Biology subjects

Murray, J. D.

Publications and source records attributed to Murray, J. D..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Transcriptomics-informed large-scale cortical model captures topography of pharmacological neuroimaging effects of LSD

Psychoactive drugs can transiently perturb brain physiology while preserving brain structure. The role of physiological state in shaping neural function can therefore be investigated through neuroimaging of pharmacologically-induced effects. This paradigm has revealed that neural and experiential effects of lysergic acid diethylamide (LSD) are attributable to its agonist activity at the serotonin-2A receptor. Here, we integrate brainwide transcriptomics with biophysically-based large-scale circuit modeling to simulate acute neuromodulatory effects of LSD on human cortical dynamics. Our model captures the topographic effects of LSD-induced changes in cortical BOLD functional connectivity. These findings suggest that serotonin-2A-mediated modulation of pyramidal cell gain is the circuit mechanism through which LSD alters cortical functional topography. Individual-subject fitting reveals that the model captures patterns of individual neural differences in drug response that predict altered states of consciousness. This work establishes a framework for linking molecular-level manipulations to salient changes in brain function, with implications for precision medicine.

neuroscience↗

bioRxiv2021

Trial-to-trial variability of spiking delay activity in prefrontal cortex constrains burst-coding models of working memory

A hallmark neuronal correlate of working memory (WM) is stimulus-selective spiking activity of neurons in prefrontal cortex (PFC) during mnemonic delays. These observations have motivated an influential computational modeling framework in which WM is supported by persistent activity. Recently this framework has been challenged by arguments that observed persistent activity may be an artifact of trial-averaging, which potentially masks high variability of delay activity at the single-trial level. In an alternative scenario, WM delay activity could be encoded in bursts of selective neuronal firing which occur intermittently across trials. However, this alternative proposal has not been tested on single-neuron spike-train data. Here, we developed a framework for addressing this issue by characterizing the trial-to-trial variability of neuronal spiking quantified by Fano factor (FF). By building a doubly stochastic Poisson spiking model, we first demonstrated that the burst-coding proposal implies a significant increase in FF positively correlated with firing rate, and thus loss of stability across trials during the delay. Simulation of spiking cortical circuit WM models further confirmed that FF is a sensitive measure that can well dissociate distinct WM mechanisms. We then tested these predictions on datasets of single-neuron recordings from macaque prefrontal cortex during three WM tasks. In sharp contrast to the burst-coding model predictions, we only found a small fraction of neurons showing increased WM-dependent burstiness, and stability across trials during delay was strengthened in empirical data. Therefore, reduced trial-to-trial variability during delay provides strong constraints on the contribution of single-neuron intermittent bursting to WM maintenance. Significance StatementThere are diverging classes of theoretical models explaining how information is maintained in working memory by cortical circuits. In an influential model class, neurons fire exhibit persistent elevated memorandum-selective firing, whereas a recently developed class of burst-coding models suggests that persistent activity is an artifact of trial-averaging, and spiking is sparse in each single trial, subserved by brief intermittent bursts. However, this alternative picture has not been characterized or tested on empirical spike-train data. Here we combine mathematical analysis, computational model simulation and experimental data analysis to test empirically theses two classes of models and show that the trial-to-trial variability of empirical spike trains is not consistent with burst coding. These findings provide constraints for theoretical models of working memory.

neuroscience↗

bioRxiv2021

Geometry of neural computation unifies working memory and planning

Real-world tasks require coordination of working memory, decision making, and planning, yet these cognitive functions have disproportionately been studied as independent modular processes in the brain. Here we propose that contingency representations, defined as mappings for how future behaviors depend on upcoming events, can unify working memory and planning computations. We designed a task capable of disambiguating distinct types of representations. Our experiments revealed that human behavior is consistent with contingency representations, and not with traditional sensory models of working memory. In task-optimized recurrent neural networks we investigated possible circuit mechanisms for contingency representations and found that these representations can explain neurophysiological observations from prefrontal cortex during working memory tasks. Finally, we generated falsifiable predictions for neural data to identify contingency representations in neural data and to dissociate different models of working memory. Our findings characterize a neural representational strategy that can unify working memory, planning, and context-dependent decision making.

