Deep immunological profiling and hybrid modelling of the brain for predicting and improving recovery from acute brain injury – AI-NIGMA

Acute cerebral ischemia (ACI) and post anoxic encephalopathy (PAE) following cardiac arrest are among the leading causes of death and neurological disability in Europe. In survivors, these pathologies are responsible for major levels of dependence, leading to a greater number ‘years of life lost’ (DALY) than cancer or all other causes of dementia combined. From a mechanistic point of view, these two clinical conditions are the consequence of sudden either focal (ACI) or global (PAE) interruption of cerebral blood flow. The medical management of both these conditions is currently focused on obtaining rapid cerebral revascularization by pharmacological/mechanical means (ACI) or cardiocirculatory resuscitation (PEA). Nonetheless, it should be noted that half of the patients suffering from ACI whose vascular occlusion has been successfully restored, and two-thirds of patients resuscitated from cardiac arrest, will have an unfavorable neurological outcome. Moreover, despite prognostication is a cornerstone for the clinical management and care of these patients, current predictors are poorly reliable and leave 30-50% of them in a grey zone of prognostication.
Providing a very promising led in this field, members of our team (ToNIC), has recently identified by using in vivo molecular imaging techniques, a link between neuroimmune activation and PAE patient’s neurological outcome. However, the deep immune profiling of ICA and PEA patients have not yet been done. We consider that the individual and high-throughput characterization of immunological responses, leveraging on the well-mastered ground-breaking methods of deep immune profiling provided by SURGE, has the potential to create a significant paradigm shift in the field.
Indeed, we postulate that AI-NIGMA, by capitalizing on recent advances in the field of high-dimensional single-cell immune profiling and multimodal neuroimaging, combined with advances in automated learning and hybrid brain modelling, will offer an unprecedented opportunity to achieve precision medicine based on the identification of individual immune fingerprints of acute brain injuries, holding promise of significant public-health and socio-economic impacts and potential knowledge transfer to alternative neurological conditions putatively implicating neuroimmune process (e.g. Alzheimer disease, traumatic brain injury, etc).