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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Non-parametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data

Directed connectivity inference has become a cornerstone in neuroscience following the recent progress in neuroimaging and elctrophysiological techniques to characterize anatomical and functional networks. This paper focuses on the detection of existing connections from the observed activity in networks of 50 to 150 nodes with linear feedback in discrete time. Through the variation of multiple network parameters, our numerical results indicate that directed connections - in the time domain - are more accurately estimated based on the coefficients obtained from multivariate autoregressive (MVAR) than Granger causality analysis, which is based on the error residuals of the same MVAR linear regression. Based on these findings, we propose a non-parametric significance test for connectivity detection, which achieves a good control of false positives (type 1 error) and is robust to various network topologies. When generating surrogate distributions, we compare the effects of circular shifts, random permutations and phase randomization of the observed time series, each breaking down covariances in a specific manner: the MVAR estimates from those shuffled covariances build a null-hypothesis distribution for each connection, from which the original connectivity estimate can be compared. We apply our method to multiunit activity data recorded from Utah electrode arrays in monkey and examine the detected interactions between 25 channels for a proof of concept. The results unravel a non-trivial underlying connectivity structure, which differentiates the effect of incoming and outgoing connections.

neuroscience↗

bioRxiv2017

Decoding the categorization of visual motion with magnetoencephalography

Brain decoding techniques are particularly efficient at deciphering weak and distributed neural patterns. Brain decoding has primarily been used in cognitive neurosciences to predict differences between pairs of stimuli (e.g. faces vs. houses), but how distinct brain/perceptual states can be decoded following the presentation of continuous sensory stimuli is unclear. Here, we developed a novel approach to decode brain activity recorded with magnetoencephalography while participants discriminated the coherence of two intermingled clouds of dots. Seven levels of visual motion coherence were tested and participants reported the colour of the most coherent cloud. The decoding approach was formulated as a ranked-classification problem, in which the model was evaluated by its capacity to predict the order of a pair of trials, each tested with two distinct visual motion coherence levels. Two brain states were decoded as a function of the degree of visual motion coherence. Importantly, perceptual motion coherence thresholds were found to match the decoder boundaries in a fully data-driven way. The algorithm revealed the earliest categorization in hMT+, followed by V1/V2, IPS, and vlPFC.

neuroscience↗

bioRxiv2017

Using computational theory to constrain statistical models of neural data

Computational neuroscience is, to first order, dominated by two approaches: the \"bottom-up\" approach, which searches for statistical patterns in large-scale neural recordings, and the \"top-down\" approach, which begins with a theory of computation and considers plausible neural implementations. While this division is not clear-cut, we argue that these approaches should be much more intimately linked. From a Bayesian perspective, computational theories provide constrained prior distributions on neural data--albeit highly sophisticated ones. By connecting theory to observation via a probabilistic model, we provide the link necessary to test, evaluate, and revise our theories in a data-driven and statistically rigorous fashion. This review highlights examples of this theory-driven pipeline for neural data analysis in recent literature and illustrates it with a worked example based on the temporal difference learning model of dopamine.

neuroscience↗

bioRxiv2017

Data-Driven Extraction of a Nested Structure of Human Cognition

AbstractDecades of cognitive neuroscience research have revealed two basic facts regarding task-driven brain activation patterns. First, distinct patterns of activation occur in response to different task demands. Second, a superordinate, dichotomous pattern of activation/de-activation, is commonly observed across a variety of task demands. We explore the possibility that a hierarchical model incorporates these two observed brain activation phenomena into a unifying framework. We apply a latent variable approach, exploratory bi-factor analysis, to a large set of brain activation patterns to determine the potential existence of a nested structure of factors that underlies a variety of commonly observed activation patterns. We find that a general factor, associated with a superordinate brain activation/de-activation pattern, explained the majority of the variance (52.37%). The bi-factor analysis also revealed several sub-factors that explained an additional 31.02% of variance in brain activation patterns, associated with different manifestations of the superordinate brain activation/de-activation pattern, each emphasizing different contexts in which the task demands occurred. Importantly, this nested factor structure provided better overall fit to the data compared with a non-nested factor structure model. These results point to domain-general psychological process, representing a focused awareness process or attentional episode that is variously manifested according to the sensory modality of the stimulus and degree of cognitive processing. This novel model provides the basis for constructing a biologically-informed, data-driven taxonomy of psychological processes.

