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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 253 records · Page 14Linked to original sources

bioRxiv2017

Variational Autoencoder: An Unsupervised Model for Modeling and Decoding fMRI Activity in Visual Cortex

Goal-driven convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Inspired by the \"free-energy principle\" in neuroscience, we modeled the brains bottom-up and top-down pathways using the VAEs encoder and decoder, respectively. Following such conceptual relationships, we found that the VAE was able to predict cortical activities observed with functional magnetic resonance imaging (fMRI) from three human subjects watching natural videos. Compared to CNN, VAE resulted in relatively lower prediction accuracies, especially for higher-order ventral visual areas. On the other hand, fMRI responses could be decoded to estimate the VAEs latent variables, which in turn could reconstruct the visual input through the VAEs decoder. This decoding strategy was more advantageous than alternative decoding methods based on partial least square regression. This study supports the notion that the brain, at least in part, bears a generative model of the visual world.

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bioRxiv2017

Predicting personality traits from resting-state fMRI

Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 884 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the \"Big Five\", as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; 3 denoising strategies; 2 alignment schemes; 3 models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=0.24, R2=0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=0.26, R2=0.044). Other factors (Extraversion, Neuroticism, Agreeableness and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors (\"\" and \"{beta}\") from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the {beta} superordinate factor (r=0.27, R2=0.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

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bioRxiv2017

Eye movement-related confounds in neural decoding of visual working memory representations

The study of visual working memory (VWM) has recently seen revitalization with the emergence of new insights and theories regarding its neural underpinnings. One crucial ingredient responsible for this progress is the rise of neural decoding techniques. These techniques promise to uncover the representational contents of neural signals, as well as the underlying code and the dynamic profile thereof. Here, we aimed to contribute to the field by subjecting human volunteers to a combined VWM/imagery task, while recording and decoding their neural signals as measured by MEG. At first sight, the results seem to provide evidence for a persistent, stable representation of the memorandum throughout the delay period. However, control analyses revealed that these findings can be explained by subtle, VWM-specific eye movements. As a potential remedy, we demonstrate the use of a functional localizer, which was specifically designed to target bottom-up sensory signals and as such avoids eye movements, to train the neural decoders. This analysis revealed a sustained representation for approximately 1 second, but no longer throughout the entire delay period. We conclude by arguing for more awareness of the potentially pervasive and ubiquitous effects of eye movement-related confounds.\n\nSignificance statementVisual working memory is an important aspect of higher cognition and has been subject of much investigation within the field of cognitive neuroscience. Over recent years, these studies have increasingly relied on the use of neural decoding techniques. Here, we show that neural decoding may be susceptible to confounds induced by stimulus-specific eye movements. Such eye movements during working memory have been reported before, and may in fact be a common phenomenon. Given the widespread use of neural decoding and the potentially contaminating effects of eye movements, we therefore believe that our results are of significant relevance for the field.

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bioRxiv2017

Is whole-brain functional connectivity a neuromarker of sustained attention? Comment on Rosenberg & al. (2016)

Identification of neuromarkers accurately predicting cognitive characteristics from a single, standardised MRI scan could be tremendously useful in basic psychology and clinical practise. In a recent article, Rosenberg et al. (Nat Neurosci, 19, 165-171, 2016) argue that whole-brain functional network strength is a broadly applicable neuromarker of sustained attention. They claim that this marker accurately predicts performance from task related as well as resting state activity. Here, we discuss the applicability and generalizability in the context of three methodological concerns: Simulations show that the statistical methods for the 1) initial validation analyses as well as 2) internal validation using leave-one-out cross validation, are biased towards significance; 3) simple and complex models are compared suboptimally. Overall, we find that the article of Rosenberg et al. provides sufficient proof that network strength is associated with attentional capacity that it is not possible to say to which extent, and for this reason we argue that it cannot be concluded that the network is a broadly applicable neuromarker.

