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The temporal dynamics of climate processes are spread across different timescales and, as such, the study of these processes at only one selected timescale might not reveal the complete mechanisms and interactions within and between the (sub-) processes. To capture the non-linear interactions between climatic events, the method of event synchronization has found increasing attention recently. The main drawback with the present estimation of event synchronization is its restriction to analysing the time series at one reference timescale only. The study of event synchronization at multiple scales would be of great interest to comprehend the dynamics of the investigated climate processes. In this paper, the wavelet-based multi-scale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization. Wavelets are used extensively to comprehend multi-scale processes and the dynamics of processes across various timescales. The proposed method allows the study of spatio-temporal patterns across different timescales. The method is tested on synthetic and real-world time series in order to check its replicability and applicability. The results indicate that MSES is able to capture relationships that exist between processes at different timescales.
The topic of synchronization forms a link between nonlinear dynamics and neuroscience. On the one hand, neurobiological research has shown that the synchronization of neuronal activity is an essential aspect of the working principle of the brain. On the other hand, recent advances in the physical theory have led to the discovery of the phenomenon of phase synchronization. A method of data analysis that is motivated by this finding - phase synchronization analysis - has already been successfully applied to empirical data. The present doctoral thesis ties up to these converging lines of research. Its subject are methodical contributions to the further development of phase synchronization analysis, as well as its application to event-related potentials, a form of EEG data that is especially important in the cognitive sciences. The methodical contributions of this work consist firstly in a number of specialized statistical tests for a difference in the synchronization strength in two different states of a system of two oscillators. Secondly, in regard of the many-channel character of EEG data an approach to multivariate phase synchronization analysis is presented. For the empirical investigation of neuronal synchronization a classic experiment on language processing was replicated, comparing the effect of a semantic violation in a sentence context with that of the manipulation of physical stimulus properties (font color). Here phase synchronization analysis detects a decrease of global synchronization for the semantic violation as well as an increase for the physical manipulation. In the latter case, by means of the multivariate analysis the global synchronization effect can be traced back to an interaction of symmetrically located brain areas.<BR> The findings presented show that the method of phase synchronization analysis motivated by physics is able to provide a relevant contribution to the investigation of event-related potentials in the cognitive sciences.
How do late proficient bilinguals process morphosyntactic and lexical-semantic information in their non-native language (L2)? How is this information represented in the L2 mental lexicon? And what are the neural signatures of L2 morphosyntactic and lexical-semantic processing? We addressed these questions in one behavioral and two ERP priming experiments on inflected German adjectives testing a group of advanced late Russian learners of German in comparison to native speaker (L1) controls. While in the behavioral experiment, the L2 learners performed native-like, the ERP data revealed clear L1/L2 differences with respect to the temporal dynamics of grammatical processing. Specifically, our results show that L2 morphosyntactic processing yielded temporally and spatially extended brain responses relative to L1 processing, indicating that grammatical processing of inflected words in an L2 is more demanding and less automatic than in the L1. However, this group of advanced L2 learners showed native-like lexical-semantic processing.
In this thesis sentence processing was investigated using a psychophysiological measure known as pupillometry as well as Event-Related Potentials (ERP). The scope of the the- sis was broad, investigating the processing of several different movement constructions with native speakers of English and second language learners of English, as well as word order and case marking in German speaking adults and children. Pupillometry and ERP allowed us to test competing linguistic theories and use novel methodologies to investigate the processing of word order. In doing so we also aimed to establish pupillometry as an effective way to investigate the processing of word order thus broadening the methodological spectrum.
Moving Beyond ERP Components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
Moving beyond ERP components
(2018)
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
This study focuses on the ability of the adult sound system to reorganise as a result of experience. Participants were exposed to existing and novel syllables in either a listening task or a production task over the course of two days. On the third day, they named disyllabic pseudowords while their electroencephalogram was recorded. The first syllable of these pseudowords had either been trained in the auditory modality, trained in production or had not been trained. The EEG response differed between existing and novel syllables for untrained but not for trained syllables, indicating that training novel sound sequences modifies the processes involved in the production of these sequences to make them more similar to those underlying the production of existing sound sequences. Effects of training on the EEG response were observed both after production training and mere auditory exposure.
