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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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Choice-induced biases in perception

Illusions provide a great opportunity to study how perception is affected by both the observers expectations and the way sensory information is represented1,2,3,4,5. Recently, Jazayeri and Movshon6 reported a new and interesting perceptual illusion, demonstrating that the perceived motion direction of a dynamic random dot stimulus is systematically biased when preceded by a motion discrimination judgment. The authors hypothesized that these biases emerge because the brain predominantly relies on those neurons that are most informative for solving the discrimination task7, but then is using the same neural weighting profile for generating the percept. In other words, they argue that these biases are \"mistakes\" of the brain, resulting from using inappropriate neural read-out weights. While we were able to replicate the illusion for a different visual stimulus (orientation), our new psychophysical data suggest that the above interpretation is likely incorrect: Biases are not caused by a read-out profile optimized for solving the discrimination task but rather by the specific choices subjects make in the discrimination task on any given trial. We formulate this idea as a conditioned Bayesian observer model and show that it can explain the new as well as the original psychophysical data. In this framework, the biases are not caused by mistake but rather by the brains attempt to remain self-consistent in its inference process. Our model establishes a direct connection between the current perceptual illusion and the well-known phenomena of cognitive consistency and dissonance8,9.

Neuroscience

Cerebrospinal Fluid and Interstitial Fluid Motion via the Glymphatic Pathway Modelled by Optimal Mass Transport

It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the glymphatic pathway plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs1,2. It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through peri-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach3 to model the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data.

Neuroscience

Bridging the gap between genes and language deficits in schizophrenia: an oscillopathic approach

Schizophrenia is characterised by marked language deficits, but it is not clear how these deficits arise from gene mutations linked to or associated with the disease. The goal of this paper is to aid the bridging of the gap between genes and schizophrenia and, ultimately, give support to the view that it represents an abnormal ontogenetic itinerary for the human faculty of language, heavily rooted in the evolutionary processes that brought about modern language. To that end we will focus on how the schizophrenia brain processes language and, particularly, on its distinctive oscillatory profile during language processing: We will argue that brain rhythms constitute the best route to interpret language deficits in this condition and map them to neural dysfunction and risk alleles of the genes. Additionally, we will show that candidate genes for schizophrenia are overrepresented among the set of genes that are believed are important for the evolution of human language. These genes crucially include (and are related to) genes involved in brain rhythmicity. We will claim that this translational effort and the links we uncover may help develop an understanding of language evolution, along with the aetiology of schizophrenia, its clinical/linguistic profile, and its high prevalence among modern populations.

Neuroscience

Using reciprocity for relating the simulation of transcranial current stimulation to the EEG forward problem

To explore the relationship between transcranial current stimulation (tCS) and the electroencephalography (EEG) forward problem, we investigate and compare accuracy and efficiency of a reciprocal and a direct EEG forward approach for dipolar primary current sources both based on the finite element method (FEM), namely the adjoint approach (AA) and the partial integration approach in conjunction with a transfer matrix concept (PI). By analyzing numerical results, comparing to analytically derived EEG forward potentials and estimating computational complexity in spherical shell models, AA turns out to be essentially identical to PI. It is then proven that AA and PI are also algebraically identical even for general head models. This relation offers a direct link between the EEG forward problem and tCS. We then demonstrate how the quasi-analytical EEG forward solutions in sphere models can be used to validate the numerical accuracies of FEM-based tCS simulation approaches. These approaches differ with respect to the ease with which they can be employed for realistic head modeling based on MRI-derived segmentations. We show that while the accuracy of the most easy to realize approach based on regular hexahedral elements is already quite high, it can be significantly improved if a geometry-adaptation of the elements is employed in conjunction with an isoparametric FEM approach. While the latter approach does not involve any additional difficulties for the user, it reaches the high accuracies of surface-segmentation based tetrahedral FEM, which is considerably more difficult to implement and topologically less flexible in practice. Finally, in a highly realistic head volume conductor model and when compared to the regular alternative, the geometry-adapted hexahedral FEM is shown to result in significant changes in tCS current flow orientation and magnitude up to 45 degrees and a factor of 1.66, respectively.

