Ben Fulcher

The paper introduces DiCER, an alternative to global signal regression for removing signal deflections in fMRI data, showing improved performance in reducing artifacts and improving functional connectivity analysis.

The paper shows that fMRI timescales correlate with structural connectivity strength in humans, mirroring findings in mice.

The paper introduces catch22, a set of 22 time-series features that balance computational efficiency and classification performance across diverse biomedical and other time-series datasets.

The paper identifies conserved large-scale cortical gradients in mice and humans, showing similar patterns in cytoarchitecture, gene expression, and connectivity across species.

This paper outlines a seven-step pipeline for integrating brain-wide gene expression and neuroimaging data and evaluates how processing choices affect results.

The paper introduces a feature-based analysis method to characterize peripheral nerve firing by unsupervised dimensionality reduction, aiming to improve decoding in bioelectronic medicine devices.

This paper reviews TMS paradigms and develops biophysically-based models to understand how LTP/LTD modulate population-level neuronal activity in response to TMS.

The paper shows that hub neurons in the C. elegans connectome have tightly coupled gene expression, which is linked to their role as command interneurons regulating locomotion.

This paper evaluates motion correction strategies for resting-state fMRI, finding that no single method is optimal and that preprocessing significantly affects results.

This paper introduces hctsa, a tool that automatically extracts interpretable time-series features to relate complex time-series data to scientific outcomes.

The paper identifies distinct transcriptional signatures in connectomically-defined subregions of the human striatum using diffusion MRI and transcriptomic data.

The paper examines the relationship between structural connectome topology and regional BOLD signal dynamics in the mouse brain, finding strong correlations between weighted in-degree and fMRI time series autocorrelation.