neuroscience↗

bioRxiv2017

VREX: An Open-Source Toolbox for Creating 3D Virtual Reality Experiments

BackgroundWe present VREX, a free open-source Unity toolbox for virtual reality research in the fields of experimental psychology and neuroscience.\n\nResultsDifferent study protocols about perception, attention, cognition and memory can be constructed using the toolbox. VREX provides a procedural generation of (interconnected) rooms that can be automatically furnished with a click of a button. VREX includes a menu system for creating and storing experiments with different stages. Researchers can combine different rooms and environments to perform end-to-end experiments including different testing situations and data collection. For fine-tuned control VREX also comes with an editor where all the objects in the virtual room can be manually placed and adjusted in the 3D world.\n\nConclusionsVREX simplifies the generation and setup of complicated VR scenes and experiments for researchers. VREX can be downloaded and easily installed from vrex.mozello.com

neuroscience↗

bioRxiv2017

Foreground Enhancement and Background Suppression in Human Early Visual System During Passive Perception of Natural Images

One of the major challenges in visual neuroscience is represented by foreground-background segmentation. Data from nonhuman primates show that segmentation leads to two distinct, but associated processes: the enhancement of neural activity during figure processing (i.e., foreground enhancement) and the suppression of background-related activity (i.e., background suppression). To study foreground-background segmentation in ecological conditions, we introduce a novel method based on parametric modulation of low-level image properties followed by application of simple computational image-processing models. By correlating the outcome of this procedure with human fMRI activity measured during passive viewing of 334 natural images, we reconstruct easily interpretable \"neural images\" from seven visual areas: V1, V2, V3, V3A, V3B, V4 and LOC. Results show evidence of foreground enhancement for all tested regions, while background suppression specifically occurs in V4 and LOC. \"Neural images\" reconstructed from V4 and LOC revealed a preserved spatial resolution of foreground textures, indicating a richer representation of the salient part of natural images, rather than a simplistic model of object shape. Our results indicate that scene segmentation is an automatic process that occurs during natural viewing, even when individuals are not required to perform any particular task.

neuroscience↗

bioRxiv2017

Monitoring large populations of locus coeruleus neurons reveals the non-global nature of the norepinephrine neuromodulatory system

Understanding the forebrain neuromodulation by the noradrenergic locus coeruleus (LC) is fundamental for cognitive and systems neuroscience. The diffuse projections of individual LC neurons and presumably their synchronous spiking have long been perceived as features of the global nature of noradrenergic neuromodulation. Yet, the commonly referenced \"synchrony\" underlying global neuromodulation, has never been assessed in a large population, nor has it been related to projection target specificity. Here, we recorded up to 52 single units simultaneously (3164 unit pairs in total) in rat LC and characterized projections by stimulating 15 forebrain sites. Spike count correlations were low and, surprisingly, only 13% of pairwise spike trains had synchronized spontaneous discharge. Notably, even noxious sensory stimulation did not activate the entire population, only evoking synchronized responses in ~16% of units on each trial. We also identified novel infra-slow (0.01-1 Hz) fluctuations of LC unit spiking that were asynchronous across the population. A minority, synchronized possibly by gap junctions, was biased toward restricted (non-global) forebrain projection patterns. Finally, we characterized two types of LC single units differing by waveform shape, propensity for synchronization, and interactions with cortex. These cell types formed finely-structured ensembles. Our findings suggest that the LC conveys a highly complex, differentiated, and potentially target-specific neuromodulatory signal.

neuroscience↗

bioRxiv2017

Reason’s Enemy Is Not Emotion: Engagement of Cognitive Control Networks Explains Biases in Gain/Loss Framing

In the classic gain/loss framing effect, describing a gamble as a potential gain or loss biases people to make risk-averse or risk-seeking decisions, respectively. The canonical explanation for this effect is that frames differentially modulate emotional processes - which in turn leads to irrational choice behavior. Here, we evaluate the source of framing biases by integrating functional magnetic resonance imaging (fMRI) data from 143 human participants performing a gain/loss framing task with meta-analytic data from over 8000 neuroimaging studies. We found that activation during choices consistent with the framing effect were most correlated with activation associated with the resting or default brain, while activation during choices inconsistent with the framing effect most correlated with the task-engaged brain. Our findings argue against the common interpretation of gain/loss framing as a competition between emotion and control. Instead, our study indicates that this effect results from differential cognitive engagement across decision frames.\n\nSignificance StatementThe biases frequently exhibited by human decision-makers have often been attributed to the presence of emotion. Using a large fMRI sample and analysis of whole-brain networks defined with the meta-analytic tool Neurosynth, we find that neural activity during frame-biased decisions are more significantly associated with default behaviors (and the absence of executive control) than with emotion. These findings point to a role for neuroscience in shaping longstanding psychological theories in decision science.

