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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 703 records · Page 39Linked to original sources

bioRxiv2016

Attention selectively reshapes the geometry of distributed semantic representation

Humans prioritize different semantic qualities of a complex stimulus depending on their behavioral goals. These semantic features are encoded in distributed neural populations, yet it is unclear how attention might operate across these distributed representations. To address this, we presented participants with naturalistic video clips of animals behaving in their natural environments while the participants attended to either behavior or taxonomy. We used models of representational geometry to investigate how attentional allocation affects the distributed neural representation of animal behavior and taxonomy. Attending to animal behavior transiently increased the discriminability of distributed population codes for observed actions in anterior intraparietal, pericentral, and ventral temporal cortices. Attending to animal taxonomy while viewing the same stimuli increased the discriminability of distributed animal category representations in ventral temporal cortex. For both tasks, attention selectively enhanced the discriminability of response patterns along behaviorally relevant dimensions. These findings suggest that behavioral goals alter how the brain extracts semantic features from the visual world. Attention effectively disentangles population responses for downstream read-out by sculpting representational geometry in late-stage perceptual areas.

Neuroscience↗

bioRxiv2016

Disentangling the representation of identity from head view along the human face processing pathway.

Neural models of a distributed system for face perception implicate a network of regions in the ventral visual stream for recognition of identity. Here, we report an fMRI neural decoding study in humans that shows that this pathway culminates in a right inferior frontal cortex face area (rIFFA) with a representation of individual identities that has been disentangled from variable visual features in different images of the same person. At earlier stages in the pathway, processing begins in early visual cortex and the occipital face area (OFA) with representations of head view that are invariant across identities, and proceeds to an intermediate level of representation in the fusiform face area (FFA) in which identity is emerging but still entangled with head view. Three-dimensional, view-invariant representation of identities in the rIFFA may be the critical link to the extended system for face perception, affording activation of person knowledge and emotional responses to familiar faces.\n\nSignificance StatementIn this fMRI decoding experiment, we address how face images are processed in successive stages to disentangle the view-invariant representation of identity from variable visual features. Representations in early visual cortex and the occipital face area distinguish head views, invariant across identities. An intermediate level of representation in the fusiform face area distinguishes identities but still is entangled with head view. The face-processing pathway culminates in the right inferior frontal area with representation of view-independent identity. This paper clarifies the homologies between the human and macaque face processing systems. The findings show further, however, the importance of the inferior frontal cortex in decoding face identity, a result that has not yet been reported in the monkey literature.

Neuroscience↗

bioRxiv2016

Prevalent Presence of Periodic Actin-spectrin-based Membrane Skeleton in a Broad Range of Neuronal Cell Types and Animal Species

Actin, spectrin and associated molecules form a periodic, sub-membrane cytoskeleton in the axons of neurons. For a better understanding of this membrane-associated periodic skeleton (MPS), it is important to address how prevalent this structure is in different neuronal types, different subcellular compartments, and across different animal species. Here, we investigated the organization of spectrin in a variety of neuronal and glial-cell types. We observed the presence of MPS in all of the tested neuronal types cultured from mouse central and peripheral nervous systems, including excitatory and inhibitory neurons from several brain regions, as well as sensory and motor neurons. Quantitative analyses show that MPS is preferentially formed in axons in all neuronal types tested here: spectrin shows a long-range, periodic distribution throughout all axons, but only appears periodic in a small fraction of dendrites, typically in the form of isolated patches in sub-regions of these dendrites. As in dendrites, we also observed patches of periodic spectrin structures in a small fraction of glial-cell processes in four types of glial cells cultured from rodent tissues. Interestingly, despite its strong presence in the axonal shaft, MPS is absent in most presynaptic boutons, but is present in a substantial fraction of dendritic spine necks, including some projecting from dendrites where such a periodic structure is not observed in the shaft. Finally, we found that spectrin is capable of adopting a similar periodic organization in neurons of a variety of animal species, including Caenorhabditis elegans, Drosophila, Gallus gallus, Mus musculus and Homo sapiens.

