Todd Samuel Braver

This study shows that caudate dopamine-D2 receptor availability influences cognitive effort decisions in both lab tasks and daily life.

This paper develops a computational framework to model how task-induced changes in brain dynamics relate to cognitive individual differences using rsfMRI and tfMRI data.

The paper models neural dynamics on an attractor landscape to show how attentional states and contexts alter the geometry of brain activity trajectories.

The paper investigates the neural mechanisms linking working memory capacity and proactive control using fMRI and behavioral data from the DMCC project, identifying a circuit involving rDLPFC and lMOT.

This paper shows that combining hierarchical Bayesian modeling with multivariate decoding improves the reliability and precision of estimating individual differences in cognitive control from fMRI data.

The paper constructs dynamical models of resting-state brain networks from fMRI data, revealing anatomically reliable attractor landscapes that go beyond conventional functional connectivity analyses.

This paper develops a framework to model brain dynamics from time-series data, revealing individual differences in alpha and beta oscillations through excitation-inhibition balance.

The paper introduces the DMCC55B dataset, a collection of fMRI data from 55 healthy young adults performing cognitive control tasks, for benchmarking and analysis of task performance and individual differences.

This paper improves task fMRI preprocessing by filtering out pre-event intrinsic activity using individualized MINDy models, enhancing statistical power and behavioral prediction.

The DMCC project investigates two cognitive control modes, proactive and reactive, using neuroimaging and behavioral tasks, finding a domain-general neural signature for reactive control.

This study examines twin effects in cognitive control using neuroimaging, finding increased similarity in fronto-parietal activation patterns between monozygotic twins under high control demands.

This paper uses representational similarity analysis to test coding schemes in the Stroop task, finding incongruency coding in DMFC and target/incongruency coding in DLPFC.

This study shows that the human dorsal anterior cingulate cortex integrates motivational value signals from liquid and monetary incentives to influence cognitive task performance and self-reported motivation.

This paper introduces MINDy, a method to create individualized dynamic brain models from resting state fMRI data, showing they are predictive and better at uncovering mechanisms than functional connectivity methods.

This paper introduces surrogate deconvolution to estimate hidden biological systems from indirect measurements, showing it is scalable and competitive with Kalman Filters in neuroscience applications.

The paper uses fMRI pattern similarity analyses to examine individual and genetic differences in neural coding of working memory and perceptual category in FrontoParietal and Visual regions.

The paper shows that a valuation network encodes the subjective value of cognitive effort, similar to other cost domains, and links this to motivation and decision-making.

This paper introduces a geometric classification method for brain network dynamics that achieves high temporal resolution and anatomical inference using conic derivative discriminants.