Lisa D. H. Nickerson

THC enhances behavioral and neural responses to rewards through mesocorticolimbic modulation centered on the SN-VTA.

The paper investigates why large-scale neuroimaging tasks yield weak brain-mental health associations, finding a signal evocation bottleneck in common fMRI tasks.

The paper introduces the Network Correspondence Toolbox (NCT) to evaluate spatial correspondence between novel neuroimaging results and existing brain atlases, enabling standardized reporting and comparison across studies.

The paper uses unsupervised machine learning to analyze fMRI data from an emotional face matching task, identifying large-scale brain networks and their relation to cognition and mental health.

This paper introduces a dual-projection based ICA denoising method to remove scanner/site effects in multi-site MRI data, showing improved performance over traditional ICA and GLM methods.

This paper introduces a tensor component analysis framework to identify shared brain networks across subjects during naturalistic stimuli, improving reproducibility and capturing individual differences.

This paper introduces a denoising method for multi-site multimodal MRI data to remove scanner and site variability, improving statistical analysis and data integration.

The paper uses the Human Connectome Project dataset to replicate a study on resting state-task network correspondence in neuroimaging.