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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Microcircuitry of Performance Monitoring

AcknowledgmentsThis work was supported by R01-MH55806, P30-EY08126, and by Robin and Richard Patton through the E. Bronson Ingram Chair in Neuroscience. We thank J. Easley M. Feurtado, M. Maddox, P. Middlebrooks, S. Motorny, J. Parker, M. Schall, C.R. Subraveti, A. Tomarken, and L. Toy for animal care and other technical assistance. We thank J. Brown, M. Cox, K. Dougherty, S. Errington, A. Maier, V. Stuphorn, A. Tomarken, and J. Westerberg for helpful discussions and comments on the manuscript.\n\nConflicts of interestNone\n\nCortical circuit mechanisms in medial frontal cortex enabling executive control are unknown. Hence, in monkeys performing a saccade countermanding task to earn larger or smaller fluid rewards, we sampled spiking and synaptic activity simultaneously across all layers of the supplementary eye field (SEF), an agranular cortical area contributing to performance monitoring in nonhuman primate and human studies. Laminar-specific synaptic currents with associated spike rate facilitation and suppression represented error production, reward gain or loss feedback, and reward delivery. The latency, polarity and magnitude of current and spike rate modulation were not predicted by the canonical cortical microcircuit. Pronounced synaptic currents in layer 2/3, which are modulated by loss magnitude, will contribute to the error-related negativity (ERN) and feedback-related negativity (FRN). These unprecedented findings reveal critical features of the cortical microcircuitry supporting performance monitoring and demonstrate that SEF can contribute to the error- and feedback-related negativity.\n\nSubject termscountermanding, stop signal task, goal selection, response inhibition, executive control, canonical cortical microcircuit, error-related negativity, reinforcement learning, reward prediction error

neuroscience↗
bioRxiv2017

Limitations of compensatory plasticity: the organization of the primary sensorimotor cortex in foot-using bilateral upper limb dysplasics

What forces direct brain organization and its plasticity? When a brain region is deprived of its input would this region reorganize based on compensation for the disability and experience, or would strong limitations of brain structure limit its plasticity? People born without hands activate their sensorimotor hand region while moving body parts used to compensate for this ability (e.g. their feet). This has been taken to suggest a neural organization based on functions, such as performing manual-like dexterous actions, rather than on body parts. Here we test the selectivity for functionally-compensatory body parts in the sensorimotor cortex of people born without hands. Despite clear compensatory foot use, the sensorimotor hand area in the dysplasic subjects showed preference for body parts whose cortical territory is close to the hand area, but which are not compensatorily used as effectors. This suggests that function-based organization, originally proposed for congenital blindness and deafness, does not apply to cases of the primary sensorimotor cortex deprivation in dysplasia. This is consistent with the idea that experience-independent functional specialization occurs at relatively high levels of representation. Indeed, increased and selective foot movement preference in the dysplasics was found in the association cortex, in the inferior parietal lobule. Furthermore, it stresses the roles of neuroanatomical constraints such as topographical proximity and connectivity in determining the functional development of brain regions. These findings reveal limitations to brain plasticity and to the role of experience in shaping the functional organization of the brain.\n\nSignificance StatementWhat determines the role of brain regions, and their plasticity when typical inputs or experience is not provided? To what extent can extreme compensatory use affect brain organization? We tested the functional reorganization of the primary sensorimotor cortex hand area in people born without hands, who use their feet for every-day tasks. We found that it is preferentially activated by close-by body-parts which cannot serve as effectors, and not by the feet. In contrast, foot-selective compensatory plasticity was found in the association cortex, in an area involved in tool use. This shows limitations of compensatory plasticity and experience in modifying brain organization of early topographical cortex, as compared to association cortices where function-based organization is the driving factor.\n\nClassificationBiological Sciences\\Neuroscience

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bioRxiv2017

Using spatiotemporal source separation to identify prominent features in multichannel data without sinusoidal filters