Neuroscience↗

bioRxiv2016

The neural correlates of two forms of spiritual love: an EEG study

Spiritual practices are gaining an increasingly wider audience as a means to enhance positive affect in healthy individuals and to treat neurological disorders such as anxiety and depression. The current study aimed to examine the neural correlates of two different forms of love generated by spiritual practices using EEG; love generated during a loving kindness meditation performed by Buddhist meditators, and love generated during prayer, in a separate group of participants from a Christian-based faith. The loving kindness meditation was associated with significant increases in delta, alpha 1, alpha 2 and beta power compared to baseline, while prayer induced significant increases in power of alpha 1 and gamma oscillations, together with an increase in the gamma: theta ratio. An increase in delta activity occurred during the loving kindness meditation but not during prayer. In contrast increases in theta, alpha 1, alpha 2, beta and gamma power were observed when comparing both types of practice to baseline, suggesting that increases in these frequency bands are the neural correlates of spiritual love, independent of the type of practice used to attain the state of this type of love. These findings show that both spiritual love practices are associated with widespread changes in neural activity across the brain, in particular at frequency ranges that have been implicated in positive emotional experience, integration of distributed neural activity, and changes in short-term and longterm neural circuitry.

Neuroscience↗

bioRxiv2016

Lack of evidence for cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus

Phase-amplitude coupling between theta and multiple gamma sub-bands hallmarks hippocampal activity and is believed to take part in information routing. More recently, theta and gamma oscillations were also reported to exhibit reliable phase-phase coupling, or n:m phase-locking. The existence of n:m phase-locking suggests an important mechanism of neuronal coding that has long received theoretical support. However, here we show that n:m phase-locking (1) is much lower than previously reported, (2) highly depends on epoch length, (3) does not statistically differ from chance (when employing proper surrogate methods), and that (4) filtered white noise has similar n:m scores as actual data. Moreover, (5) the diagonal stripes in theta-gamma phase-phase histograms of actual data can be explained by theta harmonics. These results point to lack of theta-gamma phase-phase coupling in the hippocampus, and suggest that studies investigating n:m phase-locking should rely on appropriate statistical controls, otherwise they could easily fall into analysis pitfalls.

Neuroscience↗

bioRxiv2016

Div-Seq: A single nucleus RNA-Seq method reveals dynamics of rare adult newborn neurons in the CNS

Transcriptomes of individual neurons provide rich information about cell types and dynamic states. However, it is difficult to capture rare dynamic processes, such as adult neurogenesis, because isolation from dense adult tissue is challenging, and markers for each phase are limited. Here, we developed Div-Seq, which combines Nuc-Seq, a scalable single nucleus RNA-Seq method, with EdU-mediated labeling of proliferating cells. We first show that Nuc-Seq can sensitively identify closely related cell types within the adult hippocampus. We apply Div-Seq to track transcriptional dynamics of newborn neurons in an adult neurogenic region in the hippocampus. Finally, we find rare adult newborn GABAergic neurons in the spinal cord, a non-canonical neurogenic region. Taken together, Nuc-Seq and Div-Seq open the way for unbiased analysis of any complex tissue.

Neuroscience↗

bioRxiv2016

Language impairments in ASD resulting from a failed domestication of the human brain

Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders entailing social and cognitive deficits, including marked problems with language. Numerous genes have been associated with ASD, but it is unclear how language deficits arise from gene mutation or dysregulation. It is also unclear why ASD shows such high prevalence within human populations. Interestingly, the emergence of a modern faculty of language has been hypothesised to be linked to changes in the human brain/skull, but also to the process of self-domestication of the human species. It is our intention to show that people with ASD exhibit less marked domesticated traits at the morphological, physiological, and behavioural levels. We also discuss many ASD candidates represented among the genes known to be involved in the domestication syndrome (the constellation of traits exhibited by domesticated mammals, which seemingly results from the hypofunction of the neural crest) and among the set of genes involved in language function closely connected to them. Moreover, many of these genes show altered expression profiles in the brain of autists. In addition, some candidates for domestication and language-readiness show the same expression profile in people with ASD and chimps in different brain areas involved in language processing. Similarities regarding the brain oscillatory behaviour of these areas can be expected too. We conclude that ASD may represent an abnormal ontogenetic itinerary for the human faculty of language resulting in part from changes in genes important for the domestication syndrome and, ultimately, from the normal functioning of the neural crest.

