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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,225 records · Page 68Linked to original sources

bioRxiv2016

Transcriptional signatures of connectomic subregions of the human striatum

Functionally distinct regions of the brain are thought to possess a characteristic connectional fingerprint - a profile of incoming and outgoing connections that defines the function of that area. This observation has motivated efforts to subdivide cortical and subcortical areas using their patterns of connectivity. However, it remains unclear whether these connectomically-defined subregions of the brain can be distinguished at the molecular level. Here, we combine high-resolution diffusion-weighted magnetic resonance imaging with comprehensive transcriptomic data to show that connectomically-defined subregions of the striatum carry distinct transcriptional signatures. Using data-driven clustering of diffusion tractography, seeded from the striatum, in 100 healthy individuals, we identify a tripartite organization of the caudate and putamen that comprises ventral, dorsal, and caudal subregions. We then use microarray data of gene expression levels in 19 343 genes, taken from 98 tissue samples distributed throughout the striatum, to accurately discriminate the three connectomically-defined subregions with 80-90% classification accuracy using linear support vector machines. This classification accuracy was robust at the group and individual level. Genes contributing strongly to the classification were enriched for gene ontology categories including dopamine signaling, glutamate secretion, response to amphetamine, and metabolic pathways, and were implicated in risk for disorders such as schizophrenia, autism, and Parkinsons disease. Our findings highlight a close link between regional variations in transcriptional activity and interregional connectivity in the brain, and suggest that there may be a strong genomic signature of connectomically-defined subregions of the brain.

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bioRxiv2016

Robust extraction of spatio-temporal patterns from resting state fMRI

It is well-established that patterns of functional connectivity (FC) - measures of correlated activity between pairs of voxels or regions observed in the human brain using neuroimaging - are robustly expressed in spontaneous activity during rest. These patterns are not static, but exhibit complex spatio-temporal dynamics. Over the last years, a multitude of methods have been proposed to reveal these dynamics on the level of the whole brain. One finding is that the brain transitions through different FC configurations over time, and substantial effort has been put into characterizing these configurations. However, the dynamics governing these transitions are more elusive, specifically, the contribution of stationary vs. non-stationary dynamics is an active field of inquiry. In this study, we use a whole-brain approach, considering FC dynamics between 66 ROIs covering the entire cortex. We combine an innovative dimensionality reduction technique, tensor decomposition, with a mean field model which possesses stationary dynamics. It has been shown to explain resting state FC averaged over time and multiple subjects, however, this average FC summarizes the spatial distribution of correlations while hiding their temporal dynamics. First, we apply tensor decomposition to resting state scans from 24 healthy controls in order to characterize spatio-temporal dynamics present in the data. We simultaneously utilize temporal and spatial information by creating tensors that are subsequently decomposed into sets of brain regions (\"communities\") that share similar temporal dynamics, and their associated time courses. The tensors contain pairwise FC computed inside of overlapping sliding windows. Communities are discovered by clustering features pooled from all subjects, thereby ensuring that they generalize. We find that, on the group level, the data give rise to four distinct communities that resemble known resting state networks (RSNs): default mode network, visual network, control networks, and somatomotor network. Second, we simulate data with our stationary mean field model whose nodes are connected according to results from DTI and fiber tracking. In this model, all spatio-temporal structure is due to noisy fluctuations around the average FC. We analyze the simulated data in the same way as the empirical data in order to determine whether stationary dynamics can explain the emergence of distinct FC patterns (RSNs) which have their own time courses. We find that this is the case for all four networks using the spatio-temporal information revealed by tensor decomposition if nodes in the simulation are connected according to model-based effective connectivity. Furthermore, we find that these results require only a small part of the FC values, namely the highest values that occur across time and ROI pair. Our findings show that stationary dynamics can account for the emergence of RSNs. We provide an innovative method that does not make strong assumptions about the underlying data and is generally applicable to resting state or task data from different subject populations.

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bioRxiv2016

Coordinate Based Random Effect Size meta-analysis of neuroimaging studies

1Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta- analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density.\n\nHere a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is vital to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely.

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bioRxiv2016

The Semantics of Adjective Noun Phrases in the Human Brain

As a person reads, the brain performs complex operations to create higher order semantic representations from individual words. While these steps are effortless for competent readers, we are only beginning to understand how the brain performs these actions. Here, we explore semantic composition using magnetoencephalography (MEG) recordings of people reading adjective-noun phrases presented one word at a time. We track the neural representation of semantic information over time, through different brain regions. Our results reveal two novel findings: 1) a neural representation of the adjective is present during noun presentation, but this neural representation is different from that observed during adjective presentation 2) the neural representation of adjective semantics observed during adjective reading is reactivated after phrase reading, with remarkable consistency. We also note that while the semantic representation of the adjective during the reading of the adjective is very distributed, the later representations are concentrated largely to temporal and frontal areas previously associated with composition. Taken together, these results paint a picture of information flow in the brain as phrases are read and understood.