Neuroscience

A proposed rule for updating of the head direction cell reference frame following rotations in three dimensions

In the mammalian brain, allocentric (Earth-referenced) heading direction, called azimuth, is encoded by head direction (HD) cells, which fire according to the facing direction of the rats head. If the animal is on a horizontal surface then egocentric (self-referenced) rotations of the head around the dorso-ventral axis, called yaw, correspond to changes in azimuth, and elicit appropriate updating of the HD signal. However, if the surface is sloping steeply then yaw rotations no longer map linearly to changes in azimuth. The brain could avoid the complex computations needed to compute global azimuth simply by always firing according to direction on the local surface; however, if the animal moves between surfaces having different compass orientations then errors would accumulate in the subsequent azimuth signal. These errors could be avoided if the HD system instead combines two updating rules: yaw rotations around the D-V axis and rotations of the D-V axis around the gravity-defined vertical axis. We show here that when rats move between vertical walls of different orientations then HD cells indeed rotate their activity by an amount corresponding to the amount of vertical-axis rotation. With modelling, we then show how this reference-frame rotation, which may be driven by inputs from the vestibular nuclei or vestibulocerebellum, allows animals to maintain a simple yaw-based updating rule while on a given plane, but also to avoid accumulation of heading errors when moving between planes, thus facilitating orientation during complex real-world navigation.

Neuroscience

A statistical framework for neuroimaging data analysis based on mutual information estimated via a Gaussian copula

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, uni-and multi-dimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article.\n\nHighlightsO_LINovel estimator for mutual information and other information theoretic quantities\nC_LIO_LIProvides general, efficient, flexible and robust multivariate statistical framework\nC_LIO_LIValidated statistical performance on EEG and MEG data\nC_LIO_LIApplications to spectral power and phase, 2D magnetic field gradients, temporal derivatives\nC_LIO_LIInteraction information relates information content in different responses\nC_LI

Neuroscience

Quantifying Differences and Similarities in Whole-brain White Matter Architecture Using Local Connectome Fingerprints

Quantifying differences or similarities in connectomes has been a challenge due to the immense complexity of global brain networks. Here we introduce a noninvasive method that uses diffusion MRI to characterize whole-brain white matter architecture as a single local connectome fingerprint that allows for a direct comparison between structural connectomes. In four independently acquired data sets with repeated scans (total N=213), we show that the local connectome fingerprint is highly specific to an individual, allowing for an accurate self-versus-others classification that achieved 100% accuracy across 17,398 identification tests. The estimated classification error was approximately one thousand times smaller than fingerprints derived from diffusivity-based measures or region-to-region connectivity patterns. The local connectome fingerprint also revealed neuroplasticity within an individual reflected as a decreasing trend in self-similarity across time, whereas this change was not observed in the diffusivity measures. Moreover, the local connectome fingerprint can be used as a phenotypic marker, revealing 12.51% similarity between monozygotic twins, 5.14% between dizygotic twins, and 4.51% between none-twin siblings. This novel approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome.\n\nAuthor SummaryThe local organization of white matter architecture is highly unique to individuals, making it a tangible metric of connectomic differences. The variability in local white matter architecture is found to be partially determined by genetic factors, but largely plastic across time. This approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome.

