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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 307 records · Page 17Linked to original sources

bioRxiv2018

Aversive conditioning in oddball paradigm modulates multisensory integration, attention and emotional processing

The nature of cortical plasticity in the course of learning is one of the most intriguing questions of the modern cognitive neuroscience. Aversive conditioning is a type of associative learning produced by continuous pairing of neutral and aversive stimuli. Aversive conditioning and electroencephalography together provide a good framework for expanding our knowledge about fast learning-related cortical changes. In our experiment we tested a novel paradigm to study associative learning where aversive conditioning was combined with passive oddball. We employed conditioned auditory neutral stimuli and unconditioned aversive electrical shocks and used time-frequency, connectivity and event-related potentials (ERP) analyses to explore their interaction. First, we observed changes in the cortical activity in the form of conditioning-induced multisensory integration. The integration manifested itself in (1) desynchronization of lower beta activity in the contralateral to expected electrical shocks hemisphere and (2) enhanced functional connectivity between auditory and somatosensory cortex in the gamma frequency band. Second, we found a larger amplitude of P3a and the late posterior positivity (LPP) components of ERP to conditioned stimuli, which may be related to increased attentional and emotional significance of these stimuli. Our results reproduced and extended previous findings about multisensory integration in classical conditioning and demonstrated the improved discriminability of ERP responses through incorporation of the oddball paradigm in associative learning.

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bioRxiv2018

A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains

People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to development of larger brains. We tested this hypothesis with molecular genetic data using discoveries from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We analyzed genetic, brain imaging, and cognitive test data from the UK Biobank, the Dunedin Study, the Brain Genomics Superstruct Project (GSP), and the Duke Neurogenetics Study (DNS) (combined N=8,271). We measured genetics using polygenic scores based on published GWAS. We conducted meta-analysis to test associations among participants genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants education polygenic scores and their cognitive test performance. Effect-sizes were larger in the population-based UK Biobank and Dunedin samples than in the GSP and DNS samples. Sensitivity analysis suggested this effect-size difference partly reflected restricted range of cognitive performance in the GSP and DNS samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging data to understand neurobiology linking genetics with individual differences in cognitive performance.

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bioRxiv2018

Spontaneous activity emerging from an inferred network model captures complex temporal dynamics of spiking data

Inference methods are widely used to recover effective models from observed data. However, few studies attempted to investigate the dynamics of inferred models in neuroscience, and none, to our knowledge, at the network level. We introduce a principled modification of a widely used generalized linear model (GLM), and learn its structural and dynamic parameters from in-vitro spike data. The spontaneous activity of the new model captures prominent features of the non-stationary and non-linear dynamics displayed by the biological network, where the reference GLM largely fails, and also reflects fine-grained spatio-temporal dynamical features. Two ingredients were key for success. The first is a saturating transfer function: beyond its biological plausibility, it limits the neurons information transfer, improving robustness against endogenous and external noise. The second is a super-Poisson spikes generative mechanism; it accounts for the undersampling of the network, and allows the model neuron to flexibly incorporate the observed activity fluctuations.

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bioRxiv2018

Neuronal population mechanisms of lightness perception

The way that humans and animals perceive the lightness of an object depends on its physical luminance as well as its surrounding context. While neuronal responses throughout the visual pathway are modulated by context, the relationship between neuronal responses and lightness perception is poorly understood. We searched for a neuronal mechanism of lightness by recording responses of neuronal populations in monkey primary visual cortex (V1) and area V4 to stimuli that produce a lightness illusion in humans, in which the lightness of a disk depends on the context in which it is embedded. We found that the way individual units encode the luminance (or equivalently for our stimuli, contrast) of the disk and its context is extremely heterogeneous. This motivated us to ask whether the population representation in either V1 or V4 satisfies three criteria: 1) disk luminance is represented with high fidelity, 2) the context surrounding the disk is also represented, and 3) the representations of disk luminance and context interact to create a representation of lightness that depends on these factors in a manner consistent with human psychophysical judgments of disk lightness. We found that populations of units in both V1 and V4 fulfill the first two criteria, but that we cannot conclude that the two types of information in either area interact in a manner that clearly predicts human psychophysical measurements: the interpretation of our population measurements depends on how subsequent areas read out lightness from the population responses.\n\nNew & NoteworthyA core question in visual neuroscience is how the brain extracts stable representations of object properties from the retinal image. We searched for a neuronal mechanism of lightness perception by determining whether the responses of neuronal populations in primary visual cortex and area V4 could account for a lightness illusion measured using human psychophysics. Our results suggest that comparing psychophysics with population recordings will yield insight into neuronal mechanisms underlying a variety of perceptual phenomena.