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bioRxiv2016

fMRI Correlates of Reaction Time Prolongation during intentional False Responding; an inter-individual difference study

Reaction time (RT) is chiefly longer when people lie. However, the baseline speed in answering questions and the amount of RT prolongation during lying show considerable amount of inter-individual variability. In the current study, we exploited this fact to glean insights on the contribution of each lie-related brain region to hampering of response speeds when people try to be deceitful. In an event-related fMRI session, participants were interrogated by yes-no autobiographical questions and were instructed to intentionally provide false responses to a pre-selected subset of questions. Data from twenty healthy volunteers were analyzed. Baseline speed [RTtruth] and relative appended lie RT [(RTlie - RTtruth) / RTtruth] measures were calculated for each participant and were included in the group level analysis of [lie > truth] BOLD contrasts. Lying RTs were significantly longer than truth telling RTs. Lie-related increase in activity of right ventrolateral prefrontal cortex (VLPFC) and bilateral paracingulate cortex correlated with the baseline speed of participants, while the increase in activity of Left VLPFC, left lateral occipital cortex and bilateral anterior cingulate areas directly correlated with the amount of lying reaction time cost. Activity within bilateral posterior cingulate cortex and right insular cortex inversely correlated with lying RT-cost. Bilateral supplementary motor areas, internal capsule white matter and left angular gyrus showed lie-related increase in activity but did not correlate with either of behavioral measures. Provisional implications regarding the contribution of these regions to RT prolongation and their cognitive role in deceitful behavior are discussed.

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bioRxiv2016

Statistical testing and power analysis for brain-wide association study

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression [Cheng et al., 2015a,b, 2016], the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method considerably reduces the computational complexity of a permutation-or simulation-based approach, thus, it can efficiently tackle large datasets with ultra-high resolution images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods failed. A software package is available at https://github.com/weikanggong/BWAS.

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bioRxiv2016

Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays

Simultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.\n\nAuthor SummarySimultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these recordings is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is largely stymied by electrical stimulation artifacts across the array, which are typically larger than the signals of interest. We develop a novel computational framework to estimate and subtract away this contaminating artifact, enabling the large-scale analysis of responses of possibly hundreds of cells to tailored stimulation. Importantly, we suggest that this technology may also be helpful for the development of future high-resolution neural prosthetic devices (e.g., retinal prostheses).

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bioRxiv2016

Distinct cAMP signaling modalities mediate behavioral flexibility and consolidation of social status in Drosophila aggression.

Social rituals, like male-male aggression in Drosophila, are often stereotyped and the component behavioral patterns modular. The likelihood of transition from one behavioral pattern to another is malleable by experience and confers flexibility to the behavioral repertoire. Experience-dependent modification of innate aggressive behavior in flies alters fighting strategies during fights and establishes dominant-subordinate relationships. Dominance hierarchies resulting from agonistic encounters are consolidated to longer lasting social status-dependent behavioral modifications resulting in a robust loser effect.\n\nWe show that cyclic adenosine monophosphate (cAMP) dynamics regulated by the calcium/calmodulin-dependent adenylyl cyclase, Rut and the cAMP phosphodiesterase, Dnc but not the Amn gene product, in specific neuronal groups of the mushroom body and central complex, mediate behavioral plasticity necessary to establish dominant- subordinate relationships. rut and dnc mutant flies are unable to alter fighting strategies and establish dominance relationships during agonistic interactions. This real-time flexibility during a fight is independent of changes in aggression levels. Longer-term consolidation of social status in the form of a loser effect, however, requires additional Amn-dependent inputs to cAMP signaling and involves a circuit-level association between the /{beta} and {gamma} neurons of the mushroom body.\n\nOur findings implicate cAMP signaling in mediating plasticity of behavioral patterns in aggressive behavior and in the generation of a temporally stable memory trace that manifests as a loser effect.\n\nSUMMARY STATEMENTPhasic recruitment of different cAMP signaling modalities in specific neuronal groups lead to the formation of temporally distinct components of learning and memory in fly aggression.

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bioRxiv2016

Hydrogen Bond Networks and HydrophobicEffects in the Amyloid β30-35 Chain in Water: A Molecular Dynamics Study

We study the conformational landscape of the C-terminal fragment of the Amyloid protein A{beta}30-35 in water using well-tempered metadynamics simulations and find that it resembles an intrinsically disordered protein. The conformational fluctuations of the protein are facilitated by a collective reorganization of both protein and water hydrogen bond networks, combined with electrostatic interactions between termini as well as hydrophobic interactions of the side chains. The stabilization of hydrophobic interactions in one of the conformers involves a collective collapse of the sidechains along with a squeeze out of water sandwiched in between. The charged N and C termini play a critical role in stabilizing different types of protein conformations including those involving contact ion salt-bridges as well as solvent mediated interactions of the termini and amide backbone. We examine this by probing the distribution of directed water wires forming the hydrogen bond network enveloping the polypeptide. Water wires and their fluctuations form an integral part of structural signature of the protein conformation.