In reading, word frequency is commonly regarded as the major bottom-up determinant for the speed of lexical access. Moreover, language processing depends on top-down information, such as the predictability of a word from a previous context. Yet, however, the exact role of top-down predictions in visual word recognition is poorly understood: They may rapidly affect lexical processes, or alternatively, influence only late post-lexical stages. To add evidence about the nature of top-down processes and their relation to bottom-up information in the timeline of word recognition, we examined influences of frequency and predictability on event-related potentials (ERPs) in several sentence reading studies. The results were related to eye movements from natural reading as well as to models of word recognition. As a first and major finding, interactions of frequency and predictability on ERP amplitudes consistently revealed top-down influences on lexical levels of word processing (Chapters 2 and 4). Second, frequency and predictability mediated relations between N400 amplitudes and fixation durations, pointing to their sensitivity to a common stage of word recognition; further, larger N400 amplitudes entailed longer fixation durations on the next word, a result providing evidence for ongoing processing beyond a fixation (Chapter 3). Third, influences of presentation rate on ERP frequency and predictability effects demonstrated that the time available for word processing critically co-determines the course of bottom-up and top-down influences (Chapter 4). Fourth, at a near-normal reading speed, an early predictability effect suggested the rapid comparison of top-down hypotheses with the actual visual input (Chapter 5). The present results are compatible with interactive models of word recognition assuming that early lexical processes depend on the concerted impact of bottom-up and top-down information. We offered a framework that reconciles the findings on a timeline of word recognition taking into account influences of frequency, predictability, and presentation rate (Chapter 4).
During natural reading, a parafoveal preview of the upcoming word facilitates its subsequent recognition (e.g., shorter fixation durations compared to masked preview) but nothing is known about the neural correlates of this so-called preview benefit. Furthermore, while the evidence is strong that readers preprocess orthographic features of upcoming words, it is controversial whether word meaning can also be accessed parafoveally. We investigated the timing, scope, and electrophysiological correlates of parafoveal information use in reading by simultaneously recording eye movements and fixation-related brain potentials (FRPs) while participants read word lists fluently from left to right. For one word the target (e.g., "blade") parafoveal information was manipulated by showing an identical ("blade"), semantically related ("knife"), or unrelated ("sugar") word as preview. In boundary trials, the preview was shown parafoveally but changed to the correct target word during the incoming saccade. Replicating classic findings, target words were fixated shorter after identical previews. In the EEG, this benefit was reflected in an occipitotemporal preview positivity between 200 and 280 ms. In contrast, there was no facilitation from related previews. In parafoveal-on-foveal trials, preview and target were embedded at neighboring list positions without a display change. Consecutive fixation of two related words produced N400 priming effects, but only shortly (160 ms) after the second word was directly fixated. Results demonstrate that neural responses to words are substantially altered by parafoveal preprocessing under normal reading conditions. We found no evidence that word meaning contributes to these effects. Saccade-contingent display manipulations can be combined with EEG recordings to study extrafoveal perception in vision.
Brain-electric correlates of reading have traditionally been studied with word-by-word presentation, a condition that eliminates important aspects of the normal reading process and precludes direct comparisons between neural activity and oculomotor behavior. In the present study, we investigated effects of word predictability on eye movements (EM) and fixation-related brain potentials (FRPs) during natural sentence reading. Electroencephalogram (EEG) and EM (via video-based eye tracking) were recorded simultaneously while subjects read heterogeneous German sentences, moving their eyes freely over the text. FRPs were time-locked to first-pass reading fixations and analyzed according to the cloze probability of the currently fixated word. We replicated robust effects of word predictability on EMs and the N400 component in FRPs. The data were then used to model the relation among fixation duration, gaze duration, and N400 amplitude, and to trace the time course of EEG effects relative to effects in EM behavior. In an extended Methodological Discussion section, we review 4 technical and data-analytical problems that need to be addressed when FRPs are recorded in free-viewing situations (such as reading, visual search, or scene perception) and propose solutions. Results suggest that EEG recordings during normal vision are feasible and useful to consolidate findings from EEG and eye-tracking studies.