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bioRxiv2018

An ultralight head-mounted camera system integrates detailed behavioral monitoring with multichannel electrophysiology in freely moving mice

Breakthroughs in understanding the neural basis of natural behavior require neural recording and intervention to be paired with high-fidelity multimodal behavioral monitoring. An extensive genetic toolkit for neural circuit dissection, and well-developed neural recording technology, make the mouse a powerful model organism for systems neuroscience. However, methods for high-bandwidth acquisition of behavioral signals in mice remain limited to fixed-position cameras and other off-animal devices, complicating the monitoring of animals freely engaged in natural behaviors. Here, we report the development of an ultralight head-mounted camera system combined with head-movement sensors to simultaneously monitor eye position, pupil dilation, whisking, and pinna movements along with head motion in unrestrained, freely behaving mice. The power of the combined technology is demonstrated by observations linking eye position to head orientation; whisking to non-tactile stimulation; and, in electrophysiological experiments, visual cortical activity to volitional head movements.

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bioRxiv2018

Prefrontal cortex as a meta-reinforcement learning system

Over the past twenty years, neuroscience research on reward-based learning has converged on a canonical model, under which the neurotransmitter dopamine stamps in associations between situations, actions and rewards by modulating the strength of synaptic connections between neurons. However, a growing number of recent findings have placed this standard model under strain. In the present work, we draw on recent advances in artificial intelligence to introduce a new theory of reward-based learning. Here, the dopamine system trains another part of the brain, the prefrontal cortex, to operate as its own free-standing learning system. This new perspective accommodates the findings that motivated the standard model, but also deals gracefully with a wider range of observations, providing a fresh foundation for future research.

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bioRxiv2018

Transcranial direct current stimulation alters functional network structure in humans

Transcranial direct current stimulation (tDCS) is routinely used in basic and clinical research, but its efficacy has been challenged on a methodological and statistical basis recently. The arguments against tDCS derive from insufficient understanding of how this technique interacts with brain processes physiologically. Because of its potential as a central tool in neuroscience, it is important to clarify whether and how tDCS affects neuronal activity. Here, we investigate influences of offline tDCS on network architecture measured by functional magnetic resonance imaging. Our results reveal a tDCS-induced reorganisation of a functionally-defined network that is dependent on whether we are exciting or inhibiting a node within this network, confirming in a functioning brain, and in a bias free and independent fashion that tDCS influences neuronal activity. Moreover, our results suggest that network-specific connectivity has an important role in defining the effects of tDCS and the relationship between brain states and behaviour.

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bioRxiv2018

Analysis of an open source, closed-loop, realtime system for hippocampal sharp-wave ripple disruption

