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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

Language-selective brain regions track linguistic input more closely than domain-general regions

Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by non-linguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, cingulate, and insular regions. Activity in this \"Multiple Demand (MD)\" network scales with comprehension difficulty, but also with cognitive effort across a wide range of non-linguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Critically, given that each network is sensitive to some linguistic features, prior research has assumed - implicitly or explicitly - that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time-courses in each network across different people listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks.\n\nSignificance StatementLanguage comprehension recruits both language-specific mechanisms and domain-general mechanisms that are engaged in many cognitive processes. In the human cortex, language-selective mechanisms are implemented in the left-lateralized \"core language network\", whereas domain-general mechanisms are implemented in the bilateral \"Multiple Demand (MD)\" network. Here, we report the first direct comparison of the respective contributions of these networks to naturalistic story comprehension. Using a novel combination of neuroimaging approaches we find that MD regions track stories less closely than language regions. This finding constrains the possible contributions of the MD network to comprehension, contrasts with accounts positing that this network has continuous access to linguistic input, and suggests a new typology of comprehension processes based on their extent of input tracking.

Neuroscience↗

bioRxiv2016

A comprehensive evaluation of multiband-accelerated sequences and their effects on statistical outcome measures in fMRI.

Accelerated functional Magnetic Resonance Imaging (fMRI) with multiband sequences is now relatively widespread. These sequences can be used to dramatically reduce the repetition time (TR) and produce a time-series sampled at a higher temporal resolution. We tested the effects of higher temporal resolutions for fMRI on statistical outcome measures in a comprehensive manner on two different MRI scanner platforms. Experiment 1 tested a range of acceleration factors (1-6) against a standard EPI sequence on a single composite task that maps a number of basic sensory, motor, and cognitive networks. Experiment 2 compared the standard sequence with acceleration factors of 2 and 3 on both resting-state and two task paradigms (an N-back task, and faces/places task), with a number of different analysis approaches. Results from experiment 1 showed modest but relatively inconsistent effects of the higher sampling rate on statistical outcome measures. Experiment 2 showed strong benefits of the multiband sequences on results derived from resting-state data, but more varied effects on results from the task paradigms. Notably, the multiband sequences were superior when Multi-Voxel Pattern Analysis was used to interrogate the faces/places data, but showed less benefit in conventional General Linear Model analyses of the same data. In general, ROI-derived measures of statistical effects benefitted relatively little from higher sampling resolution, with decrements even seen in one task (N-back). Across both experiments, results from the two scanner platforms were broadly comparable. The statistical benefits of high temporal resolution fMRI with multiband sequences may therefore depend on a number of factors, including the nature of the investigation (resting-state vs. task-based), the experimental design, the particular statistical outcome measure, and the type of analysis used. Higher sampling rates in fMRI are not a panacea, and it is recommended that researchers use multiband acquisition sequences conservatively.

Neuroscience↗

bioRxiv2016

Neural correlates of early sound encoding and their relationship to speech in noise perception

Speech-in-noise (SIN) perception is a complex cognitive skill that affects social, vocational, and educational activities. Poor SIN ability particularly affects young and elderly populations, yet varies considerably even among healthy young adults with normal hearing. Although SIN skills are known to be influenced by top-down processes that can selectively enhance lower-level sound representations, the complementary role and of feed-forward mechanisms and their relationship to musical training is poorly understood. Using a paradigm that eliminates the main top-down factors that have been implicated in SIN performance, we aimed to better understand how robust encoding of periodicity in the auditory system (as measured by the frequency-following response) contributes to SIN perception. Using magnetoencephalograpy, we found that the strength of encoding at the fundamental frequency in the brainstem, thalamus, and cortex is correlated with SIN accuracy, as was the amplitude of the slower cortical P2 wave, and these enhancements were related to the extent and timing of musicianship. These results are consistent with the hypothesis that basic feed-forward sound encoding affects SIN perception by providing better information to later processing stages, and that modifying this process may be one mechanism through which musical training might enhance the auditory networks that subserve both musical and language functions.\n\nHighlights- Enhancements in periodic sound encoding are correlated with speech-in-noise ability\n- This effect is observed in the absence of contextual cues and task demands\n- Better encoding is observed throughout the auditory system and is right-lateralized\n- Stronger encoding is related to stronger subsequent secondary auditory cortex activity\n- Musicianship is related to both speech-in-noise perception and enhanced MEG signals

