John D. Murray

This study shows ketamine broadens neural spatial tuning and impairs spatial working memory in healthy humans, linking NMDAR antagonism to cortical microcircuit disruptions.

This study applies a drift diffusion model to analyze decision-making dynamics in a transitive inference task, showing stable reaction times despite improved accuracy during learning.

The paper quantifies age-related changes in temporal and spatial scales of resting-state fMRI signals across the lifespan and links these changes to cortical myelination.

The paper identifies brain activity patterns associated with individual differences in spatial working memory strategies, showing distinct engagement of control and default networks for fine-grained vs. categorical representations.

This study investigates how ketamine induces distinct whole-brain functional connectivity patterns in individuals and links these patterns to gene expression and behavioral responses.

The paper evaluates the stability of CCA and PLS in high-dimensional datasets, finding instability in small samples and proposing a power calculator for reliable multivariate analysis.

The paper develops a biophysically-based circuit model to capture individual variation in cortical functional connectivity using resting-state fMRI data from the Human Connectome Project.

The paper examines how noise correlations in large-scale brain networks relate to signal correlations and their impact on information coding in a multi-task fMRI dataset.

The paper investigates brain-behavior associations using resting-state functional connectivity edges, finding that diverse edges are more informative than uniform ones, but using all edges provides the best generalizability.

The paper analyzes neural state-trait variations in healthy young adults using resting-state fMRI to identify reproducible co-activation patterns linked to behavioral and cognitive features.

The study quantifies corticothalamic connectivity patterns in humans and macaques, revealing distinct spatial extents associated with sensorimotor and associative cortical areas.

The paper shows spatial and temporal autocorrelation explain network topology measures in rs-fMRI data and how they change with aging and drug treatment.

QuNex provides a platform for reproducible neuroimaging analytics, integrating multi-modal tools and enabling scalable, high-throughput processing across species and datasets.

The paper compares static and dynamic functional connectivity methods in fMRI, finding that dynamic estimates capture similar information to static ones.

This study proposes contingency representations as a unifying framework for working memory and planning, supported by human behavior and neural network simulations.

The paper shows that animals briefly suppress motor output when new sensory evidence becomes available, and neurons in the frontal eye field show a corresponding activity dip.

This study investigates how disruptions in the excitation-inhibition balance affect decision-making in a cortical circuit model, finding that both elevated and reduced E/I ratios impair decision-making in distinct ways.

The paper shows that multi-task representations in human cortex follow a sensory-to-motor hierarchy with compression-then-expansion of dimensionality, supported by neural network models trained for sensory-to-motor transformation.

The paper tests burst-coding models of working memory by analyzing trial-to-trial variability in prefrontal cortex spiking data and finds reduced variability contradicts burst coding predictions.

The paper develops a stable brain-behavior space mapping for psychosis-spectrum disorders using symptom and brain data, showing univariate approaches outperform multivariate ones for individualized prediction.

The paper integrates transcriptomics with large-scale cortical modeling to simulate LSD's effects on brain function, revealing serotonin-2A receptor-mediated changes in cortical connectivity.

This study uses fMRI and a computational model to show that affective and task-similar distractors differentially impact working memory through distinct neural mechanisms.

This study develops a spatially-heterogeneous circuit model to simulate brain activity dynamics, showing that anatomical and functional gradients are essential for generating realistic static and dynamic functional connectivity patterns.

PsychRNN is a Python package for training RNNs on cognitive tasks, enabling neuroscience research with accessible, flexible, and extensible tools.

The paper investigates decision-making biases in health and disease using behavioural experiments, computational models, and pharmacology, revealing a neural circuit mechanism for pro-variance bias and its link to NMDA receptor hypofunction in schizophrenia.

The paper develops a circuit model of thalamic neurons to show how top-down inputs to reticular neurons enhance attentional control by improving signal detection and discrimination.

This study investigates how temporal and reward contexts influence perceptual decision-making by training monkeys and modeling their behavior with generalized drift-diffusion models.

This paper introduces a framework for simulating and fitting generalized drift-diffusion models, showing improved accuracy and efficiency over traditional methods.

The paper introduces a generative null model to create spatially autocorrelated surrogate brain maps for hypothesis testing in brain organization studies.

The paper models decision-making in a hierarchical neural network, showing how excitatory-inhibitory balance affects decision strategies under different uncertainty levels.