neuroscience↗

bioRxiv2017

Model-based spike sorting with a mixture of drifting t-distributions

Chronic extracellular recordings are a powerful tool for systems neuroscience, but spike sorting remains a challenge. A common approach is to fit a generative model, such as a mixture of Gaussians, to the observed spike data. Even if non-parametric methods are used for spike sorting, such generative models provide a quantitative measure of unit isolation quality, which is crucial for subsequent interpretation of the sorted spike trains. We present a spike sorting strategy that models the data as a mixture of drifting t-distributions. This model captures two important features of chronic extracellular recordings--cluster drift over time and heavy tails in the distribution of spikes--and offers improved robustness to outliers. We evaluate this model on several thousand hours of chronic tetrode recordings and show that it fits the empirical data substantially better than a mixture of Gaussians. We also provide a software implementation that can re-fit long datasets (several hours, millions of spikes) in a few seconds, enabling interactive clustering of chronic recordings. Using experimental data, we identify three common failure modes of spike sorting methods that assume stationarity. We also characterize the limitations of several popular unit isolation metrics in the presence of empirically-observed variations in cluster size and scale. We find that the mixture of drifting t-distributions model enables efficient spike sorting of long datasets and provides an accurate measure of unit isolation quality over a wide range of conditions.

neuroscience↗

bioRxiv2017

Dipolar extracellular potentials generated by axonal projections

Extracellular field potentials (EFPs) are an important source of information in neuroscience, but their physiological basis is in many cases still a matter of debate. Axonal sources are typically discounted in modeling and data analysis because their contributions are assumed to be negligible. Here, we show experimentally and theoretically that contributions of axons to EFPs can be significant. Modeling action potentials propagating along axons, we showed that EFPs were prominent in the presence of a terminal zone where axons branch and terminate in close succession, as found in many brain regions. Our models predicted a dipolar far field and a polarity reversal at the center of the terminal zone. We confirmed these predictions using EFPs from the barn owl auditory brainstem where we recorded in nucleus laminaris using a multielectrode array. These results demonstrate that axonal terminal zones produce EFPs with considerable amplitude and spatial reach.

neuroscience↗

bioRxiv2017

Modern machine learning far outperforms GLMs at predicting spikes

Neuroscience has long focused on finding encoding models that effectively ask \"what predicts neural spiking?\" and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a GLM. Here we compared the predictive performance of GLMs to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. In general, the modern methods (particularly XGBoost and the ensemble) produced more accurate spike predictions and were less sensitive to the preprocessing of features. This discrepancy in performance suggests that standard feature sets may often relate to neural activity in a nonlinear manner not captured by GLMs. Encoding models built with machine learning techniques, which can be largely automated, more accurately predict spikes and can offer meaningful benchmarks for simpler models.

neuroscience↗

bioRxiv2017

Limits on prediction in language comprehension: A multi-lab failure to replicate evidence for probabilistic pre-activation of phonology

