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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Egocentric and Allocentric Representations in Auditory Cortex

A key function of the brain is to provide a stable representation of an objects location in the world. In hearing, sound azimuth and elevation are encoded by neurons throughout the auditory system and auditory cortex is necessary for sound localization. However the coordinate frame in which neurons represent sound space remains undefined: classical spatial receptive fields in head-fixed subjects can be explained either by sensitivity to sound source location relative to the head (egocentric) or relative to the world (allocentric encoding). This coordinate frame ambiguity can be resolved by studying freely moving subjects and here we recorded spatial receptive fields in auditory cortex freely moving ferrets. We found two distinct populations of neurons: While the majority ([~]80%) of spatially tuned neurons represented sound source location relative to the head, we provide novel evidence for a group of neurons in which space was represented in an allocentric world-centered coordinate frame. We also use our ability to measure spatial tuning in moving subjects to explore the influence of sound source distance and speed of head movements on auditory cortical activity and spatial tuning. Modulation depth of spatial tuning increased with distance for egocentric but not allocentric units, whereas for both populations modulation was stronger at faster movement speeds. Our findings argue that auditory cortex is involved in the representation of both sound source location relative to ourselves and sound location in the world independent of our own position.

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bioRxiv2017

Integration of visual information in auditory cortex promotes auditory scene analysis through multisensory binding

How and where in the brain audio-visual signals are bound to create multimodal objects remains unknown. One hypothesis is that temporal coherence between dynamic multisensory signals provides a mechanism for binding stimulus features across sensory modalities. Here we report that when the luminance of a visual stimulus is temporally coherent with the amplitude fluctuations of one sound in a mixture, the representation of that sound is enhanced in auditory cortex. Critically, this enhancement extends to include both binding and non-binding features of the sound. We demonstrate that visual information conveyed from visual cortex, via the phase of the local field potential, and is combined with auditory information within auditory cortex. These data provide evidence that early cross-sensory binding provides a bottom-up mechanism for the formation of cross-sensory objects and that one role for multisensory binding in auditory cortex is to support auditory scene analysis.

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bioRxiv2017

Improved Kinect sensor based motion capturing system for gait assessment

Optical motion capturing systems are expensive and require substantial dedicated space to be set up. On the other hand, they provide unsurpassed accuracy and reliability. In many situations however flexibility is required and the motion capturing system can only temporarily be placed. The Microsoft Kinect v2 sensor is comparatively cheap and with respect to gait analysis promising results have been published. We here present a motion capturing system that is easy to set up, flexible with respect to the sensor locations and delivers high accuracy in gait parameters comparable to a gold standard motion capturing system (VICON). Further, we demonstrate that sensor setups which track the person only from one-side are less accurate and should be replaced by two-sided setups. With respect to commonly analyzed gait parameters, especially step width, our system shows higher agreement with the VICON system than previous reports.

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bioRxiv2017

Potassium channels contribute to activity-dependent scaling of dendritic inhibition

GABAergic inhibition plays a critical role in the regulation of neuronal activity. In the neocortex, inhibitory interneurons that target the dendrites of pyramidal cells influence both electrical and biochemical postsynaptic signaling. Voltage-gated ion channels strongly shape dendritic excitability and the integration of excitatory inputs, but their contribution to GABAergic signaling is less well understood. By combining 2-photon calcium imaging and focal GABA uncaging, we show that voltage-gated potassium channels normally suppress the GABAergic inhibition of calcium signals evoked by back-propagating action potentials in dendritic spines and shafts of cortical pyramidal neurons. Moreover, the voltage-dependent inactivation of these channels leads to enhancement of dendritic calcium inhibition following somatic spiking. Computational modeling reveals that the enhancement of calcium inhibition involves an increase in action potential depolarization coupled with the nonlinear relationship between membrane voltage and calcium channel activation. Overall, our findings highlight the interaction between intrinsic and synaptic properties and reveal a novel mechanism for the activity-dependent scaling of GABAergic inhibition.\n\nSignificance StatementGABAergic inhibition potently regulates neuronal activity in the neocortex. How such inhibition interacts with the intrinsic electrophysiological properties of single neurons is not well-understood. Here we investigate the ability of voltage-gated potassium channels to regulate the impact of GABAergic inhibition in the dendrites of neocortical pyramidal neurons. Our results show that potassium channels normally reduce inhibition directed towards pyramidal neuron dendrites. However, these channels are inactivated by strong neuronal activity, leading to an enhancement of GABAergic potency and limiting the corresponding influx of dendritic calcium. Our findings illustrate a previously unappreciated relationship between neuronal excitability and GABAergic inhibition.

