Jialin Li

This study develops a brain-based biomarker, PARES, that predicts both pain increases and relief in individuals using fMRI data.

This paper introduces RLBF, a framework for real-time fMRI-based adaptive stimulus generation using reinforcement learning, validated in a study optimizing visual stimuli to enhance V1 responses.

This paper evaluates the replicability of diffusion-weighted MRI-based brain-behavior models and finds moderate replicability with small sample sizes, but higher replicability for trait-like phenotypes.

This study compares neural mechanisms of appetitive and aversive pain-related learning using fMRI, finding shared and distinct brain activity patterns during acquisition and extinction.

The paper partially replicates and generalizes Bramson et al.'s 2023 findings on functional anatomical shifts in emotion action control related to anxiety using fMRI data from a different sample and paradigm.

The paper proposes an adaptive splitting method for external validation of machine learning models to improve generalizability and replicability in predictive modeling studies.

This paper introduces fcANNs, a model of brain dynamics based on functional connectivity, showing it can reconstruct and predict brain activity in rest, tasks, and disease states.

The paper develops a neurofunctional signature of disgust using fMRI and machine learning, showing it generalizes across core disgust, gustatory distaste, and socio-moral contexts.

This study shows that losartan improves reward learning by enhancing ventral striatal prediction errors and frontostriatal communication in healthy humans.

The paper examines associations between Internet Use Disorder symptom domains and brain structure/function in a dimensional neuroimaging study, finding opposing effects on striatal networks and volumes.

This study shows that losartan reduces memory advantage for negative emotional material by decreasing hippocampus activity and its coupling with the amygdala.