In the last few decades, the idea that people routinely and implicitly predict upcoming words during language comprehension has turned from a controversial hypothesis to a widely-accepted assumption. Current theories of language comprehension1-3 posit prediction, or context-based pre-activation, as an essential mechanism occurring at all levels of linguistic representation (semantic, morpho-syntactic and phonological/orthographic) and facilitating the integration of words into the unfolding discourse representation. The strongest evidence to date for phonological pre-activation comes from DeLong, Urbach and Kutas4, who monitored participants electrophysiological brain responses as they read sentences, presented one word at a time, with expected/unexpected indefinite article + noun combinations like, \"The day was breezy so the boy went outside to fly a kite/an airplane\". The sentences varied expectations ( cloze probability) for a consonant- or vowel-initial noun, as determined in a sentence-completion task using other participants. Expectedly, the amplitude of the N400 event-related potential (ERP) decreased (became less negative) with increasing cloze reflecting ease of processing5-6. Whereas the decreased N400 at the noun could be due to its pre-activation or because high-cloze nouns are easier to integrate, crucially, N400s at the immediately-preceding article a or an showed the same relationship with cloze, i.e., encountering an indefinite article that mismatched a highly-expected word (e.g., an when expecting kite) also elicited a larger N400. This led to the claim that participants pre-activated highly-expected nouns, including their initial phonemes, based on the preceding context, with larger N400s on mismatching articles reflecting disconfirmation of this prediction.\n\nThe Delong et al. study warranted stronger conclusions than related results available at the time. Unlike previous work, it did not rely on the precursory visual-depiction of upcoming nouns, clearly de-confounded prediction and integration effects, and tested for graded phonological pre-activation of specific word form. Correspondingly, the study has been enthusiastically received as strong evidence for probabilistic phonological pre-activation, receiving over 650 citations to date and featuring in authoritative reviews2-3. However, there is good cause to question the soundness of the original finding (and the appropriateness of the analysis used). Attempts to replicate the critical article-effect have failed7. Moreover, an earlier, alternative analysis of the same data by the authors8 failed to reach statistical significance, but was omitted from the published report.\n\nTo obtain more definitive evidence, we conducted a direct replication study spanning 9 laboratories (Ntotal = 334). We pre-registered one replication analysis that was faithful to the original, and one single-trial analysis that modeled subject- and item-level variance using linear mixed-effects models. Applying the replication analysis to our article data (Figure 1a), the original finding did not replicate: no laboratory observed a significant negative relationship between cloze and N400 at central-parietal electrodes. In contrast, the negative relationship was successfully replicated for the nouns: 6 laboratories observed such an effect and 2 laboratories observed relatively strong but non-significant effects in the expected direction (range r = .30 to .50). In the single-trial analysis (Fig. 1b-c), there was no statistically significant effect of cloze on article-N400s, also with stricter control for pre-article voltage levels (Supplementary Fig. 1). Crucially, there was a strong and significant cloze effect on noun-N400s (in all laboratories), which was significantly different from that on article-N400s. We observed no significant differences between laboratories for article or noun effects. Exploratory Bayesian analyses with priors based on DeLong et al. further support our conclusions (Fig. 1d, Supplementary Fig. 2). Finally, a control experiment confirmed our participants sensitivity to the a/an rule during online language comprehension (Supplementary Fig. 3).\n\nO_FIG O_LINKSMALLFIG WIDTH=173 HEIGHT=200 SRC=\"FIGDIR/small/111807_fig1.gif\" ALT=\"Figure 1\">\nView larger version (72K):\norg.highwire.dtl.DTLVardef@1231330org.highwire.dtl.DTLVardef@1c0eb43org.highwire.dtl.DTLVardef@95a28forg.highwire.dtl.DTLVardef@1e38ee6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1C_FLOATNO A multi-lab failure to replicate evidence for probabilistic pre-activation of phonology. (a) Pre-registered replication analysis: Pearsons r correlations between ERP amplitude and article/noun cloze probability per EEG channel (* P < 0.05) and per laboratory. (b, c) Pre-registered single-trial analysis: (b) Grand-average ERPs elicited by relatively expected and unexpected words (cloze higher/lower than 50%) at electrode Cz, with standard deviation are shown in dotted lines, and (c) the relationship between cloze and N400 amplitude as illustrated by the mean ERP