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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Inferring exemplar discriminability in brain representations

Representational distinctions within categories are important in all perceptual modalities and also in cognitive and motor representations. Recent pattern-information studies of brain activity have used condition-rich designs to sample the stimulus space more densely. To test whether brain response patterns discriminate among a set of stimuli (e.g. exemplars within a category) with good sensitivity, we can pool statistical evidence over all pairwise comparisons. A popular test statistic reflecting exemplar information is the exemplar discriminability index (EDI), which is defined as the average of the pattern dissimilarity estimates between different exemplars minus the average of the pattern dissimilarity estimates between repetitions of identical exemplars. The EDI is commonly tested with a t test (H0: population mean EDI = 0) across subjects (subject as random effect). However, it is unclear whether this approach is either valid or optimal. Here we describe a wide range of statistical tests of exemplar discriminability and assess the validity (specificity) and power (sensitivity) of each test. The tests include previously used and novel, parametric and nonparametric tests, which treat subject as a random or fixed effect, and are based on different dissimilarity measures, different test statistics, and different inference procedures. We use simulated and real data to determine which tests are valid and which are most sensitive. The popular across-subject t test of the EDI (typically using correlation distance as the pattern dissimilarity measure) requires the assumption that the EDI is 0-mean normal under H0, which is not strictly true. Reassuringly, our simulations suggest that the test controls the false-positives rate at the nominal level and is thus valid in practice. However, test statistics based on average Mahalanobis distances or average linear-discriminant t values (both accounting for the multivariate error covariance among responses) are substantially more powerful for both random- and fixed-effects inference. We suggest preferred procedures for safely and sensitively detecting subtle pattern differences between exemplars.

Neuroscience↗

bioRxiv2016

Cortical seizure propagation respects functional connectivity underlying sensory processing

Focal epilepsy involves excessive cortical activity that propagates both locally and distally. Does this propagation follow the same routes as normal cortical activity? We pharmacologically induced focal seizures in primary visual cortex (V1) of awake mice, and compared their propagation to the retinotopic organization of V1 and higher visual areas. We used simultaneous local field potential recordings and widefield imaging of a genetically encoded calcium indicator to measure prolonged seizures (ictal events) and brief interictal events. Both types of event are orders of magnitude larger than normal visual responses, and both start as standing waves: synchronous elevated activity in the V1 focus and in homotopic locations in higher areas, i.e. locations with matching retinotopic preference. Following this common beginning, however, seizures persist and propagate both locally and into homotopic distal regions, and eventually invade all of visual cortex and beyond. We conclude that seizure initiation resembles the initiation of interictal events, and seizure propagation respects the connectivity underlying normal visual processing.

Neuroscience↗

bioRxiv2017

SkinnerTrax: high-throughput behavior-dependent optogenetic stimulation of Drosophila

While red-shifted channelrhodopsin has been shown to be highly effective in activating CNS neurons in freely moving Drosophila, there were no existing high-throughput tools for closed-loop, behavior-dependent optogenetic stimulation of Drosophila. Here, we present SkinnerTrax to fill this void. SkinnerTrax stimulates individual flies promptly in response to their being at specific positions or performing specific actions. Importantly, SkinnerTrax was designed for and achieves significant throughput with simple and inexpensive components.

neuroscience↗

bioRxiv2016

Degenerate time-dependent network dynamics anticipate seizures in human epileptic brain

Epileptic seizures are known to follow specific changes in brain dynamics. While some algorithms can nowadays robustly detect these changes, a clear understanding of the mechanism by which these alterations occur and generate seizures is still lacking. Here, we provide cross-validated evidence that such changes are initiated by an alteration of physiological network state dynamics. Specifically, our analysis of long intracranial EEG recordings from a group of 10 patients identifies a critical phase of a few hours in which time-dependent network states become less variable (\"degenerate\") and is followed by a global functional connectivity reduction before seizure onset. This critical phase is characterized by an abnormal occurrence of highly correlated network instances and is shown to particularly affect the activity of resection regions in patients with validated postsurgical outcome. Our approach characterizes pre-seizure networks dynamics as a cascade of two sequential events providing new insights into seizure prediction and control.

