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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 127 records · Page 7Linked to original sources

bioRxiv2016

Possible existence of optical communication channels in the brain

Given that many fundamental questions in neuroscience are still open, it seems pertinent to explore whether the brain might use other physical modalities than the ones that have been discovered so far. In particular it is well established that neurons can emit photons, which prompts the question whether these biophotons could serve as signals between neurons, in addition to the well-known electro-chemical signals. For such communication to be targeted, the photons would need to travel in waveguides. Here we show, based on detailed theoretical modeling, that myelinated axons could serve as photonic waveguides, taking into account realistic optical imperfections. We propose experiments, both in vivo and in vitro, to test our hypothesis. We discuss the implications of our results, including the question whether photons could mediate long-range quantum entanglement in the brain.

Neuroscience↗

bioRxiv2016

Three-dimensional definition of two prominent deep brain stimulation targets based on a multimodal high-definition MNI template

Three-dimensional atlases of subcortical brain structures are valuable tools to reference anatomy in neuroscience and neurology. In the special case of deep brain stimulation (DBS), the three most common targets are the subthalamic nucleus (STN), the internal part of the pallidum (GPi) and the ventral intermediate nucleus of the thalamus (VIM). With the help of atlases that define the position and shape of these target regions within a well-defined stereotactic space, their spatial relationship to implanted deep brain stimulation (DBS) electrodes may be determined.\n\nHere we present a composite atlas based on manual segmentations of a multi-modal high-resolution MNI template series, histology and structural connectivity. To attain exact congruence to the template anatomy, key structures were defined using all four modalities of the template simultaneously. In a first step tissue probability maps were defined based on the multimodal intensity profile of each structure. These observer-independent probability maps provided an excellent basis for the subsequent manual segmentation particularly when defining the outline of the target regions.\n\nSecond, the key structures were used as an anchor point to coregister a histology based atlas into standard space. Finally, a sub-segmentation of the subthalamic nucleus into three functional zones was estimated based on structural connectivity. The resulting composite atlas uses the spatial information of the MNI template for DBS key structures that are visible on the template itself. For remaining structures, it relies on histology or structural connectivity. In this way the final atlas combines the anatomical detail of a histology based atlas with the spatial accuracy of key structures in relationship to the template anatomy. Thus, the atlas provides an ideal tool for the analysis of DBS electrode placement.\n\nHighlightsO_LIComposite subcortical atlas based on a multimodal, high definition MNI template series, histology and tractography\nC_LIO_LIHigh definition atlas of DBS targets exactly matching MNI 152 NLIN 2009b space\nC_LIO_LIMultimodal subcortical segmentation algorithm applied to MNI template\nC_LI

Neuroscience↗

bioRxiv2016

Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding predictive coding, executive control and a gamut of other brain functions. The underlying circuit mechanism, however, remains poorly understood and represents a major challenge in neuroscience. In the present work we tackled this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intra-laminar, inter-laminar, inter-areal and whole cortex) scales. The model was tested by reproducing, and shedding insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas: feedforward pathways are associated with enhanced gamma (30-70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low beta (8-15 Hz) oscillations. We found that in order for the model to account for the experimental observations, the feedback projection needs to predominantly target infragranular layers in a target area, which leads to a proposed circuit substrate for predictive coding. The model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of inter-areal signaling, as reported in recent monkey and human experiments. Taken together, this work highlights the importance of multi-scale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

Neuroscience↗

bioRxiv2016

Cytoarchitectonic similarity is a wiring principle of the human connectome

Understanding the wiring diagram of the human cerebral cortex is a fundamental challenge in neuroscience. Elemental aspects of its organization remain elusive. Here we examine which structural traits of cortical regions, particularly their cytoarchitecture and thickness, relate to the existence and strength of inter-regional connections. We use the architecture data from the classic work of von Economo and Koskinas and state-of-the-art diffusion-based connectivity data from the Human Connectome Project. Our results reveal a prominent role of the cytoarchitectonic similarity of supragranular layers for predicting the existence and strength of connections. In contrast, cortical thickness similarity was not related to the existence or strength of connections. These results are in line with findings for non-human mammalian cerebral cortices, suggesting overarching wiring principles of the mammalian cerebral cortex. The results invite hypotheses about evolutionary conserved neurobiological mechanisms that give rise to the relation of cytoarchitecture and connectivity in the human cerebral cortex.