The goal of a Brain-Computer Interface (BCI) consists of the development of a unidirectional interface between a human and a computer to allow control of a device only via brain signals. While the BCI systems of almost all other groups require the user to be trained over several weeks or even months, the group of Prof. Dr. Klaus-Robert Müller in Berlin and Potsdam, which I belong to, was one of the first research groups in this field which used machine learning techniques on a large scale. The adaptivity of the processing system to the individual brain patterns of the subject confers huge advantages for the user. Thus BCI research is considered a hot topic in machine learning and computer science. It requires interdisciplinary cooperation between disparate fields such as neuroscience, since only by combining machine learning and signal processing techniques based on neurophysiological knowledge will the largest progress be made. In this work I particularly deal with my part of this project, which lies mainly in the area of computer science. I have considered the following three main points: <b>Establishing a performance measure based on information theory:</b> I have critically illuminated the assumptions of Shannon's information transfer rate for application in a BCI context. By establishing suitable coding strategies I was able to show that this theoretical measure approximates quite well to what is practically achieveable. <b>Transfer and development of suitable signal processing and machine learning techniques:</b> One substantial component of my work was to develop several machine learning and signal processing algorithms to improve the efficiency of a BCI. Based on the neurophysiological knowledge that several independent EEG features can be observed for some mental states, I have developed a method for combining different and maybe independent features which improved performance. In some cases the performance of the combination algorithm outperforms the best single performance by more than 50 %. Furthermore, I have theoretically and practically addressed via the development of suitable algorithms the question of the optimal number of classes which should be used for a BCI. It transpired that with BCI performances reported so far, three or four different mental states are optimal. For another extension I have combined ideas from signal processing with those of machine learning since a high gain can be achieved if the temporal filtering, i.e., the choice of frequency bands, is automatically adapted to each subject individually. <b>Implementation of the Berlin brain computer interface and realization of suitable experiments:</b> Finally a further substantial component of my work was to realize an online BCI system which includes the developed methods, but is also flexible enough to allow the simple realization of new algorithms and ideas. So far, bitrates of up to 40 bits per minute have been achieved with this system by absolutely untrained users which, compared to results of other groups, is highly successful.
Combined optimization of spatial and temporal filters for improving brain-computer interfacing
(2006)
Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output de ice by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.
Electrophysiological research using verbal response paradigms faces the problem of muscle artifacts that occur during speech production or in the period preceding articulation. In this context, this paper has two related aims. The first is to show how the nature of the first phoneme influences the alignment of the ERPs. The second is to further characterize the EEG signal around the onset of articulation, both in temporal and frequency domains. Participants were asked to name aloud pictures of common objects. We applied microstate analyses and time-frequency transformations of ERPs locked to vocal onset to compare the EEG signal between voiced and unvoiced labial plosive word onset consonants. We found a delay of about 40 ms in the set of stable topographic patterns for /b/ relative to /p/ onset words. A similar shift was observed in the power increase of gamma oscillations (30-50 Hz), which had an earlier onset for /p/ trials (similar to 150 ms before vocal onset). This 40-ms shift is consistent with the length of the voiced proportion of the acoustic signal prior to the release of the closure in the vocal responses. These results demonstrate that phonetic features are an important parameter affecting response-locked ERPs, and hence that the onset of the acoustic energy may not be an optimal trigger for synchronizing the EEG activity to the response in vocal paradigms. The indexes explored in this study provide a step forward in the characterization of muscle-related artifacts in electrophysiological studies of speech and language production.