neuroscience↗

bioRxiv2020

Transient neuronal suppression for exploitation of new sensory evidence

In noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals' behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is an important component of dynamic perceptual decision making.

neuroscience↗

bioRxiv2020

PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks

Task-trained artificial recurrent neural networks (RNNs) provide a computational modeling framework of increasing interest and application in computational, systems, and cognitive neuroscience. RNNs can be trained, using deep learning methods, to perform cognitive tasks used in animal and human experiments, and can be studied to investigate potential neural representations and circuit mechanisms underlying cognitive computations and behavior. Widespread application of these approaches within neuroscience has been limited by technical barriers in use of deep learning software packages to train network models. Here we introduce PsychRNN, an accessible, flexible, and extensible Python package for training RNNs on cognitive tasks. Our package is designed for accessibility, for researchers to define tasks and train RNN models using only Python and NumPy without requiring knowledge of deep learning software. The training backend is based on TensorFlow and is readily extensible for researchers with TensorFlow knowledge to develop projects with additional customization. PsychRNN implements a number of specialized features to support applications in systems and cognitive neuroscience. Users can impose neurobiologically relevant constraints on synaptic connectivity patterns. Furthermore, specification of cognitive tasks has a modular structure, which facilitates parametric variation of task demands to examine their impact on model solutions. PsychRNN also enables task shaping during training, or curriculum learning, in which tasks are adjusted in closed-loop based on performance. Shaping is ubiquitous in training of animals in cognitive tasks, and PsychRNN allows investigation of how shaping trajectories impact learning and model solutions. Overall, the PsychRNN framework facilitates application of trained RNNs in neuroscience research. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/321752v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1541513org.highwire.dtl.DTLVardef@133c86borg.highwire.dtl.DTLVardef@aaf099org.highwire.dtl.DTLVardef@99d55b_HPS_FORMAT_FIGEXP M_FIG Example workflow for using PsychRNN. First, the task of interest is defined, and a recurrent neural network model is trained to perform the task, optionally with neurobiologically informed constraints on the network. After the network is trained, the researchers can investigate network properties including the synaptic connectivity patterns and the dynamics of neural population activity during task execution, and other studies, e.g. those on perturbations, can be explored. The dotted line shows the possible repetition of this cycle with one network, which allows investigation of training effects of task shaping, or curriculum learning, for closed-loop training of the network on a progression of tasks. C_FIG Significance StatementArtificial recurrent neural network (RNN) modeling is of increasing interest within computational, systems, and cognitive neuroscience, yet its proliferation as a computational tool within the field has been limited due to technical barriers in use of specialized deep-learning software. PsychRNN provides an accessible, flexible, and powerful framework for training RNN models on cognitive tasks. Users can define tasks and train models using the Python-based interface which enables RNN modeling studies without requiring user knowledge of deep learning software. PsychRNNs modular structure facilitates task specification and incorporation of neurobiological constraints, and supports extensibility for users with deep learning expertise. PsychRNNs framework for RNN modeling will increase accessibility and reproducibility of this approach across neuroscience subfields.

neuroscience↗

bioRxiv2020

Circuit mechanisms of top-down attentional control in a thalamic reticular model

The thalamus engages in attention by amplifying relevant signals and filtering distractors. Whether architectural features of thalamic circuitry offer a unique locus for attentional control is unknown. We developed a circuit model of excitatory thalamocortical and inhibitory reticular neurons, capturing key observations from task-engaged animals. We found that top-down inputs onto reticular neurons regulate thalamic gain effectively, compared to direct thalamocortical inputs. This mechanism enhances downstream readout, improving detection, discrimination, and cross-modal performance. The model revealed heterogeneous thalamic responses that enable decoding top-down versus bottom-up signals. Spiking activity from task-performing mice supported model predictions, with a similar coding geometry in auditory thalamus and readout strategy in auditory cortex. Dynamical systems analysis explained why reticular neurons are potent sites for control, and how lack of excitatory connectivity among thalamocortical neurons enables separation of top-down from bottom-up signals. Our work reveals mechanisms for attentional control and connects circuit architectures to computational functions.

