Gang Chen

This study compares the retrograde transduction efficiency of four viral vectors in dopaminergic neurons of rhesus monkeys after striatal injection.

This study identifies four distinct HRF shape groups in the epileptic brain, showing HRF variability is linked to both anatomical location and pathology.

This study investigates how associative learning and expectations influence pain and hedonic taste perception, finding domain-general neural circuits involved in cue effects across different modalities.

This paper examines limitations in interpreting resting-state fMRI correlations, distinguishing measurement distortion from causal non-identifiability and their implications for biological interpretation.

This study compares univariate and multivariate models of emotional valence encoding in the human amygdala using fMRI, finding that core amygdala regions use global activation rather than distributed patterns.

This paper reassesses the measurement error assumption in twin studies and introduces a hierarchical model to improve heritability estimation accuracy.

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 highlights pitfalls in covariate selection and advocates for principled approaches to improve statistical validity and reproducibility in data analysis.

This paper identifies novel neural markers of sleep depth using auditory arousal threshold instead of conventional sleep stages, revealing unique brain network correlations.

The paper examines individual variability in the relationship between resting-state fMRI metrics and cerebrovascular reactivity (CVR) using longitudinal data from 7 subjects with 10 sessions each.

The paper introduces FMRI quality control tools in AFNI, providing systematic, in-depth, and interactive QC features for preprocessing pipelines.

This paper improves fMRI analysis by capturing hemodynamic profiles instead of relying on a canonical HRF, showing better sensitivity and cross-study reproducibility.

This study uses Bayesian and frequentist methods to analyze resting-state fMRI data from females with GAD, finding reduced functional connectivity in specific brain regions.

The paper argues against hiding subthreshold results in neuroimaging studies and proposes a 'highlighting' approach to improve reproducibility and understanding.

This study investigates emotion perception in individuals with Moebius Syndrome, finding reduced sensitivity to facial expressions despite normal perceptual control tasks.

This study applies a visual attention training method to right hemisphere stroke patients, showing improved attention in some but not all participants with mild left inattention.

This study investigates how emotional faces modulate human visual cortex, finding layer-specific and retinotopically diffuse effects consistent with amygdala feedback.

The paper investigates how negative family emotional climate affects children's psychopathological symptoms through child-parent neural synchrony, finding reduced vmPFC-hippocampal correlations in such dyads.

The paper identifies information loss sources in neuroimaging and proposes a Bayesian framework to improve inference and reproducibility.

The paper examines how balancing trial and subject sample sizes affects statistical efficiency in neuroimaging, finding trial sample size has a comparable impact to subject sample size.

This study evaluates ICA-based denoising strategies for improving CVR estimates in multi-echo BOLD fMRI during breath-hold tasks, finding that a conservative ICA model reduces motion effects best.

This paper introduces multilevel smoothing splines for nonlinear analysis in neuroimaging, demonstrating their application to longitudinal structural MRI data.

This study uses fMRI to examine time-dependent brain activity during haptic 3D curve and roughness estimation, finding differential activation patterns in sensorimotor and higher-level regions.

This paper introduces a Bayesian multilevel model framework to account for cross-trial variability in fMRI data, showing improved statistical sensitivity and accuracy compared to traditional methods.

The paper proposes a data-driven Bayesian method to select an optimal primary threshold in cluster-wise fMRI analysis, improving statistical power while controlling false discovery rate.

This study investigates how emotion and action interact in the brain by examining context-dependent responses to threatening stimuli using fMRI and Bayesian analysis.

This paper uses TBS and resting-state fMRI to show that disrupting the right posterior superior temporal sulcus reduces functional connectivity across the face-processing network, including between non-stimulated regions.

This paper introduces a Bayesian multilevel framework for inter-subject correlation analysis in naturalistic scanning data, improving inference efficiency and reproducibility.

The paper proposes a Bayesian multilevel approach to address multiple testing in neuroimaging by embracing multiplicity through global calibration, aiming to improve model efficiency and reproducibility.

The paper introduces a Bayesian multilevel modeling approach for neuroimaging connectomics that integrates region, region pairs, and subject analyses to improve statistical inference and avoid multiple testing issues.