Jeanette A. Mumford

This study evaluates deep learning models for predicting task activation from resting-state fMRI, finding that architectural changes improve efficiency without sacrificing accuracy.

This paper validates data-driven factor models over RDoC-based models using fMRI and behavioral data, showing better reproducibility and clinical relevance across neural and behavioral domains.

This study validates the RDoC framework using a latent variable approach, showing a bifactor model better fits fMRI data than the current framework and highlights underrepresentation of the arousal and regulatory systems domain.

The paper identifies unintended bias from collinearity reduction strategies in fMRI analysis and introduces a contrast-based variance inflation factor to assess their impact.

The paper shares a dataset of cognitive tasks, anatomical MRI, and functional MRI data from 103 healthy adults to study self-regulation.

This study evaluates how different analytic choices affect test-retest reliability in fMRI data using the Monetary Incentive Delay task across three independent samples.

The paper addresses the response time paradox in fMRI analyses by proposing a model that incorporates response times to separate condition effects from RT-related activation.

This paper demonstrates that global activation and pattern similarity are intertwined in neural data and shows that statistical methods to separate them are not always effective.

The paper shows that different analysis workflows by 70 teams on the same fMRI dataset led to varied results, but meta-analysis found consensus in activated regions.

The paper investigates how emotional processing of stimuli affects subsequent neutral social stimuli processing, finding individual differences in affective biasing and memory.