Neuroscience↗

bioRxiv2016

Frontal cortex function derives from hierarchical predictive coding

The frontal lobes are essential for human volition and goal-directed behavior, yet their function remains unclear. While various models have highlighted working memory, reinforcement learning, and cognitive control as key functions, a single framework for interpreting the range of effects observed in prefrontal cortex has yet to emerge. Here we show that a simple computational motif based on predictive coding can be stacked hierarchically to learn and perform arbitrarily complex goal-directed behavior. The resulting Hierarchical Error Representation (HER) model simulates a wide array of findings from fMRI, ERP, single-units, and neuropsychological studies of both lateral and medial prefrontal cortex. Additionally, the model compares favorably with current machine learning approaches, learning more rapidly and with comparable performance, while self-organizing representations into efficient hierarchical groups and managing working memory storage. By reconceptualizing lateral prefrontal activity as anticipating prediction errors, the HER model provides a novel unifying account of prefrontal cortex function with broad implications both for understanding the frontal cortex and building more powerful machine learning applications.

Neuroscience↗

bioRxiv2016

Reading positional codes with fMRI: Problems and solutions

Neural mechanisms which bind items into sequences have been investigated in a large body of research in animal neurophysiology and human neuroimaging. However, a major problem in interpreting this data arises from a fact that several unrelated processes, such as memory load, sensory adaptation, and reward expectation, also change in a consistent manner as the sequence unfolds. In this paper we show that the problem of extracting neural data about the structure of a sequence is especially acute for fMRI, which is almost exclusively the modality used in human experiments. We show that such fMRI results must be treated with caution and in many cases the assumed neural representation might actually reflect unrelated processes.

Neuroscience↗

bioRxiv2016

How prior knowledge prepares perception: Prestimulus oscillations carry perceptual expectations and influence early visual responses.

Perceptual experience results from a complex interplay of bottom-up input and prior knowledge about the world, yet the extent to which knowledge affects perception, the neural mechanisms underlying these effects, and the stages of processing at which these two sources of information converge, are still unclear. In several experiments we show that language, in the form of verbal cues, both aids recognition of ambiguous \"Mooney\" images and improves objective visual discrimination performance in a match/non-match task. We then used electroencephalography (EEG) to better understand the mechanisms of this effect. The improved discrimination of images previously labeled was accompanied by a larger occipital-parietal P1 evoked response to the meaningful versus meaningless target stimuli. Time-frequency analysis of the interval between the cue and the target stimulus revealed increases in the power of posterior alpha-band (8-14 Hz) oscillations when the meaning of the stimuli to be compared was trained. The magnitude of the pre-target alpha difference and the P1 amplitude difference were positively correlated across individuals. These results suggest that prior knowledge prepares the brain for upcoming perception via the modulation of alpha-band oscillations, and that this preparatory state influences early (~120 ms) stages of visual processing.

Neuroscience↗

bioRxiv2016

Neuroprotective strategies for NMDAR-mediated excitotoxicity in Huntington’s Disease

BACKGROUNDHuntingtons Disease (HD) is an autosomal dominant neurodegenerative disease causing severe neurodegeneration of the striatum as well as marked cognitive and motor disabilities. Excitotoxicity, caused by overstimulation of NMDA receptors (NMDARs) has been shown to have a key role in the neuropathogenesis of HD, suggesting that targeting NMDAR-dependent signaling may be an effective clinical approach for HD. However, broad NMDAR antagonists are generally poor therapeutics in clinical practice. It has been suggested that GluN2A-containing, synaptically located NMDARs activate cell survival signaling pathways, while GluN2B-containing, primarily extrasynaptic NMDARs trigger cell death signaling. A better approach to development of effective therapeutics for HD may be to target, specifically, the cell-death specific pathways associated with extrasynaptic GluN2B NMDAR activation, while maintaining or potentiating the cell-survival activity of GluN2A-NMDARs.\n\nOBJECTIVEThis review outlines the role of NMDAR-mediated excitotoxicity in HD and overviews current efforts to develop better therapeutics for HD where NMDAR excitotoxicity is the target.\n\nMETHODSA systematic review process was conducted using the PubMed search engine focusing on research conducted in the past 5-10 years. 250 articles were consulted for the review, with key search terms including \"Huntingtons Disease\", \"excitotoxicity\", \"NMDAR\" and \"therapeutics\".\n\nRESULTSA wide range of NMDAR excitotoxicity-based targets for HD were identified and reviewed, including targeting NMDARs directly by blocking GluN2B, extrasynaptic NMDARs and/or potentiating GluN2A, synaptic NMDARs, targeting glutamate release or uptake, or targeting specific downstream cell-death signaling of NMDARs.\n\nCONCLUSIONThe current review identifies NMDAR-mediated excitotoxicity as a key player in HD pathogenesis and points to various excitotoxicity-focused targets as potential future preventative therapeutics for HD.

