Kristin A. Linn

This study shows that targeting cortical areas with high resting state functional connectivity to the sgACC with TMS leads to stronger sgACC responses in depressed patients.

The paper introduces NEST, a framework for testing brain-phenotype associations within functional networks, applied to neurodevelopmental imaging data.

The paper develops a multivariate residualization method for medical imaging data to better control for nuisance variables and applies it to ADNI data.

This paper introduces a method to analyze intermodal coupling between multiple brain imaging modalities and applies it to study neurodevelopmental patterns in adolescents.

This study examines how cerebral blood flow and fMRI fluctuations are coupled in youth, finding age-related decreases, sex differences, and links to executive function.

This paper introduces PeDecURe, a method to adjust for nuisance variables in multivariate pattern analysis, showing it improves accuracy and generalizability in neuroimaging studies.

This study identifies three neurocognitive subtypes of depression in youth using machine learning, showing distinct neural signatures despite similar clinical symptoms.

The paper introduces longitudinal ComBat to harmonize multi-scanner imaging data, showing it improves detection of longitudinal changes and controls type I error better than existing methods.

This study uses combined fMRI and TMS to map causal brain networks involving the sgACC and amygdala, showing individualized targeting captures causal circuitry in humans.

MIMoSA improves automated white matter lesion segmentation in multiple sclerosis by integrating multimodal MRI data through inter-modal coupling regression and local logistic regression.

The paper introduces a statistical technique to count distinct multiple sclerosis lesions from MRI data, showing it correlates with gold-standard counts and provides disease-related insights.