Physical fatigue (PF) negatively affects postural control, resulting in impaired balance performance in young and older adults. Similar effects on postural control can be observed for mental fatigue (MF) mainly in older adults. Controversial results exist for young adults. There is a void in the literature on the effects of fatigue on balance and cortical activity. Therefore, this study aimed to examine the acute effects of PF and MF on postural sway and cortical activity. Fifteen healthy young adults aged 28 ± 3 years participated in this study. MF and PF protocols comprising of an all-out repeated sit-to-stand task and a computer-based attention network test, respectively, were applied in random order. Pre and post fatigue, cortical activity and postural sway (i.e., center of pressure displacements [CoPd], velocity [CoPv], and CoP variability [CV CoPd, CV CoPv]) were tested during a challenging bipedal balance board task. Absolute spectral power was calculated for theta (4–7.5 Hz), alpha-2 (10.5–12.5 Hz), beta-1 (13–18 Hz), and beta-2 (18.5–25 Hz) in frontal, central, and parietal regions of interest (ROI) and baseline-normalized. Inference statistics revealed a significant time-by-fatigue interaction for CoPd (p = 0.009, d = 0.39, Δ 9.2%) and CoPv (p = 0.009, d = 0.36, Δ 9.2%), and a significant main effect of time for CoP variability (CV CoPd: p = 0.001, d = 0.84; CV CoPv: p = 0.05, d = 0.62). Post hoc analyses showed a significant increase in CoPd (p = 0.002, d = 1.03) and CoPv (p = 0.003, d = 1.03) following PF but not MF. For cortical activity, a significant time-by-fatigue interaction was found for relative alpha-2 power in parietal (p < 0.001, d = 0.06) areas. Post hoc tests indicated larger alpha-2 power increases after PF (p < 0.001, d = 1.69, Δ 3.9%) compared to MF (p = 0.001, d = 1.03, Δ 2.5%). In addition, changes in parietal alpha-2 power and measures of postural sway did not correlate significantly, irrespective of the applied fatigue protocol. No significant changes were found for the other frequency bands, irrespective of the fatigue protocol and ROI under investigation. Thus, the applied PF protocol resulted in increased postural sway (CoPd and CoPv) and CoP variability accompanied by enhanced alpha-2 power in the parietal ROI while MF led to increased CoP variability and alpha-2 power in our sample of young adults. Potential underlying cortical mechanisms responsible for the greater increase in parietal alpha-2 power after PF were discussed but could not be clearly identified as cause. Therefore, further future research is needed to decipher alternative interpretations.
Physical fatigue (PF) negatively affects postural control, resulting in impaired balance performance in young and older adults. Similar effects on postural control can be observed for mental fatigue (MF) mainly in older adults. Controversial results exist for young adults. There is a void in the literature on the effects of fatigue on balance and cortical activity. Therefore, this study aimed to examine the acute effects of PF and MF on postural sway and cortical activity. Fifteen healthy young adults aged 28 ± 3 years participated in this study. MF and PF protocols comprising of an all-out repeated sit-to-stand task and a computer-based attention network test, respectively, were applied in random order. Pre and post fatigue, cortical activity and postural sway (i.e., center of pressure displacements [CoPd], velocity [CoPv], and CoP variability [CV CoPd, CV CoPv]) were tested during a challenging bipedal balance board task. Absolute spectral power was calculated for theta (4–7.5 Hz), alpha-2 (10.5–12.5 Hz), beta-1 (13–18 Hz), and beta-2 (18.5–25 Hz) in frontal, central, and parietal regions of interest (ROI) and baseline-normalized. Inference statistics revealed a significant time-by-fatigue interaction for CoPd (p = 0.009, d = 0.39, Δ 9.2%) and CoPv (p = 0.009, d = 0.36, Δ 9.2%), and a significant main effect of time for CoP variability (CV CoPd: p = 0.001, d = 0.84; CV CoPv: p = 0.05, d = 0.62). Post hoc analyses showed a significant increase in CoPd (p = 0.002, d = 1.03) and CoPv (p = 0.003, d = 1.03) following PF but not MF. For cortical activity, a significant time-by-fatigue interaction was found for relative alpha-2 power in parietal (p < 0.001, d = 0.06) areas. Post hoc tests indicated larger alpha-2 power increases after PF (p < 0.001, d = 1.69, Δ 3.9%) compared to MF (p = 0.001, d = 1.03, Δ 2.5%). In addition, changes in parietal alpha-2 power and measures of postural sway did not correlate significantly, irrespective of the applied fatigue protocol. No significant changes were found for the other frequency bands, irrespective of the fatigue protocol and ROI under investigation. Thus, the applied PF protocol resulted in increased postural sway (CoPd and CoPv) and CoP variability accompanied by enhanced alpha-2 power in the parietal ROI while MF led to increased CoP variability and alpha-2 power in our sample of young adults. Potential underlying cortical mechanisms responsible for the greater increase in parietal alpha-2 power after PF were discussed but could not be clearly identified as cause. Therefore, further future research is needed to decipher alternative interpretations.
