John D Murray

• Yale University

Sensorimotor and cognitive abilities undergo substantial changes throughout the human lifespan, but the corresponding changes in the functional properties of cortical networks remain poorly understood. This can be studied using temporal and spatial scales of functional magnetic resonance imaging (fMRI) signals, which provide a robust description of...

The accumulation of evidence over time formalized in the drift diffusion model (DDM), has become one of the most prevalent models of deliberative decision-making. To better understand the role of latent variables during a serial learning task, in which decisions were made rapidly and did not show typical accuracy and response time patterns, we fit...

Deficits in working memory (WM) are a hallmark of neuropsychiatric disorders such as schizophrenia, yet their neurobiological basis remains poorly understood. Glutamate N-methyl-D-aspartate receptors (NMDARs) are critical for spatial WM (sWM), with NMDAR antagonist ketamine known to attenuate task-evoked activation and reduce sWM accuracy. Cortical...

Neural activity and behavior vary within an individual (states) and between individuals (traits). However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In...

Importance DSM criteria are polythetic, allowing for heterogeneity of symptoms among individuals with the same disorder. In empirical research, most combinations were not found or only rarely found, prompting criticism of this heterogeneity. Objective To elaborate how symptom-based definitions and assessments contribute to a distinct probability p...

Spatial locations can be encoded and maintained in working memory using different representations and strategies. Fine-grained representations provide detailed stimulus information, but are cognitively demanding and prone to inexactness. The uncertainty in fine-grained representations can be compensated by the use of coarse, but robust categorical...

Humans presented with the same problem in the same environment commonly adopt wildly different strategies for attention and learning. Indeed, psychiatric conditions are defined by qualitative differences in behavior. However, most tasks measure an individual's deviation from a single expected strategy rather than the utilization of distinct strateg...

Each cortical area has a distinct pattern of anatomical connections within the thalamus, a central subcortical structure composed of functionally and structurally distinct nuclei. Previous studies have suggested that certain cortical areas may have more extensive anatomical connections that target multiple thalamic nuclei, which potentially allows...

Each cortical area has a distinct pattern of anatomical connections within the thalamus, a central subcortical structure composed of functionally and structurally distinct nuclei. Previous studies have suggested that certain cortical areas may have more extensive anatomical connections that target multiple thalamic nuclei, which potentially allows...

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 associations is stability of their feature patterns. However, stability of CCA/PLS in high-dimensional datasets is questionab...

Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains va...

Importance: Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments. Objective: To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment. Design: Double blind randomized controlled trial. Setting:...

Resting-state functional connectivity (FC) has received considerable attention in the study of brain-behavior associations. However, the low generalizability of brain-behavior studies is a common challenge due to the limited sample- to-feature ratio. In this study, we aimed to improve the generalizability of brain-behavior associations in resting-s...

Neural activity and behavior manifest a dual nature of state and trait dynamics, exhibiting variations within and between individuals. However, the joint properties of neural state-trait variation and how they map onto individual behavior remain understudied. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patt...

Spatial locations can be encoded and maintained in working memory using high-precision, fine-grained representations that are cognitively demanding, or coarse and less demanding categorical representations. In this study, we employed an individual differences approach to identify brain activity correlates of the use of fine-grained and categorical...

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state...

Introduction: Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, res...

The world is overabundant with feature-rich information obscuring the latent causes of experience. How do people approximate the complexities of the external world with simplified internal representations that generalize to novel examples or situations? Theories suggest that internal representations could be determined by decision boundaries that d...

Functional connectivity (FC) in blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains va...

Precision psychiatry aims to identify markers of inter-individual variability that allow predicting the right treatment for each patient. However, bridging the gap between molecular-level manipulations and neural systems-level functional alterations remains an unsolved problem in psychiatry. After decades of low success rates in pharmaceutical R&D...

Background Ketamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamine’s molecular mechanisms connect to its neural and behavioral effects. Methods We conducted a double-blind placebo-control...

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...