neuroscience↗

bioRxiv2020

Mapping Brain-Behavior Space Relationships Along the Psychosis Spectrum

Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlight a need to develop a stable neurobiologically-grounded mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 cross-diagnostic PSD patients, we derived and replicated a dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits. In turn, these symptom axes mapped onto distinct, reproducible brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) do not produce stable results. Instead, we show that a univariate brain-behavioral space (BBS) can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and gene expression maps. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable path that can be iteratively optimized for personalized clinical biomarker endpoints.

neuroscience↗

bioRxiv2020

On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations

Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS solutions is stability of feature patterns driving an association. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues in a systematic manner, we developed a generative modeling framework to simulate synthetic datasets, parameterized by dimensionality, variance structure, and association strength. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the latent pattern underlying the discovered association. We confirmed these trends across two neuroimaging modalities, functional and diffusion MRI, and in independent datasets, Human Connectome Project (n{approx}1000) and UK Biobank (n{approx}20000) and found that only the latter comprised sufficient samples for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses for future studies.

neuroscience↗

bioRxiv2020

Harnessing endogenous repair mechanisms for targeted gene knock-in of bovine embryos

Introducing useful traits into livestock breeding programs through gene knock-ins has proven challenging. Typically, targeted insertions have been performed in cell lines, followed by somatic cell nuclear transfer cloning, which can be inefficient. An alternative is to introduce genome editing reagents and a homologous recombination (HR) donor template into embryos to trigger homology-directed repair (HDR). However, the HR pathway is primarily restricted to actively dividing cells (S/G2-phase) and its efficiency is low in zygotes, especially for the introduction of large DNA sequences. The homology-mediated end joining (HMEJ)-based strategy harnesses HDR by direct injection of embryos, and has been shown to have an improved knock-in efficiency in non-dividing cells. The knock-in efficiency for a 1.8kb gene was contrasted when combining a gRNA/Cas9 ribonucleoprotein complex with either a traditional HR donor template, or a HMEJ template in bovine zygotes. The HMEJ template resulted in a significantly higher rate of gene knock-in as compared to the HR template (37.0% and 13.8%; P < 0.05). Additionally, more than a third of the knock-in embryos (36.9%) were non-mosaic. This approach will facilitate the one-step introduction of gene constructs at a specific location of the bovine genome and contribute to the next generation of elite cattle.

bioengineering↗

bioRxiv2020

Evaluation of Mosaicism and Off Target Mutations in CRISPR-Mediated Genome Edited Bovine Embryos

The CRISPR/Cas9 genome editing tool has the potential to improve the livestock breeding industry by allowing for the introduction of desirable traits. Although an efficient and targeted tool, the CRISPR/Cas9 system can have some drawbacks, including off-target mutations and mosaicism, particularly when used in developing embryos. Here, we introduced genome editing reagents into single-cell bovine embryos to compare the effect of Cas9 mRNA and protein on the mutation efficiency, level of mosaicism, and evaluate potential off-target mutations utilizing next generation sequencing. We designed guide-RNAs targeting three loci (POLLED, H11, and ZFX) in the bovine genome and saw a significantly higher rate of mutation in embryos injected with Cas9 protein (84.2%) vs. Cas9 mRNA (68.5%). In addition, the level of mosaicism was higher in embryos injected with Cas9 mRNA (100%) compared to those injected with Cas9 protein (94.2%), with little to no unintended off-target mutations detected. This study demonstrates that the use of Cas9 protein, rather than Cas9 mRNA, results in a higher editing efficiency in bovine embryos while lowering the level of mosaicism. However, further optimization must be carried out for the CRISPR/Cas9 system to become feasible for single-step embryo editing in a commercial system.