The number of simultaneously recorded electrodes in neuroscience is steadily increasing, providing new opportunities for understanding brain function, but also new challenges for appropriately dealing with the increase in dimensionality. Multivariate source-separation analysis methods have been particularly effective at improving signal-to-noise ratio while reducing the dimensionality of the data, and are widely used for cleaning, classifying, and source-localizing multichannel neural time series data. Most source-separation methods produce a spatial component (that is, a weighted combination of channels to produce one time series); here, this is extended to apply sourceseparation to a time series, with the idea of obtaining a weighted combination of successive time points, such that the weights are optimized to satisfy some criteria. This is achieved via a two-stage source-separation procedure, in which an optimal spatial filter is first constructed, and then its optimal temporal basis function is computed. This second stage is achieved with a time-delay-embedding matrix, in which additional rows of a matrix are created from time-delayed versions of existing rows. The optimal spatial and temporal weights can be obtained by solving a generalized eigendecomposition of covariance matrices. The method is demonstrated in simulated data and in an empirical EEG study on theta-band activity during response conflict. Spatiotemporal source separation has several advantages, including defining empirical filters without the need to apply sinusoidal narrowband filters.

neuroscience↗
bioRxiv2017

A simplified electronic circuit for combined single-cell stimulation and recording using loose cell-attached electrodes

While tight-seal patch clamp recordings have found wide use in neuroscience and in other fields, the requirement to replace the glass pipette after every attempted recording represents an impediment to high throughput studies such as searching for monosynaptically connected pairs of neurons. Loose cell-attached recording was introduced in 2000 to circumvent this problem since it enabled combined recording and stimulation of visually-identified neurons without necessitating a tight (gigaohm) seal. Since the stimulus voltages required to evoke action potentials through low resistance seals are beyond the capacity of most commercial amplifiers, Barbour and Isope introduced a variation of classic patch clamp amplifier circuit that is able to deliver stimulus voltages that are effective in triggering action potentials under the loose cell-attached patch clamp configuration. The present report presents the design and operation of a simpler amplifier that contains only two integrated circuits and is able to effectively stimulate and record action potentials in mitral cells in rodent olfactory bulb slices. The addition of an accessory analog gating circuit enables manual control of the stimulus voltage with pulse timing controlled by a digital output from a computer. This system may be useful in studies that require surveying many potential pairs of neurons for synaptic connections and for sampling and manipulating single-cell activity in in vivo electrophysiology experiments.

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bioRxiv2017

MatClassRSA: A Matlab toolbox for M/EEG classification and visualization of proximity matrices

MatClassRSA is a Matlab toolbox that performs M/EEG classification and produces various visualizations of the resulting confusion matrices. This toolbox is aimed toward cognitive neuroscience researchers who wish to apply classification-style analyses to repeated trials of data. The functionalities of the toolbox fall into three categories: (1) M/EEG classification, (2) computation of Representational Dissimilarity Matrices (RDMs) from confusion or other proximity matrices, and (3) clustering and tree visualizations of RDMs. MatClassRSA combines out-of-the-box functionality with a variety of customization options. Usage of the toolbox requires only a high-level understanding of machine learning and the ability to call Matlab functions from scripts.

neuroscience↗
bioRxiv2017

Reproducibility of importance extraction methods in neural network based fMRI classification

Recent advances in machine learning allow faster training, improved performance and increased interpretability of classification techniques. Consequently, their application in neuroscience is rapidly increasing. While classification approaches have proved useful in functional magnetic resonance imaging (fMRI) studies, there are concerns regarding extraction, reproducibility and visualization of brain regions that contribute most significantly to the classification. We addressed these issues using an fMRI classification scheme based on neural networks and compared a set of methods for extraction of category-related voxel importances in three simulated and two empirical datasets. The simulation data revealed that the proposed scheme successfully detects spatially distributed and overlapping activation patterns upon successful classification. Application of the proposed classification scheme to two previously published empirical fMRI datasets revealed robust importance maps that extensively overlap with univariate maps but also provide complementary information. We conclude that importance maps are superior to univariate approaches for both detection of overlapping patterns and patterns with weak univariate information.

neuroscience↗
bioRxiv2017

Neocortical microdissection at columnar and laminar resolution for molecular interrogation

The heterogeneous organization of the mammalian neocortex poses a challenge to elucidate the molecular mechanisms underlying its physiological processes. Although high-throughput molecular methods are increasingly deployed in neuroscience, their anatomical specificity is often lacking. Here we introduce a targeted microdissection technique that enables extraction of high-quality RNA and proteins at high anatomical resolution from acutely prepared brain slices. We exemplify its utility by isolating single cortical columns and laminae from the mouse primary somatosensory (barrel) cortex. Tissues can be isolated from living slices in minutes, and the extracted RNA and protein are of sufficient quantity and quality to be used for RNA-sequencing and mass spectrometry. This technique will help to increase the anatomical specificity of molecular studies of the neocortex, and the brain in general as it is applicable to any brain structure that can be identified using optical landmarks in living slices.