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bioRxiv2017

Chunking as a rational strategy for lossy data compression in visual working memory tasks.

The nature of capacity limits for visual working memory has been the subject of an intense debate that has relied on models that assume items are encoded independently. Here we propose that instead, similar features are jointly encoded through a \"chunking\" process to optimize performance on visual working memory tasks. We show that such chunking can: 1) facilitate performance improvements for abstract capacity-limited systems, 2) be optimized through reinforcement, 3) be implemented by center-surround dynamics, and 4) increase effective storage capacity at the expense of recall precision. Human performance on a variant of a canonical working memory task demonstrated performance advantages, precision detriments, inter-item dependencies, and trial-to-trial behavioral adjustments diagnostic of performance optimization through center-surround chunking. Models incorporating center-surround chunking provided a better quantitative description of human performance in our study as well as in a meta-analytic dataset, and apparent differences in working memory capacity across individuals were attributable to individual differences in the implementation of chunking. Our results reveal a normative rationale for center-surround connectivity in working memory circuitry, call for re-evaluation of memory performance differences that have previously been attributed to differences in capacity, and support a more nuanced view of visual working memory capacity limitations: strategic tradeoff between storage capacity and memory precision through chunking contribute to flexible capacity limitations that include both discrete and continuous aspects.

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bioRxiv2017

Evidence for a Large-Scale Brain System Supporting Allostasis and Interoception in Humans

AbstractLarge-scale intrinsic brain systems have been identified for exteroceptive senses (e.g., sight, hearing, touch). We introduce an analogous system for representing sensations from within the body, called interoception, and demonstrate its relation to regulating peripheral systems in the body, called allostasis. Employing the recently introduced Embodied Predictive Interoception Coding (EPIC) model, we used tract-tracing studies of macaque monkeys, followed by two intrinsic functional magnetic resonance imaging samples (N = 280 and N = 270) to evaluate the existence of an intrinsic allostatic/interoceptive system in the human brain. Another sample (N = 41) allowed us to evaluate the convergent validity of the hypothesized allostatic/interoceptive system by showing that individuals with stronger connectivity between system hubs performed better on an implicit index of interoceptive ability related to autonomic fluctuations. Implications include novel insights for the brains functional architecture, dissolving the artificial boundary between mind and body, and unifying mental and physical illness.

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bioRxiv2017

Spontaneous Neural Encoding of Social Network Position

Humans form complex social networks that include numerous non-reproductive bonds with non-kin. Navigating these networks presents a considerable cognitive challenge thought to have comprised a driving force in human brain evolution. Yet, little is known about how and to what extent the human brain encodes the structure of the social networks in which it is embedded. By combining social network analysis and multi-voxel pattern analysis of functional magnetic resonance imaging (fMRI) data, we show that social network information about direct relationships, bonds between third parties, and aspects of the broader network topology is accurately perceived and automatically activated upon seeing a familiar other.

