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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Promiscuity of peripheral molecular biomarkers in major psychiatric disorders: a transdiagnostic systematic review

The search for biomarkers has been one of the leading endeavors in biological psychiatry; nevertheless, in spite of hundreds of publications, it has yet to make an impact in clinical practice. To study how biomarker research has progressed over the years, we performed a systematic review of the literature to evaluate (a) the most studied peripheral molecular markers in major psychiatric disorders, (b) the main features of studies in which they are proposed as biomarkers and (c) whether their patterns of variation are similar across disorders. Out of the six molecules most commonly present as keywords in articles studying plasmatic markers of schizophrenia, major depressive disorder or bipolar disorder, five (BDNF, TNF-alpha, IL-6, C-reactive protein and cortisol) were the same across the three diagnoses. An analysis of the literature on these molecules showed that, while 65% of studies were cross-sectional and 66% compared biomarker levels between patients and controls in specific disorders, only 10% presented an objective measure of diagnostic or prognostic efficacy. Meta-analyses showed that variation in the levels of these molecules was robust across studies, but also similar among disorders, suggesting them to reflect transdiagnostic systemic consequences of psychiatric illness rather than diagnostic markers. Based on this, we discuss how current publication practices have led to research fragmentation across diagnoses, and what steps can be taken in order to increase clinical translation in the field.

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bioRxiv2016

Neurons in the inferior temporal cortex of macaque monkeys are sensitive to multiple surface features from natural objects

Object surfaces contain a variety of visual features that help us to recognize them. To understand how this information is represented and processed in the brain, we prepared a set of images from natural object surfaces that maintained surface features but lacked contours. We examined spiking responses of neurons in the inferior temporal (IT) cortex of monkeys, which is a crucial structure needed for visual object recognition. About half of IT neurons responded to surface images with sharp selectivity, indicating that a significant fraction of these neurons contribute to object surface representation in a sparse manner. Responses of IT neurons were susceptible to image manipulations, including color removal, removal of luminance contrasts, and spatial structure degradation. This shows that multiple features are required for IT responses to surface images. Comparing neuronal response properties among IT, visual area 4 (V4), and primary visual cortex (V1) revealed properties of IT neurons that differed from those in the other visual processing regions. Additionally, some neuronal response properties were similar between IT and V4, but differed from those in V1, indicating that responses of IT neurons to surface images are constructed by hierarchical processing throughout the ventral visual pathway.

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bioRxiv2016

Feedback Dynamics Determine the Structureof Spike-Count Correlation in Visual Cortex

The variable responses of sensory neurons tend to be weakly correlated (spike-count correlation, rsc). This is widely thought to reflect noise in shared afferents, in which case rsc can limit the reliability of sensory coding. However, it could also be due to feedback from higher-order brain regions. Currently, the relative contribution of these sources is unknown. We addressed this by recording from populations of V1 neurons in macaques performing different discrimination tasks involving the same visual input. We found that the structure of rsc(the way rsc varied with neuronal stimulus preference) changed systematically with task instruction. Therefore, even at the earliest stage in the cortical visual hierarchy, rsc structure during task performance primarily reflects feedback dynamics. Consequently, previous proposals for how rsc constrains sensory processing need not apply. Furthermore, we show that correlations between the activity of single neurons and choice depend on feedback engaged by the task.

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bioRxiv2016

Multidimensional imbalances in cortical circuit activity in Fragile-X Syndrome mice

A leading theory for multiple brain disorders, such as schizophrenia and autism, is that they arise from developmental imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this simple 1-dimensional model is sufficiently rich to capture the set of key alterations of neural circuit processing in brain disorders. Here we used a combination of computational modeling and large-scale analysis of in vivo 2-photon Ca2+ imaging data from somatosensory cortex of wild-type and Fmr1 knock-out (KO) mice, a model of Fragile X syndrome, to test the E/I imbalance model. Our main findings were: 1) the E/I imbalance model was not rich enough to capture the alterations in neural activity statistics in Fmr1 KO mice; 2) in a computational model we found greatly varying magnitudes and directions of effects of synaptic and cellular properties on network activity; 3) evidence for opposite changes in circuit properties of Fmr1 KO mice at different stages of development; 4) a reduction in the entropy of circuit activity in young Fmr1 KO mice compared to wild-type, but an opposite increase in adult Fmr1 KO mice. These findings suggest qualitatively new strategies for developing treatments for Fragile-X Syndrome and related disorders.