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bioRxiv2017

Acute fluoxetine differently affects aggressive display in zebrafish phenotypes

Zebrafish have been introduced as a model organism in behavioral neuroscience and biological psychiatry, increasing the breadth of findings using fish to study the neurobiology of aggression. Phenotypic differences between leopard and longfin zebrafish were exploited in order to elucidate the role of phasic serotonin in aggressive displays on this species. The present study revealed differences in aggressive display between leopard and longfin zebrafish, and a discrepant effect of acute fluoxetine in both populations. In mirror-induced aggression, leopard animals showed higher display latencies than longfin, as well as lower display duration and frequency (Experiment 1). Moreover, 2.5 mg/kg fluoxetine decreased the duration and frequency of display in longfin, but not leopard; and 5 mg/kg fluoxetine increased display frequency in leopard, but not longfin (Experiment 2). It is suggested that zebrafish from the longfin phenotype show more aggressive motivation and readiness in the mirror-induced aggression test that leopard, and that acute fluoxetine increases aggression in leopard and decreased it in longfin zebrafish.

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bioRxiv2017

Tracking wakefulness as it fades: micro-measures of Alertness

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method based on frequency and sleep graphoelements, which is capable of detecting micro variations in alertness. The validity of the automated method was verified by training and testing the algorithm using a dataset where participants are known to fall asleep. In addition, we tested generalizability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience.

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bioRxiv2017

Hyperacuity Bayesian methods to enhance temporal resolution of two-photon recording of the complex spikes in the cerebellar Purkinje cells

Two-photon imaging is a major recording technique in neuroscience but its low sampling rate imposes a severe limit of elucidating high temporal profiles of neuronal dynamics. Here we developed two hyperacuity Bayesian algorithms to improve spike detection and spike time precision, minimizing the estimation error supervised by the ground-truth given as the electrical spike signals. The benchmark showed that our algorithms outperformed other unsupervised algorithms maximizing the likelihood of the estimates for both experimental and simulation data. We argue that the supervised algorithms are useful tools to improve spike estimation of two-photon recording in case ground truth signals are available.

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bioRxiv2017

Advanced NeuroGPS-Tree: dense reconstruction of brain-wideneuronal population close to ground truth

Recent progresses allow imaging specific neuronal populations at single-axon level across mouse brain. However, digital reconstruction of neurons in large dataset requires months of human labor. Here, we developed a tool to solve this problem. Our tool offers a special error-screening system for fast localization of submicron errors in densely packed neurites and along long projection across the whole brain, thus achieving reconstruction close to the ground-truth. Moreover, our tool equips algorithms that significantly reduce intensive manual interferences and achieve high-level automation, with speed 5 times faster compared to semi-automatic tools. We also demonstrated reconstruction of 35 long projection neurons around one injection site of a mouse brain at an affordable time cost. Our tool is applicable with datasets of 10 TB or higher from various light microscopy, and provides a starting point for the reconstruction of neuronal population for neuroscience studies at a single-cell level.

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bioRxiv2017

Static and dynamic measures of human brain connectivity predict complementary aspects of human cognitive performance

In cognitive network neuroscience, the connectivity and community structure of the brain network is related to cognition. Much of this research has focused on two measures of connectivity - modularity and flexibility - which frequently have been examined in isolation. By using resting state fMRI data from 52 young adults, we investigate the relationship between modularity, flexibility and performance on cognitive tasks. We show that flexibility and modularity are highly negatively correlated. However, we also demonstrate that flexibility and modularity make unique contributions to explain task performance, with modularity predicting performance for simple tasks and flexibility predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.

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bioRxiv2018

Selective Reporting, Analytical Flexibility and Multiple Comparisons in ERP Research: The case of Pain Empathy

Empathy has received considerable attention from the field of cognitive and social neuroscience. A significant portion of these studies used the event-related potential (ERP) technique to study the mechanisms of empathy for pain in others in different conditions and clinical populations. These show that specific ERP components measured during the observation of pain in others are modulated by several factors and altered in clinical populations. However, issues present in this literature such as analytical flexibility and lack of type 1 error control raise doubts regarding the validity and reliability of these conclusions. The current study compiled the results and methodological characteristics of 40 studies using ERP to study empathy of pain in others. The results of the meta-analysis suggest that the centro-parietal P3 and late positive potential component are sensitive to the observation of pain in others, while the early N1 and N2 components are not reliably associated with vicarious pain observation. The review of the methodological characteristics shows that the presence of selective reporting, analytical flexibility and lack of type 1 error control compromise the interpretation of these results. The implication of these results for the study of empathy and potential solutions to improve future investigations are discussed.