Neuroscience↗

bioRxiv2016

Time to wake up: Studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal

Functional magnetic resonance imaging (fMRI) has allowed the noninvasive study of task-based and resting-state brain dynamics in humans by inferring neural activity from blood-oxygenation-level dependent (BOLD) signal changes. An accurate interpretation of the hemodynamic changes that underlie fMRI signals depends on the understanding of the quantitative relationship between changes in neural activity and changes in cerebral blood flow, oxygenation and volume. While there has been extensive study of neurovascular coupling in anesthetized animal models, anesthesia causes large disruptions of brain metabolism, neural responsiveness and cardiovascular function. Here, we review work showing that neurovascular coupling and brain circuit function in the awake animal are profoundly different from those in the anesthetized state. We argue that the time is right to study neurovascular coupling and brain circuit function in the awake animal to bridge the physiological mechanisms that underlie animal and human neuroimaging signals, and to interpret them in light of underlying neural mechanisms. Lastly, we discuss recent experimental innovations that have enabled the study of neurovascular coupling and brain-wide circuit function in un-anesthetized and behaving animal models.

Neuroscience↗

bioRxiv2016

Detecting large-scale networks in the human brain using high-density electroencephalography

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can permit investigating fast dynamics of cortical electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from showing brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and eLORETA source localization, together with ICA for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the cortex as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research.

Neuroscience↗

bioRxiv2016

Fixing the stimulus-as-fixed-effect fallacy in task fMRI

Most fMRI experiments record the brains responses to samples of stimulus materials (e.g., faces or words). Yet the statistical modeling approaches used in fMRI research universally fail to model stimulus variability in a manner that affords population generalization--meaning that researchers conclusions technically apply only to the precise stimuli used in each study, and cannot be generalized to new stimuli. A direct consequence of this stimulus-as-fixed-effect fallacy is that the majority of published fMRI studies have likely overstated the strength of the statistical evidence they report. Here we develop a Bayesian mixed model (the random stimulus model; RSM) that addresses this problem, and apply it to a range of fMRI datasets. Results demonstrate considerable inflation (50 - 200 % in most of the studied datasets) of test statistics obtained from standard \"summary statistics\"-based approaches relative to the corresponding RSM models. We demonstrate how RSMs can be used to improve parameter estimates, properly control false positive rates, and test novel research hypotheses about stimulus-level variability in human brain responses.

Neuroscience↗

bioRxiv2016

Traumatic brain injury causes retention of long introns in metabolic genes via regulation of intronic Histone 3 lysine 36 methylation levels in the sub-acute phase of injury

Traumatic brain injury (TBI) can cause persistent pathological alteration of neurons. This may lead to cognitive dysfunctions, depression, and even increased susceptibility to life threatening diseases, such as epilepsy and Alzheimers Disease. To investigate the underlying genetic and molecular basis of TBI, Wasserman and colleagues developed an inexpensive and reproducible model for simulating TBI in Drosophila melanogaster (Fruit Fly). Using a modified version of this high impact trauma (HIT) device, we subjected w1118 fruit flies to mild closed head trauma. To determine the transcriptomic changes that contribute to survival post TBI, we collected fly heads from the survivors at 2 time points; 4 hours and 24 hours post-trauma. Mild TBI had limited impact on the steady state RNA levels but showed large perturbations in alternative splicing (AS) 24 hours post-trauma. Classification of these AS changes showed selective retention (RI) of long introns (>81bps), with a mean size of ~3000bps. Some of these RI genes also showed a significant reduction in transcript abundance and were specifically enriched in genes involved in mitochondrial metabolism. The RI are enriched in ACACACA motifs known to bind to Smooth (SM), an hnRNPL class of splicing factor. Mutating SM (sm4/Df) resulted in reversal of RI observed 24 hours post-trauma, and in some cases, elimination of basal levels of RI in long introns. This observation suggests that SM is critical regulator of RI and that this process is enhanced by TBI. Interestingly, chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) for histone 3 lysine 36 trimethylation (H3K36me3) revealed increased levels of this histone modification in retained introns post-trauma. Consistent with this observation, mutations in lysine specific demethylase 4A (KDM4A), which de-methylate H3K36me3, increased RI in many of the same long introns affected by TBI. Additionally, higher H3K36me3 levels are observed around intronic SM-binding motifs post-trauma, suggesting interaction between H3K36me3 and SM binding to intronic splicing suppressor sites might be responsible for increasing RI of metabolic genes as a novel mechanism to improve survival after TBI.

