Social Neuroscience Methods

One goal of our current and future research is to examine the opportunities of the integration of multi-modal data. For example, in some of our more recent projects we have focused on the integration of psychophysiological data with functional neuroimaging. We combined eyetracking data and pupil dilation measures with fMRI in order to elucidate altered frontal brain function and emotional reactivity in juvenile myoclonic epilepsy as well as impairments in empathic responses of individuals with autism spectrum disorders. These data showed compelling evidence for a common source of variance in the dynamics of the pupil size as well as neural activation in brain circuits involved in homeostatic regulation. Based on these first studies, we aim to broaden the perspective by integrating data across other modalities (i.e. skin conductance responses, blushing) to achieve a more complete picture of sources and modulations of human psychophysiological responses during emotional processing and social interactions.

Publications on this topic

Müller-Pinzler L, Gazzola V, Keysers C, Frässle S, Einhäuser W, Sommer J, Jansen A, Paulus FM, Krach S (2015): Neural pathways of embarrassment and their modulation by social anxiety. Neuroimage, 119, 252-261.

Krach S, Kamp-Becker I, Einhäuser W, Sommer J, Frässle S, Jansen A, Rademacher L, Müller-Pinzler L, Gazzola V, Paulus FM (2015): Evidence from pupillometry and fMRI indicates reduced neural response during vicarious social pain but not physical pain in autism. Hum Brain Mapp.

Paulus FM, Krach S, Blanke M, Roth C, Belke M, Sommer J, … Knake S (2015): Fronto-insula network activity explains emotional dysfunctions in juvenile myoclonic epilepsy : Combined evidence from pupillometry and fMRI. Cortex, 5, 219–231.

Frässle S, Paulus FM, Krach S, Schweinberger SR, Stephan KE, Jansen A (2015): Mechanisms of hemispheric lateralization: Asymmetric interhemispheric recruitment in the face perception network. Neuroimage

A second aim is to investigate the challenges in adapting more complex methods for modelling brain function in context of social neuroscience. During the last decade many new methods have been developed specifically to characterize how brain regions interact with each other. However, at present, very little is known about the consistency and sources of between subject variability in the parameters of e.g. functional connectivity methods, graph analyses, or dynamic causal modelling (DCM). Based on these thoughts we started to elucidate the properties of basic and complex methods in the analyses of functional brain imaging data to assess the potential for application in social neurosciences.

Publications on this topic

Bedenbender J, Paulus FM, Krach S, Pyka M, Sommer J, Krug A, … Jansen A (2011): Functional Connectivity Analyses in imaging genetics: Considerations on methods and data interpretation. PLoS One, 6(12).

Frässle S, Stephan KE, Friston KJ, Steup M, Krach S, Paulus FM, Jansen A (2015): Test-retest reliability of dynamic causal modeling for fMRI. Neuroimage, 117, 56-66.