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bioRxiv2017

Shared Spatiotemporal Category Representations in Biological and Artificial Deep Neural Networks

Understanding the computational transformations that enable invariant visual categorization is a fundamental challenge in both systems and cognitive neuroscience. Recently developed deep convolutional neural networks (CNNs) perform visual categorization at accuracies that rival humans, providing neuroscientists with the opportunity to interrogate the series of representational transformations that enable categorization in silico. The goal of the current study is to assess the extent to which sequential visual representations built by a CNN map onto those built in the human brain as assessed by high-density, time-resolved event-related potentials (ERPs). We found correspondence both over time and across the scalp: earlier ERP activity was best explained by early CNN layers at all electrodes. Later neural activity was best explained by the later, conceptual layers of the CNN. This effect was especially true both in frontal and right occipital sites. Together, we conclude that deep artificial neural networks trained to perform scene categorization traverse similar representational stages as the human brain. Thus, examining these networks will allow neuroscientists to better understand the transformations that enable invariant visual categorization.

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bioRxiv2018

Three dimensional two-photon imaging of neuronal activity in freely moving mice using a miniature fiber coupled microscope with active axial-scanning

We present a miniature head mounted two-photon fiber-coupled microscope (2P-FCM) for neuronal imaging with active axial focusing enabled using a miniature electrowetting lens. Full three-dimensional two-photon imaging of GCaMP6s showing individual neuron activity in multiple focal planes was achieved in a freely-moving mouse. Two-color simultaneous imaging of GFP and tdTomato fluorescence is also demonstrated. Additionally, dynamic control of the axial scanning of the electrowetting lens allows tilting of the focal plane enabling cells in multiple focal planes to be imaged simultaneously. Two-photon imaging allows increased penetration depth in tissue yielding a working distance of 450 m with an additional 180 m of active axial focusing. The objective NA is 0.45 with a lateral resolution of 1.8 m, an axial resolution of 10 m, and a field-of-view of 240 m diameter. The 2P-FCM has a weight of only [~]2.5 g and is capable of repeatable and stable head-attachment. The 2P-FCM with dynamic axial scanning provides a new capability to record from functionally distinct neuronal layers, opening new opportunities in neuroscience research.

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bioRxiv2017

An open resource for nonhuman primate imaging

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience.\n\nUnfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMate Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 13 independent data collections aggregated across 11 sites (total = 98 macaque monkeys). We also outline the unique pitfalls and challenges that should be considered in the analysis of the non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

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bioRxiv2017

EGL-19 AND EXP-2 ION CHANNELS MODELLING WITHIN C. ELEGANS PHARYNGEAL MUSCLE CELL ALLOWS REPRODUCTION OF CA2+ DRIVEN ACTION POTENTIALS, BOTH SINGLE EVENTS AND TRAINS

One of the current problems at the interface between neuroscience, biophysics, and computational modeling is the reverse-engineering and reproduction of Caenorhabditis elegans using computer simulation. The aim of our research was to develop the computational models and techniques for solving this problem while participating in the international open science OpenWorm Project. We have suggested models of a typical C. elegans neuron and a pharyngeal muscle cell, which were constructed and optimized using the NEURON simulation environment. The available experimental data about EGL-19 and EXP-2 ion channels allowed the model of a muscle to reproduce the action potential time profile correctly. Also, the model of a neuron reproduces quite accurately the mechanism of neural signal transmission based on passive propagation. We believe our models to be promising for better representing the specifics of various nervous and muscular cell classes when adding the corresponding ion channel models. Moreover, they can be used to construct the networks of such elements.

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bioRxiv2017

The Brain/MINDS 3D digital marmoset brain atlas

We present a new 3D digital brain atlas of the non-human primate, common marmoset monkey (Callithrix jacchus), with MRI and coregistered Nissl histology data. To the best of our knowledge this is the first comprehensive digital 3D brain atlas of the common marmoset having normalized multi-modal data, cortical and sub-cortical segmentation, and in a common file format (NIfTI). The atlas can be registered to new data, is useful for connectomics, functional studies, simulation and as a reference.\n\nThe atlas was based on previously published work but we provide several critical improvements to make this release valuable for researchers. Nissl histology images were processed to remove illumination and shape artifacts and then normalized to the MRI data. Brain region segmentation is provided for both hemispheres. The data is in the NIfTI format making it easy to integrate into neuroscience pipelines, whereas the previous atlas was in an inaccessible file format. We also provide cortical, mid-cortical and white matter boundary segmentations useful for visualization and analysis.