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bioRxiv2016

A cryptography-based approach for movement decoding

Brain decoders use neural recordings to infer a users activity or intent. To train a decoder, we generally need infer the variables of interest (covariates) using simultaneously measured neural activity. However, there are many cases where this approach is not possible. Here we overcome this problem by introducing a fundamentally new approach for decoding called distribution alignment decoding (DAD). We use the statistics of movement, much like cryptographers use the statistics of language, to find a mapping between neural activity and motor variables. DAD learns a linear decoder which aligns the distribution of its output with the typical distribution of motor outputs by minimizing their KL-divergence. We apply our approach to a two datasets collected from the motor cortex of non-human primates (NHPs): a reaching task and an isometric force production task. We study the performance of DAD and find regimes where DAD provides comparable and in some cases, better performance than a typical supervised decoder. As DAD does not rely on the ability to record motor-related outputs, it promises to broaden the set of potential applications of brain decoding.

neuroscience↗

bioRxiv2016

Measurements of morphological and biochemical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson’s disease using optical diffraction tomography

Parkinsons disease (PD) is a common neurodegenerative disease. However, therapeutic methods of PD are still limited due to complex pathophysiology in PD. Here, we present optical measurements of individual neurons from in vitro PD model using optical diffraction tomography (ODT). By measuring 3-D refractive index distribution of neurons, morphological and biochemical alterations in in-vitro PD model are quantitatively investigated. We found that neurons show apoptotic features in early PD progression. The present approach will open up new opportunities for quantitative investigation of the pathophysiology of various neurodegenerative diseases.

neuroscience↗

bioRxiv2016

High precision registration between zebrafish brain atlases using symmetric diffeomorphic normalization

Atlases provide a framework for information from diverse sources to be spatially mapped and integrated into a common reference space. In particular, brain atlases allow regional annotation of gene expression, cell morphology, connectivity and activity. In larval zebrafish, advances in genetics, imaging and computational methods have enabled the collection of large datasets providing such information on a whole-brain scale. However, datasets from different sources may not be aligned to the same spatial coordinate system, because technical considerations may necessitate use of different reference templates. Two recent brain atlases for larval zebrafish exemplify this problem. The Z-Brain atlas contains information on gene expression, neural activity and neuroanatomical segmentation acquired using immunohistochemical staining of fixed tissue. In contrast, the Zebrafish Brain Browser (ZBB) atlas was constructed from live scans of fluorescent reporter genes in transgenic larvae. Although different reference brains were used, the two atlases included several transgene patterns in common that provided potential 'bridges' for transforming each into the others coordinate space. We therefore tested multiple bridging channels and registration algorithms. The symmetric diffeomorphic normalization (SyN) algorithm in ANTs improved the precision of live brain registration while better preserving cell morphology than the previously used B-spline elastic registration algorithm. SyN could also be calibrated to correct for tissue distortion introduced during fixation and permeabilization. Finally, multi-reference channel optimization provided a transformation matrix that enabled Z-Brain and ZBB to be co-aligned with acceptable precision and minimal perturbation of cell and tissue morphology. This study demonstrates the feasibility of integrating whole brain datasets, despite disparate acquisition protocols and reference templates, when sufficient information is present for bridging.\n\nAnatomical abbreviations

neuroscience↗

bioRxiv2016

Discovering event structure in continuous narrative perception and memory

SummaryDuring realistic, continuous perception, humans automatically segment experiences into discrete events. Using a novel model of neural event dynamics, we investigate how cortical structures generate event representations during continuous narratives, and how these events are stored and retrieved from long-term memory. Our data-driven approach enables identification of event boundaries and event correspondences across datasets without human-generated stimulus annotations, and reveals that different regions segment narratives at different timescales. We also provide the first direct evidence that narrative event boundaries in high-order areas (overlapping the default mode network) trigger encoding processes in the hippocampus, and that this encoding activity predicts pattern reinstatement during recall. Finally, we demonstrate that these areas represent abstract, multimodal situation models, and show anticipatory event reinstatement as subjects listen to a familiar narrative. Our results provide strong evidence that brain activity is naturally structured into semantically meaningful events, which are stored in and retrieved from long-term memory.

