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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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

Bottom-up and top-down computations in high-level visual cortex

The ability to read a page of text or recognize a persons face depends on category-selective visual regions in ventral temporal cortex (VTC). To understand how these regions mediate word and face recognition, it is necessary to characterize how stimuli are represented and how this representation is used in the execution of a cognitive task. Here, we show that the response of a category-selective region in VTC can be computed as the degree to which the low-level properties of the stimulus match a category template. Moreover, we show that during execution of a task, the bottom-up representation is scaled by the intraparietal sulcus (IPS), and that the level of IPS engagement reflects the cognitive demands of the task. These results provide a unifying account of neural processing in VTC in the form of a model that addresses both bottom-up and top-down effects and quantitatively predicts VTC responses.

Neuroscience↗

bioRxiv2016

Dopamine neurons change the type of excitability in response to stimuli

The dynamics of neural excitability determine the neuronal response to stimuli, its synchronization and resonance properties and, ultimately, the computations it performs in the brain. We investigated the dynamical mechanisms underlying the excitability type of dopamine (DA) neurons, using a conductance based biophysical model, and its regulation by intrinsic and synaptic currents. By calibrating the model to reproduce low frequency tonic firing, NMDA excitation is balanced by GABA-mediated inhibition and leads to type I excitable behavior characterized by a continuous decrease in firing frequency in response to hyperpolarizing currents. Furthermore, we analyzed how excitability type of the DA neuron model is influenced by changes in the intrinsic current composition. A subthreshold sodium current is necessary for a continuous frequency decrease during application of a negative current, and the low-frequency balanced state during simultaneous activation of NMDA and GABA receptors. Blocking this current switches the neuron to type II. Enhancing the anomalous rectifier Ih current also switches the excitability to type II. Key characteristics of synaptic conductances that may be observed in vivo also change the type of excitability: a depolarized GABAR reversal potential or co-activation of AMPARs leads to an abrupt frequency drop to zero, which is typical for type II excitability. Coactivation of NMDARs together with AMPARs and GABARs shifts the the type I/II boundary toward more hyperpolarized GABAR reversal potentials. To better understand how altering each of the aforementioned currents leads to changes in excitability profile of DA neuron, we provide a thorough dynamical analysis. Collectively, these results imply that type I excitability in dopamine neurons might be important for low firing rates and fine-tuning basal dopamine levels, while switching excitability to type II during NMDAR and AMPAR activation may facilitate a transient increase in dopamine concentration, as type II neurons are more amenable to synchronization.\n\nAuthor summaryDopamine neurons play a central role in guiding motivated behaviors. However, complete understanding of computations these neurons perform to encode rewarding and salient stimuli is still forthcoming. Network connectivity influences neural responses to stimuli but so do intrinsic excitability properties of individual neurons, as they define their synchronization properties and neural coding strategy. We investigated the excitability type of the DA neuron and found that, depending on the synaptic and intrinsic current composition, DA neurons can switch from type I to type II excitability. In short, without synaptic inputs or under balanced excitatory and inhibitory inputs DA neurons exhibits type I excitability, while excitatory AMPAR inputs can switch the neuron to type II. Type I neurons are best suited for coding the stimulus intensity due to their ability to smoothly decrease the firing rate. Type I excitability might be important for achieving low a basal DA concentration necessary for normal brain functioning. Switching to type II excitability further enables robust transient DA release of heterogeneous DA neuron population in response to correlated inputs, partially due to evoked population synchrony.

Neuroscience↗

bioRxiv2016

Cerebellar learning using perturbations

The cerebellum aids the learning and execution of fast coordinated movements, with acquired information being stored by plasticity of parallel fibre--Purkinje cell synapses. According to the current consensus, erroneously active parallel fibre synapses are depressed by complex spikes arising when climbing fibres signal movement errors. However, this theory cannot solve the credit assignment problem of using the limited information from a global movement evaluation to optimise behaviour by guiding the plasticity in numerous neurones. We identify the possible implementation of an algorithm solving this problem, whereby spontaneous complex spikes perturb ongoing movements, create an eligibility trace for plasticity and signal resulting error changes to guide plasticity. These error changes are extracted by adaptively cancelling the average error. This framework, stochastic gradient descent with estimated global errors, generates specific predictions for synaptic plasticity rules that contradict the current consensus. However, in vitro plasticity experiments under physiological conditions verified our predictions, highlighting the sensitivity of plasticity studies to unphysiological conditions. Using numerical and analytical approaches we demonstrate the convergence and estimate the capacity of learning in our implementation. Finally, a similar mechanism may operate during optimisation of action sequences by the basal ganglia, where dopamine could both initiate movements and signal rewards, analogously to the dual perturbation and correction role of the climbing fibre outlined here.

