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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Does subjective sleep quality predict cognitive performance? - Evidence from three empirical studies

The role of sleep in cognitive performance has gained increasing attention in neuroscience and sleep research in the recent decades, however, the relationship between subjective (self-reported) sleep quality and cognitive performance has not yet been comprehensively characterized. In this paper, our aim was to test the relationship between subjective sleep quality and a wide range of cognitive functions in a healthy young adult sample combined across three studies. Sleep quality was assessed by Pittsburgh Sleep Quality Index, Athens Insomnia Scale, and a sleep diary to capture general subjective sleep quality, and Groningen Sleep Quality Scale to capture prior nights sleep quality. Within cognitive functions, we tested working memory, executive functions, and several sub-processes of procedural learning. To provide more reliable results, we included robust frequentist and Bayesian statistical analyses as well. Unequivocally across all analyses, we showed that there is no association between subjective sleep quality and cognitive performance in the domain of working memory, executive functions and procedural learning in healthy young adults. Our paper can contribute to a deeper understanding of subjective sleep quality and its measures, and we discuss various factors that may affect whether associations can be observed between subjective sleep quality and cognitive performance.

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bioRxiv2018

Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models

Neuroscience studies of human decision-making abilities commonly involve sub-jects completing a decision-making task while BOLD signals are recorded using fMRI. Hypotheses are tested about which brain regions mediate the effect of past experience, such as rewards, on future actions. One standard approach to this is model-based fMRI data analysis, in which a model is fitted to the behavioral data, i.e., a subjects choices, and then the neural data are parsed to find brain regions whose BOLD signals are related to the models internal signals. However, the internal mechanics of such purely behavioral models are not constrained by the neural data, and therefore might miss or mischaracterize aspects of the brain. To address this limitation, we introduce a new method using recurrent neural network models that are flexible enough to be jointly fitted to the behavioral and neural data. We trained a model so that its internal states were suitably related to neural activity during the task, while at the same time its output predicted the next action a subject would execute. We then used the fitted model to create a novel visualization of the relationship between the activity in brain regions at different times following a reward and the choices the subject subsequently made. Finally, we validated our method using a previously published dataset. We found that the model was able to recover the underlying neural substrates that were discovered by explicit model engineering in the previous work, and also derived new results regarding the temporal pattern of brain activity.

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bioRxiv2018

Maximum-entropy and representative samples of neuronal activity: a dilemma

The present work shows that the maximum-entropy method can be applied to a sample of neuronal recordings along two different routes: (1) apply to the sample; or (2) apply to a larger, unsampled neuronal population from which the sample is drawn, and then marginalize to the sample. These two routes give inequivalent results. The second route can be further generalized to the case where the size of the larger population is unknown. Which route should be chosen? Some arguments are presented in favour of the second. This work also presents and discusses probability formulae that relate states of knowledge about a population and its samples, and that may be useful for sampling problems in neuroscience.

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bioRxiv2018

General Functional Connectivity: shared features of resting-state and task fMRI drive reliable individual differences in functional brain networks

Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displays higher heritability on average than resting-state functional connectivity. We also show that predictions of cognitive ability from GFC generalize across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

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bioRxiv2018

Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large (with scales of TB/hour in some cases), which has presented a barrier to routine open sharing of this data, slowing progress in reproducible research. State of the art methods for analyzing this data are based on non-negative matrix factorization (NMF); these approaches solve a non-convex optimization problem, and are highly effective when good initializations are available, but can break down e.g. in low-SNR settings where common initialization approaches fail.\n\nHere we introduce an improved approach to compressing and denoising functional imaging data. The method is based on a spatially-localized penalized matrix decomposition (PMD) of the data to separate (low-dimensional) signal from (temporally-uncorrelated) noise. This approach can be applied in parallel on local spatial patches and is therefore highly scalable, does not impose non-negativity constraints or require stringent identifiability assumptions (leading to significantly more robust results compared to NMF), and estimates all parameters directly from the data, so no hand-tuning is required. We have applied the method to a wide range of functional imaging data (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets): in all cases, we observe ~2-4x increases in SNR and compression rates of 20-300x with minimal visible loss of signal, with no adjustment of hyperparameters; this in turn facilitates the process of demixing the observed activity into contributions from individual neurons. We focus on two challenging applications: dendritic calcium imaging data and voltage imaging data in the context of optogenetic stimulation. In both cases, we show that our new approach leads to faster and much more robust extraction of activity from the video data.