Transient neural activity pervades hippocampal electrophysiological activity. During more quiescent states, brief {approx}100 ms periods comprising large {approx}150-250 Hz oscillations known as sharp-wave ripples (SWR) which co-occur with ensemble bursts of spiking activity, are regularly found in local field potentials recorded from area CA1. SWRs and their concomitant neural activity are thought to be important for memory consolidation, recall, and memory-guided decision making. Temporally-selective manipulations of hippocampal neural activity upon online hippocampal SWR detection have been used as causal evidence of the importance of SWR for mnemonic process as evinced by behavioral and/or physiological changes. However, though this approach is becoming more wide spread, the performance trade-offs involved in building a SWR detection and disruption system have not been explored, limiting the design and interpretation of SWR detection experiments. We present an open source, plug-and-play, online ripple detection system with a detailed performance characterization. Our system has been constructed to interface with an open source software platform, Trodes, and two hardware acquisition platforms, Open Ephys and SpikeGadgets. We show that our in vivo results -- approximately 80% detection latencies falling in between {approx}20-66 ms with {approx}2 ms closed-loop latencies while maintaining <10 false detections per minute -- are dependent upon both algorithmic trade-offs and acquisition hardware. We discuss strategies to improve detection accuracy and potential limitations of online ripple disruptions. By characterizing this system in detail, we present a template for analyzing other closed-loop neural detection and perturbation systems. Thus, we anticipate our modular, open source, realtime system will facilitate a wide range of carefully-designed causal closed-loop neuroscience experiments.

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bioRxiv2018

word2brain

Mapping brain functions to their underlying neural substrates is a central goal of cognitive neuroscience. Functional magnetic resonance imaging (fMRI) has proven indispensable in this endeavour. Recently, there has been growing interest in tackling this problem by mapping semantic concepts onto brain regions using repositories of images and text from the neuroimaging literature. However, no study has thus far approached this problem using (dense) vector representations of words. Using data from the Neurosynth database, we sought to develop a model that could (A) capture local correlations between words in text, as well as topics, (B) capture representation of distributed brain networks in relation to word embeddings, and (C) generate synthetic images given word inputs. We show that jointly embedding words and brain imaging data on a vector space can yield semantic representations that sensibly relate concepts across biological, psychological, and observational levels of analysis. Moreover, our proposed model makes no assumption about spatial orientation of fMRI voxels, which allows for embedding of distributed brain networks onto the semantic space. We demonstrate this capability by generating synthetic brain activation vectors from word inputs. Our model has the potential to advance neuroimaging meta-analysis as well as contextual word-embedding methods more broadly.

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bioRxiv2018

Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway

Recognising an object involves rapid visual processing and activation of semantic knowledge about the object, but how visual processing activates and interacts with semantic representations remains unclear. Cognitive neuroscience research has shown that while visual processing involves posterior regions along the ventral stream, object meaning involves more anterior regions, especially perirhinal cortex. Here we investigate visuo-semantic processing by combining a deep neural network model of vision with an attractor network model of semantics, such that visual information maps onto object meanings represented as activation patterns across features. In the combined model, concept activation is driven by visual input and co-occurrence of semantic features, consistent with neurocognitive accounts. We tested the models ability to explain fMRI data where participants named objects. Visual layers explained activation patterns in early visual cortex, whereas pattern-information in perirhinal cortex was best explained by later stages of the attractor network, when detailed semantic representations are activated. Posterior ventral temporal cortex was best explained by intermediate stages corresponding to initial semantic processing, when visual information has the greatest influence on the emerging semantic representation. These results provide proof of principle of how a mechanistic model of combined visuo-semantic processing can account for pattern-information in the ventral stream.

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bioRxiv2018

Mapping molecular datasets back to the brain regions they are extracted from: Remembering the native countries of hypothalamic expatriates and refugees

This article, which includes novel unpublished data along with commentary and analysis, focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined from brain tissue to the original locations within the brain that they are derived from using digital atlas mapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomic analyses conducted in the mammalian hypothalamus. Following a brief historical overview, we highlight studies that have mined biochemical and molecular information from the hypothalamus and then lay out a strategy for how these data can be linked spatially to the mapped locations in a canonical brain atlas where the data come from, thereby allowing researchers to integrate these data with other datasets across multiple scales. A key methodology that enables atlas-based mapping of extracted datasets - laser-capture microdissection - is discussed in detail, with a view of how this technology is a bridge between systems biology and systems neuroscience.