The thalamus is a key structure in the mammalian brain, providing a hub for communication within and across distributed forebrain networks. Research in this area has undergone a revolution in the last decade, with findings that suggest an expanded role for the thalamus in sensory processing, motor control, arousal regulation, and cognition. Moving...

Neuroimaging technology has experienced explosive growth and has transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges around method integration (1-3). Specifically, researchers often have to rely on siloed approaches which...

The phenotype of schizophrenia (SZ), regardless of etiology, represents the most studied psychotic disorder with respect to neurobiology and distinct phases of illness. The early phase of illness represents a unique opportunity to provide effective and individualized interventions that can alter illness trajectories. Developmental age and illness s...

Integrating motivational signals with cognition is critical for goal-directed activities. The mechanisms that link neural changes with motivated working memory continue to be understood. Here, we tested how externally cued and non-cued (internally represented) reward and loss impact spatial working memory precision and neural circuits in human subj...

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 po...

The synaptic balance between excitation and inhibition (E/I balance) is a fundamental principle of cortical circuits, and disruptions in E/I balance are commonly linked to cognitive deficits such as impaired decision making. Explanatory gaps remain in a mechanistic understanding of how E/I balance contributes to cognitive computations, and how E/I...

People cannot access the latent causes giving rise to experience. How then do they approximate the high-dimensional feature space of the external world with lower-dimensional internal models that generalize to novel examples or contexts? Here, we developed and tested a theoretical framework that internally identifies states by feature regularity (i...

The heterogeneity of symptoms among individuals diagnosed with the same mental disorder complicates the identification of biomarkers and the development of targeted treatments. Yet, the characteristics of this heterogeneity remain largely unknown. We investigated the frequency of disorder-specific symptom combinations, a marker of symptom heterogen...

We live in a world that changes on many timescales. To learn and make decisions appropriately, the human brain has evolved to integrate various types of information, such as sensory evidence and reward feedback, on multiple timescales. This is reflected in cortical hierarchies of timescales consisting of heterogeneous neuronal activities and expres...

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 obs...

Decades of research have highlighted the importance of optimal stimulation of cortical dopaminergic receptors, particularly the D1R receptor (D1R), for prefrontal-mediated cognition. This mechanism is particularly relevant to the cognitive deficits in schizophrenia, given the abnormalities in cortical dopamine (DA) neurotransmission and in the expr...

Background Diminished synaptic gain – the sensitivity of postsynaptic responses to neural inputs – may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms. Methods Participant...

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 PSD patients spanning several diagnoses, we derived and...

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. Previously, using pharmacological neuroimaging, we found that neural and experiential effects of lysergic ac...

Cognition depends on resisting interference and responding to relevant stimuli. Distracting information, however, varies based on content, requiring distinct filtering mechanisms. For instance, affective information captures attention, disrupts performance and attenuates activation along frontal-parietal regions during cognitive engagement, while r...

Correlations are a basic object of analysis across neuroscience, but multivariate patterns of correlations can be difficult to interpret. For example, correlations are fundamental to understanding timeseries derived from resting-state functional magnetic resonance imaging (rs-fMRI), a proxy of brain activity. Networks constructed from regional corr...

We are constantly faced with decisions between alternatives defined by multiple attributes, necessitating an evaluation and integration of different information sources. Time-varying signals in multiple brain areas are implicated in decision-making; but we lack a rigorous biophysical description of how basic circuit properties, such as excitatory-i...

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 obs...

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 a...

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 attr...

Diminished synaptic gain - the sensitivity of postsynaptic responses to neural inputs - may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms. Participants with schizophrenia...

Computing plays a critical role in the biological sciences but faces increasing challenges of scale and complexity. Quantum computing, a computational paradigm exploiting the unique properties of quantum mechanical analogs of classical bits, seeks to address many of these challenges. We discuss the potential for quantum computing to aid in the merg...

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 potentia...

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 po...

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 potenti...

Decision-making biases can be features of normal behaviour, or deficits underlying neuropsychiatric symptoms. We used behavioural psychophysics, spiking-circuit modelling and pharmacological manipulations to explore decision-making biases during evidence integration. Monkeys showed a pro-variance bias (PVB): a preference to choose options with more...