Neuroscience

Object-finding skill created by repeated reward experience

AbstractFor most animals, survival depends on rapid detection of rewarding objects, but search for an object surrounded by many others is known to be difficult and time consuming. However, there is neuronal evidence for robust and rapid differentiation of objects based on their reward history in primates (Hikosaka et al., 2014). We hypothesized that such robust coding should support efficient search for high-value objects, similar to a pop-out mechanism. To test this hypothesis, we let subjects (n=4, macaque monkeys) view a large number of complex objects with consistently biased rewards with variable training durations (1, 5 or >30days). Following training, subjects searched for a high-value object (Good) among a variable number of low-value objects (Bad). Consistent with our hypothesis, we found that Good objects were accurately and quickly targeted, often by a single and direct saccade with a very short latency (<200ms). The dependence of search times on display size reduced significantly with longer reward training, giving rise to a more efficient search (40ms/item to 16ms/item). This object-finding skill showed a large capacity for value-biased objects and was maintained in the long-term memory with no interference from reward learning with other objects. Such object-finding skill, particularly its large capacity and its long term retention, would be crucial for maximizing rewards and biological fitness throughout life where many objects are experienced continuously and/or intermittently.\n\nSignificance StatementVisual objects that have become associated with reward in the past, can grab our attention even when we are not looking for them. Here, we show that this powerful attentional mechanism serves an important biological purpose: it allows one to quickly find valuable objects regardless of the number of other visual distractors present. Efficient search has long been thought to be primarily limited to objects with certain visually conspicuities (Wolfe and Horowitz, 2004). Our result shows that long-term and consistently biased reward can achieve search efficiencies that are independent of object visual features. This search efficiency is highly scalable as it develops for a large number of objects with no apparent interference between objects and is maintained in long-term memory.

Neuroscience

Transdermal electrical neuromodulation of the trigeminal sensory nuclear complex improves sleep quality and mood.

Achieving optimal human performance that involves cognitive or physical work requires quality sleep and a positive mental attitude. The ascending reticular activating system (RAS) represents a powerful set of endogenous neuromodulatory circuits that gate and tune global brain responses to internal and external cues, thereby regulating consciousness, alertness, and attention. The activity of two major RAS nuclei, the locus coeruleus (LC) and pedunculopontine nucleus (PPN), can be altered by trigeminal nerve modulation. Monosynaptic afferent inputs from the sensory components of trigeminal nerve branches project to the trigeminal sensory nuclear complex (TSNC), which has direct and polysynaptic connections to the LC and PPN. We previously found high-frequency (7 - 11 kHz) transdermal electrical neuromodulation (TEN) of the trigeminal nerve rapidly induces physiological relaxation, dampens sympathetic nervous system responses to acute stress, and suppresses levels of noradrenergic biomarkers. Given the established roles of LC and PPN neuronal activity in sleep regulation, psychophysiological arousal, and stress, we conducted three studies designed to test hypotheses that modulation of the TSNC can improve sleep quality and mood in healthy individuals (n = 99). Across a total of 1,386 days monitored, we observed TEN modulation of trigeminal and cervical nerves prior to sleep onset produced significant improvements in sleep quality and affective states, quantified using clinically validated surveys, overnight actigraph and heart rate recordings, and biochemical analyses compared to baseline or sham controls. Moreover, we observed some frequency dependence in that TEN delivered at lower frequencies (TENLF; 0.50 - 0.75 kHz) was significantly more effective at improving sleep quality and reducing anxiety than higher frequency TEN waveforms. Collectively our results indicate that transdermal electrical neuromodulation of trigeminal and cervical nerve branches can influence TSNC activity in a manner that significantly improves sleep quality and significantly reduces stress. We conclude that biasing RAS network activity to optimize sleep efficiency and enhance mood by electrically modulating TSNC activity through its afferent inputs holds tremendous potential for optimizing mental health and human performance.

Neuroscience

Macroscopic coherent structures in a stochastic neural network: from interface dynamics to coarse-grained bifurcation analysis

