SEARCH · Search bioRxiv

Results for “Neuroscience”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 361 records · Page 20Linked to original sources

bioRxiv2018

The Human Default Consciousness and its Disruption: Insights from an EEG Study of Buddhist Jhana Meditation

The "neural correlates of consciousness" (NCC) is a familiar topic in neuroscience, overlapping with research on the brains "default mode network". Task-based studies of NCC by their nature recruit one part of the cortical network to study another, and are therefore both limited and compromised in what they can reveal about consciousness itself. The form of consciousness explored in such research, we term the human default consciousness (DCs), our everyday waking consciousness. In contrast, studies of anaesthesia, coma, deep sleep, or some extreme pathological states such as epilepsy, reveal very different cortical activity; all of which states are essentially involuntary, and generally regarded as "unconscious". An exception to involuntary disruption of consciousness is Buddhist jh[a]na meditation, whose implicit aim is to intentionally withdraw from the default consciousness, to an inward-directed state of stillness referred to as jh[a]na consciousness, as a basis to develop insight. The default consciousness is sensorily-based, where information about, and our experience of, the outer world is evaluated against personal and organic needs and forms the basis of our ongoing self-experience. This view conforms both to Buddhist models, and to the emerging work on active inference and minimisation of free energy in determining the network balance of the human default consciousness. This paper is a preliminary report on the first detailed EEG study of jh[a]na meditation, with findings radically different to studies of more familiar, less focused forms of meditation. While remaining highly alert and "present" in their subjective experience, a high proportion of subjects display "spindle" activity in their EEG, superficially similar to sleep spindles of stage 2 nREM sleep, while more-experienced subjects display high voltage slow-waves reminiscent, but significantly different, to the slow waves of deeper stage 4 nREM sleep, or even high-voltage delta coma. Some others show brief posterior spike-wave bursts, again similar, but with significant differences, to absence epilepsy. Some subjects also develop the ability to consciously evoke clonic seizure-like activity at will, under full control. We suggest that the remarkable nature of these observations reflects a profound disruption of the human DCs when the personal element is progressively withdrawn.

neuroscience↗

bioRxiv2018

On the interpretation of tractography-based parcellations

Connectivity-based parcellation of subcortical structures using diffusion tractography is now a common paradigm in neuroscience. These analyses often imply voxel-level specificity of connectivity, and the formation of compact, spatially coherent clusters is often taken as strong imaging-based evidence for anatomically distinct subnuclei in an individual. In this study, we demonstrate that internal structure in diffusion anisotropy is not necessary for a plausible parcellation to be obtained, by spatially permuting diffusion parameters within the thalami and repeating the parcellation. Moreover, we show that, in a winner-takes-all paradigm, most voxels receive the same label before and after this shuffling process--a finding that is stable across image acquisitions and tractography algorithms. We therefore suggest that such parcellations should be interpreted with caution.

neuroscience↗

bioRxiv2018

Towards a \"Treadmill Test\" for Cognition: Reliable Prediction of Intelligence From Whole-Brain Task Activation Patterns

Identifying brain-based markers of general cognitive ability, i.e., \"intelligence\", has been a longstanding goal of cognitive and clinical neuroscience. Previous studies focused on relatively static, enduring features such as gray matter volume and white matter structure. In this report, we investigate prediction of intelligence based on task activation patterns during the N-back working memory task as well as six other tasks in the Human Connectome Project dataset, encompassing 19 task contrasts. We find that whole brain task activation patterns are a highly effective basis for prediction of intelligence, achieving a 0.68 correlation with intelligence scores in an independent sample, which exceeds results reported from other modalities. Additionally, we show that tasks that tap executive processing and that are more cognitively demanding are particularly effective for intelligence prediction. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in an activated task state improves brain-based prediction of intelligence.

neuroscience↗

bioRxiv2018

Nuclear transcriptomes of the seven neuronal cell types that constitute the Drosophila mushroom bodies.