Neuroscience↗

bioRxiv2016

iELVis: An open source MATLAB toolbox for localizing and visualizing human intracranial electrode data

BackgroundIntracranial electrical recordings (iEEG) and brain stimulation (iEBS) are invaluable human neuroscience methodologies. However, the value of such data is often unrealized as many laboratories lack tools for localizing electrodes relative to anatomy. To remedy this, we have developed a MATLAB toolbox for intracranial electrode localization and visualization, iELVis.\n\nNew MethodiELVis uses existing tools (BioImage Suite, FSL, and FreeSurfer) for preimplant magnetic resonance imaging (MRI) segmentation, neuroimaging coregistration, and manual identification of electrodes in postimplant neuroimaging.\n\nSubsequently, iELVis implements methods for correcting electrode locations for postimplant brain shift with millimeter-scale accuracy and provides interactive visualization on 3D surfaces or in 2D slices with optional functional neuroimaging overlays. iELVis also localizes electrodes relative to FreeSurfer-based atlases and can combine data across subjects via the FreeSurfer average brain.\n\nResultsIt takes 30-60 minutes of user time and 12-24 hours of computer time to localize and visualize electrodes from one brain. We demonstrate iELViss functionality by showing that three methods for mapping primary hand somatosensory cortex (iEEG, iEBS, and functional MRI) provide highly concordant results.\n\nComparison with Existing MethodsiELVis is the first public software for electrode localization that corrects for brain shift, maps electrodes to an average brain, and supports neuroimaging overlays. Moreover, its interactive visualizations are powerful and its tutorial material is extensive.\n\nConclusionsiELVis promises to speed the progress and enhance the robustness of intracranial electrode research. The software and extensive tutorial materials are freely available as part of the EpiSurg software project: https://github.com/episurg/episurg

Neuroscience↗

bioRxiv2016

Predicting functional neuroanatomical maps from fusing brain networks with genetic information

A central aim, from basic neuroscience to psychiatry, is to resolve how genes control brain circuitry and behavior. This is experimentally hard, since most brain functions and behaviors are controlled by multiple genes. In low throughput, one gene at a time, experiments, it is therefore difficult to delineate the neural circuitry through which these sets of genes express their behavioral effects. The increasing amount of publicly available brain and genetic data offers a rich source that could be mined to address this problem computationally. However, most computational approaches are not tailored to reflect functional synergies in brain circuitry accumulating within sets of genes. Here, we developed an algorithm that fuses gene expression and connectivity data with functional genetic meta data and exploits such cumulative effects to predict neuroanatomical maps for multigenic functions. These maps recapture known functional anatomical annotations from literature and functional MRI data. When applied to meta data from mouse QTLs and human neuropsychiatric databases, our method predicts functional maps underlying behavioral or psychiatric traits. We show that it is possible to predict functional neuroanatomy from mouse and human genetic meta data and provide a discovery tool for high throughput functional exploration of brain anatomy in silico.

Neuroscience↗

bioRxiv2016

A REPETITIVE MODULAR OSCILLATION UNDERLIES HUMAN BRAIN ELECTRIC ACTIVITY

The modular function j, central in the assessment of abstract mathematical problems, describes elliptic, intertwined trajectories that move in the planes of both real and complex numbers. Recent clues suggest that the j-function might display a physical counterpart, equipped with a quantifiable real component and a hidden imaginary one, currently undetectable by our senses and instruments. Here we evaluate whether the real part of the modular function can be spotted in the electric activity of the human brain. We assessed EEGs from five healthy males, eyes-closed and resting state, and superimposed the electric traces with the bidimensional curves predicted by the j-function. We found that the two trajectories matched in more than 85% of cases, independent from the subtending electric rhythm and the electrode location. Therefore, the real part of the j-functions peculiar wave is ubiquitously endowed all over normal EEGs paths. We discuss the implications of such correlation in neuroscience and neurology, highlighting how the j-function might stand for the one of the basic oscillations of the brain, and how the still unexplored imaginary part might underlie several physiological and pathological nervous features.\n\nSIGNIFICANCE STATEMENTOur results point towards the brain as ubiquitously equipped with j-functions oscillations, which movements take place on the plane of the complex numbers. It means that there must be, in brain electric activity, also a veiled complex part, which can be assessed with the help of imaginary numbers. The modular j-function provides further dimensions to the real numbers, in order to enlarge their predictive powers: it suggests the possible presence of hidden (functional or spatial) brain extra-dimensions. Furthermore, j-oscillations could be disrupted during pathologies, paving the way to novel approaches to central nervous systems diseases.