values per cloze value (number of observations reflected in circle size), along with the regression line and 95% confidence interval. A change in article cloze from 0 to 100 is associated with a change in amplitude of 0.296 {micro}V (95% confidence interval: -.08 to .67), {chi}2(1) = 2.31, p = .13. A change in noun-cloze from 0 to 100 is associated with a change in amplitude of 2.22 {micro}V (95% confidence interval: 1.75 to 2.69), {chi}2(1) = 56.5, p < .001. The effect of cloze on noun-N400s was statistically different from its effect on article-N400s, {chi}2(1) = 31.38, p < .001. (d) Bayes factor analysis associated with the replication analysis, quantifying the obtained evidence for the null hypothesis (H0) that N400 is not impacted by cloze, or for the alternative hypothesis (H1) that N400 is impacted by cloze with the size and direction of effect reported by DeLong et al. Scalp maps show the common logarithm of the replication Bayes factor for each electrode, capped at log(100) for presentation purposes. Electrodes that yielded at least moderate evidence for or against the null hypothesis (Bayes factor of [≥] 3) are marked by an asterisk. At posterior electrodes where DeLong et al. found their effects, our article data yielded strong to extremely strong evidence for the null hypothesis, whereas our noun data yielded extremely strong evidence for the alternative hypothesis (upper graphs). These results were also found when applying a 500 ms pre-word baseline correction (lower graphs).\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC=\"FIGDIR/small/111807_figS1.gif\" ALT=\"Figure 1\">\nView larger version (32K):\norg.highwire.dtl.DTLVardef@bad459org.highwire.dtl.DTLVardef@1cb4d98org.highwire.dtl.DTLVardef@534676org.highwire.dtl.DTLVardef@1371c54_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOSupplementary Figure 1.C_FLOATNO Exploratory single-trial analyses: The relationship between cloze and ERP amplitude as illustrated by the mean ERP values per cloze value (number of observations reflected in circle size), along with the regression line and 95% confidence interval, from four exploratory analyses. We performed tests which used longer baseline time windows (200 ms, upper left panel; 500 ms, upper right panel) to better control for pre-article voltage levels, or which used the pre-registered baseline and applied a 0.1 Hz high-pass filter (lower left panel) to better control for slow signal drift (while presumably not affecting N400 activity). All three tests reduced the initially observed effect of article-cloze (200 ms baseline, = .25, CI [-.12, .62], {chi}2(1) = 1.35, p = .19; 500 ms baseline, = .14, CI [-.25, .53], {chi}2(1) = 0.46, p = .50; 0.1 Hz filter: = 0.09, CI [-.22, .41], {chi}2(1) = 0.33, p = .56). An analysis in the 500 to 100 ms time window before article-onset (lower right panel) revealed a non-significant effect of cloze that resembled the pattern observed after article-onset, = .16, CI [-.07, .39], {chi}2(1) = 1.82, p = .18. Combined, these results suggest that the results obtained with the pre-registered analysis at least partly reflected the effects of slow signal drift that existed before the articles were presented.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=161 HEIGHT=200 SRC=\"FIGDIR/small/111807_figS2.gif\" ALT=\"Figure 2\">\nView larger version (35K):\norg.highwire.dtl.DTLVardef@124ffa2org.highwire.dtl.DTLVardef@af29aorg.highwire.dtl.DTLVardef@bd69d1org.highwire.dtl.DTLVardef@16e3b98_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOSupplementary Figure 2.C_FLOATNO Results from exploratory Bayesian mixed-effects model analyses, represented by posterior distributions for the effect of cloze on ERP amplitudes in the N400 window. The x-axis shows cloze effect sizes (i.e., changes in microvolts associated with an increase from 0% cloze probability to 100% cloze probability). The black line indicates the posterior distribution of effects; higher values of the posterior density at a given effect size indicate higher probability that this is the true effect size in the population. The peak of the posterior distribution roughly corresponds to the point estimate of the effect size (the regression coefficient) fitted from the Bayesian mixed effect model, i.e., the most likely value of the true effect size. The middle 95% of the posterior distribution, shaded in pink, corresponds to a two-tailed 95% credible interval for the effect sizei.e., an interval that we can be 95% confident contains the true effect. The green dotted line indicates the prior distribution (i.e., our expectation about where the true effect would lie before the data were collected), which is centered on 1.25V, the effect observed by Delong and colleagues (2005). The black connected dots illustrate the ratio between the posterior and prior distribution (i.e., the Bayes Factor) at the effect size of 0V; for example, a Bayes Factor of 4 suggests we can be 4 times more certain that the true effect is zero after having conducted this experiment than before, or, in other words, that the data increased our confidence in the null effect of zero fourfold. We performed these analyses for each of the linear mixed-effects model analysis we performed. We note that in all the article-analyses, the posterior probability of the estimated effect being greater than zero is around 80 or 90%, but this is also the case for the pre-stimulus variable, suggesting that the observed patterns arise before the articles are seen. In none of our article-analyses did zero lie outside the obtained credible interval, whereas for the nouns, zero lay outside the credible interval. These results are consistent with a failure to replicate the DeLong et al. article-effect and successful replication of the noun-effect.