Neuroscience↗

bioRxiv2016

Population imaging at subcellular resolution supports specific and local inhibition by granule cells in the olfactory bulb

Information processing in early sensory regions is modulated by a diverse range of inhibitory interneurons. We sought to elucidate the role of olfactory bulb interneurons called granule cells (GCs) in odor processing by imaging the activity of hundreds of these cells simultaneously in mice. Odor responses in GCs were temporally diverse and spatially disperse, with some degree of non-random, modular organization. The overall sparseness of activation of GCs was highly correlated with the extent of glomerular activation by odor stimuli. Increasing concentrations of single odorants led to proportionately larger population activity, but some individual GCs had non-monotonic relation to concentration due to local inhibitory interactions. Individual dendritic segments could sometimes respond independently to odors, revealing their capacity for compartmentalized signaling in vivo. Collectively, the response properties of GCs point to their role in specific and local processing, rather than global operations such as response normalization proposed for other interneurons.

Neuroscience↗

bioRxiv2016

Multivariate Pattern Connectivity

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern connectivity (MVPC): a technique to study the dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPC characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. Considering the fusiform face area (FFA) as a seed region, we used searchlight-based MVPC to reveal interactions between regions undetected by univariate functional connectivity analyses. MVPC (but not functional connectivity) identified significant interactions between right FFA and the right anterior temporal lobe, the right superior temporal sulcus, and the dorsal visual stream. Additionally, MVPC outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPC uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.\n\nAuthor SummaryHuman behavior is supported by systems of brain regions that exchange infor-mation to complete a task. This exchange of information between brain regions leads to statistical relationships between their responses over time. Most likely, these relationships do not link only the mean responses in two brain regions, but also their finer spatial patterns. Analyzing finer response patterns has been a key advance in the study of responses within individual regions, and can be leveraged to study between-region interactions. To capture the overall statistical relationship between two brain regions, we need to describe each regions responses with respect to dimensions that best account for the variation in that region over time. These dimensions can be different from region to region. We introduce an approach in which each regions responses are characterized in terms of region-specific dimensions that best account for its responses, and the relationships between regions are modeled with multivariate linear models. We demonstrate that this approach provides a better account of the data as compared to standard functional connectivity, and we use it to discover multiple dimensions within the fusiform face area that have different connectivity profiles with the rest of the brain.

Neuroscience↗

bioRxiv2016

Dorsal anterior cingulate and ventromedial prefrontal cortex have inverse roles in both foraging and economic choice

Recent research has highlighted a distinction between sequential foraging choices and traditional economic choices between simultaneously presented options. This was partly motivated by observations in Kolling et al. (2012) [KBMR] that these choice types are subserved by different circuits, with dorsal anterior cingulate (dACC) preferentially involved in foraging and ventromedial prefrontal cortex (vmPFC) preferentially involved in economic choice. To support this account, KBMR used fMRI to scan human subjects making either a foraging choice (between exploiting a current offer or swapping for potentially better rewards) or an economic choice (between two reward-probability pairs). This study found that dACC better tracked values pertaining to foraging, while vmPFC better tracked values pertaining to economic choice. We recently showed that dACCs role in these foraging choices is better described by the difficulty of choosing than by foraging value, when correcting for choice biases and testing a sufficiently broad set of foraging values (Shenhav et al., 2014). Here, we extend these findings in three ways. First, we replicate our original finding with a larger sample and a task modified to address remaining methodological gaps between our previous experiments and that of KBMR. Second, we show that dACC activity is best accounted for by choice difficulty alone (rather than in combination with foraging value) during both foraging and economic choices. Third, we show that patterns of vmPFC activity, inverted relative to dACC, also suggest a common function across both choice types. Overall, we conclude that both regions are similarly engaged by foraging-like and economic choice.