neuroscience↗

bioRxiv2016

Sexually dimorphic differentiation of a C. elegans hub neuron is cell-autonomously controlled by a conserved transcription factor

Functional and anatomical sexual dimorphisms in the brain are either the result of cells that are generated only in one sex, or a manifestation of sex-specific differentiation of neurons present in both sexes. The PHC neurons of the nematode C. elegans differentiate in a strikingly sex-specific manner. While in hermaphrodites the PHC neurons display a canonical pattern of synaptic connectivity similar to that of other sensory neurons, PHC differentiates into a densely connected hub sensory/interneuron in males, integrating a large number of male-specific synaptic inputs and conveying them to both male-specific and sex-shared circuitry. We describe that the differentiation into such a hub neuron involves the sex-specific scaling of several components of the synaptic vesicle machinery, including the vesicular glutamate transporter eat-4/VGLUT, induction of neuropeptide expression, changes in axonal projection morphology and a switch in neuronal function. We demonstrate that these molecular and anatomical remodeling events are controlled cell-autonomously by the phylogenetically conserved Doublesex homolog dmd-3, which is both required and sufficient for sex-specific PHC differentiation. Cellular specificity of dmd-3 action is ensured by its collaboration with non-sex specific terminal selector-type transcription factors whereas sex-specificity of dmd-3 action is ensured by the hermaphrodite-specific master regulator of hermaphroditic cell identity, the Gli-like transcription factor tra-1, which transcriptionally represses dmd-3 in hermaphrodite PHC. Taken together, our studies provide mechanistic insights into how neurons are specified in a sexually dimorphic manner.

neuroscience↗

bioRxiv2016

Motivational, proteostatic and transcriptional deficits precede synapse loss, gliosis and neurodegeneration in the B6.HttQ111/+ model of Huntington’s disease

We investigated the appearance and progression of disease-relevant signs in the B6.HttQ111/+ mouse, a genetically precise model of the mutation that causes Huntingtons disease (HD). We find that B6.HttQ111/+ mice are healthy, show no overt signs of central or peripheral inflammation, and no gross motor impairment as late as 12 months of age. Behaviorally, we find that 4-9 month old B6.HttQ111/+ mice have normal activity levels and show no clear signs of anxiety or depression, but do show clear signs of reduced motivation. The neuronal density, neuronal size, synaptic density and number of glia is normal in B6.HttQ111/+ striatum, the most vulnerable brain region in HD, up to 12 months of age. Despite this preservation of the synaptic and cellular composition of the striatum, we observe clear progressive, striatal-specific, transcriptional dysregulation and accumulation of neuronal intranuclear inclusions (NIIs). Simulation studies suggest these molecular endpoints are sufficiently robust for future preclinical studies, and that B6.HttQ111/+ mice are a useful tool for modeling disease-modifying or neuroprotective strategies for disease processes before the onset of overt phenotypes.

neuroscience↗

bioRxiv2016

Field Potential Reflects the Balance of Synaptic Excitation and Inhibition

Neural circuits sit in a dynamic balance between excitation (E) and inhibition (I). Fluctuations in this E:I balance have been shown to influence neural computation, working memory, and information processing. While more drastic shifts and aberrant E:I patterns are implicated in numerous neurological and psychiatric disorders, current methods for measuring E:I dynamics require invasive procedures that are difficult to perform in behaving animals, and nearly impossible in humans. This has limited the ability to examine the full impact that E:I shifts have in neural computation and disease. In this study, we develop a computational model to show that E:I ratio can be estimated from the power law exponent (slope) of the electrophysiological power spectrum, and validate this relationship using previously published datasets from two species (rat local field potential and macaque electrocorticography). This simple method--one that can be applied retrospectively to existing data--removes a major hurdle in understanding a currently difficult to measure, yet fundamental, aspect of neural computation.

neuroscience↗

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.

neuroscience↗

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

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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.

neuroscience↗

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.

neuroscience↗

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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