Neuroscience↗

bioRxiv2016

A brush fire feedback loop for the ramp-like rise in hominin brain size that began 2.4 million years ago

The rapid three-fold enlargement of the hominin brain1,2 began about 2.3 million years ago (myr) as Africa dried and grass replaced brush, creating great savannas3. Seeking an amplifying feedback loop, I analyzed the lightning-brush-fire ecology for grazing animals in a grassy burn scar4. Discovering the new grass by exploring brush byways could promote a population boom-but only after grass-specialized herbivores evolved from mixed feeders5 at 2.4 myr. When the brush returned several decades later, the grazer boom would turn to bust, squeezing numerous descendants back into the core grasslands. Meat-eating Homo species would boom and bust when grazers did, enriching the core in whatever alleles were earlier concentrated in the brush fringe catchment zone for that boom. This return migration for Homo is what creates the amplifying feedback loop that speeds brain enlargement rate, likely up to the mutation rate limit. It also promotes trait hitchhiking: any brush-relevant allele, not just those for hunting, can experience amplifying feedback merely by hanging out in the catchment zone4. The shade offered by brush would have been the default location for cooperative nurseries, time-consuming food preparation, and toolmaking. Increased behavioral versatility correlates with larger brain size and the more versatile brains of a current generation need only spend more-than-average time in the booms catchment zone for this recursive evolutionary process to keep average brain size increasing via assortative mating. This helps account for the time when enlargement began, why it was linear, when it ended, and why it slowed in Neanderthals and in Asian Homo erectus. Without utilizing Darwins selective survival, the feedback loop makes advance room for \"free\" future functionality in the cerebral cortex, likely relevant to the evolutionary emergence of our structured intellectual functions6 such as syntax, contingent planning, games, and logic.

Neuroscience↗

bioRxiv2016

Spatiotemporal structure of intracranial electric fields induced by transcranial electric stimulation in human and nonhuman primates

Transcranial electric stimulation (TES) is an emerging technique, developed to non-invasively modulate brain function. However, the spatiotemporal distribution of the intracranial electric fields induced by TES remains poorly understood. In particular, it is unclear how much current actually reaches the brain, and how it distributes across the brain. Lack of this basic information precludes a firm mechanistic understanding of TES effects. In this study we directly measure the spatial and temporal characteristics of the electric field generated by TES using stereotactic EEG (s-EEG) electrode arrays implanted in cebus monkeys and surgical epilepsy patients. We found a small frequency dependent decrease (10%) in magnitudes of TES induced potentials and negligible phase shifts over space. Electric field strengths were strongest in superficial brain regions with maximum values of about 0.5 mV/mm. Our results provide crucial information for the interpretation of human TES studies and the optimization and design of TES stimulation protocols. In addition, our findings have broad implications concerning electric field propagation in non-invasive recording techniques such as EEG/MEG.

Neuroscience↗

bioRxiv2016

C. elegans PVD Neurons: A Platform for Functionally Validating and Characterizing Neuropsychiatric Risk Genes

One of the primary challenges in the field of psychiatric genetics is the lack of an in vivo model system in which to functionally validate candidate neuropsychiatric risk genes (NRGs) in a rapid and cost-effective manner1-3. To overcome this obstacle, we performed a candidate-based RNAi screen in which C. elegans orthologs of human NRGs were assayed for dendritic arborization and cell specification defects using C. elegans PVD neurons. Of 66 NRGs, identified via exome sequencing of autism (ASD)4 or schizophrenia (SCZ)5-9 probands and whose mutations are de novo and predicted to result in a complete or partial loss of protein function, the C. elegans orthologs of 7 NRGs were found to be required for proper neuronal development and represent a variety of functional classes, including transcriptional regulators and chromatin remodelers, molecular chaperones, and cytoskeleton-related proteins. Notably, the positive hit rate, when selectively assaying C. elegans orthologs of ASD and SCZ NRGs, is enriched >14-fold as compared to unbiased RNAi screening10. Furthermore, we find that RNAi phenotypes associated with the depletion of NRG orthologs is recapitulated in genetic mutant animals, and, via genetic interaction studies, we show that the NRG ortholog of ANK2, unc-44, is required for SAX-7/MNR-1/DMA-1 signaling. Collectively, our studies demonstrate that C. elegans PVD neurons are a tractable model in which to discover and dissect the fundamental molecular mechanisms underlying neuropsychiatric disease pathogenesis.

