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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Asymmetry of the temporal code for space by hippocampal place cells

Hippocampal place cells convey spatial information through spike frequency (\"rate coding\") and spike timing relative to the theta phase (\"temporal coding\"). Whether rate and temporal coding are due to independent or related mechanisms has been the subject of wide debate. Here we show that the spike timing of place cells couples to theta phase before major increases in firing rate, anticipating the animals entrance into the classical, rate-based place field. In contrast, spikes rapidly decouple from theta as the animal leaves the place field and firing rate decreases. Therefore, temporal coding has strong asymmetry around the place field center. We further show that the dynamics of temporal coding along space evolves in three stages: phase coupling, phase precession and phase decoupling. These results suggest that place cells represent more future than past locations through their spike timing and that independent mechanisms govern rate and temporal coding.

Neuroscience↗

bioRxiv2016

Disrupting dorsolateral prefrontal cortex by rTMS reduces the P300 based marker of deception

It is well known that electroencephalographic event related potential component P300 is sensitive to perception of critical items in a concealed information test. However, it is not known whether the relative level of expression of P300 as a neural marker of deception can be manipulated by means of non-invasive neuromodulation. Here, we show that while P300 exhibited systematic amplitude differences in response to the more as well as the less significant stimuli items encountered at the \"crime scene\" compared to neutral items, offline rTMS to dorsolateral prefrontal cortex attenuated P300 amplitude in response to the critical items. Yet, the individual subjects showed different sensitivity of the P300 as the marker of concealment. We conclude that rTMS can be used for subduing electrophysiological markers of deception, but this effect depends on whether the subject belongs to the group of CIT-sensitive individuals.

Neuroscience↗

bioRxiv2016

Williams Syndrome-Specific Neuroarchitectural Profile and Its Associations with Cognitive Features

Williams Syndrome (WS), a rare genetic disorders caused by hemizyous deletion of ~26 genes on the chromosome 7, has unique cognitive features and neuroanatomic abnormalities. Limited in statistical power due to its rareness had led to inconsistent in many direct comparisons using structural magnetic resonance imaging (MRI), and their associations with cognitive features of WS are not clear. Here, we used a novel approach to derive a WS specific neuroarchitectural profile and tested its association with cognitive features of WS. Using a WS adult cohort (n = 43), we trained a logistic elastic-net model to extract a sparse representation of WS specific neuroarchitectural profile. The predictive performances are robust within the training cohort (leave one out cross-validation AUC = 1.0) and generalized well in an independent teenager WS cohort (n = 60, AUC = 1.0). The WS specific neuroarchitectural profile includes multiple MRI measurements in the orbitofrontal cortex, superior parietal cortex, Sylvian fissures, and basal ganglia, whereas its variations reflect the underlying size of hemizygous deletion, and mediated the disease impact on the cognitive features of WS. In this study, we demonstrate the robustness of the derived WS specific neuroarchitectural profile, suggesting the joint developmental abnormalities in the cortical-subcortical circuitry cause the unique features of WS cognition.

Neuroscience↗

bioRxiv2016

Heterogeneity of cognitive decline in dementia: taking into account variable time-zero severity

NOTEThe biases seen in simulations of Temporal Clustering appear to be even worse in real applications. While this was a novel and interesting approach, ultimately this work has been discontinued. I feel the biases are due to propagated error in estimating individual level offsets based on a single noisy measure, amplified by the fact that high MMSE scores change very slowly and therefore many estimates are from near an asymptote on the left hand of the model. We continue to work in this area, with other approaches showing significantly more promise.\n\nUnderstanding heterogeneity in Alzheimers disease (AD) progression is critically important for the optimal design of trials, allowing participants to be recruited who are correctly diagnosed and who are likely to undergo cognitive decline. Current knowledge about heterogeneity is limited by the paucity of long-term follow-up data and methodological challenges. Of the latter, a key problem is how to choose the most appropriate time zero to use in longitudinal models, a choice which affects results. Rather than a pre-specified time zero we propose a novel methodology - Temporal Clustering - that defines a new time zero using individual offsets inferred from the data. We applied this to longitudinal Mini-Mental State Examination (MMSE), where this approach ensures that individuals have similar estimated MMSE scores at this new time zero. Simulations showed that it could accurately predict cluster membership after the application of a filter. Next we applied it to a cohort of 2412 individuals, with large variability in MMSE score at first visit. Temporal Clustering was used to split individuals into two clusters. The group showing faster decline had higher average levels of AD risk factors: cerebrospinal fluid tau and APOE [isin]4. Cluster membership predicted by Temporal Clustering was less affected by individuals cognitive ability at first visit than was the case for clusters found using Latent Class Mixture Models. Further application and development of this method will help researchers to identify risk factors affecting cognitive decline.

