Integrating theory-guided and data-driven approaches for measuring consciousness

Clinical assessment of consciousness is a significant issue, with recent research suggesting some brain-damaged patients who are assessed as unconscious are in fact conscious. Misdiagnosis of consciousness can also be detrimental when it comes to general anaesthesia, causing numerous psychological problems, including post-traumatic stress disorder. Avoiding awareness with overdose of anaesthetics, however, can also lead to cognitive impairment. Currently available objective assessment of consciousness is limited in accuracy or requires expensive equipment with major barriers to translation.
In this talk, we will outline our recent theory-guided and data-driven approaches to develop new, optimized consciousness measures that will be robustly evaluated on an unprecedented breadth of high-quality neural data, recorded from the fly model system. We will overcome the subjective-choice problem in data-driven and theory-guided approaches with a comprehensive data analytic framework, which has never been applied to consciousness detection, integrating previously disconnected streams of research in consciousness detection to accelerate the translation of objective consciousness measures into clinical settings.