Tracking Consciousness

Scientific images of heads with lines coming out of them representing cognitive patterns

Reducing Misdiagnosis of Awareness after Severe Brain Injury.

Dr Srivas Chennu’s research in the School of Computing on computational brain connectomics has generated valuable insights into how complex networks of interactions between specific brain regions support cognition and consciousness, and underlie neurological and psychiatric conditions.

The challenge of assessing consciousness takes on immediate clinical and societal significance in patients diagnosed to be in vegetative and minimally conscious states, collectively termed disorders of consciousness (DoC). Dr Chennu’s research has the potential to improve diagnosis and prognosis for minimally conscious patients, up to 40% of whom might be misdiagnosed.

Dr Chennu has developed and validated robust computational tools for human brain connectomics, for assessing consciousness in patients at their bedside. Working closely with clinical centres, he has deployed the developed software at patients’ bedsides. This novel project is among the first to contribute to the scientific advancement and validation of healthcare technology for assessing consciousness after severe brain injury.


[1] Chennu, S., Annen, J., Wannez, S., Thibaut, A., Chatelle, C., Cassol, H., Martens, G., Schnakers, C., Gosseries, O., Menon, D. & Laureys, S. 2017. “Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness”. Brain, 140, 2120-2132.
[2] Chennu, S., Craston, P., Wyble, B. & Bowman, H. 2009. “Attention Increases the Temporal Precision of Conscious Perception: Verifying the Neural-ST2 Model”. PLoS Computational Biology, 5, e1000576