Graeme D. Jackson

The paper uses multilayer network modelling to analyze fMRI data and shows that reading ability correlates with specific dynamic interactions within the reading network, particularly involving the left fusiform gyrus and inferior frontal gyrus.

This study investigates postoperative atrophy in non-resected brain regions following resective neurosurgery, linking it to transneuronal degeneration and showing that structural network reorganisations may be artifacts of image processing.

The paper examines associations between epilepsy-related polygenic risk scores and brain morphology in children, finding PRS-related cortical thinning linked to structural patterns in TLE-HS patients.

Robust-tedana improves automated denoising of multi-echo fMRI data, enhancing both single-subject and group-level analysis accuracy.

This paper identifies and corrects common methodological issues in psychophysiological interaction analysis that compromise model validity.

This study examines age-related changes in grey and white matter in TLE patients across the adult lifespan, finding accelerated aging effects after age 55.

This study identifies divergent patterns of atypical cortical asymmetry and regional atrophy in temporal lobe epilepsy using MRI data from a large multi-site cohort.

This study links structural network changes in epilepsy syndromes to risk gene expression patterns using ENIGMA data.

The paper identifies microglial activation as a factor contributing to cortical thinning in epilepsy by integrating neuroimaging and gene expression data.

The paper validates that rsfMRI temporal complexity is reproducible and correlates with fluid intelligence across multiple brain networks.

This study identifies network associations with atrophy patterns in epilepsy using neuroimaging and connectome analysis across 1,021 patients and 1,564 controls.

The paper introduces a normalized participation coefficient to reduce module size bias in network analysis, demonstrating improved identification of high-participation nodes in various networks.

This paper develops an automatic EEG spike detection method for epilepsy studies, showing it can detect more spikes, identify additional brain regions, and speed up analysis compared to manual methods.

The paper models brain network switching using multilayer networks and finds it correlates with cognitive performance and sleep.

This study compares instantaneous phase synchrony analysis and correlation-based sliding window analysis for time-resolved fMRI connectivity, finding they are comparable with optimal parameters.