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bioRxiv2016

Retrotransposon Activation Contributes to Neurodegeneration in a DrosophilaTDP-43 Model of ALS.

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two incurable neurodegenerative disorders that exist on a symptomological spectrum and share both genetic underpinnings and pathophysiological hallmarks. Functional abnormality of TAR DNA-binding protein 43 (TDP-43), an aggregation-prone RNA and DNA binding protein, is observed in the vast majority of both familial and sporadic ALS cases and in [~]40% of FTLD cases, but the cascade of events leading to cell death are not understood. We have expressed human TDP-43 (hTDP-43) in Drosophila neurons and glia, a model that recapitulates many of the characteristics of TDP-43-linked human disease including protein aggregation pathology, locomotor impairment, and premature death. We report that such expression of hTDP-43 impairs small interfering RNA (siRNA) silencing, which is the major post-transcriptional mechanism of retrotransposable element (RTE) control in somatic tissue. This is accompanied by de-repression of a panel of both LINE and LTR families of RTEs, with somewhat different elements being active in response to hTDP-43 expression in glia versus neurons. hTDP-43 expression in glia causes an early and severe loss of control of a specific RTE, the endogenous retrovirus (ERV) gypsy. We demonstrate that gypsy causes the degenerative phenotypes in these flies because we are able to rescue the toxicity of glial hTDP-43 either by genetically blocking expression of this RTE or by pharmacologically inhibiting RTE reverse transcriptase activity. Moreover, we provide evidence that activation of DNA damage-mediated programmed cell death underlies both neuronal and glial hTDP-43 toxicity, consistent with RTE-mediated effects in both cell types. Our findings suggest a novel mechanism in which RTE activity contributes to neurodegeneration in TDP-43-mediated diseases such as ALS and FTLD.\n\nAUTHOR SUMMARYFunctional abnormality of TAR DNA-binding protein 43 (TDP-43), an aggregation-prone RNA and DNA binding protein, is observed in the vast majority of both familial and sporadic ALS cases and in [~]40% of FTLD cases, and mutations in TDP-43 are causal in a subset of familial ALS cases. Although cytoplasmic inclusions of this mostly nuclear protein are a hallmark of the disease, the cascade of events leading to cell death are not understood. We demonstrate that expression of human TDP-43 (hTDP-43) in Drosophila neurons or glial cells, which results in toxic cytoplasmic accumulation of TDP-43, causes broad expression of retrotransposons. In the case of glial hTDP-43 expression, the endogenous retrovirus (ERV) gypsy causally contributes to degeneration because inhibiting gypsy genetically or pharmacologically is sufficient to rescue the phenotypic effects. Moreover, we demonstrate that activation of DNA damage-mediated programmed cell death underlies hTDP-43 and gypsy mediated toxicity. Finally, we find that hTDP-43 pathology impairs small interfering RNA silencing, which is an essential system that normally protects the genome from RTEs. These findings suggest a novel mechanism in which a storm of retrotransposon activation drives neurodegeneration in TDP-43 mediated diseases such as ALS and FTLD.

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bioRxiv2016

Behavioral evidence for feedback gain control by the inhibitory microcircuit of the substantia nigra

We recently demonstrated that the collaterals of substantia nigra (SN) projection neurons can implement divisive feedback inhibition, or gain control (Brown et al., 2014). While in vivo recordings were consistent with divisive feedback inhibition, a causal test was lacking. A gain control model of the nigral microcircuit implies behavioral effects of disrupting intranigral inhibition that are distinct from previous functional models. To test the model predictions experimentally, we develop a chemogenetic approach that can selectively suppress synaptic release within the substantial nigra without affecting the propagation of activity to extranigral targets. We observe behavioral consequences of suppressing intranigral inhibition that are uniquely consistent with a gain control model. Our data further suggest that if endogenous metabotropic signaling can modulate intranigral synapses, this would provide a circuit mechanism for an exploitation/exploration trade-off in which the timing and variability of goal-directed movements are controlled independently of changes in action.

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bioRxiv2016

Estimation of time-varying decision thresholds from the choice and reaction times without assumptions on the shape

When a decision is made based on a series of samples of evidence, the threshold for decision may change over time. Here we propose a fast heuristic algorithm that estimates the time-varying thresholds without making an assumption on their shape. The algorithm gives an approximate best estimate of the time-varying thresholds for all time considered when all other parameters are fixed. The algorithm outperforms conventional methods that gradually adjust the threshold estimates during fitting, when fitting time is limited.