Electroencephalographic (EEG) research indicates changes in adults' low frequency bands of frontoparietal brain areas executing different balance tasks with increasing postural demands. However, this issue is unsolved for adolescents when performing the same balance task with increasing difficulty. Therefore, we examined the effects of a progressively increasing balance task difficulty on balance performance and brain activity in adolescents. Thirteen healthy adolescents aged 16-17 year performed tests in bipedal upright stance on a balance board with six progressively increasing levels of task difficulty. Postural sway and cortical activity were recorded simultaneously using a pressure sensitive measuring system and EEG. The power spectrum was analyzed for theta (4-7 Hz) and alpha-2 (10-12 Hz) frequency bands in pre-defined frontal, central, and parietal clusters of electrocortical sources. Repeated measures analysis of variance (rmANOVA) showed a significant main effect of task difficulty for postural sway (p < 0.001; d = 6.36). Concomitantly, the power spectrum changed in frontal, bilateral central, and bilateral parietal clusters. RmANOVAs revealed significant main effects of task difficulty for theta band power in the frontal (p < 0.001, d = 1.80) and both central clusters (left: p < 0.001, d = 1.49; right: p < 0.001, d = 1.42) as well as for alpha-2 band power in both parietal clusters (left: p < 0.001, d = 1.39; right: p < 0.001, d = 1.05) and in the central right cluster (p = 0.005, d = 0.92). Increases in theta band power (frontal, central) and decreases in alpha-2 power (central, parietal) with increasing balance task difficulty may reflect increased attentional processes and/or error monitoring as well as increased sensory information processing due to increasing postural demands. In general, our findings are mostly in agreement with studies conducted in adults. Similar to adult studies, our data with adolescents indicated the involvement of frontoparietal brain areas in the regulation of postural control. In addition, we detected that activity of selected brain areas (e.g., bilateral central) changed with increasing postural demands.
The functional significance of the N400 evoked-response component is still actively debated. An increasing amount of theoretical and computational modelling work is built on the interpretation of the N400 as a prediction error. In neural network modelling work, it was proposed that the N400 component can be interpreted as the change in a probabilistic representation of meaning that drives the continuous adaptation of an internal model of the statistics of the environment. These results imply that increased N400 amplitudes should correspond to greater adaptation, which can be measured via implicit memory. To investigate this model derived hypothesis, the current study manipulated expectancy in a sentence reading task to influence N400 amplitudes and subsequently presented the previously expected vs. unexpected words in a perceptual identification task to measure implicit memory. As predicted, reaction times in the perceptual identification task were significantly faster for previously unexpected words that induced larger N400 amplitudes in the previous sentence reading task. Additionally, it could be demonstrated that this adaptation seems to specifically depend on the process underlying N400 amplitudes, as participants with larger N400 differences during sentence reading also exhibited a larger implicit memory benefit in the perceptual identification task. These findings support the interpretation of the N400 as an implicit learning signal driving adaptation in language processing.