Neuroscience↗

bioRxiv2016

Euclidean distance as a measure of ventral and dorsal white matter connectivity

Fiber tractography based on diffusion tensor imaging (DTI) has become an important research tool for investigating the anatomical connectivity between brain regions in vivo. Combining DTI with functional magnetic resonance imaging (fMRI) allows for the mapping of structural and functional architecture of large-scale networks for cognitive processing. This line of research has shown that ventral and dorsal fiber pathways subserve different aspects of bottom-up- and top-down processing in the human brain.\n\nHere, we investigate the feasibility and applicability of Euclidean distance as a simple geometric measure to differentiate ventral and dorsal long-range white matter fiber pathways tween parietal and inferior frontal cortical regions, employing a body of studies that used probabilistic tractography.\n\nWe show that ventral pathways between parietal and inferior frontal cortex have on average a significantly longer Euclidean distance in 3D-coordinate space than dorsal pathways. We argue that Euclidean distance could provide a simple measure and potentially a boundary value to assess patterns of connectivity in fMRI studies. This would allow for a much broader assessment of general patterns of ventral and dorsal large-scale fiber connectivity for different cognitive operations in the large body of existing fMRI studies lacking additional DTI data.

Neuroscience↗

bioRxiv2016

Transformation of Independent Oscillatory Inputs into Temporally Precise Rate Codes

Complex behaviors demand temporal coordination among functionally distinct brain regions. The basal forebrains afferent and efferent structure suggests a capacity for mediating such coordination. During performance of a selective attention task, synaptic activity in this region was dominated by four amplitude-independent oscillations temporally organized by the phase of the slowest, a theta rhythm. Further, oscillatory amplitudes were precisely organized by task epoch and a robust input/output transform, from synchronous synaptic activity to spiking rates of basal forebrain neurons, was identified. For many neurons, spiking was temporally organized as phase precessing sequences against theta band field potential oscillations. Remarkably, theta phase precession advanced in parallel to task progression, rather than absolute spatial location or time. Together, the findings reveal a process by which associative brain regions can integrate independent oscillatory inputs and transform them into sequence-specific, rate-coded outputs that are adaptive to the pace with which organisms interact with their environment.

Neuroscience↗

bioRxiv2016

Experience-dependent increases in sharp-wave ripples near visual targets

We measured hippocampal sharp-wave ripples during goal-directed visual exploration in macaques. Exploratory sharp-wave ripples were more frequent on familiar trials, in the second half of search, and near the visual target (i.e., the goal location). These spatial and temporal properties may help SWRs coordinate hippocampal and extra-hippocampal firing sequences that guide actions based on past experiences.

Neuroscience↗

bioRxiv2016

Reading out olfactory receptors: feedforward circuits detect odors in mixtures without demixing

The olfactory system, like other sensory systems, can detect specific stimuli of interest amidst complex, varying backgrounds. To gain insight into the neural mechanisms underlying this ability, we imaged responses of mouse olfactory bulb glomeruli to single odors and hundreds of mixtures. We used this data to build a model of mixture responses that incorporated nonlinear interactions and trial-to-trial variability and explored potential decoding mechanisms that can mimic mouse performance from our previous study (Rokni et al., 2014) when given glomerular responses as input. We find that a linear decoder with sparse weights could match mouse performance using just a small subset of the glomeruli (~ 15). However, when such a decoder is trained only with single odors, it generalizes poorly to mixture stimuli due to nonlinear mixture responses. We show that mice similarly fail to generalize, although they could in principle fully decompose the mixtures. This suggests that mice learn this segregation task discriminatively by adjusting task-specific decision boundaries without taking advantage of a demixed representation of odors.

