Mats van Es

I am a Principal Investigator at the Oxford Centre for Human Brain Activity, where I study how cognition emerges from coordinated activity across the brain. My research combines cognitive and systems neuroscience with advanced neuroimaging and computational methods.

I primarily use magnetoencephalography and electroencephalography, known collectively as M/EEG, to measure brain activity with millisecond precision. This allows me to investigate how large scale brain networks communicate, how their activity changes over time, and how brain rhythms contribute to perception, attention, memory, and other cognitive functions.

A central aim of my research is to understand the temporal organisation of brain activity. Rather than remaining continuously active, brain networks appear to switch on and off in structured patterns. For example, my colleagues and I recently showed that functional brain networks activate in recurring cycles lasting approximately 300 to 1,000 milliseconds. This provides evidence that large scale networks follow organised temporal rules, which may help the brain coordinate different cognitive processes efficiently.

Alongside this empirical research, I develop computational methods and open source software for analysing neuroimaging data (e.g., OSL-Ephys, OSL-Dynamics). These tools allow researchers to study aspects of brain activity that cannot be captured using conventional approaches, particularly rapid changes in network activity and communication. Another goal of these tools is to improve the reliability, reproducibility, and scalability of neuroimaging research, so that complex analyses can be applied consistently across large datasets and clinical populations.

A further focus of my work is translating advances in basic neuroscience into clinically useful methods. I investigate whether patterns of dynamic brain activity measured with MEG can be used as biomarkers for neurological disorders, including Alzheimer’s disease. In particular, I advocate moving beyond conventional static measures, which average brain activity over long periods, towards dynamic measures that capture how brain networks change, interact, and reorganise from moment to moment.