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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

Measuring speaker-listener neural coupling with functional near infrared spectroscopy

The present study investigates brain-to-brain coupling, defined as inter-subject correlations in the hemodynamic response, during natural verbal communication. We used functional near-infrared spectroscopy (fNIRS) to record brain activity of speakers telling stories and listeners comprehending audio recordings of these stories. Listeners brain activity was correlated with speakers with a delay. This between-brain correlation disappeared when verbal communication failed. We further compared the fNIRS and functional Magnetic Resonance Imaging (fMRI) recordings of listeners comprehending the same story and found a relationship between the fNIRS oxygenated-hemoglobin concentration changes and the fMRI BOLD in brain areas associated with speech comprehension. This correlation between fNIRS and fMRI was only present when data from the same story were compared between the two modalities and vanished when data from different stories were compared; this cross-modality consistency further highlights the reliability of the spatiotemporal brain activation pattern as a measure of story comprehension. Our findings suggest that fNIRS is a powerful tool for investigating brain-to-brain coupling during verbal communication. As fNIRS sensors are relatively low-cost and can even be built into wireless, portable, battery-operated systems, these results highlight the potential of broad utilization of this approach in everyday settings for augmenting communication and interaction.

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bioRxiv2016

Temporal order of Alzheimer’s disease-related cognitive marker changes in BLSA and WRAP longitudinal studies

Investigation of the temporal trajectories of currently used neuropsychological tests is critical to identifying earliest changing measures on the path to dementia due to Alzheimers disease (AD). We used the Progression Score (PS) method to characterize the temporal trajectories of measures of verbal memory, executive function, attention, processing speed, language, and mental state using data spanning normal cognition, mild cognitive impairment (MCI), and AD from 1661 participants with a total of 7839 visits (age at last visit 77.6 SD 9.2) in the Baltimore Longitudinal Study of Aging and 1542 participants with a total of 4467 visits (age at last visit 59.9 SD 7.3) in the Wisconsin Registry for Alzheimers Prevention. This method aligns individuals in time based on the similarity of their longitudinal measurements to reveal temporal trajectories. As a validation of our methodology, we explored the associations between the individualized cognitive progression scores (Cog-PS) computed by our method and clinical diagnosis. Digit span tests were the first to show declines in both data sets, and were detected mainly among cognitively normal individuals. These were followed by tests of verbal memory, which were in turn followed by Trail Making Tests, Boston Naming Test, and Mini-Mental State Examination. Differences in Cog-PS across the clinical diagnosis groups were statistically significant, highlighting the potential use of Cog-PS as individualized indicators of disease progression. Identifying cognitive measures that are changing in preclinical AD can lead to the development of novel cognitive tests that are finely tuned to detecting earliest changes.\n\nABBREVIATIONS

neuroscience↗

bioRxiv2016

How we transmit memories to other brains: constructing shared neural representations via communication

It is striking that humans are able to encode and later verbally share their memories of an episode with listeners, who are in turn able to imagine (mentally construct) details of the episode which they have not personally experienced. However, it is unknown how strongly the neural patterns elicited by imagining specific episodes resemble the neural states elicited during the original encoding of those episodes. In the current study, using fMRI and a natural communication task, we traced how neural patterns associated with specific scenes depicted in a movie are encoded, verbally recalled, and then transferred to a group of naive listeners who construct the scenes of the movie in their imagination. By comparing neural patterns across the three conditions, we report, for the first time, that event-specific neural patterns are observed in the default mode network (DMN) and shared across the encoding, reinstatement (spoken recall), and new construction (imagination) of the same real-life episode. This study uncovers the intimate correspondences between memory encoding and imagination, and highlights the essential role that our common language plays in the process of transmitting ones experiences to other brains.

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bioRxiv2016

Ring attractor dynamics emerge from a spiking model of the entire protocerebral bridge

