE striatal representations. In order to test these ideas, we developed a novel decision-making paradigm to assess reward prediction errors (RPEs) and PRPEs in two studies (fMRI study: n = 20; behavioural study: n = 19). Our results show that overall participants developed a preference for the most pseudo-rewarding option throughout the task, even though it did not lead to more monetary rewards. fMRI analyses revealed that this preference was predicted by individual differences in the relative striatal sensitivity to PRPEs vs RPEs. Together, our results indicate that pseudo-rewards generate learning signals in the striatum and subsequently bias choice behavior despite their lack of association with actual reward.

neuroscience↗
bioRxiv2017

How context alters value: Price information recruits the brain’s valuation and affective regulation system for shaping experienced taste pleasantness

Informational cues such as the price of a wine can trigger expectations about its taste quality and thereby modulate the sensory experience on a reported and neural level. Yet it is unclear how the brain translates such expectations into sensory pleasantness. We used multilevel mediation analysis of neural and behavioral data obtained in participants who tasted identical wines cued with different prices. We found that the brain's valuation system (BVS) in concert with the anterior prefrontal cortex explained the effect of price cues on taste pleasantness ratings. The sensitivity of the BVS to rewards outside the taste domain moderated the strength of these effects. Moreover, brain mediators of price cue effects overlapped with brain regions previously found to be involved in placebo analgesia. These findings provide novel evidence for the fundamental role that neural pathways linked to motivation and affective regulation play for the effect of informational cues on sensory experiences.

neuroscience↗
bioRxiv2017

Lateral geniculate neurons show robust ocular dominance plasticity

Experience-dependent plasticity in the mature visual system is considered exclusively cortical. Using chronic two-photon Ca2+ imaging, we found evidence against this tenet: dLGN cells showed robust ocular dominance shifts after monocular deprivation. Most, but not all responses of dLGN cell boutons in binocular visual cortex were monocular during baseline. Following deprivation, however, deprived-eye dominated boutons became responsive to the non-deprived eye. Thus, plasticity of dLGN neurons contributes to cortical ocular dominance shifts.

neuroscience↗
bioRxiv2017

Tracking Real Time Changes in Working Memory Updating and Gating with Event-Based Eye-Blink Rate

Effective working memory (WM) functioning depends on the gating process that regulates the balance between maintenance and updating of WM. The present study used the event-based eye-blink rate (ebEBR), which presumably reflects phasic striatal dopamine activity, to examine how the cognitive processes of gating and updating separately facilitate flexible updating of WM contents and the potential involvement of dopamine in these processes. Realtime changes in eye blinks were tracked during performance on the reference-back task, in which demands on these two processes were independently manipulated. In all three experiments, trials that required WM updating and trials that required gate switching were both associated with increased ebEBR. These results may support the prefrontal cortex basal ganglia WM model (PBWM) by linking updating and gating to striatal dopaminergic activity. In Experiment 3, the ebEBR was used to determine what triggers gate switching. We found that switching to an updating mode (gate opening) was more stimulus driven and retroactive than switching to a maintenance mode, which was more context driven. Together, these findings show that the ebEBR - an inexpensive, non-invasive, easy-to-use measure - can be used to track changes in WM demands during task performance and, hence, possibly striatal dopamine activity.

neuroscience↗
bioRxiv2017

Encoding variable cortical states with short-term spikepatterns

Neurons in the primary somatosensory cortex (S1) respond to peripheral stimulation with synchronised bursts of spikes, which lock to the macroscopic 600 Hz EEG waves. The mechanism of burst generation and synchronisation in S1 is not yet understood. Using models of single-neuron responses fitted to unit recordings from macaque monkeys, we show that these synchronised bursts are the consequence of correlated synaptic inputs combined with a refractory mechanism. In the presence of noise these models reproduce also the observed trial-to-trial response variability, where individual bursts represent one of many stereotypical temporal spike patterns. When additional slower and global excitability fluctuations are introduced the single-neuron spike patterns are correlated with the population activity, as demonstrated in experimental data. The underlying biophysical mechanism of S1 responses involves thalamic inputs arriving through depressing synapses to cortical neurons in a high-conductance state. Our findings show that a simple feedforward processing of peripheral inputs could give rise to neuronal responses with non-trivial temporal and population statistics. We conclude that neural systems could use refractoriness to encode variable cortical states into stereotypical short-term spike patterns amenable to processing at neuronal time scales (tens of milliseconds).\n\nSignificance statementNeurons in the hand area of the primary somatosensory cortex respond to repeated presentation of the same stimulus with variable sequences of spikes, which can be grouped into distinct temporal spike patterns. In a simplified model, we show that such spike patterns are product of synaptic inputs and intrinsic neural properties. This model can reproduce both single-neuron and population responses only when a private variability in each neuron is combined with a multiplicative gain shared over whole population, which fluctuates over trials and might represent the dynamical state of the early stages of sensory processing. This phenomenon exemplifies a general mechanism of transforming the ensemble cortical states into precise temporal spike patterns at the level of single neurons.