Neuroscience↗

bioRxiv2016

Best Practices in Data Analysis and Sharing in Neuroimaging using MRI

Neuroimaging enables rich noninvasive measurements of human brain activity, but translating such data into neuroscientific insights and clinical applications requires complex analyses and collaboration among a diverse array of researchers. The open science movement is reshaping scientific culture and addressing the challenges of transparency and reproducibility of research. To advance open science in neuroimaging the Organization for Human Brain Mapping created the Committee on Best Practice in Data Analysis and Sharing (COBIDAS), charged with creating a report that collects best practice recommendations from experts and the entire brain imaging community. The purpose of this work is to elaborate the principles of open and reproducible research for neuroimaging using Magnetic Resonance Imaging (MRI), and then distill these principles to specific research practices. Many elements of a study are so varied that practice cannot be prescribed, but for these areas we detail the information that must be reported to fully understand and potentially replicate a study. For other elements of a study, like statistical modelling where specific poor practices can be identified, and the emerging areas of data sharing and reproducibility, we detail both good practice and reporting standards. For each of seven areas of a study we provide tabular listing of over 100 items to help plan, execute, report and share research in the most transparent fashion. Whether for individual scientists, or for editors and reviewers, we hope these guidelines serve as a benchmark, to raise the standards of practice and reporting in neuroimaging using MRI.

Neuroscience↗

bioRxiv2016

Top-down modulation of stimulus drive via beta-gamma cross-frequency interaction.

AbstractSeveral recent studies have demonstrated that the bottom-up signaling of a visual stimulus is subserved by interareal gamma-band synchronization, whereas top-down influences are mediated by alpha-beta band synchronization. These processes may implement top-down control of stimulus processing if top-down and bottom-up mediating rhythms are coupled via cross-frequency interaction. To test this possibility, we investigated Granger-causal influences among awake male macaque primary visual area V1, higher visual area V4 and parietal control area 7a during attentional task performance. Top-down 7a-to-V1 beta-band influences enhanced visually driven V1-to-V4 gamma-band influences. This enhancement was spatially specific and largest when beta-band activity preceded gamma-band activity by [~]0.1 s, suggesting a causal effect of top-down processes on bottom-up processes. We propose that this cross-frequency interaction mechanistically subserves the attentional control of stimulus selection.\n\nSignificance StatementContemporary research indicates that the alpha-beta frequency band underlies top-down control, while the gamma-band mediates bottom-up stimulus processing. This arrangement inspires an attractive hypothesis, which posits that top-down beta-band influences directly modulate bottom-up gamma band influences via cross-frequency interaction. We evaluate this hypothesis determining that beta-band top-down influences from parietal area 7a to visual area V1 are correlated with bottom-up gamma frequency oscillations from V1 to area V4, in a spatially specific manner, and that this correlation is maximal when top-down activity precedes bottom-up activity. These results show that for top-down processes such as spatial attention, elevated top-down beta-band influences directly enhance feedforward stimulus induced gamma-band processing, leading to enhancement of the selected stimulus.

Neuroscience↗

bioRxiv2016

High-throughput mapping of single neuron projections by sequencing of barcoded RNA

Neurons transmit information to distant brain regions via long-range axonal projections. In the mouse, area-to-area connections have only been systematically mapped using bulk labeling techniques, which obscure the diverse projections of intermingled single neurons. Here we describe MAPseq (Multiplexed Analysis of Projections by Sequencing), a technique that can map the projections of thousands or even millions of single neurons by labeling large sets of neurons with random RNA sequences (\"barcodes\"). Axons are filled with barcode mRNA, each putative projection area isdissected, and the barcode mRNA is extracted and sequenced. Applying MAPseq to the locus coeruleus (LC), we find that individual LC neurons have preferred cortical targets. By recasting neuroanatomy, which is traditionallyviewed as a problem of microscopy, as a problem of sequencing, MAPseq harnesses advances in sequencing technology to permit high-throughput interrogation of brain circuits.

Neuroscience↗

bioRxiv2016

Brain structure in pediatric Tourette syndrome

Previous studies of brain structure in Tourette syndrome (TS) have produced mixed results, and most had modest sample sizes. In the present multi-center study, we used structural MRI to compare 103 children and adolescents with TS to a well-matched group of 103 children without tics. We applied voxel-based morphometry methods to test gray matter (GM) and white matter (WM) volume differences between diagnostic groups, accounting for MRI scanner and sequence, age, sex and total GM + WM volume. The TS group demonstrated greater GM volume in posterior thalamus, hypothalamus and midbrain, and lower WM volume bilaterally in orbital and medial prefrontal cortex. These results demonstrate evidence for abnormal brain structure in children and youth with TS, consistent with and extending previous findings. As orbital cortex is reciprocally connected with hypothalamus, our results suggest that structural abnormalities in these regions may relate to abnormal behavioral inhibition and somatic monitoring in TS.