Animal navigation is accomplished by a combination of landmark-following and dead reckoning based on estimates of self motion. Both of these approaches require the encoding of heading information, which can be represented as an allocentric or egocentric azimuthal angle. Recently, Ca2+ correlates of landmark position and heading direction, in egocentric coordinates, were observed in the ellipsoid body (EB), a ring-shaped processing unit in the fly central complex (Seelig and Jayaraman, 2015). These correlates displayed key dynamics of so-called ring attractors, namely: 1) responsiveness to the position of external stimuli, 2) persistence in the absence of external stimuli, 3) locking onto a single external stimulus when presented with two competitors, 4) stochastically switching between competitors with low probability, and 5) sliding or jumping between positions when an external stimulus moves. We hypothesized that ring attractor-like activity in the EB arises from reciprocal neuronal connections to a related structure, the protocerebral bridge (PB). Using recent light-microscopy resolution catalogues of neuronal cell types in the PB (Wolff et al., 2015; Lin et al., 2013), we determined a connectivity matrix for the PB-EB circuit. When activity in this network was simulated using a leaky-integrate-and-fire model, we observed patterns of activity that closely resemble the reported Ca2+ phenomena. All qualitative ring attractor behaviors were recapitulated in our model, allowing us to predict failure modes of the PB ring attractor and the circuit dynamic phenotypes of thermogenetic or optogenetic manipulations. Ring attractor dynamics emerged under a wide variety of parameter configurations, even including non-spiking leaky-integrator implementations. This suggests that the ring-attractor computation is a robust output of this circuit, apparently arising from its high-level network properties (topological configuration, local excitation and long-range inhibition) rather than biological nitty gritty.

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bioRxiv2016

Hierarchical Frequency Tagging reveals neural markers of predictive coding under varying uncertainty

Understanding the integration of top-down and bottom-up signals is essential for the study of perception. Current accounts of predictive coding describe this in terms of interactions between state units encoding expectations or predictions, and error units encoding prediction error. However, direct neural evidence for such interactions has not been well established. To achieve this, we combined EEG methods that preferentially tag different levels in the visual hierarchy: Steady State Visual Evoked Potential (SSVEP at 10Hz, tracking bottom-up signals) and Semantic Wavelet-Induced Frequency Tagging (SWIFT at 1.3Hz tracking top-down signals). Importantly, we examined intermodulation components (IM, e.g., 11.3Hz) as a measure of integration between these signals. To examine the influence of expectation and predictions on the nature of such integration, we constructed 50-second movie streams and modulated expectation levels for upcoming stimuli by varying the proportion of images presented across trials. We found SWIFT, SSVEP and IM signals to differ in important ways. SSVEP was strongest over occipital electrodes and was not modified by certainty. Conversely, SWIFT signals were evident over temporo- and parieto-occipital areas and decreased as a function of increasing certainty levels. Finally, IMs were evident over occipital electrodes and increased as a function of certainty. These results link SSVEP, SWIFT and IM signals to sensory evidence, predictions, prediction errors and hypothesis-testing - the core elements of predictive coding. These findings provide neural evidence for the integration of top-down and bottom-up information in perception, opening new avenues to studying such interactions in perception while constraining neuronal models of predictive coding.\n\nSIGNIFICANCE STATEMENTThere is a growing understanding that both top-down and bottom-up signals underlie perception. But how do these signals interact? And how does this process depend on the signals probabilistic properties? Predictive coding theories of perception describe this in terms how well top-down predictions fit with bottom-up sensory input. Identifying neural markers for such signal integration is therefore essential for the study of perception and predictive coding theories in particular. The novel Hierarchical Frequency Tagging method simultaneously tags top-down and bottom-up signals in EEG recordings, while obtaining a measure for the level of integration between these signals. Our results suggest that top-down predictions indeed integrate with bottom-up signals in a manner that is modulated by the predictability of the sensory input.

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bioRxiv2016

Complete coverage of space favors modularity of the grid system in the brain

Grid cells in the entorhinal cortex fire when animals that are exploring a certain region of space occupy the vertices of a triangular grid that spans the environment. Different neurons feature triangular grids that differ in their properties of periodicity, orientation and ellipticity. Taken together, these grids allow the animal to maintain an internal, mental representation of physical space. Experiments show that grid cells are modular, i.e. there are groups of neurons which have grids with similar periodicity, orientation and ellipticity. We use statistical physics methods to derive a relation between variability of the properties of the grids within a module and the range of space that can be covered completely (i.e. without gaps) by the grid system with high probability. Larger variability shrinks the range of representation, providing a functional rationale for the experimentally observed co-modularity of grid cell periodicity, orientation and ellipticity. We obtain a scaling relation between the number of neurons and the period of a module, given the variability and coverage range. Specifically, we predict how many more neurons are required at smaller grid scales than at larger ones.