Neuroscience↗

bioRxiv2016

The visual white matter: The application of diffusion MRI and fiber tractography to vision science

Visual neuroscience has traditionally focused much of its attention on understanding the response properties of neurons along the visual pathways. This review focuses instead on the properties of the white matter connections between these neurons. Specifically, we provide an introduction to methods to study the human visual white matter using diffusion MRI (dMRI). This method allows us to measure the white matter connections in individual visual systems in vivo, allows us to trace long-range connections between different parts of the visual system, and to measure the biophysical properties of these connections. We explain the principles underlying dMRI measurements and the basics of modeling these data. We review a range of findings from recent studies on connections between different visual field maps, on the effects of visual impairment on the white matter, and on the properties underlying networks that process visual information that supports visual face recognition. Finally, we discuss a few promising directions for future studies. These include new methods for analysis of MRI data, open data-sets that are becoming available to study brain connectivity and white matter properties, and open-source software for the analysis of these data.

Neuroscience↗

bioRxiv2016

A Bayesian method for reducing bias in neural representational similarity analysis

1In neuroscience, the similarity matrix of neural activity patterns in response to different sensory stimuli or under different cognitive states reflects the structure of neural representational space. Existing methods derive point estimations of neural activity patterns from noisy neural imaging data, and the similarity is calculated from these point estimations. We show that this approach translates structured noise from estimated patterns into spurious bias structure in the resulting similarity matrix, which is especially severe when signal-to-noise ratio is low and experimental conditions cannot be fully randomized in a cognitive task. We propose an alternative Bayesian framework for computing representational similarity in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data, and directly estimate this covariance structure from imaging data while marginalizing over the unknown activity patterns. Converting the estimated covariance structure into a correlation matrix offers an unbiased estimate of neural representational similarity. Our method can also simultaneously estimate a signal-to-noise map that informs where the learned representational structure is supported more strongly, and the learned covariance matrix can be used as a structured prior to constrain Bayesian estimation of neural activity patterns.

Neuroscience↗

bioRxiv2016

Cortical correlates of the auditory frequency-following and onset responses: EEG and fMRI evidence

The frequency following response (FFR) is a measure of the brains periodic sound encoding. It is of increasing importance for studying the human auditory nervous system due to numerous associations with auditory cognition and dysfunction. Although the FFR is widely interpreted as originating from brainstem nuclei, a recent study using magnetoencephalography (MEG) suggested that there is also a right-lateralized contribution from the auditory cortex at the fundamental frequency (Coffey et al., 2016c). Our objectives in the present work were to validate and better localize this result using a completely different neuroimaging modality, and document the relationships between the FFR and the onset response, and cortical activity. Using a combination of electroencephalography, fMRI, and diffusion-weighted imaging, we show that activity in the right auditory cortex is related to individual differences in FFR-f0 strength, a finding that was replicated with two independent stimulus sets, with and without acoustic energy at the fundamental frequency. We demonstrate a dissociation between this FFR-f0-sensitive response in the right and an area in left auditory cortex that is sensitive to individual differences in the timing of initial response to sound onset. Relationships to timing and their lateralization are supported by parallels in the microstructure of the underlying white matter, implicating a mechanism involving neural conduction efficiency. These data confirm that the FFR has a cortical contribution, and suggest ways in which auditory neuroscience may be advanced by connecting early sound representation to measures of higher-level sound processing and cognitive function.\n\nSignificance StatementThe frequency following response (FFR) is an electroencephalograph signal that is used to explore how the auditory system encodes temporal regularities in sound, and which is related to differences in auditory function between individuals. It is known that brainstem nuclei contribute to the FFR, but recent findings of an additional cortical source are more controversial. Here, we use functional MRI to validate and extend the prediction from magnetoencephalography data of a right auditory cortex contribution to the FFR. We also demonstrate a dissociation between FFR-related cortical activity from that related to the latency of the response to sound onset, which is found in left auditory cortex. The findings provide a clearer picture of cortical processes for analysis of sound features.