We study coarse pattern formation in a cellular automaton modelling a spatially-extended stochastic neural network. The model, originally proposed by Gong and Robinson [36], is known to support stationary and travelling bumps of localised activity. We pose the model on a ring and study the existence and stability of these patterns in various limits using a combination of analytical and numerical techniques. In a purely deterministic version of the model, posed on a continuum, we construct bumps and travelling waves analytically using standard interface methods from neural fields theory. In a stochastic version with Heaviside firing rate, we construct approximate analytical probability mass functions associated with bumps and travelling waves. In the full stochastic model posed on a discrete lattice, where a coarse analytic description is unavailable, we compute patterns and their linear stability using equation-free methods. The lifting procedure used in the coarse time-stepper is informed by the analysis in the deterministic and stochastic limits. In all settings, we identify the synaptic profile as a mesoscopic variable, and the width of the corresponding activity set as a macroscopic variable. Stationary and travelling bumps have similar meso- and macroscopic profiles, but different microscopic structure, hence we propose lifting operators which use microscopic motifs to disambiguate between them. We provide numerical evidence that waves are supported by a combination of high synaptic gain and long refractory times, while meandering bumps are elicited by short refractory times.

Neuroscience

The Deceptively Simple N170 Reflects Network Information Processing Mechanisms Involving Visual Feature Coding and Transfer Across Hemispheres

A key to understanding visual cognition is to determine where, when and how brain responses reflect the processing of the specific visual features that modulate categorization behavior--the what. The N170 is the earliest Event-Related Potential (ERP) that preferentially responds to faces. Here, we demonstrate that a paradigmatic shift is necessary to interpret the N170 as the product of an information processing network that dynamically codes and transfers face features across hemispheres, rather than as a local stimulus-driven event. Reverse-correlation methods coupled with information-theoretic analyses revealed that visibility of the eyes influences face detection behavior. The N170 initially reflects coding of the behaviorally relevant eye contralateral to the sensor, followed by a causal communication of the other eye from the other hemisphere. These findings demonstrate that the deceptively simple N170 ERP hides a complex network information processing mechanism involving initial coding and subsequent cross-hemispheric transfer of visual features.

Neuroscience

Contour junctions underlie neural representations of scene categories in high-level human visual cortex

Humans efficiently grasp complex visual environments, making highly consistent judgments of entry-level category despite their high variability in visual appearance. How does the human brain arrive at the invariant neural representations underlying categorization of real-world environments? We here show that the neural representation of visual environments in scenes-selective human visual cortex relies on statistics of contour junctions, which provide cues for the three-dimensional arrangement of surfaces in a scene. We manipulated line drawings of real-world environments such that statistics of contour orientations or junctions were disrupted. Manipulated and intact line drawings were presented to participants in an fMRI experiment. Scene categories were decoded from neural activity patterns in the parahippocampal place area (PPA), the occipital place area (OPA) and other visual brain regions. Disruption of junctions but not orientations led to a drastic decrease in decoding accuracy in the PPA and OPA, indicating the reliance of these areas on intact junction statistics. Accuracy of decoding from early visual cortex, on the other hand, was unaffected by either image manipulation. We further show that the correlation of error patterns between decoding from the scene-selective brain areas and behavioral experiments is contingent on intact contour junctions. Finally, a searchlight analysis exposes the reliance of visually active brain regions on different sets of contour properties. Statistics of contour length and curvature dominate neural representations of scene categories in early visual areas and contour junctions in high-level scene-selective brain regions.

Neuroscience

Language deficits in schizophrenia and autism as related oscillatory connectomopathies: an evolutionary account

Schizophrenia (SZ) and autism spectrum disorders (ASD) are characterised by marked language deficits, but it is not clear how these arise from gene mutations associated with the disorders. Our goal is to narrow the gap between SZ and ASD and, ultimately, give support to the view that they represent abnormal (but related) ontogenetic itineraries for the human faculty of language. We will focus on the distinctive oscillatory profiles of the SZ and ASD brains, in turn using these insights to refine our understanding of how the brain computes language by exploring a novel model of linguistic feature-set composition. We will argue that brain rhythms constitute the best route to interpreting language deficits in both conditions and mapping them to neural dysfunction and risk alleles of the genes. Importantly, candidate genes for SZ and ASD are overrepresented among the gene sets believed to be important for language evolution. This translational effort may help develop an understanding of the aetiology of SZ and ASD and their high prevalence among modern populations.