The insect mushroom body (MB) is a conserved brain structure that plays key roles in a diverse array of behaviors. The Drosophila melanogaster MB is the primary invertebrate model of neural circuits related to memory formation and storage, and its development, morphology, wiring, and function has been extensively studied. MBs consist of intrinsic Kenyon Cells that are divided into three major neuron classes ({gamma}, /{beta} and /{beta}) and 7 cell subtypes ({gamma}d, {gamma}m, /{beta}ap, /{beta}m, /{beta}p, /{beta}s and /{beta}c) based on their birth order, morphology, and connectivity. These subtypes play distinct roles in memory processing, however the underlying transcriptional differences are unknown. Here, we used RNA sequencing (RNA-seq) to profile the nuclear transcriptomes of each MB neuronal cell subtypes. We identified 350 MB class- or subtype-specific genes, including the widely used /{beta} class marker Fas2 and the /{beta} class marker trio. Immunostaining corroborates the RNA-seq measurements at the protein level for several cases. Importantly, our data provide a full accounting of the neurotransmitter receptors, transporters, neurotransmitter biosynthetic enzymes, neuropeptides, and neuropeptide receptors expressed within each of these cell types. This high-quality, cell type-level transcriptome catalog for the Drosophila MB provides a valuable resource for the fly neuroscience community.

neuroscience↗

bioRxiv2018

The community structure of functional brain networks exhibits scale-specific patterns of variability across individuals and time

The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brains modular network organization, which can be assessed quantitatively using powerful computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Though the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability - which would be undetectable using past approaches - is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advancement over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints.

neuroscience↗

bioRxiv2018

Endothelial Cells Filopodia Participation In The Anastomosis Of CNS Capillaries

By combining the classic Golgi method and the electron microscope, we have gained a better understanding of the anastomosis of CNS blood capillaries. The participation of growing capillary leading endothelial cells filopodia in the anastomotic process is described. The two approaching capillaries leading endothelial cells filopodia intermingle and interact forming complex conglomerates with narrow spaces filled with proteinaceous material (possibly basal lamina) secreted by them. The presence of tight junctions among the filopodia corroborates their vascular nature. Their presence also suggests a different endothelial cells origin as will those from the two approaching capillaries. The original narrow spaces coalesce into larger ones leading to the eventual formation of a single one that will interconnect (anastomose) the two capillaries. The newly formed post-anastomotic CNS capillaries are rather small with irregular and narrow lumina that might permit the passage of fluid but not yet of blood cells. Eventually, the new capillaries lumina will enlarge permitting the passage of blood cells.\n\nFunding informationThe M. M-P. Golgi studies were supported by a \"Jacob Javits Neurosciences Investigator Award\". NIH Grant NS-22897. And the L. H. EM studies were supported by the Gilman Fund/Class of 1978 Life Sciences Center. M. M-P. is Emeritus Professor of Pathology and Pediatrics and L. H. is a Consulting Electron Microscopists. Both from the Geisel School of Medicine at Dartmouth. Hanover, NH 03755, USA

neuroscience↗

bioRxiv2018

Novel childhood experience suggests eccentricity drives organization of human visual cortex

The functional organization of human high-level visual cortex, such as face and place-selective regions, is strikingly consistent across individuals. A fundamental, unanswered question in neuroscience is what dimensions of visual information constrain the development and topography of this shared brain organization? To answer this question, we scanned with fMRI a unique group of adults who, as children, engaged in extensive experience with a novel stimulus-Pokemon-which are dissimilar from other ecological categories such as faces and places along critical dimensions (foveal bias, rectilinearity, size, animacy) from. We find that experienced adults not only demonstrate distinct and consistent distributed cortical responses to Pokemon, but their activations suggest that it is the experienced retinal eccentricity during childhood that predicts the locus of distributed responses to Pokemon in adulthood. These data advance our understanding about how childhood experience and functional constraints shape the functional organization of the human brain.