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bioRxiv2017

A quantitative description of the mouse piriform cortex

In this report, we provide a quantitative description for 2 aspects of the mouse piriform cortex. First, we give volumetric estimates of regions within the anterior and posterior piriform cortex. Second, we estimate the neuronal densities in each of these regions. From these two estimates, we calculate that the mouse piriform contains around half a million neurons equally distributed over the anterior and posterior piriform cortex. Quantitative descriptions such as these are important because they make it possible to construct realistic models and provide a constraint that theories of the olfactory circuit must fulfil. We show how quantitative descriptions can be useful for modelling by using our data to refine and improve earlier models of piriform cortex activity.

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bioRxiv2017

Saccadic suppression as a perceptual consequence of efficient sensorimotor estimation

Humans perform saccadic eye movements two to three times per second. When doing so, the nervous system strongly suppresses sensory feedback for extended periods of time in comparison with the movement time. Why does the brain discard so much visual information? Here we suggest that perceptual suppression may arise from efficient sensorimotor computations, assuming that perception and control are fundamentally linked. More precisely, we show that a Bayesian estimator should reduce the weight of sensory information around the time of saccades, as a result of signal dependent noise and of sensorimotor delays. Such reduction parallels the behavioral suppression occurring prior to and during saccades, and the reduction in neural responses to visual stimuli observed across the visual hierarchy. We suggest that saccadic suppression originates from efficient sensorimotor processing, indicating that the brain shares neural resources for perception and control.

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bioRxiv2017

Using high-throughput barcode sequencing to efficiently map connectomes

The function of a neural circuit is determined by the details of its synaptic connections. At present, the only available method for determining a neural wiring diagram with single synapse precision--a \"connectome\"--is based on imaging methods that are slow, labor-intensive and expensive. Here we present SYNseq, a method for converting the connectome into a form that can exploit the speed and low cost of modern high-throughput DNA sequencing. In SYNseq, each neuron is labeled with a unique random nucleotide sequence--an RNA \"barcode\"--which is targeted to the synapse using engineered proteins. Barcodes in pre- and postsynaptic neurons are then associated through protein-protein crosslinking across the synapse, extracted from the tissue, and then joined into a form suitable for sequencing. Although at present the inefficiency in our hands of barcode joining precludes the widespread application of this approach, we expect that with further development SYNseq will enable tracing of complex circuits at high speed and low cost.

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bioRxiv2017

Visual motion computation in recurrent neural networks

Populations of neurons in primary visual cortex (V1) transform direct thalamic inputs into a cortical representation which acquires new spatio-temporal properties. One of these properties, motion selectivity, has not been strongly tied to putative neural mechanisms, and its origins remain poorly understood. Here we propose that motion selectivity is acquired through the recurrent mechanisms of a network of strongly connected neurons. We first show that a bank of V1 spatiotemporal receptive fields can be generated accurately by a network which receives only instantaneous inputs from the retina. The temporal structure of the receptive fields is generated by the long timescale dynamics associated with the high magnitude eigenvalues of the recurrent connectivity matrix. When these eigenvalues have complex parts, they generate receptive fields that are inseparable in time and space, such as those tuned to motion direction. We also show that the recurrent connectivity patterns can be learnt directly from the statistics of natural movies using a temporally-asymmetric Hebbian learning rule. Probed with drifting grating stimuli and moving bars, neurons in the model show patterns of responses analogous to those of direction-selective simple cells in primary visual cortex. These computations are enabled by a specific pattern of recurrent connections, that can be tested by combining connectome reconstructions with functional recordings.*\n\nAuthor summaryDynamic visual scenes provide our eyes with enormous quantities of visual information, particularly when the visual scene changes rapidly. Even at modest moving speeds, individual small objects quickly change their location causing single points in the scene to change their luminance equally fast. Furthermore, our own movements through the world add to the velocities of objects relative to our retinas, further increasing the speed at which visual inputs change. How can a biological system process efficiently such vast amounts of information, while keeping track of objects in the scene? Here we formulate and analyze a solution that is enabled by the temporal dynamics of networks of neurons.

