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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 937 records · Page 52Linked to original sources
bioRxiv2016

Occipital white matter tracts in human and macaque

We compare the major white matter tracts in human and macaque occipital lobe using diffusion MRI. The comparison suggests similarities but also significant differences in spatial arrangement and relative sizes of the tracts. There are several apparently homologous tracts in the two species, including the vertical occipital fasciculus (VOF), optic radiation, forceps major, and inferior longitudinal fasciculus (ILF). There is one large human tract, the inferior fronto-occipital fasciculus, with no corresponding fasciculus in macaque. The macaque VOF is compact and its fibers intertwine with the dorsal segment of the ILF, but the human VOF is much more elongated in the anterior-posterior direction and may be lateral to the ILF. These similarities and differences will be useful in establishing which circuitry in the macaque can serve as an accurate model for human visual cortex.\n\nContact informationHiromasa Takemura, Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Japan htakemur@nict.go.jp\n\nAuthor contributionDesigned the study: HT FP BAW. Performed the experiments. HT FP GAK SML JS FQY DAL WAF NKL. Analyzed the data. HT FP KSW MAB BAW. Contributed analysis tools. FP KSW MAB. Wrote the paper. HT FP KSW BAW.

Neuroscience↗
bioRxiv2016

Changes in brain morphology and working memory capacity over childhood

1Developmental improvements in working memory are important in the acquisition of new skills, like reading and maths. Current accounts of the brain systems supporting working memory rarely take development into account. However, understanding the development of these skills, and in turn where this development can go awry, will require more sophsiticated neuropsychological accounts that fully consider the role of development. The current study investigated how structural brain correlates of components of the working memory system change over developmental time. Verbal and visuospatial short-term and working memory were assessed in 153 children between 6 and 16 years and latent components of the working memory system were derived using principal component analysis. Further, fractional anisotropy and cortical thickness maps were derived from T1-weighted and diffusion-weighted MRI and processed using eigenanatomy decomposition, an advanced dimensionality reduction method for neuroimaging data. We were then able to explore how the structural brain correlates of working memory gradually shifted across childhood. Regression modelling indicated greater involvement of the corpus callosum and posterior temporal white matter in younger children for performance associated with the executive part of the working memory system, while thickness of the occipitotemporal cortex was more predictive in older children. These findings are consistent with an account in which increasing specialisation leads to shifts in the contribution of neural substrates over developmental time, from early reliance on a distributed system supported by long-range connections to later reliance on specialised local circuitry. Furthemore, our findings emphasise the importance of taking development into account when considering the neural systems that support complex cognitive skills, like working memory.

Neuroscience↗
bioRxiv2016

Memory Replay in Balanced Recurrent Networks

Complex patterns of neural activity appear during up-states in the neocortex and sharp waves in the hippocampus, including sequences that resemble those during prior behavioral experience. The mechanisms underlying this replay are not well understood. How can small synaptic footprints engraved by experience control large-scale network activity during memory retrieval and consolidation? We hypothesize that sparse and weak synaptic connectivity between Hebbian assemblies are boosted by pre-existing recurrent connectivity within them. To investigate this idea, we connect sequences of assemblies in randomly connected spiking neuronal networks with a balance of excitation and inhibition. Simulations and analytical calculations show that recurrent connections within assemblies allow for a fast amplification of signals that indeed reduces the required number of inter-assembly connections. Replay can be evoked by small sensory-like cues or emerge spontaneously by activity fluctuations. Global--potentially neuromodulatory--alterations of neuronal excitability can switch between network states that favor retrieval and consolidation.\n\nAuthor SummarySynaptic plasticity is the basis for learning and memory, and many experiments indicate that memories are imprinted in synaptic connections. However, basic mechanisms of how such memories are retrieved and consolidated remain unclear. In particular, how can one-shot learning of a sequence of events achieve a sufficiently strong synaptic footprint to retrieve or replay this sequence? Using both numerical simulations of spiking neural networks and an analytic approach, we provide a biologically plausible model for understanding how minute synaptic changes in a recurrent network can nevertheless be retrieved by small cues or even manifest themselves as activity patterns that emerge spontaneously. We show how the retrieval of exceedingly small changes in the connections across assemblies is robustly facilitated by recurrent connectivity within assemblies. This interaction between recurrent amplification within an assembly and the feed-forward propagation of activity across the network establishes a basis for the retrieval of memories.