neuroscience↗
bioRxiv2017

Semaphorin-Plexin signaling influences early ventral telencephalic development and thalamocortical axon guidance

BackgroundSensory processing relies on projections from the thalamus to the neocortex being established during development. Information from different sensory modalities reaching the thalamus is segregated into specialized nuclei, whose neurons then send inputs to cognate cortical areas through topographically defined axonal connections.\n\nDeveloping thalamocortical axons (TCAs) normally approach the cortex by extending through the subpallium; here, axonal navigation is aided by distributed guidance cues and discrete cell populations, such as the corridor neurons and the internal capsule (IC) guidepost cells. In mice lacking Semaphorin-6A, axons from the dorsal lateral geniculate nucleus (dLGN) bypass the IC and extend aberrantly in the ventral subpallium. The functions normally mediated by Semaphorin-6A in this system remain unknown, but might depend on interactions with Plexin-A2 and Plexin-A4, which have been implicated in other neurodevelopmental processes.\n\nMethodsWe performed immunohistochemical and neuroanatomical analyses of thalamocortical wiring and subpallial development in Sema6a and Plxna2;Plxna4 null mutant mice and analyzed the expression of these genes in relevant structures.\n\nResultsIn Plxna2;Plxna4 double mutants we discovered TCA pathfinding defects that mirrored those observed in Sema6a mutants, suggesting that Semaphorin-6A-Plexin-A2/Plexin-A4 signaling might mediate dLGN axon guidance at subpallial level.\n\nIn order to understand where and when Semaphorin-6A, Plexin-A2 and Plexin-A4 may be required for proper subpallial TCA guidance, we then characterized their spatiotemporal expression dynamics during early TCA development. We observed that the thalamic neurons whose axons are misrouted in these mutants normally express Semaphorin-6A but not Plexin-A2 or Plexin-A4. By contrast, all three proteins are expressed in corridor cells and other structures in the developing basal ganglia.\n\nThis could be consistent with the Plexins acting as guidance signals through Sema6A as a receptor on dLGN axons, and/or with an indirect effect on TCA guidance due to functions in morphogenesis of subpallial intermediate targets. In support of the latter possibility, we observed that in both Plxna2;Plxna4 and Sema6a mutants some IC guidepost cells abnormally localize in correspondence of the ventral path misrouted TCAs elongate into.\n\nConclusionsThese findings implicate Semaphorin-6A-Plexin-A2/Plexin-A4 interactions in dLGN axon guidance and in the spatiotemporal organization of guidepost cell populations in the mammalian subpallium.

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bioRxiv2017

The Modulation of Pain by Circadian and Sleep-Dependent Processes: A Review of the Experimental Evidence

This proceedings paper is the first in a series of three papers developing mathematical models for the complex relationship between pain and the sleep-wake cycle. Here, we briefly review what is known about the relationship between pain and the sleep-wake cycle in humans and laboratory rodents in an effort to identify constraints for the models. While it is well accepted that sleep behavior is regulated by a daily (circadian) timekeeping system and homeostatic sleep drive, the joint modulation of these two primary biological processes on pain sensitivity has not been considered. Under experimental conditions, pain sensitivity varies across the 24 h day, with highest sensitivity occurring during the evening in humans. Pain sensitivity is also modulated by sleep behavior, with pain sensitivity increasing in response to the build up of homeostatic sleep pressure following sleep deprivation or sleep disruption. To explore the interaction between these two biological processes using modeling, we first compare the magnitude of their effects across a variety of experimental pain studies in humans. To do this comparison, we normalize the results from experimental pain studies relative to the range of physiologicallymeaningful stimulation levels. Following this normalization, we find that the estimated impact of the daily rhythm and of sleep deprivation on experimental pain measurements is surprisingly consistent across different pain modalities. We also review evidence documenting the impact of circadian rhythms and sleep deprivation on the neural circuitry in the spinal cord underlying pain sensation. The characterization of sleep-dependent and circadian influences on pain sensitivity in this review paper is used to develop and constrain the mathematical models introduced in the two companion articles.

neuroscience↗
bioRxiv2017

Metacognitive impairments extend perceptual decision making weaknesses in compulsivity

Awareness of ones own abilities is of paramount importance in adaptive decision making. Psychotherapeutic theories assume such metacognitive insight is impaired in compulsivity, though this is supported by scant empirical evidence. In this study, we investigate metacognitive abilities in compulsive participants using computational models, where these enable a segregation between metacognitive and perceptual decision making impairments. We examined twenty low-compulsive and twenty high-compulsive participants, recruited from a large population-based sample, and matched for other psychiatric and cognitive dimensions. Hierarchical computational modelling of the participants metacognitive abilities on a visual global motion detection paradigm revealed that high-compulsive participants had a reduced metacognitive ability. This impairment was accompanied by a perceptual decision making deficit whereby motion-related evidence was accumulated more slowly in high compulsive participants. Our study shows that the compulsivity spectrum is associated with a reduced ability to monitor ones own performance, over and above any perceptual decision making difficulty.

