Ralph Adolphs

The paper shows that BOLD fMRI amygdala signals are confounded by venous signals, leading to mislocalization and unreliable subnuclear attribution.

This study investigates how the human medial frontal cortex and hippocampus encode task context over time, revealing distinct coding strategies for maintaining and switching task sets.

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 investigates how neurons in the human medial frontal cortex represent cognitive conflict across tasks, showing both task-specific and generalized representations.

This study shows video-evoked fMRI BOLD responses are consistent across different data acquisition sites, both for matched and unmatched datasets.

The paper investigates gaze patterns in autism spectrum disorder (ASD) and finds they are heterogeneous across individuals but reliable within individuals, supporting a hypothesis for subtype investigation.

The paper identifies common effective connectivity patterns between auditory cortex and fronto-temporal regions in humans and monkeys using functional imaging and electrical stimulation.

This study shows that intracranial electrical stimulation alters meso-scale network integration depending on the topological role of the stimulated brain regions.

The paper estimates heritability of 37 behavioral measures from the HCP dataset using machine learning and compares results to twin correlations.

The paper shows that the medial frontal cortex flexibly uses memory-based representations during decision-making, while the medial temporal lobe does not show similar task-dependent changes.

The paper presents a human es-fMRI resource with data from 26 patients undergoing deep-brain stimulation and whole-brain functional MRI, providing raw and preprocessed data for analysis.

This study investigates the association between social network index and gray matter volume in adults using data-driven methods and finds no significant association after multiple comparison corrections.

The paper predicts general intelligence from resting-state fMRI data using a distributed brain network approach, explaining 20% of variance in intelligence scores.

The paper predicts personality traits from resting-state fMRI data, finding that openness to experience is the only reliably predicted factor.

The paper introduces a framework combining es-fMRI and causal discovery to map emotion networks in the human brain and presents preliminary findings from resting-state datasets and case studies.

This study identifies brain activity patterns in the fusiform gyrus and superior temporal sulcus that distinguish seen from unseen faces and upright from inverted faces using ECoG data.

The paper tests if integrated information theory applies to conscious experience by analyzing brain recordings and finding that integrated information structures classify visual experiences more accurately than other measures.