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bioRxiv2018

A Meta-Analysis of Executive Functions in Frontal Cortex: Comparing Healthy and Neuropsychiatric Groups

BackgroundThe neural architecture of executive functions remains a topic of considerable clinical and academic interest in the clinical neurosciences, given its strength as a transdiagnostic predictor of adaptive functioning with high heritability. In recent years, meta-analyses have shown a consistent relationship between prefrontal cortex size and executive functioning task performance in healthy adults and lesion patients, with increases in measures of cortical size (i.e., volume or thickness) associated with better executive functioning performance. There is a gap in meta-analytic literature assessing these relationships in neuropsychiatric populations, their effects relative to healthy controls, and differential contributions of brains regions and neuropsychological paradigms.\n\nMethodsWe conducted a meta-analysis of published studies (k =30) that assessed the relationship between executive functions and frontal regions in vivo (N = 1935) for both healthy (20 samples) and neuropsychiatric (21 samples) adults. Random effects modeling was used to calculate mean effect sizes and CIs.\n\nResultsLarger volumes and thickness were associated with better executive functioning in both healthy (r =.35, 95% CI =.29 -.39) and neuropsychiatric populations (r =.47, 95% CI =.40 -.51), with the effect size for neuropsychiatric populations being significantly larger compared to healthy controls. While there was variability between tasks, there were no significant differences in effect size between neuropsychological paradigms or brain region classification.\n\nConclusionsThese results indicate the relationship between healthy adult performance on neuropsychological testing is less associated with cortical size compared to neuropsychiatric adults.

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bioRxiv2018

Combining Multi-Site/Multi-Study MRI Data: Linked-ICA Denoising for Removing Scanner and Site Variability from Multimodal MRI Data

Large multi-site studies that pool magnetic resonance imaging (MRI) data across research sites or studies, or that utilize shared data from imaging repositories, present exceptional opportunities to advance neuroscience and enhance reproducibility of neuroimaging research. However, both scanner and site variability are confounds that hinder pooling data collected across different sites or across different operating systems on the same scanner, even when all acquisition protocols are harmonized. These confounds degrade statistical analyses and can lead to spurious findings. Unfortunately, methods to address this problem are scant. In this study, we propose a novel denoising approach for multi-site multimodal MRI data that implements a data-driven linked independent component analysis (LICA) to efficiently identify scanner/site-related effects for removal. Removing these effects results in denoised data that can then be combined across sites/studies to improve modality-specific statistical processing. We use data from six different studies collected on the same scanner across major hardware (gradient and head coil) and software upgrades to demonstrate our LICA-based denoising approach. The proposed method is superior compared to the existing methods we tested and has great potential for large-scale multi-site studies to produce combined data free from study/site confounds.

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bioRxiv2018

White matter connectivity between occipital and temporal regions involved in face and voice processing in hearing and early deaf individuals.

Neuroplasticity following sensory deprivation has long inspired neuroscience research in the quest of understanding how sensory experience and genetics interact in developing the brain functional and structural architecture. Many studies have shown that sensory deprivation can lead to cross-modal functional recruitment of sensory deprived cortices. Little is known however about how structural reorganization may support these functional changes. In this study, we examined early deaf, hearing signer and hearing non-signer individuals using diffusion MRI to evaluate the potential structural connectivity linked to the functional recruitment of the temporal voice area by face stimuli in deaf individuals. More specifically, we characterized the structural connectivity between occipital, fusiform and temporal regions typically supporting voice- and face-selective processing. Despite the extensive functional reorganization for face processing in the temporal cortex of the deaf, macroscopic properties of these connections did not differ across groups. However, both occipito- and fusiform-temporal connections showed significant microstructural changes between groups (fractional anisotropy reduction, radial diffusivity increase). We propose that the reorganization of temporal regions after early auditory deprivation builds on intrinsic and mainly preserved anatomical connectivity between functionally specific temporal and occipital regions.\n\nHighlightsO_LIMacrostructural connectivity of the face-voice system is preserved in early deafness\nC_LIO_LIEarly deafness impacts on the microstructural connectivity of the face-voice system\nC_LIO_LIBoth genetics and experience shape structural connections in the face-voice system\nC_LIO_LIInnate anatomical networks might constrain the expression of cross-modal plasticity\nC_LI\n\nAbbreviations

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bioRxiv2018

Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity

Estimating causal connectivity between spiking neurons from measured spike sequences is one of the main challenges of systems neuroscience. In this paper we introduce two nonparametric Bayesian methods for spike-membrane and spikespike causal connectivity based on Gaussian process regression. For spike-spike connectivity, we derive a new semi-analytic variational approximation of the response functions of a non-linear dynamical model of interconnected neurons. This semi-analytic method exploits the tractability of GP regression when the membrane potential is observed. The resulting posterior is then marginalized analytically in order to obtain the posterior of the response functions given the spike sequences alone. We validate our methods on both simulated data and real neuronal recordings.

