Daniel Tranel

This study examines how domain-general cognition (g) affects lesion-deficit mapping by analyzing 473 patients with focal brain damage using structural and functional lesion network mapping.

The paper introduces an open-source MATLAB toolkit for inferential and predictive modeling of imaging-behavior and lesion-deficit relationships, demonstrating its use in analyzing language impairments linked to brain lesions.

The paper identifies a brain network associated with religious fundamentalism using lesion data from two independent datasets, showing strong right hemisphere lateralization and reproducibility.

This study examines how focal brain damage affects voluntary respiratory and motor control, finding impaired regulation and increased anxiety in specific brain regions.

This paper introduces a novel unsupervised method, Causal Feature Learning (CFL), to discover lesion-behavior maps by learning the appropriate level of analysis and the relation between brain lesions and cognitive outcomes.

This study shows that right inferior frontal cortex damage increases trigger failures in inhibitory control tasks but does not impair the implementation of inhibition once initiated.

This study identifies brain regions and networks associated with time disorientation in patients with focal brain lesions.

The paper investigates thalamic hubs in the human brain and finds that lesions in highly connected subregions cause broader cognitive deficits than those in less connected regions.

This paper identifies and removes high-frequency motion artifacts in single-band fMRI data, improving motion estimates and reducing data loss without biasing functional connectivity.

The paper examines functional connectivity of surgically disconnected temporal pole using resting-state fMRI and finds significant connectivity with remote regions despite anatomical disconnection.