Neuroscience↗

bioRxiv2016

A Whole-Brain Computational Modeling Approach to Explain the Alterations in Resting-State Functional Connectivity during Progression of Alzheimers Disease

Understanding the mechanisms behind Alzheimers disease (AD) is one of the most challenging problems in neuroscience. Recent efforts provided valuable insights on the genetic, biochemical and neuronal correlates of AD. The advances in structural and functional neuroimaging provided massive evidence for the AD related alterations in brain connectivity. In this study, we investigated the whole-brain resting state functional connectivity (FC) and variability in dynamic functional connectivity (v-FC) of the subjects with preclinical condition (PC), mild cognitive impairment (MCI) and Alzheimers disease (AD). The synchronization in the whole-brain was monotonously decreasing during the course of the progression. However, only in the AD group the reduced synchronization produced significant widespread effects in FC. Furthermore, we found elevated variability of FC in PC group, which was reversed in AD group. We proposed a whole-brain computational modeling approach to study the mechanisms behind these alterations. We estimated the effective connectivity (EC) between brain regions in the model to reproduce observed FC of each subject. First, we compared ECs between groups to identify the changes in underlying connectivity structure. We found that the significant EC changes were restricted to temporal lobe. Then, based on healthy control subjects we systematically manipulated the dynamics in the model to investigate its effect on FC. The model showed FC alterations similar to those observed in clinical groups providing a mechanistic explanation to AD progression.

Neuroscience↗

bioRxiv2016

Multidimensional stimulus-response correlation reveals supramodal neural responses to naturalistic stimuli

In neuroscience, stimulus-response relationships have traditionally been analyzed using either encoding or decoding models. Here we combined both techniques by decomposing neural activity into multiple components, each representing a portion of the stimulus. We tested this hybrid approach on encephalographic responses to auditory and audiovisual narratives identically experienced across subjects, as well as uniquely experienced video game play. The highest stimulus-response correlations (SRC) were detected for dynamic visual features. During narratives both auditory and visual SRC were modulated by attention and tracked correlations between subjects. During video game play, SRC was modulated by task difficulty and attentional state. Importantly, the strongest component extracted for visual and auditory features had nearly identical spatial distributions, suggesting that the predominant encephalographic response to naturalistic stimuli is supramodal. The variety of novel findings demonstrates the utility of measuring multidimensional stimulus-response correlations.

Neuroscience↗

bioRxiv2016

Change-point detection without needing to detect change-points?

Understanding how humans and animals learn about statistical regularities in stable and volatile environments, and utilize these regularities to make predictions and decisions, is an important problem in neuroscience and psychology. Using a Bayesian modeling framework, specifically the Dynamic Belief Model (DBM), it has previously been shown that humans tend to make the default assumption that environmental statistics undergo abrupt, unsignaled changes, even when environmental statistics are actually stable. Because exact Bayesian inference in this setting, an example of switching state space models, is computationally intensive, a number of approximately Bayesian and heuristic algorithms have been proposed to account for learning/prediction in the brain. Here, we examine a neurally plausible algorithm, a special case of leaky integration dynamics we denote as EXP (for exponential filtering), that is significantly simpler than all previously suggested algorithms except for the delta-learning rule, and which far outperforms the delta rule in approximating Bayesian prediction performance. We derive the theoretical relationship between DBM and EXP, and show that EXP gains computational efficiency by foregoing the representation of inferential uncertainty (as does the delta rule), but that it nevertheless achieves near-Bayesian performance due to its ability to incorporate a \"persistent prior\" influence unique to DBM and absent from the other algorithms. Furthermore, we show that EXP is comparable to DBM but better than all other models in reproducing human behavior in a visual search task, suggesting that human learning and prediction also incorporates an element of persistent prior. More broadly, our work demonstrates that when observations are information-poor, detecting changes or modulating the learning rate is both difficult and (thus) unnecessary for making Bayes-optimal predictions.

Neuroscience↗

bioRxiv2016

Multiresolution functional brain parcellation in an elderly population with no or mild cognitive impairment

We present group brain parcellations for clusters generated from resting-state functional magnetic resonance images for 99 cognitively normal elderly persons and 129 patients with mild cognitive impairment, pooled from four independent datasets. The brain parcellations have been registered to both symmetric and asymmetric MNI brain templates and generated using a method called bootstrap analysis of stable clusters (BASC, Bellec et al., 2010). Eight resolutions of clusters were selected using a data-driven method called MSTEPS (Bellec, 2013). We present two variants of these parcellations. One variant contains bihemisphereic parcels (4, 6, 12, 22, 33, 65, 111, and 208 total parcels across eight resolutions). The second variant contains spatially connected regions of interest (ROIs) that span only one hemisphere (10, 17, 30, 51, 77, 199, and 322 total ROIs across eight resolutions). We also present maps illustrating functional connectivity differences between patients and controls for four regions of interest (superior medial frontal cortex, dorsomedial prefrontal cortex, striatum, middle temporal lobe). The brain parcels and associated statistical maps have been publicly released as 3D volumes, available in .mnc and .nii file formats on figshare (http://dx.doi.org/10.6084/m9.figshare.1480461) and on Neurovault (http://neurovault.org/collections/1003/). This dataset was generated as part of the following study: Tam A, Dansereau C, Badhwar A, Orban P, Belleville S, Chertkow H, Dagher A, Hanganu A, Monchi O, Rosa-Neto P, Shmuel A, Wang S, Breitner J, Bellec P for the Alzheimers Disease Neuroimaging Initiative (2015) Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies. Front. Aging Neurosci. 7:242. doi: 10.3389/fnagi.2015.00242 Finally, the code used to generate this dataset is available on Github (https://github.com/SIMEXP/mcinet).\n\n\nO_TABLECAPTIONSpecifications Table\n\nC_TABLECAPTION[Table 1]