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bioRxiv2017

Multivariate cross-frequency coupling via generalized eigendecomposition

This paper presents a new framework for analyzing cross-frequency coupling in multichannel electrophysiological recordings. The generalized eigendecomposition-based cross-frequency coupling framework (gedCFC) is inspired by source separation algorithms combined with dynamics of mesoscopic neurophysiological processes. It is unaffected by factors that confound traditional CFC methods such as non-stationarities, non-sinusoidality, and non-uniform phase angle distributions--attractive properties considering that brain activity is neither stationary nor perfectly sinusoidal. The gedCFC framework opens new opportunities for conceptualizing CFC as network interactions with diverse spatial/topographical distributions. five specific methods within the gedCFC framework are detailed, with validations in simulated data and applications in several empirical datasets. gedCFC accurately recovers physiologically plausible CFC patterns embedded in noise where traditional CFC methods perform poorly. It is also demonstrated that spike-field coherence in multichannel local field potential data can be analyzed using the gedCFC framework, with significant advantages over traditional spike-field coherence analyses. Null-hypothesis testing is also discussed.

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bioRxiv2017

Overlap among spatial memories triggers divergence of hippocampal representations

Across the domains of spatial navigation and episodic memory, the hippocampus is thought to play a critical role in disambiguating (pattern separating) representations of overlapping events. However, the mechanisms underlying hippocampal pattern separation are not fully understood. Here, using a naturalistic route-learning paradigm and spatiotemporal pattern analysis of human fMRI data, we found that hippocampal representations of overlapping routes gradually diverged with learning to the point that they became less similar than representations of non-overlapping events. This representational reversal of the objective route similarity (a) was selective to the hippocampus, (b) only occurred for the specific route segments that were shared across routes, and (c) was predicted by the degree to which individual hippocampal voxels were initially shared across route representations. These findings indicate that event overlap triggers a repulsion of hippocampal representations--a finding that provides critical mechanistic insight into how and why hippocampal representations become separated.

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bioRxiv2017

NAPR: a cloud-based framework for neuroanatomical age prediction

This paper describes NAPR, a cloud-based framework for accessing age prediction models created using machine learning-based analysis of neuroimaging data. The NAPR service is provided at https://www.cloudneuro.org. The NAPR system allows external users to predict the age of individual subjects using their own MRI data. As a demonstration of the NAPR approach, age prediction models were trained using healthy control data from the ABIDE, CoRR, DLBS and NKI Rockland neuroimaging datasets (total N = 2367). MRI scans were processed using Freesurfer v5.3. Age prediction models were trained using relevance vector machines and Gaussian processes machine learning techniques. NAPR will allow for rigorous and transparent out-of-sample assessment of age prediction model performance, and may therefore assist in the translation of neuroimaging-based modelling techniques to the clinic.

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bioRxiv2017

Glutamate release is inhibitory and unnecessary for the long-term potentiation of presynaptic function

Long-term potentiation (LTP) and long-term depression (LTD) of transmitter release probability (Pr) are thought to be triggered by the activation of glutamate receptors. Here, we demonstrate that glutamate release at CA3-CA1 synapses is in fact inhibitory and unnecessary for increases in Pr. Instead, at active presynaptic terminals, postsynaptic depolarization alone can increase Pr by promoting the release of nitric oxide from neuronal dendrites in a manner dependent on L-type voltage-gated Ca2+ channels. The release of glutamate, in contrast, decreases Pr by activating presynaptic NMDA receptors (NMDAR). Thus, net changes in Pr are determined by the combined effect of both LTP-promoting and LTD-promoting processes, that is, by the amount of glutamate release and postsynaptic depolarization that accompany presynaptic activity, respectively. Neither of these processes directly depends on the activation of postsynaptic NMDARs. We further show that presynaptic changes can be captured by a simple learning rule, in which the role of presynaptic plasticity is to ensure that the ability for a presynaptic terminal to release glutamate is matched with its ability to predict postsynaptic spiking.