Neuroscience↗

bioRxiv2016

Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

Although atypical social behaviour remains a key characterisation of ASD, the presence of sensory and perceptual abnormalities has been given a more central role in recent classification changes. An understanding of the origins of such aberrations could thus prove a fruitful focus for ASD research. Early neurocognitive models of ASD suggested that the study of high frequency activity in the brain as a measure of cortical connectivity might provide the key to understanding the neural correlates of sensory and perceptual deviations in ASD. As our review shows, the findings from subsequent research have been inconsistent, with a lack of agreement about the nature of any high frequency disturbances in ASD brains. Based on the application of new techniques using more sophisticated measures of brain synchronisation, direction of information flow, and invoking the coupling between high and low frequency bands, we propose a framework which could reconcile apparently conflicting findings in this area and would be consistent both with emerging neurocognitive models of autism and with the heterogeneity of the condition.\n\nHighlightsO_LISensory and perceptual aberrations are becoming a core feature of the ASD symptom prolife.\nC_LIO_LIBrain oscillations and functional connectivity are consistently affected in ASD.\nC_LIO_LIRelationships (coupling) between high and low frequencies are also deficient.\nC_LIO_LINovel framework proposes the ASD brain is marked by local dysregulation and reduced top-down connectivity\nC_LIO_LIThe ASD brains ability to predict stimuli and events in the environment may be affected\nC_LIO_LIThis may underlie perceptual sensitives and cascade into social processing deficits in ASD\nC_LI

Neuroscience↗

bioRxiv2016

Mechanisms for Stable Bump Activity, Winner-Take-All and Group Selection in Neuronal Spiking Networks

A major function of central nervous systems is to discriminate different categories or types of sensory input. Neuronal networks accomplish such tasks by learning different sensory maps at several stages of neural hierarchy, such that different neurons fire selectively to reflect different internal or external patterns and states. The exact mechanisms of such map formation processes in the brain are not totally understood. Here we study the mechanism by which a simple recurrent/reentrant neuronal network accomplish group selection and discrimination to different inputs in order to generate sensory maps. We describe the conditions and mechanism of transition from a rhythmic epileptic state (in which all neurons fire synchronized and indiscriminately to any input) to a winner-take-all state in which only a subset of neurons fire for a specific input. We prove an analytic condition under which a stable bump solution and a soft winner-take-all state can emerge from the local recurrent excitation-inhibition interactions in a three-layer spiking network with distinct excitatory and inhibitory populations, and demonstrate the importance of surround inhibitory connection topology on the stability of dynamic patterns in spiking neural network.

Neuroscience↗

bioRxiv2016

Variational Bayesian parameter estimation techniques for the general linear model

Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation techniques are rarely covered in introductory statistical texts. Because of the widespread practical use of VB, VML, ReML, and ML in the neuroimaging community, we reasoned that. a theoretical treatment of their relationships and their application in a basic modelling scenario may be helpful for both neuroimaging novices and practitioners alike. In this technical study, we thus revisit the conceptual and formal underpinnings of VB, VML, ReML, and ML and provide a detailed account of their mathematical relationships and implementational details. We further apply VB, VML, ReML, and ML to the general linear model (GLM) with non-spherical error covariance as commonly encountered in the first-level analysis of fMRI data. To this end, we explicitly derive the corresponding free energy objective functions and ensuing iterative algorithms. Finally, in the applied part of our study, we evaluate the parameter and model recovery properties of VB, VML, ReML, and ML, first in an exemplary setting and then in the analysis of experimental fMRI data acquired from a single participant under visual stimulation.

