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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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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↗

bioRxiv2016

Predictions shape confidence in right inferior frontal gyrus

It is clear that prior expectations shape perceptual decision-making, yet their contribution to the construction of subjective decision confidence remains largely unexplored. We recorded fMRI data while participants made perceptual decisions and confidence judgements, controlling for potential confounds of attention. Results show that subjective confidence increases as perceptual prior expectations increasingly support the decision, and that this relationship is associated with BOLD activity in right inferior frontal gyrus (rIFG). Specifically, rIFG is sensitive to the discrepancy between expectation and decision (mismatch), and, crucially, higher mismatch responses are associated with lower decision confidence. Connectivity analyses revealed the source of the expectancy information to be bilateral orbitofrontal cortex (OFC) and the source of sensory signals to be intracalcarine sulcus. Altogether, our results indicate that predictive information is integrated into subjective confidence in rIFG, and reveal an occipital-frontal network that constructs confidence from top-down and bottom-up signals. This interpretation was further supported by exploratory findings that the white matter density of intracalcarine sulcus and OFC negatively predicted their respective contributions to the construction of confidence. Our findings advance our understanding of the neural basis of subjective perceptual processes by revealing an occipito-frontal functional network that integrates prior beliefs into the construction of confidence.

Neuroscience↗

bioRxiv2016

Language impairment resulting from a de novo deletion of 7q32.1-q33: a case report

Chromosome 7 is a hot spot for cognitive disorders involving language deficits. We report on a girl who presents with a cognitive and speech delay, motor problems, hearing loss, and behavioral disturbances, and a de novo deletion within 7q32.1-q33 (chromosome position: chr7:127109685-132492196, hg 18). Several genes involved in brain development and function are located within the deleted region. Many of them are related to developmental disorders encompassing language deficits (dyslexia, speech-sound disorder, and autism). The probands phenotype may result from a change in the expression level of some of these genes.

Neuroscience↗

bioRxiv2016

Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and Quantification

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study the spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we formalize a heuristic method for approximating the interpretability of multivariate brain maps in a binary magnetoencephalography (MEG) decoding scenario. Third, we pro pose to combine the approximated interpretability and the performance of the brain decoding into a new multi-objective criterion for model selection. Our results for the MEG data show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.

Neuroscience↗

bioRxiv2016

A switching cost for motor planning

Movement planning consists of choosing the endpoint of the movement and selecting the motor program that will bring the effector on the endpoint. It is widely accepted that movement endpoint is updated on a trial-by-trial basis with respect to the observed errors and that the motor program for a given movement follows the rules of optimal feedback control. Here, we show clear limitations of these predictions because of the existence of a switching cost for motor planning. First, this cost prevented participants from tuning their motor program appropriately for each individual trial. This was true even when the participants selected the width of the target that they reached toward or when they had learned the appropriate motor program previously. These data are compatible with the existence of a switching cost such as those found in cognitive studies. Interestingly, this cost of switching shares many features of costs reported in cognitive task switching experiments and, when tested in the same participants, was correlated with it. Second, we found that randomly changing the width of a target over the course of a reaching experiment prevents the motor system from updating the endpoint of movements on the basis of the performance on the previous trial if the width of the target has changed. These results provide new insights into the process of motor planning and how it relates to optimal control theory and to a selection by consequences process rather than to an error-based process for action selection.

Neuroscience↗

bioRxiv2016

The thalamus is a gateway for stimulus-evoked activity in the habenula

The thalamus mediates diverse brain functions including arousal, perception and memory formation, and is characterized by widespread connectivity. One thalamus projection that is evolutionarily conserved is to the habenula, a regulator of broadly acting neuromodulators. The function of this projection is unknown, as the information transmitted has not been identified. By two-photon calcium imaging of larval zebrafish, we show that change in irradiance triggers dynamic responses in the habenula. Neuroanatomical tracing shows that the dorsal left habenula neuropil, which has the most prominent response to irradiance change and influences irradiance-dependent behavior and raphe response, is innervated by retinorecipient nuclei in the thalamus. Optogenetic stimulation of the thalamus triggers habenula activity, while lesion reduces light-evoked habenula activity. These data establish that the thalamus controls habenula activity following irradiance change, thereby suggesting that the thalamo-habenula projection provides a pathway for visual stimuli to affect neuromodulator release and behavior.