bioengineering↗

bioRxiv2020

A flexible framework for simulating and fitting generalized drift-diffusion models

The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building, simulating, and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are simulated numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We show that a GDDM fit with our framework explains a classic open dataset with better accuracy and fewer parameters than several DDMs implemented using the latest methodology. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

neuroscience↗

bioRxiv2020

Intragenic Complementation of Cellulose Synthase-Like D1 alleles in Root Hair Development

Root hair cells form the primary interface of plants with the soil environment, playing key roles in nutrient uptake and plant defense. In addition, they are typically the first cells infected by nitrogen-fixing soil bacteria during the root nodule symbiosis. Here we report a role for the Cellulose Synthase-Like D1 (CSLD1) gene in root hair development in Lotus japonicus. CSLD1 belongs to the cellulose synthase protein family that includes cellulose synthases, and cellulose synthase-like proteins, the latter thought to be involved in the biosynthesis of hemicellulose. We describe 11 csld1 mutant alleles that have either short (Ljcsld1-1) or variable length roots hairs (Ljcsld1-2 to 11). Examination of Ljcsld1-1 and one variable-length root hair mutant, Ljcsld1-6 showed increased root hair cell wall thickness, which in Ljcsld1-1 was more pronounced, suggesting a possible link with the defect in root nodule symbiosis. In addition, Ljcsld1-1 heterozygotes had intermediate root hair lengths, between those of wild type and the homozygotes. Intragenic complementation was observed between alleles with mutations in the N-terminal domain and other alleles, suggesting modularity of CSLD1 function and that it may operate as a homodimer or multimer. One sentence summaryThis research describes novel gain- and loss-of-function mutations at the Lotus japonicus CELLULOSE SYNTHASE-LIKE D1 locus and analyzes their impact on root hair development.

plant biology↗

bioRxiv2020

Generative modeling of brain maps with spatial autocorrelation

Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene ontology enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization.

neuroscience↗

bioRxiv2020

Hierarchical Network Model Excitatory-Inhibitory Tone Shapes Alternative Strategies for Different Degrees of Uncertainty in Multi-Attribute Decisions

We investigated two-attribute, two-alternative decision-making in a hierarchical neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a choice layer producing the decision. Depending on intermediate layer excitatory-inhibitory (E/I) tone, the network displays three distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each options attribute information is additively integrated. To maximize reward at low environmental uncertainty, the system should operate in regime I. At high environmental uncertainty, reward maximization is achieved in regime III, with each attribute module selecting a favored alternative, and the ultimate decision based upon comparison between outputs of attribute processing modules. We then use these principles to examine multi-attribute decisions with autism-related deficits in E/I balance, leading to predictions of different choice patterns and overall performance between autism and neurotypicals.

neuroscience↗

bioRxiv2019

Confluence of timing and reward biases in perceptual decision-making dynamics

Although the decisions of our daily lives often occur in the context of temporal and reward structures, the impact of such regularities on decision-making strategy is poorly understood. Here, to explore how temporal and reward context modulate strategy, we trained rhesus monkeys to perform a novel perceptual decision-making task with asymmetric rewards and time-varying evidence reliability. To model the choice and response time patterns, we developed a computational framework for fitting generalized drift-diffusion models (GDDMs) which flexibly accommodates diverse evidence accumulation strategies. We found that a dynamic urgency signal and leaky integration, in combination with two independent forms of reward biases, best capture behavior. We also tested how temporal structure influences urgency by systematically manipulating the temporal structure of sensory evidence, and found that the time course of urgency was affected by temporal context. Overall, our approach identified key components of cognitive mechanisms for incorporating temporal and reward structure into decisions.