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bioRxiv2016

Hypercomplex properties of Drosophila object detecting neurons

Many animals rely on vision to detect objects such as conspecifics, predators, and prey. Hypercomplex cells of the feline cortex and small target motion detectors of the dragonfly and hoverfly optic lobe demonstrate robust tuning for small objects with weak or no response to elongated edges or movement of the visual panorama [1-4]. However, the relationship between anatomical, molecular, and functional properties of object detection circuitry is not understood. Here, we characterize a previously identified lobula columnar neuron (LC11) in Drosophila [5]. By imaging calcium dynamics with two-photon excitation microscopy we show that LC11 responds to the non-directional motion of a small object darker than the background, with little or no responses to static flicker, elongated bars, or panoramic gratings. LC11 dendrites reside at the boundary between GABA-ergic and cholinergic layers of the lobula, each dendrite spans enough columns to sample 75-degrees of visual space, yet the functional receptive field is only 20-degrees wide, and shows robust responses to an object spanning less than one 5-degree facet of the compound eye. The dendrites of neighboring LC11s encode object motion retinotopically, but the axon terminals fuse into a glomerular structure in the central brain where retinotopy is lost. Blocking inhibitory ionic currents abolishes small object sensitivity and facilitates responses to elongated bars and gratings. Our results reveal high acuity small object motion detection in the Drosophila optic lobe.

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bioRxiv2016

Oscillatory integration windows in neurons

Oscillatory synchrony among neurons occurs in many species and brain areas, and has been proposed to help neural circuits process information. One hypothesis states that oscillatory input creates cyclic integration windows: specific times in each oscillatory cycle when postsynaptic neurons become especially responsive to inputs. With paired local field potential (LFP) and intracellular recordings and controlled stimulus manipulations we directly tested this idea in the locust olfactory system. We found that inputs arriving in Kenyon cells (KCs) sum most effectively in a preferred window of the oscillation cycle. With a computational model, we found that the non-uniform structure of noise in the membrane potential helps mediate this process. Further experiments performed in vivo demonstrated that integration windows can form in the absence of inhibition and at a broad range of oscillation frequencies. Our results reveal how a fundamental coincidence-detection mechanism in a neural circuit functions to decode temporally organized spiking.

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bioRxiv2016

Neuropilin 1 sequestration by neuropathogenic mutant glycyl-tRNA synthetase is permissive to vascular development and homeostasis

It remains a mystery how dominantly inherited mutations in the housekeeping gene GARS, which encodes glycyl-tRNA synthetase (GlyRS), mediate selective peripheral nerve toxicity resulting in the currently incurable Charcot-Marie-Tooth disease type 2D (CMT2D). A recent study identified the transmembrane receptor protein neuropilin 1 (Nrp1) as a substrate for aberrant extracellular binding of mutant GlyRS. Formation of the Nrp1/mutant GlyRS complex antagonises the interaction of Nrp1 with one of its main natural ligands, vascular endothelial growth factor-A (VEGF-A), contributing to neurodegeneration. Reduced binding of Nrp1 to VEGF-A is known to disrupt blood vessel development and growth. We therefore analysed capillary architecture at early and later symptomatic time points in CMT2D mouse muscles, retina, and sciatic nerve, as well as in embryonic hindbrain. Assessing capillary diameter, density, and branching, we observed no differences between wild-type and mutant animals from embryonic development to three months, spanning the duration over which numerous sensory and neuromuscular phenotypes manifest. This work indicates that mutant GlyRS-mediated disruption of Nrp1/VEGF-A signalling is permissive to capillary maturation and maintenance in CMT2D mice.\n\nSummary StatementAlthough the multi-functional neuropilin 1/VEGF-A signalling pathway is impaired by dominant pathogenic GlyRS variants, the vascular system remains unperturbed in Gars mutant mice.

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bioRxiv2016

Shared neural basis for experiencing the beauty of human faces and visual art: Evidence from a meta-analyses of fMRI studies

The existence of a common beauty is a long-standing debate in philosophy and related disciplines. In the last two decades, cognitive neuroscientists have sought to elucidate this issue by exploring the common neural basis of the experience of beauty. Still, empirical evidence for such common neural basis of different forms of beauty is not conclusive. To address this question, we performed an activation likelihood estimation (ALE) meta-analysis on the existing neuroimaging studies of beauty appreciation of faces and visual art by non-expert adults (49 studies, 982 participants, meta-data are available at https://osf.io/s9xds/). We observed that perceiving these two forms of beauty activated distinct brain regions: while the beauty of faces convergently activated the left ventral striatum, the beauty of visual art convergently activated the anterior medial prefrontal cortex (aMPFC). However, a conjunction analysis failed to reveal any common brain regions for the beauty of visual art and faces. The implications of these results are discussed.