Neuroscience↗

bioRxiv2016

Parvalbumin interneuron dysfunction in a thalamus - prefrontal cortex circuit in Disc1 deficiency mice

Two of the most consistent findings across disrupted-in-schizophrenia-1 (DISC1) mouse models are impaired working memory and reduced number or function of parvalbumin interneurons within the prefrontal cortex. While these findings suggest parvalbumin interneuron dysfunction in DISC1-related pathophysiology, to date, cortical inhibitory circuit function has not been investigated in depth in DISC1 deficiency mouse models. Here we assessed the function of a feedforward circuit between the mediodorsal thalamus (MD) and the medial prefrontal cortex (mPFC) in mice harboring a deletion in one allele of the Disc1 gene. We found that the inhibitory drive onto layer 3 pyramidal neurons in the mPFC was significantly reduced in the Disc1 deficient mice. This reduced inhibition was accompanied by decreased GABA release from local parvalbumin, but not somatostatin, inhibitory interneurons, and by impaired feedforward inhibition in the MD-mPFC circuit. Our results reveal a cellular mechanism by which deficiency in DISC1 causes neural circuit dysfunction frequently implicated in psychiatric disorders.

Neuroscience↗

bioRxiv2016

Dissection of Major Depressive Disorder using polygenic risk scores for Schizophrenia in two independent cohort.

Major depressive disorder (MDD) is known for its substantial clinical and suspected causal heterogeneity. It is characterised by low mood, psychomotor slowing, and increased levels of the personality trait neuroticism; factors which are also associated with schizophrenia (SCZ). It is possible that some cases of MDD may have a substantial genetic loading for SCZ. A sign of the presence of SCZ-like MDD sub-groups would be indicated by an interaction between MDD status and polygenic risk of SCZ on cognitive, personality and mood measures. In the current study, we hypothesised that higher SCZ-polygenic risk would define larger MDD case-control differences in cognitive ability, and smaller differences in distress and neuroticism. Polygenic risk scores (PGRS) for SCZ and their association with cognitive variables, neuroticism, mood, and psychological distress were estimated in a large population-based cohort (Generation Scotland: Scottish Family Health Study, GS:SFHS). Individuals were divided into those with, and without, depression (n=2587 & n=16,764 respectively) to test whether there was an interaction between MDD status and schizophrenia risk. Replication was sought in UK Biobank (n=33,525). In both GS:SFHS and UK Biobank we found significant interactions between SCZ-PGRS and MDD status for measures of psychological distress and neuroticism. In both cohorts there was a reduction of case-control differences on a background of higher genetic risk of SCZ. These findings suggest that depression on a background of high genetic risk for SCZ may show attenuated associations with distress and neuroticism. This may represent a causally distinct form of MDD more closely related to SCZ.

Neuroscience↗

bioRxiv2016

Decoding the contents of working memory tracks strategy and performance in prospective remembering

Theories of prospective memory (PM) posit that it can be subserved either by working memory (WM) or episodic memory (EM). Testing and refining these multiprocess theories of PM requires a way of tracking participants reliance on WM versus EM. Here we use multi-voxel pattern analysis (MVPA) to derive a trial-by-trial measure of WM use in prospective memory. We manipulated strategy demands by varying the degree of proactive interference (which impairs EM) and the memory load required to perform the secondary task (which impairs WM). For the condition in which participants were pushed to rely more on WM, our MVPA measures showed 1) greater WM use and 2) a trial-by-trial correlation between WM use and PM behavior. Finally, we also showed that MVPA measures of WM use are not redundant with other behavioral measures: in the condition in which participants were pushed more to rely on WM, using neural and behavioral measures together led to better prediction of PM accuracy than either measure on its own.

Neuroscience↗

bioRxiv2016

Activity flow over resting-state networks shapes cognitive task activations

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-stateFC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allows prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

Neuroscience↗