neuroscience↗
bioRxiv2017

EEG correlates of working memory performance in females

BackgroundThe study investigates oscillatory brain activity during working memory (WM) tasks. The tasks employed varied in two dimensions. First, they differed in complexity from average to highly demanding. Second, we used two types of tasks, which required either only retention of stimulus set or retention and manipulation of the content. We expected to reveal EEG correlates of temporary storage and central executive components of WM and to assess their contribution to individual differences.\n\nResultsGenerally, as compared with the retention condition, manipulation of stimuli in WM was associated with distributed suppression of alpha1 activity and with the increase of the midline theta activity. Load and task dependent decrement of beta1 power was found during task performance. Beta2 power increased with the increasing WM load and did not significantly depend on the type of the task.\n\nAt the level of individual differences, we found that the high performance (HP) group was characterized by higher alpha rhythm power. The HP group demonstrated task-related increment of theta power in the left anterior area and a gradual increase of theta power at midline area. In contrast, the low performance (LP) group exhibited a drop of theta power in the most challenging condition. HP group was also characterized by stronger desynchronization of beta1 rhythm over the left posterior area in the manipulation condition. In this condition, beta2 power increased in the HP group over anterior areas, but in the LP group over posterior areas.\n\nConclusionsWM performance is accompanied by changes in EEG in a broad frequency range from theta to higher beta bands. The most pronounced differences in oscillatory activity between individuals with high and low WM performance can be observed in the most challenging WM task.

neuroscience↗
bioRxiv2017

A Two-Process Model for Circadian and Sleep-dependent Modulation of Pain Sensitivity

Pain sensitivity is strongly modulated by time of day and by prior sleep behavior. These two factors, governed by the circadian rhythm and homeostatic sleep drive, respectively, likewise dictate the timing and duration of sleep. The fields of sleep and circadian research have identified much of the physiology underlying the circadian rhythm and homeostatic sleep drive with mathematical modeling playing an important role in understanding how these two processes interact to affect sleep behavior. We hypothesize that the daily rhythm of pain sensitivity and its sleep-dependent modulation reflect an interaction of the circadian rhythm and homeostatic sleep drive. To investigate this hypothesis, we adapt the formalism of a classic mathematical model for the regulation of sleep behavior by the circadian rhythm and homeostatic sleep drive, called the Two Process model, to simulate the interaction of these two processes on pain sensitivity. To construct the model, we utilize data from experimental reports on the daily rhythmicity of pain sensitivity in humans to define a \"daily pain sensitivity\" function. We decompose this function into two processes: a sleep-dependent process S(t) that follows the homeostatic sleep drive and a circadian process C(t) that is dictated by the circadian rhythm. By simulating different sleep schedules with the original Two Process model, we compute changes in the sleep-dependent process S(t) that modulates pain sensitivity. By combining S(t) with the circadian process C(t), our model predicts resultant changes in the daily pain sensitivity rhythm. We illustrate model predictions for changes in pain sensitivity due to sleep deprivation, sleep restriction and shift work schedules. We believe that this model may be a useful tool for pain management by providing predictions of the variations in pain sensitivity due to changing sleep schedules.

neuroscience↗
bioRxiv2017

Network cloning using DNA barcodes

The connections between neurons determine the computations performed by both artificial and biological neural networks. Recently, we have proposed SYNseq, a method for converting the connectivity of a biological network into a form that can exploit the tremendous efficiencies of high-throughput DNA sequencing. In SYNseq, each neuron is tagged with a random sequence of DNA--a \"barcode\"--and synapses are represented as barcode pairs. SYNseq addresses the analysis problem, reducing a network into a suspension of barcode pairs. Here we formulate a novel and complementary synthesis problem: How can the suspension of barcode pairs be used to \"clone\" or copy the network back into an uninitialized tabula rasa network? Although this synthesis problem might be expected to be computationally intractable, we find that, surprisingly, this problem can be solved efficiently, using only neuron-local information. We present the \"one barcode one cell\" (OBOC) algorithm, which forces all barcodes of a given sequence to coalesce into the same neuron, and show that it converges in a number of steps that is a power law of the network size. Rapid and reliable network cloning with single synapse precision is thus theoretically possible.

neuroscience↗
bioRxiv2017

When invisible noise obscures the signal: the consequences of nonlinearity in motion detection

1The motion energy model is the standard account of motion detection in animals from beetles to humans. Despite this common basis, we show here that a difference in the early stages of visual processing between mammals and insects leads this model to make radically different behavioural predictions. In insects, early filtering is spatially lowpass, which makes the surprising prediction that motion detection can be impaired by \"invisible\" noise, i.e. noise at a spatial frequency that elicits no response when presented on its own as a signal. We confirm this prediction using the optomotor response of praying mantis Sphodromantis lineola. This does not occur in mammals, where spatially bandpass early filtering means that linear systems techniques, such as deriving channel sensitivity from masking functions, remain approximately valid. Counter-intuitive effects such as masking by invisible noise may occur in neural circuits wherever a nonlinearity is followed by a difference operation.

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