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bioRxiv2016

I TRIED A BUNCH OF THINGS: THE DANGERS OF UNEXPECTED OVERFITTING IN CLASSIFICATION

Machine learning is a powerful set of techniques that has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, and MEG data. With these powerful techniques comes the danger of overfitting of hyper-parameters which can render results invalid, and cause a failure to generalize beyond the data set. We refer to this problem as over-hyping and show that it is pernicious despite commonly used precautions. In particular, over-hyping occurs when an analysis is run repeatedly with slightly different analysis parameters and one set of results is selected based on the analysis. When this is done, the resulting method is unlikely to generalize to a new dataset, rendering it a partially, or perhaps even completely spurious result that will not be valid outside of the data used in the original analysis. While it is commonly assumed that cross-validation is an effective protection against such spurious results generated through overfitting or overhyping, this is not actually true. In this article, we show that both one-shot and iterative optimization of an analysis are prone to over-hyping, despite the use of cross-validation. We demonstrate that non-generalizable results can be obtained even on non-informative (i.e. random) data by modifying hyper-parameters in seemingly innocuous ways. We recommend a number of techniques for limiting over-hyping, such as lock-boxes, blind analyses, pre-registrations, and nested cross-validation. These techniques, are common in other fields that use machine learning, including computer science and physics. Adopting similar safeguards is critical for ensuring the robustness of machine-learning techniques in the neurosciences.

Neuroscience↗

bioRxiv2016

Linking structure and activity in nonlinear spiking networks

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities--including those of different cell types--combine with connectivity to shape population activity and function.

neuroscience↗

bioRxiv2016

SimTracker tool and code template to design, manage and analyze neural network model simulations in parallel NEURON

Advances in technical computing enable larger and more detailed neural models that can incorporate ever more of the rapidly expanding body of quantitative neuroscience data. In principle, such complex network models that are strongly constrained by experimental data could direct experimental research and provide novel insights into experimental observations. However, as network models grow in complexity and scale, the necessary tasks of development and organization become unwieldy. Further, the models risk becoming inaccessible to experimentalists and other modelers, and their results may then be seen as less relevant to experimental work. To address these obstacles, we developed a tool for managing simulations called SimTracker. It supports users at each step of the modeling process, including execution of large scale parallel models on supercomputers. SimTracker is suitable for users with a range of modeling experience. SimTracker can be a valuable modeling resource that promotes iterative progress between experiment and model.

neuroscience↗

bioRxiv2016

The demise of the synapse as the locus of memory: A looming paradigm shift?

Synaptic plasticity is widely considered to be the neurobiological basis of learning and memory by neuroscientists and researchers in adjacent fields, though diverging opinions are increasingly being recognised. From the perspective of what we might call \"classical cognitive science\" it has always been understood that the mind/brain is to be considered a computational-representational system. Proponents of the information-processing approach to cognitive science have long been critical of connectionist or network approaches to (neuro-)cognitive architecture, pointing to the shortcomings of the associative psychology that underlies Hebbian learning as well as to the fact that synapses are practically unfit to implement symbols. Recent work on memory has been adding fuel to the fire and current findings in neuroscience now provide first tentative neurobiological evidence for the cognitive scientists doubts about the synapse as the (sole) locus of memory in the brain. This paper briefly considers the history and appeal of synaptic plasticity as a memory mechanism, followed by a summary of the cognitive scientists objections regarding these assertions. Next, a variety of tentative neuroscientific evidence that appears to substantiate questioning the idea of the synapse as the locus of memory is presented. On this basis, a novel way of thinking about the role of synaptic plasticity in learning and memory is proposed.

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