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bioRxiv2018

Structurally Constrained Effective Brain Connectivity

The relationship between structure and function is of interest in many research fields involving the study of complex biological processes. In neuroscience in particular, the fusion of structural and functional data can help understanding the underlying principles of the operational networks in the brain. To address this issue, this paper proposes a constrained autoregressive model leading to a representation of "effective" connectivity that can be used to better understand how the structure modulates the function. Or simply, it can be used to find novel biomarkers characterizing groups of subjects. In practice, an initial structural connectivity representation is re-weighted to explain the functional co-activations. This is obtained by minimizing the reconstruction error of an autoregressive model constrained by the structural connectivity prior. The model has been designed to also include indirect connections, allowing to split direct and indirect components in the functional connectivity, and it can be used with raw and deconvoluted BOLD signal. The derived representation of dependencies was compared to the well known dynamic causal model, giving results closer to known ground-truth. Further evaluation of the proposed effective network was performed on two typical tasks. In a first experiment the direct functional dependencies were tested on a community detection problem, where the brain was partitioned using the effective networks across multiple subjects. In a second experiment the model was validated in a case-control task, which aimed at differentiating healthy subjects from individuals with autism spectrum disorder. Results showed that using effective connectivity leads to clusters better describing the functional interactions in the community detection task, while maintaining the original structural organization, and obtaining a better discrimination in the case-control classification task. HighlightsO_LIA method to combine structural and functional connectivity by using autoregressive model is proposed. C_LIO_LIThe autoregressive model is constrained by structural connectivity defining coefficients for Granger causality. C_LIO_LIThe usefulness of the generated effective connections is tested on simulations, ground-truth default mode network experiments, a classification and clustering task. C_LIO_LIThe method can be used for direct and indirect connections, and with raw and deconvoluted BOLD signal. C_LI

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bioRxiv2018

Physical activity predicts population-level age-related differences in frontal white matter

Physical activity has positive effects on brain health and cognitive function throughout the lifespan. Thus far, few studies have examined the effects of physical activity on white matter (WM) microstructure and psychomotor speed within the same, population-based sample (critical if conclusions are to extend to the wider population). Here, using diffusion tensor imaging and a simple reaction time task within a relatively large population-derived sample (N = 399; 18-87 years) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN), we demonstrate that physical activity mediates the effect of age on white matter integrity, measured with fractional anisotropy. Higher self-reported daily physical activity was associated with greater preservation of WM in several frontal tracts, including the genu of corpus callosum, uncinate fasciculus, external capsule and anterior limb of the internal capsule. We also show that the age-related slowing is mediated by WM integrity in the genu. Our findings contribute to a growing body of work suggesting that a physically active lifestyle may protect against age-related structural disconnection and slowing.

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bioRxiv2018

Automated cellular structure extraction in biological images with applications to calcium imaging data

Recent advances in experimental methods in neuroscience enable measuring in-vivo activity of large populations of neurons at cellular level resolution. To leverage the full potential of these complex datasets and analyze the dynamics of individual neurons, it is essential to extract high-resolution regions of interest, while addressing demixing of overlapping spatial components and denoising of the temporal signal of each neuron. In this paper, we propose a data-driven solution to these challenges, by representing the spatiotemporal volume as a graph in the image plane. Based on the spectral embedding of this graph calculated across trials, we propose a new clustering method, Local Selective Spectral Clustering, capable of handling overlapping clusters and disregarding clutter. We also present a new nonlinear mapping which recovers the structural map of the neurons and dendrites, and global video denoising. We demonstrate our approach on in-vivo calcium imaging of neurons and apical dendrites, automatically extracting complex structures in the image domain, and denoising and demixing their time-traces.