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bioRxiv2016

The effect of acquisition resolution on orientation decoding from V1 BOLD fMRI at 7 Tesla

A decade after it was shown that the orientation of visual grating stimuli can be decoded from human visual cortex activity by means of multivariate pattern classification of BOLD fMRI data, numerous studies have investigated which aspects of neuronal activity are reflected in BOLD response patterns and are accessible for decoding. However, it remains inconclusive what the effect of acquisition resolution on BOLD fMRI decoding analyses is. The present study is the first to provide empirical ultra high-field fMRI data recorded at four spatial resolutions (0.8 mm, 1.4 mm, 2 mm, and 3 mm isotropic voxel size) on this topic -- in order to test hypotheses on the strength and spatial scale of orientation discriminating signals. We present detailed analysis, in line with predictions from previous simulation studies, about how the performance of orientation decoding varies with different acquisition resolutions. Moreover, we also examine different spatial filtering procedures and its effects on orientation decoding. Here we show that higher-resolution scans with subsequent down-sampling or low-pass filtering yield no benefit over scans natively recorded in the corresponding lower resolution regarding decoding accuracy. The orientation-related signal in the BOLD fMRI data is spatially broadband in nature, includes both high spatial frequency components, as well as large-scale biases previously proposed in the literature. Moreover, we found above chance-level contribution from large draining veins to orientation decoding. Acquired raw data were publicly released to facilitate further investigation.

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bioRxiv2016

Inferring the brain’s internal model from sensory responses in a probabilistic inference framework

Perception can be characterized as an inference process in which beliefs are formed about the world given sensory observations. The sensory neurons implementing these computations, however, are classically characterized with firing rates, tuning curves, and correlated noise. To connect these two levels of description, we derive expressions for how inferences themselves vary across trials, and how this predicts task-dependent patterns of correlated variability in the responses of sensory neurons. Importantly, our results require minimal assumptions about the nature of the inferred variables or how their distributions are encoded in neural activity. We show that our predictions are in agreement with existing measurements across a range of tasks and brain areas. Our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function. HighlightsO_LIGeneral connection between neural covariability and approximate Bayesian inference based on variability in the encoded posterior density. C_LIO_LIOptimal learning of a discrimination task predicts top-down components of noise correlations and choice probabilities in agreement with existing data. C_LIO_LIDifferential correlations are predicted to grow over the course of perceptual learning. C_LIO_LINeural covariability can be used to reverse-engineer the subjects internal model. C_LI

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bioRxiv2016

Activity Dynamics and Signal Representation in Striatal Network Model with Distance-dependent Connectivity

Striatum is predominantly inhibitory and the main input nucleus of the basal ganglia. A functional characterization of its activity dynamics is crucial for understanding the mechanisms underlying phenomenon such as action selection and initiation. Here, we investigated the effects of the spatial connectivity structure on the emergence and maintenance of localized bumps of activity in large-scale striatal networks (~10,000 neurons). We show that in striatal network model in which the distance-dependent connection probability varies in a Gaussian fashion (Gaussian networks), the activity remains asynchronous irregular (AI) and spatially homogeneous, independent of the background input. By contrast, when the distance-dependent connectivity varies according to a Gamma distribution (Gamma networks), with short-range connectivity suppressed, a repertoire of activity dynamics can be observed: While weak background inputs induce spatially homogeneous AI activity, stronger background inputs induce stable, spatially localized activity bumps as in winner-take-all (WTA) dynamics. Interestingly, for intermediate background inputs, the networks exhibit spatially localized, but unstable activity bumps (Transition Activity, TA), resembling the experimentally observed neuronal assembly dynamics in the striatum.\n\nAmong the three main regimes of network activity (AI, WTA, TA) we found that in the AI and TA regimes, network dynamics are flexible and can be easily modified by external stimuli. Moreover, the dynamical state of the network returns to the baseline after the stimulus is removed. By contrast, the dynamics in the WTA state are rigid and can only be changed by very strong external stimuli. These results support the hypothesis that the flexibility of the striatal network state in response to stimuli is important for its normal function and the rigid network states (WTA) correspond to brain disorders such as Parkinsons disease, where the striatum looses its repertoire of dynamic states and is only receptive to very strong inputs.

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bioRxiv2016

Reactive Oxygen Species Regulate Activity-Dependent Neuronal Structural Plasticity

Neurons are inherently plastic, adjusting their structure, connectivity and excitability in response to changes in activity. How neurons sense changes in their activity level and then transduce these to structural changes remains to be fully elucidated. Working with the Drosophila larval locomotor network, we show that neurons use reactive oxygen species (ROS), metabolic byproducts, to monitor their activity. ROS signals are both necessary and sufficient for activity-dependent structural adjustments of both pre- and postsynaptic terminals and for network output, as measured by larval crawling behavior. We find the highly conserved Parkinsons disease-linked protein DJ-1{beta} acts as a redox sensor in neurons where it regulates pre- and postsynaptic structural plasticity, in part via modulation of the PTEN-PI3Kinase pathway. Neuronal ROS thus play an important physiological role as second messengers required for neuronal and network tuning, whose dysregulation in the ageing brain and under neurodegenerative conditions may contribute to synaptic dysfunction.

