Medically unexplained symptoms (MUS) are highly prevalent in Western societies, with a substantial proportion accounting for visits to physicians and often resulting in multiple, invasive diagnostic procedures. Yet, there is a dearth of evidence on the processes underlying the aetiology of MUS, in particular MUS-associated psychobiological mechanisms. The perception-filter-model posits that the perception of MUS are a result of the interplay between (a) increased bodily signals, and (b) filter processes, e.g., due to selective attention or health anxiety.

Existing studies investigating interoceptive signal processing in MUS have focused on heartbeat perception tasks. This methodology has, however, important shortcomings: it relies on subjective reports and cannot, therefore, differentiate between actual bodily signals and the perception of these signals. In contrast to conventional heartbeat perception tasks, there are at least two methods available to assess the central representation of afferent cardiac signals independent of their perception: First, cardiac modulation of startle (CMS) is considered to reflect cardiac interoceptive signals at the level of the brainstem. Second, heartbeat-evoked potentials (HEPs) are interpreted as indicators for cardiac interoceptive signal processing at cortical level.

Previous research has demonstrated the modulating effect of stress on interoception. Although MUS may be associated with dysregulations in physiological stress axes, as yet there are no studies investigating the relationship between interoceptive signal processing and physiological stress markers in MUS. Thus, the objectives of the current project are: (a) to clarify whether the central representation of afferent bodily signals is altered in MUS, (b) to elucidate possible associations of interoceptive signal processing with physiological stress indicators at rest and in response to an acute stressor, and (c) to investigate whether relationship between bodily signals, filter processes and MUS severity as postulated by the perception-filter model can be supported by empirical data.

This project is funded by the University of Luxembourg (2015-2017).

Involved members of staff

André Schulz
Angelika Dierolf
Claus Vögele