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bioRxiv2017

Glucocorticoids change neural decoding in the middle cingulate cortex accompanied by a reduction of subjective fear in patients with spider phobia

Glucocorticoids--stress hormones released from the adrenal cortex--reduce phobic fear in anxiety disorders and enhance psychotherapy, possibly by reducing the retrieval of fear memories and enhancing the consolidation of new corrective memories. Glucocorticoid signaling in the basolateral amygdala can influence connected fear and memory-related cortical regions, but this is not fully understood. Previous studies investigated specific pathways moderated by glucocorticoids, for example, visual-temporal pathways; however, these analyses were limited to a-priori selected regions. Here, we performed whole-brain pattern analysis to localize phobic stimulus decoding related to the fear-reducing effect of glucocorticoids. We reanalyzed functional magnetic resonance imaging (fMRI) data from a previously published study with spider-phobic patients and healthy controls. The patients received either glucocorticoids or a placebo treatment before the exposure to spider images. There was moderate evidence that patients with phobia had higher decoding of phobic content in the anterior cingulate cortex (ACC) and the left and right anterior insula compared to controls. Decoding in the ACC and the right insula showed strong evidence for correlation with experienced fear. Patients with cortisol reported a reduction of fear by 10-13%; however, there was only weak evidence for changes in neural decoding compared to placebo which was found in the precuneus, the opercular cortex, and the left cerebellum.

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bioRxiv2017

Locomotor activity modulates associative learning in mouse cerebellum

Changes in behavioral state are associated with modulation of sensory responses across visual, auditory and somatosensory cortices. Here we show that locomotor activity independently modulates performance in delay eyeblink conditioning, a cerebellum-dependent form of associative learning. Increased locomotor speed in head-fixed mice was associated with earlier onset of learning and trial-by-trial enhancement of learned responses. The influence of locomotion on conditioned responses was dissociable from changes in arousal and was independent of the sensory modality of the conditioned stimulus. Eyelid responses evoked by optogenetic stimulation of mossy fiber terminals within the cerebellar cortex, but not at sites downstream, were also positively modulated by ongoing locomotion. We conclude that locomotor activity modulates delay eyeblink conditioning through mechanisms acting on the mossy fiber pathway within the cerebellar cortex. Taken together, these results suggest a novel role for behavioral state modulation in associative learning and provide a potential mechanism through which engaging in movement can improve an individuals ability to learn.

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bioRxiv2017

Identification of Mechanisms of Functional Signaling Between Human Hippocampus Regions

BackgroundStandard BOLD connectivity analyses depend on aggregating the signals of individual voxels within regions of interest (ROIs). In certain cases, this spatial aggregation implies a loss of valuable functional and anatomical information about subsets of voxels that drive the ROI level connectivity.\n\nNew MethodWe use the FGES algorithm, a data-driven score-based graphical search method, to identify subsets of voxels that are chiefly responsible for exchanging signals between ROIs. We apply the method to high-resolution resting state functional magnetic resonance imaging (rs-fMRI) data from medial temporal lobe regions of interest of a single healthy individual measured repeated times over a year and a half.\n\nResultsThe FGES algorithm recovered subsets of voxels within larger medial temporal lobe ROIs of entorhinal cortex and hippocampus subfields that show spatially consistency across different scanning sessions, and are statistically significant under tests that validate the role of these subsets as main drivers of effective connectivity between hippocampal regions of interest.\n\nComparison with Existing MethodsIn contrast to standard functional connectivity methods, the FGES algorithm is robust against false positive connections produced by transitive closures of adjacencies (correlation methods) and common effect conditioning (Markov random field methods).\n\nConclusionsThe FGES algorithm allows for identification of communication subsets of voxels driving the connectivity between regions of interest, recovering valuable anatomical and functional information that is lost when ROIs are aggregated. The FGES algorithm is specially suited for voxelwise connectivity research, given its short running time and scalability to big data problems.

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