neuroscience↗

bioRxiv2018

A comprehensive data-driven model of cat primary visual cortex

1Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue in the domain of the visual system: a comprehensive spiking model of cat primary visual cortex. The presented model satisfies an extensive range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modelled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward drive elicited by a high diversity of visual contexts, the simulated network produces a realistic, quantitatively accurate interplay between visually evoked excitatory and inhibitory conductances; contrast-invariant orientation-tuning width; center surround interactions; and stimulus-dependent changes in the precision of the neural code. This integrative model offers insights into how the studied properties interact, contributing to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of low-level perception. 2 Author summaryComputational modeling can integrate fragments of understanding generated by experimental neuroscience. However, most models considered only a few features of neural computation at a time, leading to either poorly constrained models with many parameters, or lack of expressiveness in over-simplified models. A solution is to develop detailed models, but constrain them with a broad range of anatomical and functional data to prevent overfitting. This requires a long-term systematic approach. Here we present a first snapshot of such an integrative program: a large-scale spiking model of cat primary visual cortex, that is constrained by an extensive range of anatomical and functional features. Together with the associated modeling infrastructure, this study lays the groundwork for a broad integrative modeling program seeking an in-depth understanding of vision.

neuroscience↗

bioRxiv2018

Automatic adaptation of model neurons and connections to build hybrid circuits with living networks

Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, drift compensation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates building hybrid circuits and closed-loop communication among living and artificial model neurons and connections. Furthermore contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

neuroscience↗

bioRxiv2018

Optimisation and validation of hydrogel-based brain tissue clearing shows uniform expansion across anatomical regions and spatial scales

Imaging of fixed tissue is routine in experimental neuroscience, but is limited by the depth of tissue that can be imaged using conventional methods. Optical clearing of brain tissue using hydrogel-based methods (e.g. CLARITY) allows imaging of large volumes of tissue and is rapidly becoming commonplace in the field. However, these methods suffer from a lack of standardised protocols and validation of the effect they have upon tissue morphology. We present a simple and reliable protocol for tissue clearing along with a quantitative assessment of the effect of tissue clearing upon morphology. Tissue clearing caused tissue swelling (compared to conventional methods), but this swelling was shown to be similar across spatial scales and the variation was within limits acceptable to the field. The results of many studies rely upon an assumption of uniformity in tissue swelling, and by demonstrating this quantitatively, research using these methods can be interpreted more reliably.

neuroscience↗

bioRxiv2018

Topological exploration of artificial neuronal network dynamics

One of the paramount challenges in neuroscience is to understand the dynamics of individual neurons and how they give rise to network dynamics when interconnected. Historically, researchers have resorted to graph theory, statistics, and statistical mechanics to describe the spatiotemporal structure of such network dynamics. Our novel approach employs tools from algebraic topology to characterize the global properties of network structure and dynamics.\n\nWe propose a method based on persistent homology to automatically classify network dynamics using topological features of spaces built from various spike-train distances. We investigate the efficacy of our method by simulating activity in three small artificial neural networks with different sets of parameters, giving rise to dynamics that can be classified into four regimes. We then compute three measures of spike train similarity and use persistent homology to extract topological features that are fundamentally different from those used in traditional methods. Our results show that a machine learning classifier trained on these features can accurately predict the regime of the network it was trained on and also generalize to other networks that were not presented during training. Moreover, we demonstrate that using features extracted from multiple spike-train distances systematically improves the performance of our method.

neuroscience↗

bioRxiv2018

Propagation of orientation selectivity in a spiking network model of layered primary visual cortex

We studied the propagation of orientation selectivity over layers in a model of rodent primary visual cortex in terms of the underlying connectivity. While significant progress has been made in measuring the activity in the different sub-populations as well as the connectivity between those in experiments, a comprehensive theoretical explanation of how network structure and its dynamics are related is still missing. We suggest a model of layered mouse visual cortex by extending the model suggested by Potjans and Diesmann (2014) with thalamic input that has an orientation bias according to Sadeh et al. (2014). We then studied the response properties of the network to such non-homogeneous input. We found that, without further assumptions, the connectivity derived from experimental data leads to layer-specific distributions of orientation selectivity very similar to what has been observed in mouse experiments. Interestingly, the network settles in a dynamical operating point, in which the efficacy of the different projections and their contribution to orientation tuning deviate strongly from the expectations based on the underlying anatomical connectivity. To understand the processes that shape the observed dynamics, it is essential to perform the analysis of the microcircuit on the system level. With that perspective, we find in particular that the difference in tuning of L2/3 and L4 neurons is an immediate, albeit unexpected consequence of the specific network connectivity. Furthermore, we introduce a novel method for predicting the effects of optogenetic stimulation of specific neuronal sub-populations and demonstrate its power in network simulations.\n\nSignificance StatementUnderstanding the precise roles of neuronal sub-populations in shaping the activity of neuronal networks is a fundamental objective of neuroscience research. To this end, our work makes three important contributions. First, we show that the experimentally extracted connectivity suffices to explain the degree of selectivity of sub-populations in mouse visual cortex to visual stimulation. Second, we introduce a novel system-level approach for the analysis of input-output relations of recurrent networks, which lead to distinct activity patterns. Third, we present a method for the design of optogenetic experiments that can be used to devise specific stimuli which result in a desired and predictable change of neuronal activity.