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bioRxiv2018

Deep learning models reveal internal structure and diverse computations in the retina under natural scenes

Understanding how the visual system encodes natural scenes is a fundamental goal of sensory neuroscience. We show here that a three-layer network model predicts the retinal response to natural scenes with an accuracy nearing the fundamental limits of predictability. The models internal structure is interpretable, in that model units are highly correlated with interneurons recorded separately and not used to fit the model. We further show the ethological relevance to natural visual processing of a diverse set of phenomena of complex motion encoding, adaptation and predictive coding. Our analysis uncovers a fast timescale of visual processing that is inaccessible directly from experimental data, showing unexpectedly that ganglion cells signal in distinct modes by rapidly (< 0.1 s) switching their selectivity for direction of motion, orientation, location and the sign of intensity. A new approach that decomposes ganglion cell responses into the contribution of interneurons reveals how the latent effects of parallel retinal circuits generate the response to any possible stimulus. These results reveal extremely flexible and rapid dynamics of the retinal code for natural visual stimuli, explaining the need for a large set of interneuron pathways to generate the dynamic neural code for natural scenes.

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bioRxiv2018

Improving Corticostriatal Parcellation Through Multilevel Bagging with PyBASC

Increasing the reproducibility of neuroimaging measurement addresses a central impediment to the advancement of human neuroscience and its clinical applications. Recent efforts demonstrating variance in functional brain organization within and between individuals shows a need for improving reproducibility of functional parcellations without long scan times. We apply bootstrap aggregation, or bagging, to the problem of improving reproducibility in functional parcellation. We use two large datasets to demonstrate that compared to a standard clustering framework, bagging improves the reproducibility and test-retest reliability of both cortical and subcortical functional parcellations across a range of sites, scanners, samples, scan lengths, clustering algorithms, and clustering parameters (e.g., number of clusters, spatial constraints). With as little as six minutes of scan time, bagging creates more reproducible group and individual level parcellations than standard approaches with twice as much data. This suggests that regardless of the specific parcellation strategy employed, bagging may be a key method for improving functional parcellation and bringing functional neuroimaging-based measurement closer to clinical impact.

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bioRxiv2018

Reconstruction of post-synaptic potentials by reverse modeling of local field potentials

Among electrophysiological signals, Local Field Potentials (LFPs) are extensively used to study brain activity, either in vivo or in vitro. LFPs are recorded with extracellular electrodes implanted in brain tissue. They reflect intermingled excitatory and inhibitory processes in neuronal assemblies. In cortical structures, LFPs mainly originate from the summation of post-synaptic potentials (PSPs), either excitatory (ePSPs) and inhibitory (iPSPs) generated at the level of pyramidal cells. The challenging issue, addressed in this paper, is to estimate, from a single extracellularly-recorded signal, both ePSP and iPSP components of the LFP. The proposed method is based on a model-based reverse engineering approach in which the measured LFP is fed into a physiologically-grounded neural mass model (mesoscopic level) in order to estimate the synaptic activity of a sub-population of pyramidal cells interacting with local GABAergic interneurons. The method was first validated using simulated LFPs for which excitatory and inhibitory components are known a priori and can thus serve as a ground truth. It was then evaluated on in vivo data (PTZ-induced seizures, rat; PTZ-induced excitability increase, mouse; epileptiform discharges, mouse) and on in clinico data (human seizures recorded with depth-EEG electrodes). Under these various conditions, results showed that the proposed reverse engineering method provides a reliable estimation of the average excitatory and inhibitory post-synaptic potentials at the origin of the measured LFPs. They also indicated that the method allows for monitoring of the excitation/inhibition ratio. The method has potential for multiple applications in neuroscience, typically when a time tracking of local excitability changes is required.