Neuroscience↗
bioRxiv2016

Stability and plasticity of contextual modulation in the mouse visual cortex

Activity of neurons in primary sensory cortex is shaped by visual and behavioural context. However the long-term stability of the influence of contextual factors in the mature cortex remains poorly understood. To investigate this we used 2-photon calcium imaging to track the influence of surround suppression and locomotion on individual neurons over 14 days. We found that highly active excitatory neurons and PV+ interneurons exhibited relatively stable modulation by visual context. Similarly most neurons exhibited a stable yet distinct degree modulation by locomotion. In contrast less active excitatory neurons exhibited plasticity in visual context influence resulting in increased suppression. These findings suggest that the mature visual cortex possesses stable subnetworks of neurons, differentiated by cell-type and activity level, which have distinctive and stable interactions with sensory and behavioural context, as well as other less active and more labile neurons which are sensitive to visual experience.

neuroscience↗
bioRxiv2016

Orientation tuning depends on spatial frequency in mouse visual cortex

The response properties of neurons to sensory stimuli have been used to identify their receptive fields and functionally map sensory systems. In primary visual cortex, most neurons are selective to a particular orientation and spatial frequency of the visual stimulus. Using two-photon calcium imaging of neuronal populations from the primary visual cortex of mice, we have characterized the response properties of neurons to various orientations and spatial frequencies. Surprisingly, we found that the orientation selectivity of neurons actually depends on the spatial frequency of the stimulus. This dependence can be easily explained if one assumed spatially asymmetric Gabor-type receptive fields. We propose that receptive fields of neurons in layer 2/3 of visual cortex are indeed spatially asymmetric, and that this asymmetry could be used effectively by the visual system to encode natural scenes.\n\nSignificance StatementIn this manuscript we demonstrate that the orientation selectivity of neurons in primary visual cortex of mouse is highly dependent on the stimulus SF. This dependence is realized quantitatively in a decrease in the selectivity strength of cells in non-optimum SF, and more importantly, it is also evident qualitatively in a shift in the preferred orientation of cells in non-optimum SF. We show that a receptive-field model of a 2D asymmetric Gabor, rather than a symmetric one, can explain this surprising observation. Therefore, we propose that the receptive fields of neurons in layer 2/3 of mouse visual cortex are spatially asymmetric and this asymmetry could be used effectively by the visual system to encode natural scenes.\n\nHighlights- Orientation selectivity is dependent on spatial frequency.\n- Asymmetric Gabor model can explain this dependence.

Neuroscience↗
bioRxiv2016

Internal states drive nutrient homeostasis by modulating exploration-exploitation trade-off

Internal states can deeply alter the behavior of animals. Which aspects of behavior change upon metabolic challenges and how these allow the animal to achieve nutrient homeostasis is poorly understood. We used an automated video tracking setup to characterize how amino acid and reproductive states interact to shape exploitation and exploration decisions taken by adult Drosophila melanogaster, to achieve nutritional homeostasis. We find that these two states have specific effects on the decisions to engage and leave proteinaceous food patches. Furthermore, the internal nutrient state defines the exploration-exploitation trade-off: nutrient deprived flies focus on specific patches while satiated flies explore more globally. Finally, we show that olfaction mediates the efficient recognition of yeast as an appropriate protein source and that octopamine is specifically required to mediate homeostatic postmating responses without affecting internal nutrient sensing. Internal states therefore modulate specific aspects of exploitation and exploration to change nutrient selection.

Neuroscience↗
bioRxiv2016

Personalized epilepsy seizure detection using random forest classification over one-dimension transformed EEG data

This work presents a computational method for improving seizure detection for epilepsy diagnosis. Epilepsy isthe second most common neurological disease impacting between 40 and 50 million of patients in the world and it proper diagnosis using electroencephalographic signals implies a long and expensive process which involves medical specialists. The proposed system is a patient-dependent offline system which performs an automatic detection of seizures in brainwaves applying a random forest classifier. Features are extracted using one-dimension reduced information from a spectro-temporal transformation of the biosignals which pass through an envelope detector. The performance of this method reached 97.12% of specificity, 99.29% of sensitivity, and a 0.77 h-1 false positive rate. Thus, the method hereby proposed has great potential for diagnosis support in clinical environments.