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bioRxiv2017

Accurate estimation of neural population dynamics without spike sorting

A central goal of systems neuroscience is to relate an organisms neural activity to behavior. Neural population analysis often begins by reducing the dimensionality of the data to focus on the patterns most relevant to a given task. A major practical hurdle to data analysis is spike sorting, and this problem is growing rapidly as the number of neurons measured increases. Here, we investigate whether spike sorting is necessary to estimate neural dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We re-analyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multi-unit threshold crossings in place of sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.

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bioRxiv2017

Methods for chair restraint and training of the common marmoset on oculomotor tasks

The oculomotor system is the most thoroughly understood sensorimotor system in the brain, due in large part to electrophysiological studies carried out in macaque monkeys trained to perform ocuolomotor tasks. A disadvantage of the macaque model is that many cortical oculomotor areas of interest lie within sulci, making high-density array and laminar recordings impractical. Further, many techniques of molecular biology developed in rodents, such as transgenic animals and optogenetic manipulation of neuronal subtypes, are limited in this species. The common marmoset (Callithrix jacchus) may potentially bridge the gap between systems neuroscience in macaques and molecular techniques, and additionally possesses a smooth cortex allowing easy access to frontoparietal oculomotor areas. To date, techniques for restraint and training of these animals to perform oculomotor tasks remain in an early stage of development. Here we provide details of a custom-designed chair for restraint of marmosets, a combination head restraint/recording chamber providing stability suitable for eye movement and neural recordings, and a training protocol for oculomotor tasks. As proof-of-principle, we report the results of a psychophysical study in marmosets trained to perform a saccade task using these methods, showing that, as in rhesus and humans, marmosets exhibit a \"gap effect\" - a decrease in reaction time when the fixation stimulus is removed prior to the onset of a visual saccade target. These results provide evidence that the common marmoset is a suitable model for neurophysiogical investigations of oculomotor control.\n\nNEW AND NOTEWORTHYThe ability to carry out neuronal recordings in behaving primates has provided a wealth of information regarding the neural circuits underlying the control of eye movements. Such studies require restraint of the animal within a primate chair, head fixation, methods of acclimating the animals to this restraint, and the use of operant conditioning methods for training on oculomotor tasks. In contrast to the macaque model, relatively few studies have reported in detail methods for use in the common marmoset. Here we detail custom-designed equipment and methods by which we have used to successfully train head-restrained marmosets to perform basic oculomotor tasks.

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bioRxiv2018

A Recurrent Neural Network Approach for Automated Neural Tracing in Multispectral 3D Images

Neuronal morphology reconstruction in fluorescence microscopy 3D images is essential for analyzing neuronal cell type and connectivity. Manual tracing of neurons in these images is time consuming and subjective. Automated tracing is highly desired yet is one of the foremost challenges in computational neuroscience. The multispectral labeling technique, Brainbow utilizes high dimensional spectral information to distinguish intermingled neuronal processes. It is particular interesting to develop new algorithms to include the spectral information into the tracing process. Recently, deep learning approaches achieved state-of-the-art in different computer vision and medical imaging applications. To benefit from the power of deep learning, in this paper, we propose an automated neural tracing approach in multispectral 3D Brainbow images based on recurrent neural net-work. We first adopt VBM4D approach to denoise multispectral 3D images. Then we generate cubes as training samples along the ground truth, manually traced paths. These cubes are the input to the recur-rent neural network. The proposed approach is simple and effective. The approach can be implemented with the deep learning toolbox Keras in 100 lines. Finally, to evaluate our approach, we computed the average and standard deviation of DIADEM metric from the ground truth results to our tracing results, and from our tracing results to the ground truth results. Extensive experimental results on the collected dataset demonstrate that the proposed approach performs well in Brainbow labeled mouse brain images.

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