Neuroscience

Mechanisms for action prediction operate differently in observers with motor experience

Recent theoretical and empirical work has suggested an important role for the motor system in generating predictions about the timing of external events. We tested the hypothesis that motor experience with an observed action changes how observers generated predictions about these actions by comparing the performance of naive and experienced observers on a task that required participants to predict the timing of particular critical points in a ongoing observed action. Crucially, we employed action and non-action stimuli with identical temporal dynamics, and we predicted that motor experience would enhance prediction accuracy specifically for actions and would have a reduced or negligible effect on enhancing prediction accuracy for non-action stimuli. Our results showed that motor experience did modulate prediction accuracy for action stimuli relative to non-action stimuli. No difference between conditions was observed for the naive observers.

Neuroscience

Probing the causal role of prestimulus interregional synchrony for perceptual integration via tACS

The phase of prestimulus oscillations at 7-10 Hz has been shown to modulate perception of briefly presented visual stimuli. Specifically, a recent combined EEG-fMRI study suggested that a prestimulus oscillation at around 7 Hz represents open and closed windows for perceptual integration by modulating connectivity between lower order occipital and higher order parietal brain regions. We here utilized brief event-related transcranial alternating current stimulation (tACS) to specifically modulate this prestimulus 7 Hz oscillation, and the synchrony between parietal and occipital brain regions. To this end we tested for a causal role of this particular prestimulus oscillation for perceptual integration. The EEG was acquired at the same time allowing us to investigate frequency specific after effects phase-locked to stimulation offset. On a behavioural level our results suggest that the tACS did modulate perceptual integration, however, in an unexpected manner. On an electrophysiological level our results suggest that brief tACS does induce oscillatory entrainment, as visible in frequency specific activity phase-locked to stimulation offset. Together, our results do not strongly support a causal role of prestimulus 7 Hz oscillations for perceptual integration. However, our results suggest that brief tACS is capable of modulating oscillatory activity in a temporally sensitive manner.

Neuroscience

A neural basis for the spatial suppression of visual motion perception

In theory, sensory perception should be more accurate when more neurons contribute to the representation of a stimulus. However, psychophysical experiments that use larger stimuli to activate larger pools of neurons sometimes report impoverished perceptual performance. To determine the neural mechanisms underlying these paradoxical findings, we trained monkeys to discriminate the direction of motion of visual stimuli that varied in size across trials, while simultaneously recording from populations of motion-sensitive neurons in cortical area MT. We used the resulting data to constrain a computational model that explained the behavioral data as an interaction of three main mechanisms: noise correlations, which prevented stimulus information from growing with stimulus size; neural surround suppression, which decreased sensitivity for large stimuli; and a read-out strategy that emphasized neurons with receptive fields near the stimulus center. These results suggest that paradoxical percepts reflect tradeoffs between sensitivity and noise in neuronal populations.

Neuroscience

Synaptic Transmission Parallels Neuromodulation in a Central Food-Intake Circuit

NeuromedinU is a potent regulator of food intake and activity in mammals. In Drosophila, neurons producing the homologous neuropeptide hugin regulate feeding and locomotion in a similar manner. Here, we use EM-based reconstruction to generate the entire connectome of hugin-producing neurons in the Drosophila larval CNS. We demonstrate that hugin neurons use synaptic transmission in addition to peptidergic neuromodulation and identify acetylcholine as a key transmitter. Hugin neuropeptide and acetylcholine are both necessary for the regulatory effect on feeding. We further show that subtypes of hugin neurons connect chemosensory to endocrine system by combinations of synaptic and peptide-receptor connections. Targets include endocrine neurons producing DH44, a CRH-like peptide, and insulin-like peptides. Homologs of these peptides are likewise downstream of neuromedinU, revealing striking parallels in flies and mammals. We propose that hugin neurons are part of a physiological control system that has been conserved at functional, molecular and network architecture level.

Neuroscience

Neural codes of architectural styles

Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on peoples visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

Neuroscience