Neuroscience↗

bioRxiv2016

Two-Dimensional Hermite Filters Simplify the Description of High-Order Statistics of Natural Images

Natural image statistics play a crucial role in shaping biological visual systems, understanding their function and design principles, and designing effective computer-vision algorithms. High-order statistics are critical for conveying local features, but they are challenging to study, largely because their number and variety is large. Here, via the use of two-dimensional Hermite (TDH) functions, we identify a covert symmetry in high-order statistics of natural images that simplifies this task. This emerges from the structure of TDH functions, which are an orthogonal set of functions that are organized into a hierarchy of ranks. Specifically, we find that the shape (skewness and kurtosis) of the distribution of filter coefficients depends only on the projection of the function onto a 1-dimensional subspace specific to each rank. The characterization of natural image statistics provided by TDH filter coefficients reflects both their phase and amplitude structure, and we suggest an intuitive interpretation for the special subspace within each rank.

Neuroscience↗

bioRxiv2016

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n = 25) and healthy controls (n = 25), SVM learning accurately (70%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.

Neuroscience↗

bioRxiv2016

CLARITY-compatible lipophilic dyes for electrode marking and neuronal tracing

Fluorescent lipophilic dyes, such as DiI, stain cellular membranes and are used extensively for retrograde/anterograde labelling of neurons as well as for marking the position of extracellular electrodes after electrophysiology. Convenient histological clearing techniques, such as CLARITY, enable immunostaining and imaging of large volumes for 3D-reconstruction. However, such clearing works by removing lipids and, as an unintended consequence, also removes lipophilic dyes. To remedy this wash-out, the molecular structure of the dye can be altered to adhere to both membranes and proteins so the dye remains in the tissue after lipid-clearing. Nevertheless, the capacity of such modified dyes to remain in tissue has not yet been tested. Here, we test dyes with molecular modifications that make them aldehyde-fixable to proteins. We use three Dil-analogue dyes, CM-DiI, SP-DiI and FM 1-43FX that are modified to be CLARITY-compatible candidates. We use the challenging adult, myelin-rich spinal cord tissue, which requires prolonged lipid-clearing, of rats and mice. All three dyes remained in the tissue after lipid-clearing, but CM-DiI had the sharpest and FM 1-43FX the strongest fluorescent signal.

Neuroscience↗

bioRxiv2016

Decoding the Infant Mind: Multichannel Pattern Analysis (MCPA) using fNIRS

The MRI environment restricts the types of populations and tasks that can be studied by cognitive neuroscientists (e.g., young infants, face-to-face communication). FNIRS is a neuroimaging modality that records the same physiological signal as fMRI but without the constraints of MRI, and with better spatial localization than EEG. However, research in the fNIRS community largely lacks the analytic sophistication of analogous fMRI work, restricting the application of this imaging technology. The current paper presents a method of multivariate pattern analysis for fNIRS that allows the authors to decode the infant mind (a key fNIRS population). Specifically, multi-channel pattern analysis (MCPA) employs a correlation-based decoding method where a group model is constructed for all infants except one; both average patterns (i.e., infant-level) and single trial patterns (i.e., trial-level) of activation are decoded. Between subjects decoding is a particularly difficult task, because each infant has their own somewhat idiosyncratic patterns of neural activation. The fact that our method succeeds at across-subject decoding demonstrates the presence of group-level multi-channel regularities across infants. The code for implementing these analyses has been made readily available online to facilitate the quick adoption of this method to advance the methodological tools available to the fNIRS researcher.