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bioRxiv2018

Omitted variable bias in GLMs of neural spiking activity

Generalized linear models (GLMs) have a wide range of applications in systems neuroscience describing the encoding of stimulus and behavioral variables as well as the dynamics of single neurons. However, in any given experiment, many variables that impact neural activity are not observed or not modeled. Here we demonstrate, in both theory and practice, how these omitted variables can result in biased parameter estimates for the effects that are included. In three case studies, we estimate tuning functions for common experiments in motor cortex, hippocampus, and visual cortex. We find that including traditionally omitted variables changes estimates of the original parameters and that modulation originally attributed to one variable is reduced after new variables are included. In GLMs describing single-neuron dynamics, we then demonstrate how post-spike history effects can also be biased by omitted variables. Here we find that omitted variable bias can lead to mistaken conclusions about the stability of single neuron firing. Omitted variable bias can appear in any model with confounders - where omitted variables modulate neural activity and the effects of the omitted variables covary with the included effects. Understanding how and to what extent omitted variable bias affects parameter estimates is likely to be important for interpreting the parameters and predictions of many neural encoding models.

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bioRxiv2018

The molecular pathogenesis of superoxide dismutase 1-linked ALS is promoted by low oxygen tension

Mutations that destabilize superoxide dismutase 1 (SOD1) are a cause of amyotrophic lateral sclerosis (ALS). SOD1, which is located in the reducing cytosol, contains an oxidized disulfide bond required for stability. We show that the bond is an Achilles heel of the protein because it is sensitive to the oxygen tension. Culture of ALS patient-derived fibroblasts, astrocytes and induced pluripotent stem cell-derived mixed motor neuron and astrocyte cultures (MNACs) under lowered oxygen tensions caused reductive bond cleavage and misfolding. The effects were greatest in cells expressing mutant SOD1s, but also occurred in wild type SOD1 in cultures derived from patients carrying ALS-linked mutations in C9orf72, FUS and TBK1, as well as from controls. MNACs showed a greater response than the other cell types, including enhanced SOD1 aggregation, in line with the vulnerability of the motor system. Our results show that oxygen tension is a principal determinant of SOD1 stability and shed light on how risk factors for ALS, such as aging and other conditions causing reduced vascular perfusion, could lead to disease initiation and progression.\n\nSubject categories Neuroscience; Molecular Biology of Disease

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bioRxiv2018

Individual differences in human retinotopic maps revealed by Bayesian analysis of retinotopic organization

Human visual cortex is organized into multiple retinotopic maps. Characterizing the arrangement of these maps on the cortical surface is essential to many visual neuroscience studies. Typically, maps are obtained by voxel-wise analysis of fMRI data. This method, while useful, maps only a portion of the visual field and is limited by measurement noise and subjective assessment of boundaries. We developed a novel Bayesian mapping approach which combines observation-a subjects retinotopic measurements from small amounts of fMRI time-with a prior-a learned retinotopic atlas. This process automatically draws areal boundaries, corrects discontinuities in the measured maps, and predicts validation data more accurately than an atlas alone or independent datasets alone. This new method can be used to improve the accuracy of retinotopic mapping, to analyze large fMRI datasets automatically, and to quantify differences in map properties as a function of health, development and natural variation between individuals.

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bioRxiv2018

The spatial correspondence and genetic influence of inter-hemispheric connectivity with white matter microstructure

Microscopic features (i.e., microstructure) of axons affect neural circuit activity through characteristics such as conduction speed. Deeper understanding of structure-function relationships and translating this into human neuroscience has been limited by the paucity of studies relating axonal microstructure in white matter pathways to functional connectivity (synchrony) between macroscopic brain regions. Using magnetic resonance imaging data in 11354 subjects, we constructed multi-variate models that predict the functional connectivity of pairs of brain regions from the microstructural signature of white matter pathways that connect them. Microstructure-derived models provide predictions of functional connectivity that were significant in up to 86% of the brain region pairs considered. These relationships are specific to the relevant white matter pathway and have high reproducibility. The microstructure-function relationships are associated to genetic variants (single-nucleotide polymorphisms), co-located with genes DAAM1 and LPAR1, that have previously been reported to play a role in neural development. Our results demonstrate that variation in white matter microstructure across individuals consistently and specifically predicts functional connectivity, and that this relationship is underpinned by genetic variability.

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