Arjan Hillebrand

This paper develops a method combining fMRI and MEG to non-invasively map the temporal hierarchy of visual processing in the human brain with millisecond precision.

This paper develops a multivariate signal processing pipeline to build high-order brain networks from fMRI data and identifies high-order hubs associated with distinct brain systems.

This study shows that multimodal multilayer network centrality in the frontoparietal network is positively associated with executive functioning, outperforming unilayer centrality.

The paper examines functional connectivity and network dynamics in disorders of consciousness, finding reduced metastability and network repertoire in unresponsive wakefulness syndrome compared to minimally conscious state.

The paper simulates individual functional connectivity using individual structural connectivity data and neural mass modeling, finding that individual simulations better match empirical data than group-averaged simulations.

The paper links functional network integration in the default mode network to cellular properties in TLE patients, showing correlations between network centrality and memory performance.

This study investigates reduced brain flexibility in Parkinson's disease using magnetoencephalography, finding stereotyped dynamics and beta-band hypersynchronization linked to symptom severity.

Once the aperiodic 1/f component is modelled, the childhood shift of MEG power toward higher frequencies no longer holds; only beta power rises with age, alongside a flatter spectrum.

This study compares scalp- and source-reconstructed EEG networks to assess their accuracy in estimating network topology and identifies metrics that reduce distortions caused by volume conduction.

The paper examines how functional brain networks become more segregated during childhood using magnetoencephalography and minimum spanning tree analysis.

This study examines how EEG functional network topology relates to disability in ALS patients, finding significant correlations between network parameters and disease severity.