Neuroscience↗

bioRxiv2016

Preparatory encoding of the fine scale of human spatial attention

Our attentional focus is constantly shifting: in one moment our vision may be intently concentrated on a specific spot, while in another moment we might spread our attention more broadly. While much is known about the mechanisms by which we shift our visual attention from place to place, relatively little is know about how we shift the aperture of attention from more narrowly-to more broadly-focused. Here we introduce a novel attentional distribution task to examine the neural mechanisms underlying this process. In this task, participants are presented with an informative cue that indicates the location of an upcoming target. This cue can be perfectly predictive of the exact target location, or it can indicate--with varying degrees of certainty--approximately where the target might appear. This cue is followed by a preparatory period in which there is nothing on the screen except a central fixation cross. Using scalp EEG, we examined neural activity during this preparatory period. We find that with decreasing certainty regarding the precise location of the impending target, participant response times increased while target identification accuracy decreased. Additionally, N1 amplitude in response to the cue parametrically increased with spatial certainty while the multivariate pattern of preparatory period visual cortical alpha (8-12 Hz) activity encoded attentional distribution. Both of these electrophysiological parameters were predictive of behavioral performance nearly one second later. These results offer insight into the neural mechanisms underlying how we use information to guide our attentional distribution, and how that influences behavior.\n\nAuthors contributionsB.V. and A.G. conceived of the study; B.V. and A.G. designed the experimental task; B.V. and J.S. analyzed the EEG data; B.V., J.S., Z.G., N.G., S.P., T.K., S.R., and R.M. collected and analyzed behavioral data; all co-authors assisted in writing the manuscript.\n\nB.V. is funded by an NIH IRACDA (Institutional Research and Academic Career Development Award), a University of California Presidential Postdoctoral Fellowship, the University of California, San Diego CalIt2 Strategic Research Opportunities Program, and a Sloan Research Fellowship. A.G. is funded by the National Institutes of Health Grant R01-AG30395.\n\nSignificant StatementAnimals--including humans--frequently shift their visual attentional focus more narrowly or broadly depending on expectations. For example, a predator feline may focus their visual attention on a burrow hole, waiting for their prey to emerge. In contrast, a grizzly bear hunting salmon doesn't know precisely where the fish will jump out of the water, so it must spread its attention more broadly. In a series of novel experiments, we show that this broadening of attention comes at a behavioral cost. We find that multivariate changes in preparatory visual cortical oscillatory alpha (8-12 Hz) encode attentional distribution. These results shed light on the potential neural mechanisms by which preparatory information is used to guide attentional focus.

Neuroscience↗

bioRxiv2016

Causal Relationships Underlying Episodic Memory Network Dynamics in Older Adults

Lesion and functional neuroimaging evidence suggest that local network deficits can be compensated by global network over-recruitment during a cognitive task. However, the neural mechanisms linking local and global network changes remain uncertain. To investigate this issue, we manipulated local function using repetitive transcranial magnetic stimulation (rTMS) and measured structural and task-based functional network organization during memory encoding. The study yielded two main findings. First, consistent with the compensation hypothesis, 1-Hz rTMS attenuated local activity in the stimulated left prefrontal cortex (PFC) but increased the connectivity of this region with other brain areas, including contralateral PFC. Second, this 1-Hz related increase in global connectivity effects was associated with the volume of more global connections, suggesting white-matter constraints adaptive changes in functional connectivity. Taken together, these results clarify the neural mechanisms linking local and global network changes and the role of structural connectivity.

Neuroscience↗

bioRxiv2016

How to evaluate phase differences between trial groups in ongoing electrophysiological signals