neuroscience↗
bioRxiv2017

Imbalanced amplification: A mechanism of amplification and suppression from local imbalance of excitation and inhibition in cortical circuits

Understanding the relationship between external stimuli and the spiking activity of cortical populations is a central problem in neuroscience. Dense recurrent connectivity in local cortical circuits can lead to counterintuitive response properties, raising the question of whether there are simple arithmetical rules for relating circuits connectivity structure to their response properties. One such arithmetic is provided by the mean field theory of balanced networks, which is derived in a limit where excitatory and inhibitory synaptic currents precisely balance on average. However, balanced network theory is not applicable to some biologically relevant connectivity structures. We show that cortical circuits with such structure are susceptible to an amplification mechanism arising when excitatory-inhibitory balance is broken at the level of local subpopulations, but maintained at a global level. This amplification, which can be quantified by a linear correction to the classical mean field theory of balanced networks, explains several response properties observed in cortical recordings.

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bioRxiv2017

Shared High Value Research Resources: The CamCAN Human Lifespan Neuroimaging Dataset Processed on the Open Science Grid

The CamCAN Lifespan Neuroimaging Dataset [1,2], Cambridge (UK) Centre for Ageing and Neuroscience, was acquired and processed beginning in December, 2016. The referee consensus solver deployed to the Open Science Grid [3,4] was used for this task. The dataset includes demographic and screening measures, a high-resolution MRI scan of the brain, and whole-head magnetoencephalographic (MEG) recordings during eyes closed rest (560 sec), a simple task (540 sec), and passive listening/viewing (140 sec). The data were collected from 619 neurologically normal individuals, ages 18-87. The processed results from the resting recordings are completed and available for download at http://stash.osgconnect.net/+krieger/. These constitute {approx}1.7 TBytes of data including the location within the brain (1 mm resolution), time stamp (1 msec resolution), and 80 msec time course for each of 3.7 billion validated neuroelectric events, i.e. mean 6.1 million events for each of the 619 participants.\n\nThe referee consensus solver provides high yield (mean 11,000 neuroelectric currents/sec; standard deviation (sd): 3500/sec) high confidence (p < 10-12 for each identified current) measures of the neuroelectric currents whose magnetic fields are detected in the MEG recordings. We describe the solver, the implementation of the solver deployed on the Open Science Grid, the workflow management system, the opportunistic use of high performance computing (HPC) resources to add computing capacity to the Open Science Grid reserved for this project, and our initial findings from the recently completed processing of the resting recordings. This required {approx}14 million core hours, i.e. {approx}40 core hours per second of data.

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bioRxiv2017

Rats optimally accumulate and discount evidence in a dynamic environment

How choices are made within noisy environments is a central question in the neuroscience of decision making. Previous work has characterized temporal accumulation of evidence for decision-making in static environments. However, real-world decision-making involves environments with statistics that change over time. This requires discounting old evidence that may no longer inform the current state of the world. Here we designed a rat behavioral task with a dynamic environment, to probe whether rodents can optimally discount evidence by adapting the timescale over which they accumulate it. Extending existing results about optimal inference in a dynamic environment, we show that the optimal timescale for evidence discounting depends on both the stimulus statistics and noise in sensory processing. We found that when both of these components were taken into account, rats accumulated and temporally discounted evidence almost optimally. Furthermore, we found that by changing the dynamics of the environment, experimenters could control the rats accumulation timescale, switching them from accumulating over short timescales to accumulating over long timescales and back. The theoretical framework also makes quantitative predictions regarding the timing of changes of mind in the dynamic environment. This study establishes a quantitative behavioral framework to control and investigate neural mechanisms underlying the adaptive nature of evidence accumulation timescales and changes of mind.

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bioRxiv2017

Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under a Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against the established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.