\n\nC_FIG O_FIG O_LINKSMALLFIG WIDTH=133 HEIGHT=200 SRC=\"FIGDIR/small/111807_figS3.gif\" ALT=\"Figure 3\">\nView larger version (25K):\norg.highwire.dtl.DTLVardef@ab14forg.highwire.dtl.DTLVardef@1fed126org.highwire.dtl.DTLVardef@55369corg.highwire.dtl.DTLVardef@747668_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOSupplementary Figure 3.C_FLOATNO P600 effects at electrode Pz per lab associated with flouting of the English a/an rule in the control experiment. Plotted ERPs show the grand-average difference waveform and standard deviation for ERPs elicited by ungrammatical expressions (an kite) minus those elicited by grammatical expressions (a kite). This control experiment followed in the same experimental session as the main experiment and was carried to rule out that an observed lack of a statistically significant, article-elicited prediction effect in the main experiment reflected a general insensitivity of our participants to the a/an rule. In each laboratory, nouns following incorrect articles elicited a late positive-going waveform compared to nouns following correct articles, starting at about 500 ms after word onset and strongest at parietal electrodes. This standard P600 effect was confirmed in a single-trial analysis, {chi}2(1) = 83.09, p < .001, and did not significantly differ between labs, {chi}2(8) = 8.98, p = .35.\n\nC_FIG\n\nDespite a sample size 10 times larger than the original and improved statistical analysis, we observed no statistically significant effect of cloze on article-N400s, while replicating the strong and statistically significant effect of cloze on noun-N400s4,6. The effect of cloze on article-N400s, if existent, must be very small to evade detection given our expansive approach. Whether such an effect would constitute convincing evidence for routine phonological pre-activation as assumed in theories of language comprehension3 can be questioned, but, more generally, such an effect cannot be meaningfully studied in typical small-scale studies. Consequently, current theoretical positions may be based on potentially unreliable findings and require revision. In particular, the strong prediction view that claims that pre-activation routinely occurs across all - including phonological - levels3, can no longer be viewed as having strong empirical support.\n\nOur results do not constitute evidence against prediction in general. We note a lack of convincing evidence specifically for phonological pre-activation, which would have to be measured before a noun appears and unobscured by processes instigated by the noun itself.\n\nHowever, our results neither support nor necessarily exclude phonological pre-activation. Unlike gender-marked articles9 (e.g., in Dutch or Spanish) that agree with nouns irrespective of intervening words, English a/an articles index the subsequent word, which is not always a noun. Maybe our participants did not use mismatching articles to disconfirm predicted nouns, possibly because it was not a viable strategy (American and British English corpus data show a mere 33% chance that a noun follows such articles). Perhaps a revision of the predicted meaning is required to trigger differential ERPs.\n\nDeLong et al. recently described filler-sentences in their experiment10, cf. 7, which were omitted from their original report, and were neither provided nor mentioned to us upon our request for their stimuli. DeLong used the existence of these filler-sentences to dismiss an alternative explanation of their results, namely that an unusual experimental context wherein every sentence contains an article-noun combination leads participants to strategically predict upcoming nouns. Importantly, we failed to replicate their article-effects despite an experimental context that could inadvertently encourage strategic prediction. Therefore, the difference between their experiment and ours cannot explain the different results, and may even strengthen our conclusions.\n\nIn sum, our findings do not support a strong prediction view involving routine and probabilistic pre-activation of phonological word form based on preceding context.\n\nMoreover, our results further highlight the importance of direct replication, large sample size studies, transparent reporting and of pre-registration to advance reproducibility and replicability in the neurosciences.