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bioRxiv2016

Structural covariance networks across the lifespan, from 6-94 years of age

Structural covariance examines covariation of grey matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete lifespan perspective--bridging childhood with early, middle, and late adulthood--on the development of structural covariance networks. Here, we investigate the lifespan trajectories of structural covariance in six canonical neurocognitive networks: default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual. By combining data from five open access data sources, we examine the structural covariance trajectories of these networks from 6-94 years of age in a sample of 1580 participants. Using partial least squares, we show that structural covariance patterns across the lifespan exhibit two significant, age-dependent trends. The first trend is a stable pattern whose integrity declines over the lifespan. The second trend is an inverted-U that differentiates young adulthood from other age groups. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, our results suggest that structural covariance provides a reliable definition of neurocognitive networks across the lifespan and reveal both shared and network-specific trajectories.

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bioRxiv2016

Multimodal representations of person identity individuated with fMRI

AbtractRecognizing the identity of a person is fundamental to guide social interactions. We can recognize the identity of a person looking at her face, but also listening to her voice. An important question concerns how visual and auditory information come together, enabling us to recognize identity independently of the modality of the stimulus. This study reports converging evidence across univariate contrasts and multivariate classification showing that the posterior superior temporal sulcus (pSTS), previously known to encode polymodal visual and auditory representations, encodes information about person identity with invariance within and across modality. In particular, pSTS shows selectivity for faces, selectivity for voices, classification of face identity across image transformations within the visual modality, and classification of person identity across modality.

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bioRxiv2016

Temporal anticipation based on memory

The fundamental role that our long-term memories play in guiding perception is increasingly recognised, but the functional and neural mechanisms are just beginning to be explored. Though experimental approaches are being developed to investigate the influence of long-term memories on perception, these remain mostly static and neglect their temporal and dynamic nature. Here we show we show that our long-term memories can guide attention proactively and dynamically based on learned temporal associations. Across two experiments we found that detection and discrimination of targets appearing within previously learned contexts are enhanced when the timing of target appearance matches the learned temporal contingency. Neural markers of temporal preparation revealed that the learned temporal associations trigger specific temporal predictions. Our findings emphasize the ecological role that memories play in predicting and preparing perception of anticipated events, calling for revision of the usual conceptualisation of contextual associative memory as a reflective and retroactive function.

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bioRxiv2016

Recurring functional interactions predict network architecture of interictal and ictal states in neocortical epilepsy

Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 hours of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (i) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (ii) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (iii) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that the epileptic network drives dysfunction by controlling dynamics of functional interactions between brain regions that generate seizures and those that underlie normal function.

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bioRxiv2016

Monozygotic twin pairs discordant for amyotrophic lateral sclerosis carry both common and unique epigenetic differences relevant to disease

Amyotrophic lateral sclerosis (ALS) is a devastating late-onset neurodegenerative disorder in which only a small proportion of patients carry an identifiable causative genetic lesion. Despite high heritability estimates, a genetic etiology for most sporadic ALS remains elusive. Here we report the epigenetic profiling of five monozygotic twin pairs discordant for ALS in whom previous genome sequencing excluded a genetic basis for their disease discordance. By studying cytosine methylation patterns in peripheral blood DNA we identified thousands of large between-twin differences at individual CpGs. While the specific sites of difference were largely idiosyncratic to a twin pair, a proportion (involving GABA signalling) were common to all affected individuals. In both instances the differences occurred within genes and pathways related to neurobiological function and dysfunction. Our findings reveal widespread changes in epigenetic marks in ALS patients, consistent with an epigenetic contribution to disease. These findings may be exploited to develop blood-based biomarkers of ALS and develop further insight into disease pathogenesis. We expect that our findings will provide a useful point of reference for further large scale studies of sporadic ALS.

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bioRxiv2016

A Bayesian Heteroscedastic GLM with Application to fMRI Data with Motion Spikes

We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data. The model is analyzed from a Bayesian perspective and has the benefit of automatically down-weighting time points close to motion spikes in a data-driven manner. We develop a highly efficient Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable selection among the regressors to model both the mean (i.e., the design matrix) and variance. This makes it possible to include a broad range of explanatory variables in both the mean and variance (e.g., time trends, activation stimuli, head motion parameters and their temporal derivatives), and to compute the posterior probability of inclusion from the MCMC output. Variable selection is also applied to the lags in the autoregressive noise process, making it possible to infer the lag order from the data simultaneously with all other model parameters. We use both simulated data and real fMRI data from OpenfMRI to illustrate the importance of proper modeling of heteroscedasticity in fMRI data analysis. Our results show that the GLMH tends to detect more brain activity, compared to its homoscedastic counterpart, by allowing the variance to change over time depending on the degree of head motion.

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