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bioRxiv2017

Neurobehavioural Correlates of Obesity are Largely Heritable

Recent molecular genetic studies have shown that the majority of genes associated with obesity are expressed in the central nervous system. Obesity has also been associated with neurobehavioural factors such as brain morphology, cognitive performance, and personality. Here, we tested whether these neurobehavioural factors were associated with the heritable variance in obesity measured by body mass index (BMI) in the Human Connectome Project (N=895 siblings). Phenotypically, cortical thickness findings supported the \"right brain hypothesis\" for obesity. Namely, increased BMI associated with decreased cortical thickness in right frontal lobe and increased thickness in the left frontal lobe, notably in lateral prefrontal cortex. In addition, lower thickness and volume in entorhinal-parahippocampal structures, and increased thickness in parietal-occipital structures in obese participants supported the role of visuospatial function in obesity. Brain morphometry results were supported by cognitive tests, which outlined obesitys negative association with visuospatial function, verbal episodic memory, impulsivity, and cognitive flexibility. Personality-obesity correlations were inconsistent. We then aggregated the effects for each neurobehavioural factor for a behavioural genetics analysis and demonstrated the factors genetic overlap with obesity. Namely, cognitive test scores and brain morphometry had 0.25 - 0.45 genetic correlations with obesity, and the phenotypic correlations with obesity were 77-89% explained by genetic factors. Neurobehavioural factors also had some genetic overlap with each other. In summary, obesity has considerable genetic overlap with brain and cognitive measures. This supports the theory that obesity is inherited via brain function, and may inform intervention strategies.\n\nSignificance StatementObesity is a widespread heritable health condition. Evidence from psychology, cognitive neuroscience, and genetics has proposed links between obesity and the brain. The current study tested whether the heritable variance in obesity is explained by brain and behavioural factors in a large brain imaging cohort that included multiple related individuals. We found that the heritable variance in obesity had genetic correlations 0.25 - 0.45 with cognitive tests, cortical thickness, and regional brain volume. In particular, obesity was associated with frontal lobe asymmetry and differences in temporal-parietal perceptual systems. Further, we found genetic overlap between certain brain and behavioural factors. In summary, the genetic vulnerability to obesity is expressed in the brain. This may inform intervention strategies.

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bioRxiv2017

Pitfalls in the Assessment of Brain-Machine Interfaces Using Information Transfer Rate

Information transfer rate (ITR), measured in bits/s, can be applied to evaluate motor performance, including the capacity of brain-machine interfaces (BMIs) to control external actuators. In a 2013 article entitled \"Transfer of information by BMI\" and published in Neuroscience, Tehovnik and his colleagues utilized ITR to assess the performance of several BMIs reported in the literature. We examined these analyses closely and found several fundamental flaws in their evaluation of ITR. Here we discuss the pitfalls in Tehovniks measurements of ITR, as well as several other issues raised in \"Transfer of information by BMI\", including the claim that BMIs cannot be a reasonable option for paralyzed patients.\n\nHighlightsO_LIInformation transfer rate is discussed for BMI experiments, where subjects reach to targets.\nC_LIO_LITask settings, not just the number of possible targets, are important to calculate information correctly.\nC_LIO_LIActive tactile exploration can be quantified as information transfer, but the number of targets is insufficient for such quantification.\nC_LIO_LIInformation transfer rate increases with the number of neural recording channels.\nC_LIO_LIFor practical applications, improvement in quality of life is essential, not information transfer rate per se.\nC_LI

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bioRxiv2017

Topographical and spectral signatures of infant and adult movement artifacts in naturalistic EEG

Electroencephalography (EEG) is perhaps the most widely used brain-imaging technique for paediatric populations. However, EEG signals are prone to distortion by motion. Compared to adults, infants motion is both more frequent and less stereotypical yet motion effects on the infant EEG signal are largely undocumented. Here, we present a systematic assessment of naturalistic motion effects on the infant and adult EEG signal. In Study 1, we documented the prevalence of 27 naturally occurring facial and body motions by video-coding five mother-infant pairs during naturalistic play. In Study 2, we elicited a subset of the most common facial, limb and postural motions from one adult and one infant actor while their EEG was recorded. In Study 3, we recorded EEG signals from a larger group of 12 infants whilst they produced the same motions spontaneously. Our findings from Study 2 suggested that adult movements mainly generated increases in spectral power relative to resting state, primarily at peripheral sites and in delta and high-beta frequency bands. In infants, both elicited motions (N=1, Study 2) and spontaneously-occurring motions (N=12, Study 3) produced decreases in theta, alpha and beta power over central regions and increased beta/delta power at peripheral sites. We also observed that infants jaw and upper limb movements generated more pronounced EEG artifacts than lower limb movements. It is intended that this work will inform future development of methods for addressing EEG motion-related artifacts and support wider use of naturalistic paradigms in social and developmental neuroscience.