neuroscience↗

bioRxiv2017

Clinically Useful Brain Imaging for Neuropsychiatry: How Can We Get There?

Despite decades of research, visions of transforming neuropsychiatry through the development of brain imaging-based growth charts or lab tests have remained out of reach. In recent years, there is renewed enthusiasm about the prospect of achieving clinically useful tools capable of aiding the diagnosis and management of neuropsychiatric disorders. The present work explores the basis for this enthusiasm. We assert that there is no single advance that currently has the potential to drive the field of clinical brain imaging forward. Instead, there has been a constellation of advances that, if combined, could lead to the identification of objective brain imaging-based markers of illness. In particular, we focus on advances that are helping to: 1) elucidate the research agenda for biological psychiatry (e.g., neuroscience focus, precision medicine), 2) shift research models for clinical brain imaging (e.g., big data exploration, standardization), 3) break down research silos (e.g., open science, calls for reproducibility and transparency), and 4) improve imaging technologies and methods. While an arduous road remains ahead, these advances are repositioning the brain imaging community for long-term success.

neuroscience↗

bioRxiv2017

Dynamical networks: finding, measuring, and tracking neural population activity using network theory

Systems neuroscience is in a head-long rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons comes the inescapable problems of visualising, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualise and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analysing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.

neuroscience↗

bioRxiv2017

Inferring hidden structure in multilayered neural circuits

A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we develop a computational framework to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. For example, with our regularization framework, we can learn the STA and STC using 5 and 10 minutes of data, respectively, at a level of accuracy that otherwise requires 40 minutes of data without regularization. We apply our framework to retinal ganglion cell processing, learning cascaded linear-nonlinear (LN-LN) models of retinal circuitry, consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits had consistently high thresholds, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model's parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.\n\nAuthor SummaryComputation in neural circuits arises from the cascaded processing of inputs through multiple cell layers. Each of these cell layers performs operations such as filtering and thresholding in order to shape a circuits output. It remains a challenge to describe both the computations and the mechanisms that mediate them given limited data recorded from a neural circuit. A standard approach to describing circuit computation involves building quantitative encoding models that predict the circuit response given its input, but these often fail to map in an interpretable way onto mechanisms within the circuit. In this work, we build two layer linear-nonlinear cascade models (LN-LN) in order to describe how the retinal output is shaped by nonlinear mechanisms in the inner retina. We find that these LN-LN models, fit to ganglion cell recordings alone, identify filters and nonlinearities that are readily mapped onto individual circuit components inside the retina, namely bipolar cells and the bipolar-to-ganglion cell synaptic threshold. This work demonstrates how combining simple prior knowledge of circuit properties with partial experimental recordings of a neural circuits output can yield interpretable models of the entire circuit computation, including parts of the circuit that are hidden or not directly observed in neural recordings.

neuroscience↗

bioRxiv2017

Understanding Neural Circuit Development Through Theory And Models

How are neural circuits organized and tuned to achieve stable function and produce robust behavior? The organization process begins early in development and involves a diversity of mechanisms unique to this period. We summarize recent progress in theoretical neuroscience that has substantially contributed to our understanding of development at the single neuron, synaptic and network level. We go beyond classical models of topographic map formation, and focus on the generation of complex spatiotemporal activity patterns, their role in refinements of particular circuit features, and the emergence of functional computations. Aided by the development of novel quantitative methods for data analysis, theoretical and computational models have enabled us to test the adequacy of specific assumptions, explain experimental data and propose testable hypotheses. With the accumulation of larger data sets, theory and models will likely play an even more important role in understanding the development of neural circuits.

neuroscience↗

bioRxiv2017

The 100 Euro Lab: A 3-D Printable Open Source Platform For Fluorescence Microscopy, Optogenetics And Accurate Temperature Control During Behaviour Of Zebrafish, Drosophila And C. elegans

SUMMARY SUMMARY INTRO RESULTS DISCUSSION CONCLUSION METHODS AUTHOR CONTRIBUTION SUPPLEMENTARY FIGURE 1 -... SUPPLEMENTARY TABLE 1 -... SUPPLEMENTARY ASSEMBLY AND USER... SUPPLEMENTARY VIDEOS REFERENCES Small, genetically tractable species such as larval zebrafish, Drosophila or C. elegans have become key model organisms in modern neuroscience. In addition to their low maintenance costs and easy sharing of strains across labs, one key appeal is the possibility to monitor single or groups of animals in a behavioural arena while controlling the activity of select neurons using optogenetic or thermogenetic tools. However, the purchase of a commercial solution for these types of ...

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bioRxiv2017

Concurrent tACS-fMRI Reveals Causal Influence Of Power Synchronized Neural Activity On Resting State fMRI Connectivity

Resting state fMRI (rs-fMRI) is commonly used to study the brains intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSN). It has been hypothesized that slow rs-fMRI oscillations (<0.1 Hz) are driven by underlying electrophysiological rhythms that typically occur at much faster timescales (>5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices.\n\nThe two driving tACS signals were tailored to the individuals alpha rhythm (8-12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either alpha oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS).\n\nPower-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared to phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brains intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity.\n\nSignificance StatementResting state fMRI has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes.\n\nIt was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to resting state networks derived from rs-fMRI. Here we take a novel approach to address this problem and establish a causal link between the power fluctuations of electrophysiological signals and rs-fMRI via a new neuromodulation paradigm, which exploits these power-synchronization mechanisms.\n\nThese novel mechanistic insights bridge different scientific domains and are of broad interest to researchers in the fields of Medical Imaging, Neuroscience, Physiology and Psychology.

neuroscience↗