Neuroscience↗

bioRxiv2016

Unconscious integration of multisensory bodily inputs in the peripersonal space shapes bodily self-consciousness

Recent studies have highlighted the role of multisensory integration as a key mechanism of self-consciousness. In particular, integration of bodily signals within the peripersonal space (PPS) underlies the experience of the self in a body we own (self-identification) and that is experienced as occupying a specific location in space (self-location), two main components of bodily self-consciousness (BSC). Experiments investigating the effects of multisensory integration on BSC have typically employed supra-threshold sensory stimuli, neglecting the role of unconscious sensory signals in BSC, as tested in other consciousness research. Here, we used psychophysical techniques to test whether multisensory integration of bodily stimuli underlying BSC may also occur for multisensory inputs presented below the threshold of conscious perception. Our results indicate that visual stimuli rendered invisible (through continuous flash suppression) boost processing of tactile stimuli on the body (Exp. 1), and enhance the perception of near-threshold tactile stimuli (Exp. 2), only once they entered peripersonal space. We then employed unconscious multisensory mechanisms to manipulate BSC. Participants were presented with tactile stimulation on their body and with visual stimuli on a virtual body, seen at a distance, which were either visible or rendered invisible. We report that if visuo-tactile stimulation was synchronized, participants self-identified with the virtual body (Exp. 3), and shifted their self-location toward the virtual body (Exp.4), even if visual stimuli were fully invisible. Our results indicate that multisensory inputs, even outside of awareness, are integrated and affect the phenomenological content of self-consciousness, grounding BSC firmly in the field of psychophysical consciousness studies.

Neuroscience↗

bioRxiv2016

Minimal Data Needed for Valid & Accurate Image-Based fMRI Meta-Analysis

Meta-analysis is a powerful statistical tool to combine results from a set of studies. When image data is available for each study, a number of approaches have been proposed to perform such meta-analysis including combination of standardised statistics, just effect estimates or both effects estimates and their sampling variance. While the latter is the preferred approach in the statistical community, often only standardised estimates are shared, reducing the possible meta-analytic approaches. Given the growing interest in data sharing in the neuroimaging community there is a need to identify what is the minimal data to be shared in order to allow for future image-based meta-analysis. In this paper, we compare the validity and the accuracy of eight meta-analytic approaches on simulated and real data. In one-sample tests, combination of contrast estimates into a random-effects General Linear Model or non-parametric statistics provide a good approximation of the reference approach. If only standardised statistical estimates are shared, permutations of z-score is the preferred approach.

Neuroscience↗

bioRxiv2016

Detecting distortions of peripherally-presented letter stimuli under crowded conditions

When visual features in the periphery are close together they become difficult to recognise: something is present but it is unclear what. This is called \"crowding\". Here we investigated sensitivity to features in highly familiar shapes (letters) by applying spatial distortions. In Experiment 1, observers detected which of four peripherally-presented (8 deg of retinal eccentricity) target letters was distorted (spatial 4AFC). The letters were presented either isolated or surrounded by four undistorted flanking letters, and distorted with one of two types of distortion at a range of distortion frequencies and amplitudes. The bandpass noise distortion (\"BPN\") technique causes spatial distortions in cartesian space, whereas radial frequency distortion (\"RF\") causes shifts in polar coordinates. Detecting distortions in target letters was more difficult in the presence of flanking letters, consistent with the effect of crowding. The BPN distortion type showed evidence of tuning, with sensitivity to distortions peaking at approximately 6.5 c/deg for un-flanked letters. The presence of flanking letters causes this peak to rise to approximately 8.5 c/deg. In contrast to the tuning observed for BPN distortions, RF distortion sensitivity increased as the radial frequency of distortion increased. In a series of follow-up experiments we found that sensitivity to distortions is reduced when flanking letters were also distorted, that this held when observers were required to report which target letter was undistorted, and that this held when flanker distortions were always detectable. The perception of geometric distortions in letter stimuli is impaired by visual crowding.

Neuroscience↗