Samuel J. Harrison

This paper introduces mPFMs, a new method for multiscale functional brain connectivity analysis that improves prediction of UK Biobank traits compared to standard techniques.

This study compares parcellation and gradient methods for predicting behavior from resting-state functional connectivity in two datasets, finding that individual-specific hard parcellations perform best in HCP and principal gradients perform similarly in ABCD.

This study investigates how anxiety relates to interoceptive processing of breathing using fMRI and computational models, finding that anxiety traits affect interoceptive prediction certainty and errors in the anterior insula.

The paper introduces a task and analysis procedure to quantify breathing-related interoception dimensions using simulated and empirical data.

This paper introduces sPROFUMO, a hierarchical model for estimating functional brain networks in individuals and populations from large fMRI datasets, showing improved performance over traditional methods.

TAPAS provides open-source tools for computational assays in psychiatry to support differential diagnosis and treatment prediction through end-to-end pipelines.

This paper introduces regression dynamic causal modeling (rDCM) for resting-state fMRI, providing directional connectivity estimates and scalability to whole-brain networks.

This paper introduces a Hilbert-based method to estimate respiratory volume per unit time from bellows recordings, improving fMRI noise correction by better capturing atypical breathing events.

This paper introduces an automated pipeline for processing neonatal resting-state fMRI data, improving motion correction and quality assurance in the developing Human Connectome Project.

The paper extends probabilistic functional modes to model subject variability in fMRI data, showing spatial maps capture most cross-subject variability and functional coupling reflects cognitive state.

The paper revisits the relationship between spatial configuration and functional connectivity, showing spatial overlap biases connectivity estimates and introducing an improved method to address this bias.

This paper introduces temporal ICA to remove global structured noise from fMRI data while preserving global neural signals, improving upon traditional global signal regression methods.

This study examines how the spatial arrangement of brain regions influences functional connectivity and behavioral predictions.