A growing number of studies endeavor to reveal periodicities in sensory and cognitive functions, by comparing the distribution of ongoing (pre-stimulus) oscillatory phases between two (or more) trial groups reflecting distinct experimental outcomes. A systematic relation between the phase of spontaneous electrophysiological signals, before a stimulus is even presented, and the eventual result of sensory or cognitive processing for that stimulus, would be indicative of an intrinsic periodicity in the underlying neural process. Prior studies of phase-dependent perception have used a variety of analytical methods to measure and evaluate phase differences, and there is currently no established standard practice in this field. The present report intends to remediate this need, by systematically comparing the statistical power of various measures of \"phase opposition\" between two trial groups, in a number of real and simulated experimental situations. Seven measures were evaluated: one parametric test (circular Watson-Williams test), and three distinct measures of phase opposition (phase bifurcation index, phase opposition sum and phase opposition product) combined with two procedures for non-parametric statistical testing (permutation, or a combination of z-score and permutation). While these are obviously not the only existing or conceivable measures, they have all been used in recent studies. All tested methods performed adequately on a previously published dataset (Busch, Dubois & VanRullen, 2009). On a variety of artificially constructed datasets, no single measure was found to surpass all others, but instead the suitability of each measure was contingent on several experimental factors: the time, frequency and depth of oscillatory phase modulation; the absolute and relative amplitudes of post-stimulus event-related potentials for the two trial groups; the absolute and relative trial numbers for the two groups; and the number of permutations used for non-parametric testing. The concurrent use of two phase opposition measures, the parametric Watson-Williams test and a non-parametric test based on summing inter-trial coherence values for the two trial groups, appears to provide the most satisfactory outcome in all situations tested. Matlab code is provided to automatically compute these phase opposition measures.

Neuroscience↗

bioRxiv2016

Aberrant prefrontal beta oscillations predict episodic memory encoding deficits in schizophrenia

Verbal episodic memory is one of the core cognitive functions affected in patients suffering from schizophrenia (SZ). Although this verbal memory impairment in SZ is a well-known finding, our understanding about its underlying neurophysiological mechanisms is rather scarce. Here we address this issue by recording brain oscillations during a memory task in a sample of healthy controls and patients suffering from SZ. Brain oscillations represent spectral fingerprints of specific neurocognitive operations and are therefore a promising tool to identify neurocognitive mechanisms that are affected by SZ. Healthy controls showed a prominent suppression of left prefrontal beta oscillatory activity during successful memory formation, which replicates several previous oscillatory memory studies. In contrast, patients failed to exhibit such left prefrontal beta power suppression. Utilizing a new topographical pattern similarity approach, we further demonstrate that the degree of similarity between a patient's beta power decrease to that of the controls reliably predicted memory performance. This relationship between beta power decreases and memory was such that the patients' memory performance improved as they showed a more similar topographical beta desynchronization pattern compared to that of healthy controls. These findings suggest that left prefrontal beta power suppression (or lack thereof) during memory encoding is a possible biomarker for the observed encoding impairments in SZ in verbal memory. This lack of left prefrontal beta power decreases might indicate a specific semantic processing deficit of verbal material in patients with schizophrenia.

Neuroscience↗

bioRxiv2016

The rhythms of predictive coding: pre-stimulus phase modulates the influence of shape perception on luminance judgments

Predictive coding is an influential model emphasizing interactions between feedforward and feedback signals. Here, we investigated its temporal dynamics. Two gray disks with different versions of the same stimulus, one enabling predictive feedback (a 3D-shape) and one impeding it (random-lines), were simultaneously presented on the left and right of fixation. Human subjects judged the luminance of the two disks while EEG was recorded. Independently of the spatial response (left/right), we found that the choice of 3D-shape or random-lines as the brighter disk (our measure of post-stimulus predictive coding efficiency on each trial) fluctuated along with the pre-stimulus phase of two spontaneous oscillations: a ~5Hz oscillation in contralateral frontal electrodes and a ~16Hz oscillation in contralateral occipital electrodes. This pattern of results demonstrates that predictive coding is a rhythmic process, and suggests that it could take advantage of faster oscillations in low-level areas and slower oscillations in high-level areas.

Neuroscience↗

bioRxiv2016

Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels

Advances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Here we introduce Kilosort, a spike sorting framework that meets these criteria, and show that it allows rapid and accurate sorting of large-scale in vivo data. Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved. Rapid processing is achieved thanks to a novel low-dimensional approximation for the spatiotemporal distribution of each template, and to batch-based optimization on GPUs. A novel post-clustering merging step based on the continuity of the templates substantially reduces the requirement for subsequent manual curation operations. We compare Kilosort to an established algorithm on data obtained from 384-channel electrodes, and show superior performance, at much reduced processing times. Data from 384-channel electrode arrays can be processed in approximately realtime. Kilosort is an important step towards fully automated spike sorting of multichannel electrode recordings, and is freely available (github.com/cortex-lab/Kilosort).