Neuroscience↗
bioRxiv2016

Reward-based training of recurrent neural networks for cognitive and value-based tasks

Trained neural network models, which exhibit many features observed in neural recordings from behaving animals and whose activity and connectivity can be fully analyzed, may provide insights into neural mechanisms. In contrast to commonly used methods for supervised learning from graded error signals, however, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when the optimal behavior depends on an animals internal judgment of confidence or subjective preferences. Here, we describe reward-based training of recurrent neural networks in which a value network guides learning by using the selected actions and activity of the policy network to predict future reward. We show that such models capture both behavioral and electrophysiological findings from well-known experimental paradigms. Our results provide a unified framework for investigating diverse cognitive and value-based computations, including a role for value representation that is essential for learning, but not executing, a task.

Neuroscience↗
bioRxiv2016

Intrinsic functional connectivity resembles cortical architecture at various levels of isoflurane anesthesia

Cortical activity patterns change in different depths of general anesthesia. Here we investigate the associated network level changes of functional connectivity. We recorded ongoing electrocorticographic (ECoG) activity from the ferret temporo-parieto-occipital cortex under various levels of isoflurane and determined the functional connectivity by computing amplitude envelope correlations. Through hierarchical clustering, we derived typical connectivity patterns corresponding to light, intermediate and deep anesthesia. Generally, amplitude correlation strength increased strongly with depth of anesthesia across all cortical areas and frequency bands. This was accompanied by the emergence of burstsuppression activity in the ECoG signal and a change of the spectrum of the amplitude envelope. Normalizing the functional connectivity patterns showed that the topographical structure remained similar across depths of anesthesia, resembling the functional association of the underlying cortical areas. Thus, while strength and temporal properties of amplitude co-modulation vary depending on the activity of local neural circuits, their network-level interaction pattern is presumably most strongly determined by the underlying structural connectivity.

Neuroscience↗
bioRxiv2016

Neuronal gamma-band synchronization regulated by instantaneous modulations of the oscillation frequency

Neuronal gamma-band synchronization shapes information flow during sensory and cognitive processing. A common view is that a stable and shared frequency over time is required for robust and functional synchronization. To the contrary, we found that non-stationary instantaneous frequency modulations were essential for synchronization. First, we recorded gamma rhythms in monkey visual area V1, and found that they synchronized by continuously modulating their frequency difference in a phase-dependent manner. The frequency modulation properties regulated both the phase-locking and the preferred phase-relation between gamma rhythms. Second, our experimental observations were in agreement with a biophysical model of gamma rhythms and were accurately predicted by the theory of weakly coupled oscillators revealing the underlying theoretical principles that govern gamma synchronization. Thus, synchronization through instantaneous frequency modulations represents a fundamental principle of gamma-band neural coordination that is likely generalizable to other brain rhythms.

Neuroscience↗
bioRxiv2016

Elevated serum adenosine deaminase levels in neuroleptic-naive patients with recent-onset schizophrenia

Schizophrenia is characterized by pathophysiological alterations of multiple neurotransmitter systems such as dopaminergic, glutamatergic, GABA-ergic and serotonergic pathways. Adenosine, a homeostatic neuromodulator that mediates signaling through multiple neurotransmitter pathways, is an emerging candidate neurobiological substrate of schizophrenia. The present study examined peripheral blood levels of adenosine deaminase, an adenosine metabolizing enzyme, in 16 neuroleptic-naive patients with recent-onset schizophrenia (mean age=25.59 years (range: 16-35)) and 18 age-matched healthy comparison subjects (mean age=25.17 years (range: 18-28)). Serum adenosine deaminase levels were assayed at two time points; before (7 p.m.) and after (7 a.m.) sleep. The adenosine deaminase levels were compared between groups and were correlated to positive and negative symptom severity measures. Adenosine deaminase levels were found to be higher at both evening (p=0.013) and morning (p<0.001) time points in our sample of patients with recent-onset schizophrenia who were never exposed to neuroleptic medications. Correlational analysis revealed evidence for a possible link between evening rise in adenosine deaminase and severity of auditory hallucinations (p=0.003) as well as morning rise in adenosine deaminase and severity of avolition-apathy in patients with schizophrenia (p=0.013). The results of the study provide strong support to the adenosine hypothesis of schizophrenia and highlight the potential utility of serum adenosine deaminase as a peripheral biomarker of schizophrenia.

