About

Conscious experience is central to our existence, and although important advances have been made in our scientific understanding of the phenomenon, radically different theories are still debated within the field, and clinically, prognoses for disorders of consciousness can still be improved substantially. The main aim of this Action is examine the role of cortical neural architecture in consciousness from a basic science as well as a clinical perspective. This will be achieved in a joint effort aimed at building detailed neuroarchitectural models from different kinds of brain data and relating these to meticulously gathered behavioural data from healthy normal participants performing tasks associated with conscious perception/behaviour as well as to clinical data from patients with disorders of consciousness.

The relation between neural architecture and consciousness will be made using advanced statistical modelling, including machine learning. If the Action is successful, the resulting models can be compared to identify neuroarchitectural characteristics related to each consciousness phenomenon individually and to all phenomena. This can be used to form a tentative data driven neuroarchitectural model of consciousness. Furthermore, successfully reaching the clinical sub-aims can result in a substantial increase in the predictive accuracy of prognoses for disorders of consciousness. Accurate prognoses could have a substantial positive impact on the lives of patients and relatives, and they could facilitate clinical decisions regarding whether escalate or stop treatment.

Main Aim

Over the last years, important progress has been made within the field of consciousness research, and recently, not only functional activity, but also generally stable neuroarchitectural characteristics have been examined in relation to their role in generating conscious experience. One part of the debate has focused on whether the prefrontal cortex has a causal role in consciousness. Dominant theories have emphasised the role of the prefrontal cortex for the generation of conscious experience, and support for this was published in prestigious journals for healthy normal participants and patients with disorders of consciousness and/or prefrontal injury. Recently, however, studies have proposed an alternative explanation: that the findings are instead related to simply reporting one’s experience. Studies using so-called no-report paradigms found that a late frontal electroencephalography (EEG) component and the frontal functional magnetic resonance imaging (fMRI) BOLD signal were reduced dramatically when participants did not report a perceived stimulus. Similarly, it has been reported that specific contents of awareness and the presence/absence of stimulus awareness were better decoded from intermediate occipital/temporal rather than late frontal source activity. Citing these and many other – including clinical and sleep – studies, a recent Nature Reviews Neuroscience article concluded that consciousness is related to a posterior cortical hot zone. Although it is important to note that the debate is still not settled, these recent articles indicate a general openness in the field towards considering a substantial role in consciousness for cortical neural architecture not just limited to that of the prefrontal cortex, and a recent article, also published in Nature Reviews Neuroscience, proposed examining exactly this.

The Main Aim of this Action is to follow that proposal and examine the role of cortical neural architecture from a basic science as well as a clinical perspective. This will be achieved in a joint effort aimed at building detailed neuroarchitectural models from different kinds of brain data and relating these to meticulously gathered behavioural from healthy normal participants performing tasks associated with conscious perception/behaviour as well as to clinical data from patients with disorders of consciousness. The relation between neural architecture and consciousness will be made using advanced statistical modelling, including machine learning.