Neuroscience↗

bioRxiv2016

Suite2p: beyond 10,000 neurons with standard two-photon microscopy

Two-photon microscopy of calcium-dependent sensors has enabled unprecedented recordings from vast populations of neurons. While the sensors and microscopes have matured over several generations of development, computational methods to process the resulting movies remain inefficient and can give results that are hard to interpret. Here we introduce Suite2p: a fast, accurate and complete pipeline that registers raw movies, detects active cells, extracts their calcium traces and infers their spike times. Suite2p runs on standard workstations, operates faster than real time, and recovers ~2 times more cells than the previous state-of-the-art method. Its low computational load allows routine detection of ~10,000 cells simultaneously with standard two-photon resonant-scanning microscopes. Recordings at this scale promise to reveal the fine structure of activity in large populations of neurons or large populations of subcellular structures such as synaptic boutons.

Neuroscience↗

bioRxiv2016

Hearing scenes: A neuromagnetic signature of perceived auditory spatial extent

Perceiving the geometry of surrounding space is a multisensory process, crucial to contextualizing object perception and guiding navigation behavior. Auditory cues are informative about the shape and extent of large-scale environments: humans can make judgments about surrounding spaces from reverberation cues. However, how the scale of auditory space is represented neurally is unknown. Here, by orthogonally varying the spatial extent and sound source content of auditory scenes during magnetoencephalography (MEG) recording, we report a neural signature of auditory space size perception, starting ~145 ms after stimulus onset. Importantly, this neuromagnetic response is readily dissociable in form and time into representations of the source and its reverberant enclosing space: while the source exhibits an early and transient response, the neural signature of space is sustained and independent of the original source that produced it. Further, the space size response is robust to variations in sound source, and vice versa. The MEG decoding signal was distributed primarily across bilateral temporal sensor locations, significantly correlated with behavioral responses in a separate experiment. Together, our results provide the first neuromagnetic evidence for a robust auditory space size representation in the human brain, sensitive to reverberant decay, and reveal the temporal dynamics of how such a code emerges over time from the transformation of complex naturalistic auditory signals.

Neuroscience↗

bioRxiv2016

Novel agonist of GDNF family ligand receptor RET for the treatment of experimental neuropathy

Neuropathic pain is a chronic pain condition caused by lesion or disease affecting the somatosensory system. The glial cell line-derived neurotrophic factor (GDNF) family ligands (GFLs) alleviate symptoms of NP and stimulate regeneration of sensory neurons in vivo. Here we report the development of the compound BT18 that selectively activates GFLreceptors, alleviates pain and restores damaged dorsal root ganglion (DRG) neurons in rat models of NP.\n\nSignificance statementNeuropathic pain (NP) is a chronic syndrome caused by different diseases and lesions affecting nervous system. Earlier studies demonstrated that neurotrophic factors - the glial cell line-derived neurotrophic factor (GDNF) and artemin - could reverse the damage done by lesions in animal models of NP. We demonstrate for the first time that a small molecule can activate receptor of GDNF and artemin, it alleviates pain symptoms in vivo in two animal models of NP and restores to normal the molecular markers expressed in sensory neurons. This compound, termed BT18, can pave way for creating novel disease modifying therapies for NP.

Neuroscience↗

bioRxiv2016

Uncoupling of dysgranular retrosplenial “head direction” cells from the global head direction network

Spatial cognition is an important model system with which to investigate how sensory signals are transformed into cognitive representations. Head direction cells, found in several cortical and subcortical regions, fire when an animal faces a given direction and express a global directional signal which is anchored by visual landmarks and underlies the \"sense of direction\". We investigated the interface between visual and spatial cortical brain regions and report the discovery that a population of neurons in the dysgranular retrosplenial cortex, which we co-recorded with classic head direction cells in a rotationally symmetrical two-compartment environment, were dominated by a local visually defined reference frame and could be decoupled from the main head direction signal. A second population showed rotationally symmetric activity within a single sub-compartment suggestive of an acquired interaction with the head direction cells. These observations reveal an unexpected incoherence within the head direction system, and suggest that dysgranular retrosplenial cortex may mediate between visual landmarks and the multimodal sense of direction. Importantly, it appears that this interface supports a bi-directional exchange of information, which could explain how it is that landmarks can inform the direction sense while at the same time, the direction sense can be used to interpret landmarks.

Neuroscience↗