Neuroscience↗
bioRxiv2016

Genetically targeted 3D visualisation of Drosophila neurons under Electron Microscopy and X-Ray Microscopy using miniSOG

Large dimension, high-resolution imaging is important for neural circuit visualisation as neurons have both long- and short-range patterns: from axons and dendrites to the numerous synapses at their endings. Electron Microscopy (EM) is the favoured approach for synaptic resolution imaging but how such structures can be segmented from high-density images within large volume datasets remains challenging.\n\nFluorescent probes are widely used to localise synapses, identify cell-types and in tracing studies. The equivalent EM approach would benefit visualising such labelled structures from within sub-cellular, cellular, tissue and neuroanatomical contexts.\n\nHere we developed genetically-encoded, electron-dense markers using miniSOG. We demonstrate their ability in 1) labelling cellular sub-compartments of genetically-targeted neurons, 2) generating contrast under different EM modalities, and 3) segmenting labelled structures from EM volumes using computer-assisted strategies. We also tested non-destructive X-ray imaging on whole Drosophila brains to evaluate contrast staining. This enables us to target specific regions for EM volume acquisition.

Neuroscience↗
bioRxiv2016

Opponent surrounds explain diversity of contextual phenomena across visual modalities

AsbtractContext is known to affect how a stimulus is perceived. A variety of illusions have been attributed to contextual processing -- from orientation tilt effects to chromatic induction phenomena, but their neural underpinnings remain poorly understood. Here, we present a recurrent network model of classical and extra-classical receptive fields that is constrained by the anatomy and physiology of the visual cortex. A key feature of the model is the postulated existence of two spatially disjoint near-vs. far-extra-classical regions with complementary facilitatory and suppressive contributions to the classical receptive field. The model accounts for a variety of contextual illusions, reveals commonalities between seemingly disparate phenomena, and helps organize them into a novel taxonomy. It explains how center-surround interactions may shift from attraction to repulsion in tilt effects, and from contrast to assimilation in induction phenomena. The model further explains enhanced perceptual shifts generated by a class of patterned background stimuli that activate the two opponent extra-classical regions cooperatively. Overall, the ability of the model to account for the variety and complexity of contextual illusions provides computational evidence for a novel canonical circuit that is shared across visual modalities.

Neuroscience↗
bioRxiv2016

Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation

The so-called steady-state evoked potentials (SSEPs) are rhythmic brain responses to rhythmic sensory stimulation, and are often used to study perceptual and attentional processes. We present a data analysis method for maximizing the signal-to-noise ratio of the narrow-band steady-state response in the frequency and time-frequency domains. The method, termed rhythmic entrainment source separation (RESS), is based on denoising source separation approaches that take advantage of the simultaneous but differential projection of neural activity to many non-invasively placed electrodes or sensors. Our approach is a combination and extension of existing multivariate source separation methods. We demonstrate that RESS performs well on both simulated and empirical data, and outperforms conventional SSEP analysis methods based on selecting electrodes with the strongest SSEP response. We also discuss the potential confound of overfitting--whereby the filter captures noise in absence of a signal. Matlab scripts are available to replicate and extend our simulations and methods. We conclude with some practical advice for optimizing SSEP data analyses and interpreting the results.