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bioRxiv2016

Resting-state fMRI correlations: from link-wiseunreliability to whole brain stability

The functional architecture of spontaneous BOLD fluctuations has been characterized in detail by numerous studies, demonstrating its potential relevance as a biomarker. However, the systematic investigation of its consistency is still in its infancy. Here, we analyze both the within- and between-subject variability as well as the test-retest reliability of resting-state functional connectivity (FC) estimates in a unique data set comprising multiple fMRI scans (42) from 5 subjects, and 50 single scans from 50 subjects. To this aim we adopted a statistical framework enabling us to disentangle the contribution of different sources of variability and their dependence on scan duration, and showed that the low reliability of single links can be largely improved using multiple scans per subject. Moreover, we show that practically all observed inter-region variability (at the link-level) is not significant and due to the statistical uncertainty of the estimator itself rather than to genuine variability among areas. Finally, we use the proposed statistical framework to demonstrate that, despite the poor consistency of single links, the information carried by the whole-brain spontaneous correlation structure is indeed robust, and can in fact be used as a functional fingerprint.

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bioRxiv2016

Potent optogenetic inhibition of behavior with anion channelrhodopsins

Optogenetics employs light exposure to manipulate physiology in genetically modified organisms. There are abundant tools for optogenetic excitation of neuronal activity, but the limitations of current activity photo-inhibitors present an obstacle to demonstrating the necessity of specific neuronal circuits. Here we show that anion channelrhodopsins can be used to specifically and rapidly inhibit a range of systems involved in Drosophila locomotion, wing expansion, memory retrieval and gustation, demonstrating their broad utility to the circuit analysis of behavior.

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bioRxiv2016

Shorter neural adaptation to sounds accounts for dyslexics’ abnormal perceptual and reading dynamics

Dyslexia is a prevalent reading disability whose underlying mechanisms are still disputed. We studied the neural mechanisms underlying dyslexia using a simple frequency-discrimination task. Though participants were asked to compare the 2-tones in each trial, implicit memory of previous trials affected their responses. We hypothesized that implicit memory decays faster among dyslexics. We tested this by increasing the temporal intervals between consecutive trials, and measuring the behavioral impact and ERP responses from the auditory cortex. Dyslexics showed a faster decay of implicit memory effects on both measures, with similar time constants. Finally, faster decay also characterized dyslexics benefits in oral reading rate. It decreased faster as a function of the time interval from the previous reading of the same non-word. We propose that dyslexics shorter neural adaptation paradoxically accounts for their longer reading times, since it induces noisier and less reliable predictions for both simple and complex stimuli.

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bioRxiv2016

Estimating the implicit component of visuomotor rotation learning by constraining movement preparation time.

When sensory feedback is perturbed, accurate movement is restored by a combination of implicit processes and deliberate re-aiming to strategically compensate for errors. Here, we directly compare two methods used previously to dissociate implicit from explicit learning on a trial-by-trial basis: 1) asking participants to report the direction that they aim their movements, and contrasting this with the directions of the target and the movement that they actually produce, 2) manipulating movement preparation time. By instructing participants to re-aim without a sensory perturbation, we show that re-aiming is possible even with the shortest possible preparation times, particularly when targets are narrowly distributed. Nonetheless, re-aiming is effortful and comes at the cost of increased variability, so we tested whether constraining preparation time is sufficient to suppress strategic re-aiming during adaptation to visuomotor rotation with a broad target distribution. The rate and extent of error reduction under preparation time constraints were similar to estimates of implicit learning obtained from self-report without time pressure, suggesting that participants chose not to apply a re-aiming strategy to correct visual errors under time pressure. Surprisingly, participants who reported aiming directions showed less implicit learning according to an alternative measure, obtained during trials performed without visual feedback. This suggests that the process of reporting can affect the extent or persistence of implicit learning. The data extend existing evidence that restricting preparation time can suppress explicit re-aiming, and provide an estimate of implicit visuomotor rotation learning that does not require participants to report their aiming directions.\n\nNew and NoteworthyDuring sensorimotor adaptation, implicit, error-driven learning can be isolated from explicit strategy-driven re-aiming by subtracting self-reported aiming directions from movement directions, or by restricting movement preparation time. Here, we compared the two methods. Restricting preparation times did not eliminate re-aiming, but was sufficient to suppress reaiming during adaptation with widely-distributed targets. The self-report method produced a discrepancy in implicit learning estimated by subtracting aiming directions, and implicit learning measured in no-feedback trials.

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