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bioRxiv2018

Observing action sequences elicits sequence-specific neural representations in frontoparietal brain regions

Learning new skills by watching others is important for social and motor development throughout the lifespan. Prior research has suggested that observational learning shares common substrates with physical practice at both cognitive and brain levels. In addition, neuroimaging studies have used multivariate analysis techniques to understand neural representations in a variety of domains including vision, audition, memory and action, but few studies have investigated neural plasticity in representational space. As such, although movement sequences can be learned by observing other peoples actions, a largely unanswered question in neuroscience is how experience shapes the representational space of neural systems. Here we combined pre- and post-training fMRI sessions with six days of observational practice to examine whether the observation of action sequences elicits sequence-specific representations in frontoparietal brain regions and the extent to which these representations become more pronounced with observational practice. Our results showed that observed action sequences are modelled by distinct patterns of activity in frontoparietal cortex and that such representations largely generalise to very similar, but untrained, sequences. These findings advance our understanding of what is modelled during observational learning (sequence-specific information), as well as how it is modelled (reorganisation of frontoparietal cortex is similar manner to that of physical practice). Thus, on a more fine-grained neural level than demonstrated previously, we show the representational structure of how frontoparietal cortex maps visual information onto motor circuits to order to enhance motor performance.\n\nSignificance statementLearning by watching others is a cornerstone in the development of expertise and skilled behaviour. However, it remains unclear how visual signals are mapped onto motor circuits for such learning to occur. Here we show that observed action sequences are modelled by distinct patterns of activity in frontoparietal cortex and that such representations largely generalise to very similar, but untrained, sequences. These findings advance our understanding of what is modelled during observational learning (sequence-specific information), as well as how it is modelled (reorganisation of frontoparietal cortex is similar manner to that of physical practice). More generally, these findings demonstrate how motor circuit involvement in the perception of action sequences shows high fidelity to the physical performance of action sequences.

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bioRxiv2018

Neural tracking of the speech envelope in cochlear implant users

ObjectiveWhen listening to speech, the brain tracks the speech envelope. It is possible to reconstruct this envelope from EEG recordings. However, in people who hear using a cochlear implant (CI), the artifacts caused by electrical stimulation of the auditory nerve contaminate the EEG. This causes the decoder to produce an artifact-dominated reconstruction, which does not reflect the neural signal processing. The objective of this study is to develop and validate a method for assessing the neural tracking of speech envelope in CI users.\n\nApproachTo obtain EEG recordings free of stimulus artifacts, the electrical stimulation is periodically in-terrupted. During these stimulation gaps, artifact-free EEG can be sampled and used to train a linear envelope decoder. Different recording conditions were used to characterize the artifacts and their influence on the envelope reconstruction.\n\nMain resultsThe present study demonstrates for the first time that neural tracking of the speech envelope can be measured in response to ongoing electrical stimulation. The responses were validated to be truly neural and not affected by stimulus artifact.\n\nSignificanceBesides applications in audiology and neuroscience, the characterization and elimination of stimulus artifacts will enable future EEG studies involving continuous speech in CI users. Measures of neural tracking of the speech envelope reflect interesting properties of the listeners perception of speech, such as speech intelligibility or attentional state. Successful decoding of neural envelope tracking will open new possibilities to investigate the neural mechanisms of speech perception with a CI.

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bioRxiv2018

Limits on visual awareness for multiple objects: a bottleneck in low-level edge detection rather than high-level representational architecture

Our capacity to become aware of visual stimuli is limited. Investigating these limits, Cohen et al. (2015, Journal of Cognitive Neuroscience) found that certain object categories (e.g., faces) were more effective in blocking awareness of other categories (e.g., buildings) than other combinations (e.g., cars/chairs) in the continuous flash suppression (CFS) task. They also found that more category-pair representational similarity in higher visual cortex was related to longer category-pair breakthrough times suggesting a high-level representational architecture bottleneck for visual awareness. As the cortical representations of hands and tools overlap, these categories are ideal to test this further. We conducted CFS experiments and predicted longer breakthrough times for hands/tools compared to other pairs. In contrast to these predictions, participants were generally faster at detecting targets masked by hands or tools compared to other mask categories when giving manual (Experiment 1) or vocal responses (Experiment 2). Furthermore, we found the same inefficient mask effect for hands in the context of the categories used by Cohen et al. (2015) and found a similar behavioural pattern as the original paper (Experiment 3). Exploring potential low-level explanations, we found that the category average for edges (e.g. hands have less detail compared to cars) was the best predictor for the data. However, these category-specific image characteristics could not completely account for the Cohen et al. (2015) category pattern or for the hand/tool effects. Thus, several low- and high-level object category-specific limits for visual awareness are plausible and more investigations are needed to further tease these apart.

