Prisca Bauer - ‘Engaged phenomenology: neurology beyond the brain’

<p>Season five of our podcast continues with a panel presentation from our 2020 annual conference: ‘Engaged Phenomenology’ Online. This episode features Prisca Bauer who was one of three speakers (along with Valeria Bizzari and Francesca Brencio) on the preconstituted panel “Engaging phenomenology in the neurosciences”. Bizzari and Brencio’s presentations will be released in the next two episodes of the BSP Podcast. To begin, here is Bauer from the Department of Psychosomatic Medicine and Psychotherapy University Medical Center, Freiburg Faculty of Medicine, University of Freiburg.</p> <p> </p> <p>ABSTRACT: The burden of neurological conditions is enormous and steadily increasing. When including stroke, neurological conditions are the main cause of disability-adjusted life-years. Modern medicine is based on a strict division between body and mind. As a consequence, neurological conditions are reduced to conditions of the brain, yet they have a profound impact on the experience of people affected by them. Phenomenological accounts are not routinely considered in the diagnostic or therapeutic processes in neurology. I will show the potential of using a systematic phenomenological approach to improve care in people with neurological conditions by taking epilepsy as an example. The main symptom of epilepsy, a condition affecting 1% of the population, is unpredictable seizures, which severely impact people’s lives. Our hypothesis is that through systematic interviews, people with epilepsy can learn to recognise subjective seizure “warning signs”. The recognition of these may help people to increase their safety around seizures, and to regain a sense of control over their unpredictability. I will present preliminary data from phenomenological interviews with people with epilepsy, and explorative analyses of the neural correlates of these subjective seizure “warning signs”. The combination of phenomenological and neural data has the potential to help to improve data-based seizure prediction algorhythms. This study i