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bioRxiv2017

Mapping the human brain’s cortical-subcortical functional network organization

Highlights O_LILarge-scale functional network map of the entire human brain\nC_LIO_LICortical networks based on multiband fMRI, recently-identified regions\nC_LIO_LISubcortical extension of networks covering all subcortical structures\nC_LIO_LIMultiple quality assessments demonstrate robustness of functional networks\nC_LIO_LINetwork atlas released as public resource, providing framework for future studies\nC_LI\n\nABSTRACTUnderstanding complex systems such as the human brain requires characterization of the systems architecture across multiple levels of organization - from neurons, to local circuits, to brain regions, and ultimately large-scale brain networks. Here we focus on characterizing the human brains large-scale network organization, as it provides an overall framework for the organization of all other levels. We developed a highly principled approach to identify cortical network communities at the level of functional systems, calibrating our community detection algorithm using extremely well-established sensory and motor systems as guides. Building on previous network partitions, we replicated and expanded upon well-known and recently identified networks, including several higher-order cognitive networks such as a left-lateralized language network. We expanded these cortical networks to subcortex, revealing 358 highly organized subcortical parcels that take part in forming whole-brain functional networks. Notably, the identified subcortical parcels are similar in number to a recent estimate of the number of cortical parcels (360). This whole-brain network atlas - released as an open resource for the neuroscience community - places all brain structures across both cortex and subcortex into a single large-scale functional framework, with the potential to facilitate a variety of studies investigating large-scale functional networks in health and disease.

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bioRxiv2017

Elucidating relations between fMRI, ECoG and EEG through a common natural stimulus

Human brain mapping relies heavily on fMRI, ECoG and EEG, which capture different physiological signals. Relationships between these signals have been established in the context of specific tasks or during resting state, often using spatially confined concurrent recordings in animals. But it is not certain whether these correlations generalize to other contexts relevant for human cognitive neuroscience. Here, we address the case of complex naturalistic stimuli and ask two basic questions. First, how reliable are the responses evoked by a naturalistic audio-visual stimulus in each of these imaging methods, and second, how similar are stimulus-related responses across methods? To this end, we investigated a wide range of brain regions and frequency bands. We presented the same movie clip twice to three different cohorts of subjects (NEEG = 45, NfMRI = 11, NECoG = 5) and assessed stimulus-driven correlations across viewings and between imaging methods, thereby ruling out task-irrelevant confounds. All three imaging methods had similar repeat-reliability across viewings when fMRI and EEG data were averaged across subjects, highlighting the potential to achieve large signal-to-noise ratio by leveraging large sample sizes. The fMRI signal correlated positively with high-frequency ECoG power across multiple task-related cortical structures but positively with low-frequency EEG and ECoG power. In contrast to previous studies, these correlations were as strong for low-frequency as for high frequency ECoG. We also observed links between fMRI and infra-slow EEG voltage fluctuations. These results extend previous findings to the case of natural stimulus processing.

neuroscience↗
bioRxiv2017

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can be used to identify communities or modules in brain graphs: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the utility of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

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bioRxiv2017

Selective Recruitment of Cortical Neurons by Electrical Stimulation

Despite its critical importance in experimental and clinical neuroscience, at present there is no systematic method to predict which neural elements will be activated by a given stimulation regime. Here we develop a novel approach to model the effect of cortical stimulation on spiking probability of neurons in a volume of tissue, by applying an analytical estimate of stimulation-induced activation of different cell types across cortical layers. We utilize the morphology and properties of axonal arborization profiles obtained from publicly available anatomical reconstructions of the twelve main categories of neocortical neurons to derive the dependence of activation probability on cell type, layer and distance from the source. We then propagate this activity through the local network incorporating connectivity, synaptic and cellular properties. Our work predicts that (a) intracranial cortical stimulation induces selective activation across cell types and layers; (b) superficial anodal stimulation is more effective than cathodal at cell activation; (c) cortical surface stimulation focally activates layer I axons, and (d) an optimal stimulation intensity exists capable of eliciting cell activation lasting beyond the end of stimulation.

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