neuroscience↗

bioRxiv2018

Modeling visual performance differences with polar angle: A computational observer approach

Visual performance depends on polar angle, even when eccentricity is held constant; on many psychophysical tasks observers perform best when stimuli are presented on the horizontal meridian, worst on the upper vertical, and intermediate on the lower vertical meridian. This variation in performance around the visual field can be as pronounced as that of doubling the stimulus eccentricity. The causes of these asymmetries in performance are largely unknown. Some factors in the eye, e.g. cone density, are positively correlated with the reported variations in visual performance with polar angle. However, the question remains whether such correlations can quantitatively explain the perceptual differences observed around the visual field. To investigate the extent to which the earliest stages of vision -optical quality and cone density- contribute to performance differences with polar angle, we created a computational observer model. The model uses the open-source software package ISETBIO to simulate an orientation discrimination task for which visual performance differs with polar angle. The model starts from the photons emitted by a display, which pass through simulated human optics with fixational eye movements, followed by cone isomerizations in the retina. Finally, we classify stimulus orientation using a support vector machine to learn a linear classifier on the photon absorptions. To account for the 30% increase in contrast thresholds for upper vertical compared to horizontal meridian, as observed psychophysically on the same task, our computational observer model would require either an increase of ~7 diopters of defocus or a reduction of 500% in cone density. These values far exceed the actual variations as a function of polar angle observed in human eyes. Therefore, we conclude that these factors in the eye only account for a small fraction of differences in visual performance with polar angle. Substantial additional asymmetries must arise in later retinal and/or cortical processing.\n\nAuthor SummaryA fundamental goal in computational neuroscience is to link known facts from biology with behavior. Here, we considered visual behavior, specifically the fact that people are better at visual tasks performed to the left or right of the center of gaze, compared to above or below at the same distance from gaze. We sought to understand what aspects of biology govern this fundamental pattern in visual behavior. To do so, we implemented a computational observer model that incorporates known facts about the front end of the human visual system, including optics, eye movements, and the photoreceptor array in the retina. We found that even though some of these properties are correlated with performance, they fall far short of quantitatively explaining it. We conclude that later stages of processing in the nervous system greatly amplify small differences in the way the eye samples the visual world, resulting in strikingly different performance around the visual field.

neuroscience↗

bioRxiv2018

Gene Expression Correlates of the Cortical Network Underlying Sentence Processing

A pivotal question in modern neuroscience is which genes regulate brain circuits that underlie cognitive functions. However, the field is still in its infancy. Here we report an integrated investigation of the high-level language network (i.e., sentence processing network) in the human cerebral cortex, combining regional gene expression profiles, task fMRI, large-scale neuroimaging meta-analysis, and resting-state functional network approaches. We revealed reliable gene expression-functional network correlations using three different network definition strategies, and identified a consensus set of genes related to connectivity within the sentence-processing network. The genes involved showed enrichment for neural development and actin-related functions, as well as association signals with autism, which can involve disrupted language functioning. Our findings help elucidate the molecular basis of the brains infrastructure for language. The integrative approach described here will be useful to study other complex cognitive traits.