Neuroscience↗

bioRxiv2016

Metacognitive ability predicts hippocampal and prefrontal microstructure

The ability to introspectively evaluate our experiences to form accurate metacognitive beliefs, or insight, is an essential component of decision-making. Previous research suggests individuals vary substantially in their level of insight, and that this variation predicts brain volume and function, particularly in the anterior prefrontal cortex (aPFC). However, the neurobiological mechanisms underlying these effects are unclear, as qualitative, macroscopic measures such as brain volume can be related to a variety of microstructural features. Here we used a newly developed, high-resolution (800{micro}m isotropic) multi-parameter mapping technique in 48 healthy individuals to delineate quantitative markers of in vivo histological features underlying metacognitive ability. Specifically, we examined how neuroimaging markers of local grey matter myelination, macromolecular and iron content relate to insight. Extending previous volumetric findings, we found that metacognitive ability, as determined by a signal-detection theoretic model, was positively related to the myelo-architectural integrity of aPFC grey matter. Interestingly, perceptual metacognition predicted decreased macromolecule content coupled with increased iron in the hippocampus and precuneus, areas previously implicated in meta-memory rather than meta-perception. Further, the relationship of hippocampal-precuneus and prefrontal microstructure to an auditory memory measure was respectively mediated or suppressed by metacognitive ability, suggesting a dynamic trade-off between participants memory and metacognition. These results point towards a novel understanding of the relationship between memory, brain microstructure, and metacognition.\n\nSignificance StatementBy combining a signal-theoretic model of individual metacognitive ability with state of the art quantitative neuroimaging, our results shed new light on the neurobiological mechanisms underlying introspective insight. Myelination and iron are core determinants of both healthy brain maturation and neurodegeneration; particularly in the hippocampus where iron accumulation is linked to oxidative stress and inflammation. Our results may thus indicate that metacognition depends upon the development and integrity of a memory-related brain network, potentially revealing novel biomarkers of neurodegeneration. These results highlight the power of quantitative mapping to reveal neurobiological correlates of behaviour.

Neuroscience↗

bioRxiv2016

Multi-Connection Pattern Analysis: Decoding the Representational Content of Neural Communication

The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. Applying MCPA to fMRI data shows that interactions between visual cortex regions are sensitive to information that distinguishes individual natural images, suggesting that image individuation occurs through interactive computation across the visual processing network. MCPA-based representational similarity analyses (RSA) results support models of error coding in interactions among regions of the network. Further RSA analyses relate the non-linear information transformation operations between layers of a computational model (HMAX) of visual processing to the information transformation between regions of the visual processing network. Additionally, applying MCPA to human intracranial electrophysiological data demonstrates that the interaction between occipital face area and fusiform face area contains information about individual faces. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions.

Neuroscience↗

bioRxiv2016

MicroRNA miR-29 controls a compensatory response to limit neuronal iron accumulation during adult life and aging

Iron is an essential metal cofactor for enzymes involved in many cellular functions such as energy generation and cell proliferation. However, excessive iron concentration leads to increased oxidative stress and toxicity. As such, iron homeostasis is strictly controlled by two RNA binding proteins known as Iron Regulatory Proteins (IRPs) that regulate at post-transcriptional level the expression of iron management genes. Despite this fine regulation, impairment of iron homeostasis occurs during aging: iron progressively accumulates in several organs and in turn, it exacerbates cellular vulnerability and tissue decay. Moreover, excessive iron accumulation within the CNS is observed in many neurodegenerative diseases. We investigated the age-dependent changes of iron homeostasis using the short lived fish Nothobranchius furzeri. Here, we show that i) both iron content and expression of microRNA family miR-29 increase during adult life and aging in the N. furzeri brain; ii) iron up-regulates miR-29 expression in fish brain and murine neurons, while in turn miR-29 targets the 3'-UTR of IREB2 mRNA, reducing iron intake; iii) Transgenic fish with knock-down of miR-29 show significant adult-onset up-regulation of IRP2 and its target TFR1 in neurons and display enhanced age-dependent accumulation of brain iron; iv) miR-29 triggers a global gene expression response that partially overlaps with that induced by aging.\n\nOur studies indicate that miR-29 modulates intracellular iron homeostasis and is up-regulated as an adaptive response to limit excessive iron accumulation and prevent early-onset aging processes.