neuroscience↗

bioRxiv2019

Mildew Locus O facilitates colonization by arbuscular mycorrhiza in angiosperms

O_LILoss of barley Mildew Resistance Locus O (MLO) is known to confer durable and robust resistance to powdery mildew (Blumeria graminis), a biotrophic fungal leaf pathogen. Based on the increased expression of MLO in mycorrhizal roots and its presence in a clade of the MLO family that is specific to mycorrhizal-host species, we investigated the potential role of MLO in arbuscular mycorrhizal interactions. C_LIO_LIUsing mutants from barley, wheat, and Medicago truncatula, we demonstrate a role for MLO in colonization by the arbuscular mycorrhizal fungi Rhizophagus irregularis. C_LIO_LIEarly mycorrhizal colonization was reduced in mlo mutants of barley, wheat and Medicago truncatula, and this was accompanied by a pronounced decrease in the expression of many of the key genes required for intracellular accommodation of arbuscular mycorrhizal fungi. C_LIO_LIThese findings suggest that the primary role of MLO in angiosperms is in the establishment of symbiotic associations with beneficial fungi, which has been appropriated by powdery mildew. C_LI

plant biology↗

bioRxiv2019

A circuit mechanism for irrationalities in decision-making and NMDA receptor hypofunction: behaviour, computational modelling, and pharmacology

Decision-making biases can be systematic features of normal behaviour, or deficits underlying neuropsychiatric symptoms. We used behavioural psychophysics, spiking-circuit modelling and pharmacological manipulations to explore decision-making biases in health and disease. Monkeys performed an evidence integration task in which they showed a pro-variance bias (PVB): a preference to choose options with more variable evidence. The PVB was also present in a spiking circuit model, revealing a neural mechanism for this behaviour. Because NMDA receptor (NMDA-R) hypofunction is a leading hypothesis for neuropathology in schizophrenia, we simulated behavioural effects of NMDA-R hypofunction onto either excitatory or inhibitory neurons in the model. These were tested experimentally using the NMDA-R antagonist ketamine, yielding changes in decision-making consistent with lowered cortical excitation/inhibition balance from NMDA-R hypofunction onto excitatory neurons. These results provide a circuit-level mechanism that bridges across explanatory scales, from the synaptic to the behavioural, in neuropsychiatric disorders where decision-making biases are prominent.\n\nSignificancePeople can make apparently irrational decisions because of underlying features in their decision circuitry. Deficits in the same neural circuits may also underlie debilitating cognitive symptoms of neuropsychiatric patients. Here, we reveal a neural circuit mechanism explaining an irrationality frequently observed in healthy humans making binary choices - the pro-variance bias. Our circuit model could be perturbed by introducing deficits in either excitatory or inhibitory neuron function. These two perturbations made specific, dissociable predictions for the types of irrational decisionmaking behaviour produced. We used the NMDA-R antagonist ketamine, an experimental model for schizophrenia, to test if these predictions were relevant to neuropsychiatric pathophysiology. The results were consistent with impaired excitatory neuron function, providing important new insights into the pathophysiology of schizophrenia.

neuroscience↗

bioRxiv2019

Salmonella Enhances Osteogenic Differentiation in Adipose-Derived Mesenchymal Stem Cells

The potential of mesenchymal stem cells (MSCs) for tissue repair and regeneration has garnered great attention. While MSC interaction with microbes at sites of tissue damage and inflammation is likely, especially in the gut, the consequences of bacterial association have yet to be elucidated. This study investigated the effect of Salmonella enterica ssp enterica serotype Typhimurium on MSC trilineage differentiation path and mechanism. Through examination of key markers of differentiation, immunomodulatory regulators, and inflammatory cytokines, we demonstrated that Salmonella altered osteogenic and chondrogenic differentiation pathways in human and goat adipose-derived MSCs. Gene expression profiles defined signaling pathway alterations in response to Salmonella association not observed in epithelial cells. We uncovered significant differential expression (P < 0.05) of genes associated with anti-apoptotic and pro-proliferative responses in MSCs during Salmonella challenge. These observations led us to conclude that bacteria, specifically Salmonella, induce pathways that influence functional differentiation trajectories in MSCs, thus implicating substantial microbial influence on MSC physiology and immune activity.

microbiology↗