Neuroscience↗

bioRxiv2016

Synaptic vesicle glycoprotein 2C (SV2C) modulates dopamine release and is disrupted in Parkinsons disease

The synaptic vesicle glycoprotein 2 (SV2) family of proteins are involved in synaptic function throughout the brain. The ubiquitously expressed SV2A has been widely implicated in epilepsy, though SV2C with its restricted basal ganglia distribution has no known function. SV2C is emerging as a potentially relevant protein in Parkinsons disease, as it is a genetic modifier of nicotine neuroprotection and sensitivity to L-DOPA. Here we identify SV2C as a mediator of dopamine homeostasis and report that disrupted expression of SV2C within the basal ganglia is a pathological feature of Parkinsons disease (PD). Genetic deletion of SV2C leads to reduced dopamine release in the dorsal striatum as measured by fast-scan cyclic voltammetry, reduced striatal dopamine content, disrupted alpha-synuclein expression, deficits in motor function, and alterations in neurochemical effects of nicotine. Further, SV2C expression is dramatically altered in postmortem brain tissue from PD cases, but not in Alzheimers disease, progressive supranuclear palsy or multiple system atrophy. This disruption was paralleled in mice overexpressing mutated -synuclein. These data establish SV2C as a novel mediator of dopamine neuron function and suggest that SV2C disruption is a unique feature of PD that likely contributes to dopaminergic dysfunction

Neuroscience↗

bioRxiv2016

A shotgun approach to identify mechanical nociception genes.

The molecular mechanisms of sensing noxious mechanical force by nociceptive sensory neurons remain poorly understood. Traditional methods for probing mechanical nociception behavioral responses are labor intensive and involve the testing of one animal at a time. This time consuming process of behavioral testing has largely precluded large scale analyses. Indeed, large scale genetic screens that have been performed thus far have been largely restricted to the investigation of ion channel genes [1]. Here we describe a new behavioral assay for mechanical nociception in which tens of animals can be stimulated simultaneously. In this assay, third instar larvae of the genetically tractable organism Drosophila melanogaster are mechanically stimulated with tungsten particles that are fired from a gun. We have used the new assay to carry out a genetic screen in which we investigated the function of 231 nociceptor enriched genes with tissue-specific RNA interference. Targeting of 21 genes resulted in mechanically insensitive phenotypes and targeting of a single gene resulted in a hypersensitive mechanical nociception phenotype. Six of the identified genes were previously uncharacterized and these were named after famed Roman gladiators (Spartacus (CG14186), Commodus (CG1311), Flamma (CG10914), Crixus(CG6685), Spiculus (CG10932), and Verus (CG31324)).

Neuroscience↗

bioRxiv2016

Resting high frequency heart rate variability is not associated with the recognition of emotional facial expressions in healthy human adults.

This study explores whether the myelinated vagal connection between the heart and the brain is involved in emotion recognition. The Polyvagal theory postulates that the activity of the myelinated vagus nerve underlies socio-emotional skills. It has been proposed that the perception of emotions could be one of this skills dependent on heart-brain interactions. However, this assumption was differently supported by diverging results suggesting that it could be related to confounded factors. In the current study, we recorded the resting state vagal activity (reflected by High Frequency Heart Rate Variability, HF-HRV) of 77 (68 suitable for analysis) healthy human adults and measured their ability to identify dynamic emotional facial expressions. Results show that HF-HRV is not related to the recognition of emotional facial expressions in healthy human adults. We discuss this result in the frameworks of the polyvagal theory and the neurovisceral integration model.

Neuroscience↗

bioRxiv2016

When the Good Guy Becomes the Bad Boy: Social Information Modulates the Neural, Physiological and Subjective Responses to Emotional Facial Expressions

In the past decade, different studies have suggested that high-order factors could influence the perceptual processing of emotional stimuli. In this study, we aimed to evaluate the effect of congruent vs. incongruent social information (positive, negative or no information related to the character of the target) on subjective (perceived and felt valence and arousal), physiological (facial mimicry) as well as on neural (P100 and N170) responses to dynamic emotoional facial expressions (EFE) that varied from neutral to one of the six basic emotions. Across three studies, the results showed (1) reduced valence and arousal evaluation of EFE when associated with incongruent social information (Study 1), (2) increased electromyographical responses (Study 2) and significant modulation of P100 and N170 components (Study 3) when EFE were associated with social (positive and negative) information (vs. no information). These studies revealed that positive or negative social information reduced subjective responses to incongruent EFE and produces a similar neural and physiological boost of the early perceptual processing of EFE irrespective of their congruency. In conclusion, this study suggested that social context (positive or negative) enhances the necessity to be alert to any subsequent cues.

Neuroscience↗