Neuroscience↗

bioRxiv2016

An extension of the studyforrest dataset for vision research

The studyforrest (http://studyforrest.org) dataset is likely the largest neuroimag-ing dataset on natural language and story processing publicly available today. In this article, along with a companion publication, we present an update of this dataset that extends its scope to vision and multi-sensory research. 15 participants of the original cohort volunteered for a series of additional studies: a clinical examination of visual function, a standard retinotopic mapping procedure, and a localization of higher visual areas -- such as the fusiform face area. The combination of this update, the previous data releases for the dataset, and the companion publication, which includes neuroimaging and eye tracking data from natural stimulation with a motion picture, form an extremely versatile and comprehensive resource for brain imaging research -- with almost six hours of functional neuroimaging data across five different stimulation paradigms for each participant. Furthermore, we describe employed paradigms and present results that document the quality of the data for the purpose of characterising major properties of participants visual processing stream.

Neuroscience↗

bioRxiv2016

Simultaneous fMRI and eye gaze recordings during prolonged natural stimulation -- a studyforrest extension

Here we present an update of the studyforrest (http://studyforrest.org) dataset that complements the previously released functional magnetic resonance imaging (fMRI) data for natural language processing with a new two-hour 3 Tesla fMRI acquisition while 15 of the original participants were shown an audio-visual version of the stimulus motion picture. We demonstrate with two validation analyses that these new data support modeling specific properties of the complex natural stimulus, as well as a substantial within-subject BOLD response congruency in brain areas related to the processing of auditory inputs, speech, and narrative when compared to the existing fMRI data for audio-only stimulation. In addition, we provide participants' eye gaze location as recorded simultaneously with fMRI, and an additional sample of 15 control participants whose eye gaze trajectories for the entire movie were recorded in a lab setting -- to enable studies on attentional processes and comparative investigations on the potential impact of the stimulation setting on these processes.

Neuroscience↗

bioRxiv2016

Low parental melatonin levels increases autism spectrum disorder risk in children

Low melatonin levels are a frequent finding in autism spectrum disorder (ASD) patients. Melatonin is important for normal neurodevelopment and embryonic growth and highly effective in protecting DNA from oxidative damage. Melatonin deficiency, possibly due to low CYP1A2 activity, could be an etiologic factor. As the fetus does not produce melatonin, low maternal melatonin levels should be involved. We measured 6-sulfatoxymelatonin in urine of 60 mothers of a child with ASD that attended our sleep clinic for people with an intellectual disability (ID), and asked for coffee consumption habits, as these are known to be related to CYP1A2 activity. 6-Sulfatoxymelatonin levels were significantly lower in mothers than in controls (p = 0.005), as well as evening coffee consumption (p = 0.034).\n\nAbbreviations

Neuroscience↗

bioRxiv2016

Spontaneous Blink Rate Correlates With Financial Risk Taking

Dopamine has long been thought to play a role in risky decision-making, with higher tonic levels of dopamine associated with more risk seeking behavior. In this work, we aimed to shed more light on this relationship using spontaneous blink rate as an indirect measure of dopamine. In particular we used video recording to measure blink rate and a decision-making survey to measure risk taking in 45 participants. Consistent with previous work linking dopamine to risky decisions, we found a strong positive correlation between blink rate and the number of risky choices a participant made. This correlation was not dependent on age or gender and was identical for both gain and loss framing. This work suggests that dopamine plays a crucial and quite general role in determining risk attitude across the population and validates this simple method of probing dopamine for decision-making research.

Neuroscience↗

bioRxiv2016

CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging data in Matlab / GNU Octave

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto-and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens.\n\nCoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species.\n\nIt uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets.\n\nCoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques.\n\nCoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality.\n\nCoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian.\n\nCoSMoMVPA is Free/Open Source Software under the permissive MIT license.\n\nWebsite: https://cosmomvpa.org\n\nSource code: https://github.com/CoSMoMVPA/CoSMoMVPA

Neuroscience↗