neuroscience↗

bioRxiv2018

Optimal evidence accumulation on social networks

A fundamental question in biology is how organisms integrate sensory and social evidence to make decisions. However, few models describe how both these streams of information can be combined to optimize choices. Here we develop a normative model for collective decision making in a network of agents performing a two-alternative forced choice task. We assume that rational (Bayesian) agents in this network make private measurements, and observe the decisions of their neighbors until they accumulate sufficient evidence to make an irreversible choice. As each agent communicates its decision to those observing it, the flow of social information is described by a directed graph. The decision-making process in this setting is intuitive, but can be complex. We describe when and how the absence of a decision of a neighboring agent communicates social information, and how an agent must marginalize over all unobserved decisions. We also show how decision thresholds and network connectivity affect group evidence accumulation, and describe the dynamics of decision making in social cliques. Our model provides a bridge between the abstractions used in the economics literature and the evidence accumulator models used widely in neuroscience and psychology.

neuroscience↗

bioRxiv2018

A flexible and generalizable model of online latent-state learning

Many models of classical conditioning fail to describe important phenomena, notably the rapid return of fear after extinction. To address this shortfall, evidence converged on the idea that learning agents rely on latent-state inferences, i.e. an ability to index disparate associations from cues to rewards (or penalties) and infer which index (i.e. latent state) is presently active. Our goal was to develop a model of latent-state inferences that uses latent states to predict rewards from cues efficiently and that can describe behavior in a diverse set of experiments. The resulting model combines a Rescorla-Wagner rule, for which updates to associations are proportional to prediction error, with an approximate Bayesian rule, for which beliefs in latent states are proportional to prior beliefs and an approximate likelihood based on current associations. In simulation, we demonstrate the models ability to reproduce learning effects both famously explained and not explained by the Rescorla-Wagner model, including rapid return of fear after extinction, the Hall-Pearce effect, partial reinforcement extinction effect, backwards blocking, and memory modification. Lastly, we derive our model as an online algorithm to maximum likelihood estimation, demonstrating it is an efficient approach to outcome prediction. Establishing such a framework is a key step towards quantifying normative and pathological ranges of latent-state inferences in various contexts.\n\nAuthor summaryComputational researchers are increasingly interested in a structured form of learning known as latent-state inferences. Latent-state inferences is a type of learning that involves categorizing, generalizing, and recalling disparate associations between observations in ones environment and is used in situations when the correct association is latent or unknown. This type of learning has been used to explain overgeneralization of a fear memory and the cognitive role of certain brain regions important to cognitive neuroscience and psychiatry. Accordingly, latent-state inferences are an important area of inquiry. Through simulation and theory, we establish a new model of latent-state inferences. Moving forward, we aim to use this framework to measure latent-state inferences in healthy and psychiatric populations.

neuroscience↗

bioRxiv2018

Deep Convolutional modeling of human face selective columns reveals their role in pictorial face representation

Despite the massive accumulation of systems neuroscience findings, their functional meaning remains tentative, largely due to the absence of realistically performing models. The discovery that deep convolutional networks achieve human performance in realistic tasks offers fresh opportunities for such modeling. Here we show that the face-space topography of face-selective columns recorded intra-cranially in 32 patients significantly matches that of a DCNN having human-level face recognition capabilities. Three modeling aspects converge in pointing to a role of human face areas in pictorial rather than person identification: First, the match was confined to intermediate layers of the DCNN. Second, identity preserving image manipulations abolished the brain to DCNN correlation. Third, DCNN neurons matching face-column tuning displayed view-point selective receptive fields. Our results point to a \"convergent evolution\" of pattern similarities in biological and artificial face perception. They demonstrate DCNNs as a powerful modeling approach for deciphering the function of human cortical networks.

neuroscience↗

bioRxiv2018

An empirical evaluation of multivariate lesion behaviour mapping using support vector regression

Multivariate lesion behaviour mapping based on machine learning algorithms has recently been suggested to complement the methods of anatomo-behavioural approaches in cognitive neuroscience. Several studies applied and validated support vector regression-based lesion symptom mapping (SVR-LSM) to map anatomo-behavioural relations. However, this promising method, as well as the multivariate approach per se, still bears many open questions. By using large lesion samples in three simulation experiments, the present study empirically tested the validity of several methodological aspects. We found that i) correction for multiple comparisons is required in the current implementation of SVR-LSM, ii) that sample sizes of at least 100 to 120 subjects are required to optimally model voxel-wise lesion location in SVR-LSM, and iii) that SVR-LSM is susceptible to misplacement of statistical topographies along the brains vasculature to a similar extent as mass-univariate analyses.

neuroscience↗