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bioRxiv2016

Asymmetrical representation of the upper and lower visual fields in oculomotor maps

The striate area devoted to the lower visual field (LVF) is larger than that devoted to the upper visual field (UVF). A similar anatomical asymmetry also exists in the LGN. Here we take advantage of two experimental tasks that are known to modulate the direction and amplitude of saccades to demonstrate a visual field asymmetry in oculomotor maps. Participants made visually guided saccades. In Experiment 1, the saccade target was accompanied by a visual distractor. The distractors presence modulated the direction of saccades, and this effect was much stronger for LVF targets. In Experiment 2, the temporal gap between the offset of the fixation stimulus and the onset of the saccade target was manipulated. This manipulation modulated the amplitude of saccades and this modulation was stronger for saccades towards UVF targets. Taken together, these results suggest that the representation of both meridians and eccentricities in the LVF is compressed in oculomotor maps.

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bioRxiv2016

Segregation between the parietal memory network and the default mode network: Effects of spatial smoothing and model order in ICA

A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network (DMN), its function has been associated with memory processing, and it has been referred to as the parietal memory network (PMN). Independent component analysis (ICA) is the most common data-driven method of extracting PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determination in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high-order model across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.

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bioRxiv2016

A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing

Experimental studies have revealed evidence of both parts-based and holistic representations of objects and faces in the primate visual system. However, it is still a mystery how such seemingly contradictory types of processing can coexist within a single system. Here, we propose a novel theory called mixture of sparse coding models, inspired by the formation of category-specific subregions in the inferotemporal (IT) cortex. We developed a hierarchical network that constructed a mixture of two sparse coding submodels on top of a simple Gabor analysis. The submodels were each trained with face or non-face object images, which resulted in separate representations of facial parts and object parts. Importantly, evoked neural activities were modeled by Bayesian inference, which had a top-down explaining-away effect that enabled recognition of an individual part to depend strongly on the category of the whole input. We show that this explaining-away effect was indeed crucial for the units in the face submodel to exhibit significant selectivity to face images over object images in a similar way to actual face-selective neurons in the macaque IT cortex. Furthermore, the model explained, qualitatively and quantitatively, several tuning properties to facial features found in the middle patch of face processing in IT as documented by Freiwald, Tsao, and Livingstone (2009). These included, in particular, tuning to only a small number of facial features that were often related to geometrically large parts like face outline and hair, preference and anti-preference of extreme facial features (e.g., very large/small inter-eye distance), and reduction of the gain of feature tuning for partial face stimuli compared to whole face stimuli. Thus, we hypothesize that the coding principle of facial features in the middle patch of face processing in the macaque IT cortex may be closely related to mixture of sparse coding models.

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bioRxiv2016

A TOPOLOGICAL/ECOLOGICAL APPROACH TO PERCEPTION

During the exploration of the surrounding environment, the brain links together external inputs, giving rise to perception of a persisting object. During imaginative processes, the same object can be recalled in mind even if it is out of sight. In this proof of concept study, Borsuks theory of shape and the Borsuk-Ulam theorem provide a mathematical foundation for Gibsons notion of persistence perception. Gibsons ecological theory of perception accounts for our knowledge of world objects by borrowing a concept of invariance in topology. A series of transformations can be gradually applied to a pattern, in particular to the shape of an object, without affecting its invariant properties, such as boundedness of parts of a visual scene. High-level representations of objects in our environment are mapped to simplified views (our interpretations) of the objects, in order to construct a symbolic representation of the environment. The representations can be projected continuously to an ecological object that we have seen and continue to see, thanks to the mapping from shapes in our memory to shapes in Euclidean space.

