Research

Google Scholar · GitHub

Selected Papers

My most cited work, according to Google Scholar.

• 
  Whole-Brain Network Models: From Physics to Bedside

  A review of the field of large-scale whole-brain computational models, tracing how physics-based approaches to neural dynamics are increasingly finding translational application at the clinical bedside.

• 
  Lifespan associated global patterns of coherent neural communication

  Using resting-state MEG recorded across the adult lifespan, we show that the global coherence and metastability of neural oscillations track healthy brain aging, offering compact markers of large-scale change in neural communication.

• 
  Biophysical mechanism underlying compensatory preservation of neural synchrony over the adult lifespan

  We combine dynamical systems modelling with MEG analysis to show that global synaptic scaling compensates for age-related white matter decline, preserving neural synchrony at the peak alpha frequency across the adult lifespan.

• 
  The virtual multiple sclerosis patient

  We integrate diffusion tensor imaging and MEG into individualized virtual brain models to estimate conduction velocities in MS patients versus controls, offering a route past the clinical-radiological paradox that standard tract-specific measures miss.

• Automatic seizure detection by modified line length and Mahalanobis distance function

  We modify the classical line-length feature for seizure detection and combine it with a Mahalanobis-distance classifier across multichannel intracranial EEG, improving seizure-detection accuracy on the Freiburg dataset without added computational cost.

• 
  Metastability indexes global changes in the dynamic working point of the brain following brain stimulation

  We characterize how single-pulse TMS transiently reduces metastability and increases coherence in global brain network dynamics, with higher EEG frequencies recovering faster than lower ones, offering a way to quantify how long stimulation effects linger.

• 
  Emotion arousal but not valence is strongly represented in aperiodic EEG activity stemming from thalamocortical interactions

  Using the DEAP dataset, we find that the aperiodic EEG exponent and offset track emotional arousal but not valence, and use a thalamocortical neural field model to show this stems from enhanced inhibitory coupling between thalamic reticular and relay populations.

• 
  Inhibition of thalamic relay nuclei scales the aperiodic and alpha band oscillations associated with arousal during naturalistic stimulus viewing

  We show that arousal during naturalistic viewing is tracked by a rise in the aperiodic EEG exponent/offset and a drop in alpha power, and use a corticothalamic neural field model to trace both effects to stronger inhibitory drive onto thalamic relay nuclei.