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bioRxiv2018

Illuminating spatial A-to-I RNA editing signatures within the Drosophila brain

SummaryAdenosine-to-inosine (A-to-I) RNA editing, catalyzed by ADAR enzymes, is a ubiquitous mechanism that generates transcriptomic diversity. This process is particularly important for proper neuronal function; however, little is known about how RNA editing is dynamically regulated between the many functionally distinct neuronal populations of the brain. In this resource paper, we present a spatial RNA editing map in the Drosophila brain and show that different neuronal populations possess distinct RNA editing signatures. After purifying and sequencing RNA from genetically marked neuronal nuclei, we identified a large number of novel editing sites and compared editing levels in hundreds of transcripts across nine functionally different neuronal populations. We found distinct editing repertoires for each population, including novel sites in repeat regions of the transcriptome and differential editing in highly conserved and likely functional regions of transcripts that encode essential neuronal genes. These changes are site-specific and not driven by changes in Adar expression, suggesting a complex, targeted regulation of editing levels in key transcripts. This fine-tuning of the transcriptome between different neurons by RNA editing may account for functional differences between distinct populations in the brain.\n\nSignificance StatementA fundamental question in contemporary neuroscience is how the remarkable cellular diversity required for the intricate function of the nervous system is achieved. In this manuscript, we bridge the gap between a cellular machinery that is known to diversify the transcriptome and the existence of distinct neuronal populations that compose Drosophila brain. Adenosine-to-inosine (A-to-I) RNA-editing is a ubiquitous mechanism that generates transcriptomic diversity in cells by recoding certain adenosines within the pre-mRNA sequence into inosines. We present a spatial map of RNA editing across different neuronal populations in Drosophila brain. Each neuronal population has a distinct editing signature, with the majority of differential editing occurring in highly conserved regions of transcripts that encode ion channels and other essential neuronal genes.

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bioRxiv2018

The correlated state in balanced neuronal networks

Understanding the magnitude and structure of inter-neuronal correlations and their relationship to synaptic connectivity structure is an important and difficult problem in computational neuroscience. Early studies show that neuronal network models with excitatory-inhibitory balance naturally create very weak spike train correlations. Later work showed that, under some connectivity structures, balanced networks can produce larger correlations between some neuron pairs, even when the average correlation is very small. All of these previous studies assume that the local neuronal network receives feedforward synaptic input from a population of uncorrelated spike trains. We show that when spike trains providing feedforward input are correlated, the downstream recurrent neuronal network produces much larger correlations. We provide an in-depth analysis of the resulting \"correlated state\" in balanced networks and show that, unlike the asynchronous state of previous work, it produces a tight excitatory-inhibitory balance consistent with in vivo cortical recordings.

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bioRxiv2018

Addressing challenges of high spatial resolution, UHF field fMRI for group analysis of higher-order cognitive tasks; an inter-sensory task directing attention between visual and somatosensory domains.

Functional MRI at ultra-high field (UHF, [≥]7T) provides significant increases in BOLD contrast-to-noise ratio (CNR) compared with conventional field strength (3T), and has been exploited for reduced field-of-view, high spatial resolution mapping of primary sensory areas. Applying these high spatial resolution methods to investigate whole brain functional responses to higher-order cognitive tasks leads to a number of challenges, in particular how to perform robust group-level statistical analyses.\n\nThis study addresses these challenges using an inter-sensory cognitive task which modulates top-down attention at graded levels between the visual and somatosensory domains. At the individual level, highly focal functional activation to the task and task difficulty (modulated by attention levels) were detectable due to the high CNR at UHF. However, to assess group level effects, both anatomical and functional variability must be considered during analysis. We demonstrate the importance of surface over volume normalization and the requirement of no spatial smoothing when assessing highly focal activity. Using novel group analysis on anatomically parcellated brain regions, we show that in higher cognitive areas (parietal and dorsal-lateral-prefrontal cortex) fMRI responses to graded attention levels were modulated quadratically, whilst in visual cortex and VIP, responses were modulated linearly. These group fMRI responses were not seen clearly using conventional second-level GLM analyses, illustrating the limitations of a conventional approach when investigating such focal responses in higher cognitive regions which are more anatomically variable. The approaches demonstrated here complement other advanced analysis methods such as multi-variate pattern analysis, allowing UHF to be fully exploited in cognitive neuroscience.

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