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bioRxiv2016

Spatial tuning shifts increase the discriminability and fidelity of population codes in visual cortex

AcknowledgementsMany thanks to the lab and particularly to Rosanne Rademaker and Edward Vul for comments on analyses and on the manuscript. This work was supported by National Science Foundation Graduate Research Fellowships to V.A.V. and T.C.S., a grant from the National Eye Institute (R01-EY025872) and a Scholar Award from the James S. McDonnell Foundation to J.T.S.\n\nABSTRACTSelective visual attention enables organisms to enhance the representation of behaviorally relevant stimuli by altering the encoding properties of single receptive fields (RFs). Yet we know little about how the attentional modulations of single RFs contribute to the encoding of an entire visual scene. Addressing this issue requires (1) measuring a group of RFs that tile a continuous portion of visual space, (2) constructing a population-level measurement of spatial representations based on these RFs, and (3) linking how different types of RF attentional modulations change the population-level representation. To accomplish these aims, we used fMRI to characterize the responses of thousands of voxels in retinotopically organized human cortex. First, we found that the response modulations of voxel RFs (vRFs) depend on the spatial relationship between the RF center and the visual location of the attended target. Second, we used two analyses to assess the spatial encoding quality of a population of voxels. We found that attention increased fine spatial discriminability and representational fidelity near the attended target. Third, we linked these findings by manipulating the observed vRF attentional modulations and recomputing our population measures. Surprisingly, we discovered that attentional enhancements of population-level representations largely depend on position shifts of vRFs, rather than changes in size or gain. Our data suggest that position shifts of single RFs are a principal mechanism by which attention enhances population-level representations in visual cortex.

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bioRxiv2016

Spike interval coding of translatory optic flow and depth from motion in the fly visual system

Many animals use the visual motion generated by travelling in a line, the translatory optic flow, to successfully navigate obstacles: near objects appear larger and to move more quickly than distant ones. Flies are experts at navigating cluttered environments, and while their visual processing of rotatory optic flow is understood in exquisite detail, how they process translatory optic flow remains a mystery. Here, we present novel cell types that have motion receptive fields matched to translation self-motion, the vertical translation (VT) cells. One of these, the VT1 cell, encodes forwards sideslip self-motion, and fires action potentials in clusters of spikes, spike bursts. We show that the spike burst coding is size and speed-tuned, and is selectively modulated by parallax motion, the relative motion experienced during translation. These properties are spatially organized, so that the cell is most excited by clutter rather than isolated objects. When the fly is presented with a simulation of flying past an elevated object, the spike burst activity is modulated by the height of the object, and the single spike rate is unaffected. When the moving object alone is experienced, the cell is weakly driven. Meanwhile, the VT2-3 cells have motion receptive fields matched to the lift axis. In conjunction with previously described horizontal cells, the VT cells have the properties required for the fly to successfully navigate clutter and encode its movements along near cardinal axes of thrust, lift and forward sideslip.

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bioRxiv2016

Recent progress in multi-electrode spike sorting methods

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.

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bioRxiv2016

Genetic overlap between in-scanner head motion and the default network connectivity

The association between in-scanner head motion and intrinsic functional connectivity (iFC) may confound explanations for individual differences in functional connectomics. However, the etiology of the correlation between head motion and iFC has not been established. This study aimed to investigate genetic and environmental contributions on the association between head motion and iFC using a twin dataset (175 same-sex twin pairs, aged 14-23 years, 48% females). After establishing that both head motion and default network iFC are moderately heritable, we found large genetic correlations (-0.52 to -0.73) between head motion and the default network iFCs. Common genes can explain 48% - 61% of the negative phenotypic correlation between the two phenotypes. These results advance our understanding of the relationship between head motion and iFC, and may have profound implications for interpreting individual differences in default network connectivity in clinical research and brain-behavior association.

