@phdthesis{Endres2019,
  author    = {Endres, Ralph Julian},
  title     = {Networks of fear: Functional connectivity of the amygdala, the insula and the anterior cingulate cortex in two subtypes of specific phobia},
  doi       = {10.25972/OPUS-18095},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180950},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {Neuroimaging research has highlighted the relevance of well-balanced functional brain interactions as an essential basis for efficient emotion regulation. In contrast, abnormal coupling of fear-processing regions such as the amygdala, the anterior cingulate cortex (ACC) and the insula could be an important feature of anxiety disorders. Although activity alterations of these regions have been frequently reported in specific phobia, little is known about their functional interactions during phobogenic stimulus processing. To explore these interrelationships in two subtypes of specific phobia - i.e., the blood-injection-injury subtype and the animal subtype - functional connectivity (FC) was analyzed in three fMRI studies. Two studies examined fear processing in a dental phobia group (DP), a snake phobia group (SP) and a healthy control group (HC) during visual phobogenic stimuli presentation while a third study investigated differences between auditory and visual stimuli presentation in DP and HC. Due to a priori hypotheses of impaired interactions between the amygdala, the ACC and the insula, a first analysis was conducted to explore the FC within these three regions of interest. Based on emerging evidence of functionally diverse subregions, the ACC was further divided into a subgenual, pregenual and dorsal ACC and the insula was divided into a ventral-anterior, dorsal-anterior and posterior region. Additionally, an exploratory seed-to-voxel analysis using the amygdala, ACC and insula as seeds was conducted to scan for connectivity patterns across the whole brain. The analyses revealed a negative connectivity of the ACC and the amygdala during phobogenic stimulus processing in controls. This connectivity was predominantly driven by the affective ACC subdivision. By contrast, SP was characterized by an increased mean FC between the examined regions. Interestingly, this phenomenon was specific for auditory, but not visual symptom provocation in DP. During visual stimulus presentation, however, DP exhibited further FC alterations of the ACC and the insula with pre- and orbitofrontal regions. These findings mark the importance of balanced interactions between fear-processing regions in specific phobia, particularly of the inhibitory connectivity between the ACC and the amygdala. Theoretically, this is assumed to reflect top-down inhibition by the ACC during emotion regulation. The findings support the suggestion that SP particularly is characterized by excitatory, or missing inhibitory, (para-) limbic connectivity, reflecting an overshooting fear response based on evolutionary conserved autonomic bottom-up pathways. Some of these characteristics applied to DP as well but only under the auditory stimulation, pointing to stimulus dependency. DP was further marked by altered pre- and orbitofrontal coupling with the ACC and the insula which might represent disturbances of superordinate cognitive control on basal emotion processes. These observations strengthen the assumption that DP is predominantly based on evaluation-based fear responses. In conclusion, the connectivity patterns found may depict an intermediate phenotype that possibly confers risks for inappropriate phobic fear responses. The findings presented could also be of clinical interest. Particularly the ACC - amygdala circuit may be used as a predictive biomarker for treatment response or as a promising target for neuroscience-focused augmentation strategies as neurofeedback or repetitive transcranial magnetic stimulation.},
  subject      = {Kernspintomografie},
  language  = {en}
}
@phdthesis{Anderson2011,
  author    = {Anderson, Christina},
  title     = {Idiosyncratic Facial Movement in Face Perception and Recognition},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-70355},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2011},
  abstract  = {It has been proposed that different features of a face provide a source of information for separate perceptual and cognitive processes. Properties of a face that remain rather stable over time, so called invariant facial features, yield information about a face's identity, and changeable aspects of faces transmit information underlying social communication such as emotional expressions and speech movements. While processing of these different face properties was initially claimed to be independent, a growing body of evidence suggests that these sources of information can interact when people recognize faces with whom they are familiar. This is the case because the way a face moves can contain patterns that are characteristic for that specific person, so called idiosyncratic movements. As a face becomes familiar these idiosyncratic movements are learned and hence also provide information serving face identification. While an abundance of experiments has addressed the independence of invariant and variable facial features in face recognition, little is known about the exact nature of the impact idiosyncratic facial movements have on face recognition. Gaining knowledge about the way facial motion contributes to face recognition is, however, important for a deeper understanding of the way the brain processes and recognizes faces. In the following dissertation three experiments are reported that investigate the impact familiarity of changeable facial features has on processes of face recognition. Temporal aspects of the processing of familiar idiosyncratic facial motion were addressed in the first experiment via EEG by investigating the influence familiar facial movement exerts on event-related potentials associated to face processing and face recognition. After being familiarized with a face and its idiosyncratic movement, participants viewed familiar or unfamiliar faces with familiar or unfamiliar facial movement while their brain potentials were recorded. Results showed that familiarity of facial motion influenced later event-related potentials linked to memory processes involved in face recognition. The second experiment used fMRI to investigate the brain areas involved in processing familiar facial movement. Participants' BOLD-signal was registered while they viewed familiar and unfamiliar faces with familiar or unfamiliar idiosyncratic movement. It was found that activity of brain regions, such as the fusiform gyrus, that underlie the processing of face identity, was modulated by familiar facial movement. Together these two experiments provide valuable information about the nature of the involvement of idiosyncratic facial movement in face recognition and have important implications for cognitive and neural models of face perception and recognition. The third experiment addressed the question whether idiosyncratic facial movement could increase individuation in perceiving faces from a different ethnic group and hence reduce impaired recognition of these other-race faces compared to own-race faces, a phenomenon named the own-race bias. European participants viewed European and African faces that were each animated with an idiosyncratic smile while their attention was either directed to the form or the motion of the face. Subsequently recognition memory for these faces was tested. Results showed that the own-race bias was equally present in both attention conditions indicating that idiosyncratic facial movement was not able to reduce or diminish the own-race bias. In combination the here presented experiments provide further insight into the involvement of idiosyncratic facial motion in face recognition. It is necessary to consider the dynamic component of faces when investigating face recognition because static facial images are not able to provide the full range of information that leads to recognition of a face. In order to reflect the full process of face recognition, cognitive and neural models of face perception and recognition need to integrate dynamic facial features as a source of information which contributes to the recognition of a face.},
  subject      = {Gesicht},
  language  = {en}
}