Neuroscience↗
bioRxiv2016

Subcortical volume and white matter integrity abnormalities in major depressive disorder: findings from UK Biobank (N=4446)

BackgroundPrevious reports of altered grey and white matter structure in Major Depressive Disorder (MDD) have been inconsistent. Recent meta-analyses have, however, reported reduced hippocampal grey matter volume in MDD and reduced white matter integrity in several brain regions. The use of different diagnostic criteria, different scanners and imaging sequences may, however, obscure further anatomical differences.\n\nMethodsIn this study, we tested for differences in subcortical grey matter volume and white matter integrity between depressed individuals and controls in a large sample of subjects from the first data release of the UK Biobank imaging study of 4446 individuals, which used consistent diagnostic criteria at a single assessment centre, with a single MRI scanner and protocol.\n\nResultsWhilst we found no significant differences in subcortical volumes, we report significant reductions in depressed individuals versus controls in global white matter integrity, as measured by fractional anisotropy (FA) ({beta} = -0.187, p = 0.017). We also report reductions in FA in association/commissural fibres ({beta} = -0.184, p = 0.019) and thalamic radiations ({beta} = -0.175, p = 0.027). Examining tracts individually, we report tract-specific FA reductions in the left superior longitudinal fasciculus ({beta} = -0.218, pcorrected = 0.012) and superior thalamic radiation ({beta} = -0.258, pcorrected = 0.010) in subjects with depression.\n\nConclusionsOur findings highlight the need for further large adequately-powered studies of depression and provide further evidence for disrupted white matter integrity in the disorder. Future studies would focus on exploring the typical neuro-phenotype in homogenous subgroups of depression.

Neuroscience↗
bioRxiv2016

Prevalence of Congenital Amusia

Congenital amusia (commonly known as tone-deafness) is a lifelong musical disorder that should affect 4% of the population according to a single estimate based on a single test from 1980. Here we present the first large-based measure of prevalence with a sample of 20,000 participants that does not rely on self-referral. On the basis of three objective tests and a questionnaire, we show that (a) the prevalence of congenital amusia is only 1.5% with slightly more females than males, unlike other developmental disorders where males often predominate; (b) self-disclosure is a reliable index of congenital amusia, that suggests that congenital amusia is hereditary with 46% first-degree relatives similarly affected; c) that the deficit is not attenuated by musical training and d) it emerges in relative isolation from other cognitive disorder except for spatial orientation problems. Hence, we suggest that congenital amusia is likely to result from genetic variations that affect musical abilities specifically.

Neuroscience↗
bioRxiv2016

Empirical validation of directed functional connectivity

Mapping directions of influence in the human brain connectome represents the next phase in understanding its functional architecture. However, a host of methodological uncertainties have impeded the application of directed connectivity methods, which have primarily been validated via 'ground truth' connectivity patterns embedded in simulated functional MRI (fMRI) and magneto-/electro-encephalography (MEG/EEG) datasets. Such simulations rely on many generative assumptions, and we hence utilized a different strategy involving empirical data in which a ground truth directed connectivity pattern could be anticipated with confidence. Specifically, we exploited the established 'sensory reactivation' effect in episodic memory, in which retrieval of sensory information reactivates regions involved in perceiving that sensory modality. Subjects performed a paired associate task in separate fMRI and MEG sessions, in which a ground truth reversal in directed connectivity between auditory and visual sensory regions was instantiated across task conditions. This directed connectivity reversal was successfully recovered across different algorithms, including Granger causality and Bayes network (IMAGES) approaches, and across fMRI ('raw' and deconvolved) and source-modeled MEG. These results extend simulation studies of directed connectivity, and offer practical guidelines for the use of such methods in clarifying causal mechanisms of neural processing.

Neuroscience↗
bioRxiv2016

What the Success of Brain Imaging Implies about the Neural Code

The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite limitations in what fMRI measures, implies that certain neural coding schemes are more likely than others. For fMRI to be successful given its low temporal and spatial resolution, the neural code must be smooth at the sub-voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we evaluate a number of reasonable coding schemes and demonstrate that only a subset are plausible given both fMRIs successes and its limitations in measuring neural activity. Deep neural network approaches, which have been forwarded as computational accounts of the ventral stream, are consistent with the success of fMRI, though functional smoothness breaks down in the later network layers. These results have implications for the nature of neural code and ventral stream, as well as what can be successfully investigated with fMRI.

Neuroscience↗