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bioRxiv2016

Response variability and population coupling of cortical synaptic inputs are strongly influenced by network properties

The highly variable spiking of a cortical neuron is \"coupled\" to that of other neurons in the network. This has implications for sensory coding, and appears to represent a fundamental property of cortical sensory processing. To date, most studies of population coupling have focused on recorded spiking activity, an approach that suffers from several confounding issues. Moreover, the contributions of various network properties to population coupling are largely unexplored. To this end, we recorded the membrane potential (V) and the nearby LFP in the visual cortex of the turtle ex vivo wholebrain preparation during ongoing and visually-evoked activity. We used an algorithm to infer the excitatory conductance (g) from V, and calculated the g-LFP coupling. We found that g-LFP coupling was highly variable across neurons, and increased following visual stimulation before relaxing to intermediate values. To investigate the role of the network, we implemented a driven small-world network of leaky integrate-and-fire neurons. This model reproduces the large across-trial response variability and g-LFP coupling dynamic, and suggests crucial roles for anatomical and emergent network properties.

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bioRxiv2016

Adaptation modulates correlated response variability in visual cortex

Cortical sensory responses are highly variable across stimulus presentations. This variability can be correlated across neurons (due to some combination of dense intracortical connectivity, cortical activity level, and cortical state), with fundamental implications for population coding. Yet the interpretation of correlated response variability (or \"noise correlation\") has remained fraught with difficulty, in part because of the restriction to extracellular neuronal spike recordings. Here, we measured response variability and its correlation at the most microscopic level of electrical neural activity, the membrane potential, by obtaining dual whole-cell recordings from pairs of cortical pyramidal neurons during visual processing. We found that during visual stimulation, correlated variability adapts towards an intermediate level and that this correlation dynamic is mediated by intracortical mechanisms. A model network with external inputs, synaptic depression, and structure reproduced the observed dynamics of correlated variability. These results establish that intracortical adaptation self-organizes cortical circuits towards a balanced regime at which network coordination maintains an intermediate level.

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bioRxiv2016

Learning enhances sensory processing in mouse V1 before improving behavior

A fundamental property of visual cortex is to enhance the representation of those stimuli that are relevant for behavior, but it remains poorly understood how such enhanced representations arise during learning. Using classical conditioning in mice, we show that orientation discrimination is learned in a sequence of distinct behavioral stages, in which animals first rely on stimulus appearance before exploiting its orientation to guide behavior. After confirming that orientation discrimination under classical conditioning requires primary visual cortex (V1), we measured, during learning, response properties of V1 neurons. Learning improved neural discriminability, sharpened orientation tuning and led to higher contrast sensitivity. Remarkably, these learning-related improvements in the V1 representation were fully expressed before successful orientation discrimination was evident in the animals behavior. We propose that V1 plays a key role early in discrimination learning to enhance behaviorally relevant sensory information.

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bioRxiv2016

Anterior cingulate is a source of valence-specificinformation about value and uncertainty

Expectations of rewards and punishments can promote similar behavioral states, such as vigilance, as well as distinct behavioral states, such as approach or avoidance. However, the cortical circuits that underlie this behavioral diversity are poorly understood. In a Pavlovian procedure in which monkeys displayed a diverse repertoire of reward, punishment, and uncertainty related behaviors not mandated by the task, I show that many anterior-cingulate (ACC) neurons represent expected value and uncertainty in a valence-specific manner, for example about either rewards or punishments. This flexibility may facilitate the top-down control of many reward- and punishment-related actions and behavioral states.

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bioRxiv2016

A first-principle for the nervous system

Higher brain functions such as perception and memory are first-person internal sensations whose mechanisms can have options to concurrently activate motor neurons for behavioral action. By setting up all the required constraints using available information from different levels, a theoretical examination from a first-person frame of reference led to the derivation of a first-principle of the structure-function units. These units operate in synchrony with the synaptically-connected neural circuitry. These units are capable of explaining and interconnecting findings from various levels and enable triangulation of a large number of observations from both the normal and \"loss of function\" states of the system. Indirect evidence for the presence of a comparable circuitry in a remote species - Drosophila - suggests the universal presence of these units across the animal kingdom. The key features of the basic unit meet the expectations of the K-lines proposed for the development of artificial intelligence.

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bioRxiv2016

T-SNE visualization of large-scale neural recordings.

Electrophysiology is entering the era of Big Data. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention [22, 23], but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially.\n\nHere we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method [27] as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid [23] and paired juxtacellular/extracellular recordings [15]. We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curration of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.

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