Decision-making biases can be features of normal behaviour, or deficits underlying neuropsychiatric symptoms. We used behavioural psychophysics, spiking-circuit modelling and pharmacological manipulations to explore decision-making biases during evidence integration. Monkeys showed a pro-variance bias (PVB): a preference to choose options with more...

The thalamus is a key brain structure engaged in attentional functions, such as selectively amplifying task-relevant signals of one sensory modality while filtering distractors of another. To investigate computational mechanisms of attentional modulation, we developed a biophysically grounded thalamic reticular circuit model, comprising excitatory...

Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlights a need to develop a neurobiologically-grounded, quantitatively stable 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 deri...

Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlights a need to develop a neurobiologically-grounded, quantitatively stable 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 deri...

Associations between high-dimensional datasets, each comprising many features, can be discovered through multivariate statistical methods, like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). CCA and PLS are widely used methods which reveal which features carry the association. Despite the longevity and popularity of CCA/PLS ap...

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 two male rhesus monkeys to perform a novel perceptual decision-making task with asymm...

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 and fitting DDM extensions, and provide a software package which implements the fra...

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 and fitting DDM extensions, and provide a software package which implements the fra...

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 and fitting DDM extensions, and provide a software package which implements the fra...

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 speci...

Background Impairments in spatial working memory (sWM) have been well-documented in schizophrenia. Here we provide a comprehensive test of a microcircuit model of WM performance in schizophrenia, which predicts enhanced effects of increasing delay duration and distractors based on a hypothesized imbalance of excitatory and inhibitory processes. Me...

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 imple...

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 speci...

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,...

Background: The use of psilocybin in scientific and experimental clinical contexts has triggered renewed interest in the mechanism of action of psychedelics. However, its time-dependent systems-level neurobiology remains sparsely investigated in humans. Methods: We conducted a double-blind, randomized, counterbalanced, crossover study comprising...

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 rew...

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of th...

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-var...

Distributed neural dysconnectivity is considered a hallmark feature of schizophrenia (SCZ), yet a tension exists between studies pinpointing focal disruptions versus those implicating brain-wide disturbances. The cerebellum and the striatum communicate reciprocally with the thalamus and cortex through monosynaptic and polysynaptic connections, form...

Schizophrenia (SCZ) is recognized as a disorder of distributed brain dysconnectivity. While progress has been made delineating large-scale functional networks in SCZ, little is known about alterations in grey matter integrity of these networks. We used a multivariate approach to identify the structural covariance of the salience, default, motor, vi...

Background A key challenge in the field of neuropsychiatry lies in matching patients to effective treatments. Most studies operate under the canonical assumption that categorical diagnostic groupings (such as schizophrenia and bipolar disorder) and/or pre-existing clinical assessments (such as the PANSS) are the ‘gold standard’ for describing behav...

Significance The brain is organized into processing streams, along which incoming sensory information is processed at increasingly abstract and integrative levels. This specialization of function is thought to be underpinned by corresponding changes in the brain’s local circuitry. Here we combine a wide range of measurements across the mouse brain,...

The large-scale organization of dynamical neural activity across cortex emerges through long-range interactions among local circuits. We hypothesized that large-scale dynamics are also shaped by heterogeneity of intrinsic local properties across cortical areas. One key axis along which microcircuit properties are specialized relates to hierarchical...

Background: Lysergic acid diethylamide (LSD) has agonist activity at various serotonin (5-HT) and dopamine receptors. Despite the therapeutic and scientific interest in LSD, specific receptor contributions to its neurobiological effects remain unknown. Methods: We therefore conducted a double-blind, randomized, counterbalanced, cross-over studyd...

Background:Lysergic acid diethylamide (LSD) has agonist activity at various serotonin (5-HT) and dopamine receptors. Despite the therapeutic and scientific interest in LSD, specific receptor contributions to its neurobiological effects remain unknown. Methods: We therefore conducted a double-blind, randomized, counterbalanced, cross-over study (Cli...