Scott T. Grafton

The paper investigates how incentive valence engages different basal ganglia circuits during movement initiation, finding distinct neural patterns for reward and threat conditions.

This study uses TREV to measure cardiac contractility during threat anticipation and finds it correlates with neural activity and emotional responses better than skin conductance responses.

The paper investigates how amygdala pathways to frontopolar cortex and medial temporal lobe modulate temporal memory for emotional events, finding distinct effects on memory accuracy and recency bias.

The paper shows cardiac-sympathetic outflow predicts action restraint during a task, with effects modulated by reward and loss conditions and participant success.

The paper examines how different regions of the lateral prefrontal cortex represent threat and controllability during threat anticipation, finding that the lateral frontal pole uniquely integrates these signals.

The paper identifies a functional open loop circuit (OLC) in humans connecting ventral putamen to motor cortical areas via subcortical limbic nodes using high-resolution fMRI.

The paper dissociates the functional roles of a novel open-loop circuit and a traditional closed-loop circuit in the human brain during incentive-based motor tasks.

This study evaluates head motion correction methods in diffusion MRI, showing both Eddy and SHORELine perform well but are affected by denoising and sampling schemes.

This study evaluates six compressed sensing diffusion spectrum imaging (CS-DSI) schemes, finding they provide accurate and reliable white matter structure measures with up to 80% reduced scan time compared to full DSI.

ReTrace evaluates tractography algorithms using Reeb graphs, showing no single algorithm consistently outperforms others and deterministic methods better capture fiber bundle properties.

The paper identifies two subtypes of limb apraxia based on distinct neuroanatomic lesion profiles using structural MRI and graph analysis in stroke patients.

The paper assesses reproducibility and variability in multi-modal, longitudinal brain networks across different modalities and tasks, finding structural networks more reliable than functional ones for individual identification.

The paper investigates whether heuristic decision-making in action selection correlates with skill in action execution, finding that heuristic users show skill advantages in spatially relevant aspects.

This paper introduces a Reeb graph model to represent white matter fiber bundles and applies it to study Alzheimer's disease progression by analyzing fiber density changes.

This study examines how sex steroid hormones affect cerebellar network dynamics across the menstrual cycle using resting-state functional connectivity.

The paper uses dynamic community detection to analyze brain imaging data from a female over her menstrual cycle, revealing transient reorganization of functional brain networks during ovulation.

QSIPrep is a software platform for preprocessing diffusion MRI data that supports multiple sampling schemes and ensures reproducibility through standardized workflows and quality measures.

This study combines repetition suppression and pattern analysis to show that M1 and parietal areas exhibit different representational changes during motor sequence learning.

This study investigates how progesterone fluctuations affect medial temporal lobe volume in humans across the menstrual cycle and finds that progesterone dynamically shapes this volume, with chronic suppression altering these effects.

This study shows how sex hormones like estrogen and progesterone influence brain network dynamics across the menstrual cycle, with estrogen promoting network coherence and progesterone reducing it, particularly in hormone-sensitive hubs.

This study investigates how the ventromedial prefrontal cortex (vmPFC) tracks subjective value and confidence during decision-making, finding that these signals are temporally dissociated.

The paper identifies structural disconnection patterns in the brain that predict severity of limb apraxia after left hemisphere stroke.

The paper predicts future motor learning from baseline sensorimotor network connectivity in humans.

This study uses machine learning to uncover dynamic subgraphs in brain networks, revealing how they constrain modular organization and explain inter-individual differences in network reconfiguration.