Mental health problems remain among the main generators of costs within and beyond the health care system. Psychotherapy, the tool of choice in their treatment, is qualified by social interaction, and cooperation within the therapist-patient-dyad. Research into the factors influencing therapy success to date is neither exhaustive nor conclusive. Among many others, the quality of the relationship between therapist and patient stands out regardless of the followed psychotherapy school. Emerging research points to a connection between interpersonal synchronization within the sessions and therapy outcome. Consequently, it can be considered significant for the shaping of this relationship. The framework of Embodied Cognition assumes bodily and neuronal correlates of thinking. Therefore, the present paper reviews investigations on interpersonal, non-verbal synchrony in two domains: firstly, studies on interpersonal synchrony in psychotherapy are reviewed (synchronization of movement). Secondly, findings on neurological correlates of interpersonal synchrony (assessed with EEG, fMRI, fNIRS) are summarized in a narrative manner. In addition, the question is asked whether interpersonal synchrony can be achieved voluntarily on an individual level. It is concluded that there might be mechanisms which could give more insights into therapy success, but as of yet remain uninvestigated. Further, the framework of embodied cognition applies more to the current body of evidence than classical cognitivist views. Nevertheless, deeper research into interpersonal physical and neurological processes utilizing the framework of Embodied Cognition emerges as a possible route of investigation on the road to lower drop-out rates, improved and quality-controlled therapeutic interventions, thereby significantly reducing healthcare costs.
Mental health problems remain among the main generators of costs within and beyond the health care system. Psychotherapy, the tool of choice in their treatment, is qualified by social interaction, and cooperation within the therapist-patient-dyad. Research into the factors influencing therapy success to date is neither exhaustive nor conclusive. Among many others, the quality of the relationship between therapist and patient stands out regardless of the followed psychotherapy school. Emerging research points to a connection between interpersonal synchronization within the sessions and therapy outcome. Consequently, it can be considered significant for the shaping of this relationship. The framework of Embodied Cognition assumes bodily and neuronal correlates of thinking. Therefore, the present paper reviews investigations on interpersonal, non-verbal synchrony in two domains: firstly, studies on interpersonal synchrony in psychotherapy are reviewed (synchronization of movement). Secondly, findings on neurological correlates of interpersonal synchrony (assessed with EEG, fMRI, fNIRS) are summarized in a narrative manner. In addition, the question is asked whether interpersonal synchrony can be achieved voluntarily on an individual level. It is concluded that there might be mechanisms which could give more insights into therapy success, but as of yet remain uninvestigated. Further, the framework of embodied cognition applies more to the current body of evidence than classical cognitivist views. Nevertheless, deeper research into interpersonal physical and neurological processes utilizing the framework of Embodied Cognition emerges as a possible route of investigation on the road to lower drop-out rates, improved and quality-controlled therapeutic interventions, thereby significantly reducing healthcare costs.
Background: Empirical evidence suggests substantial deficits regarding emotion recognition in bulimia nervosa (BN). The aim of the current study was to investigate electrophysiologic evidence for deficits in emotional face processing in patients with BN. Methods: Event-related potentials were recorded from 13 women with BN and 13 matched healthy controls while viewing neutral, happy, fearful, and angry facial expressions. Participants' recognition performance for emotional faces was tested in a subsequent categorization task. In addition, the degree of alexithymia, depression, and anxiety were assessed via questionnaires. Results: Categorization of emotional faces was hampered in BN (p = .01). Amplitudes of event-related potentials differed during emotional face processing: face-specific N170 amplitudes were less pronounced for angry faces in patients with BN (mean [M] [standard deviation {SD}] = 1.46 [0.56] mu V versus M [SD] = -1.23 [0.61] mu V, p = .02). In contrast, P3 amplitudes were more pronounced in patients with BN as compared with controls (M [SD] = 2.64 [0.46] mu V versus M [SD] = 1.25 [0.39] mu V, p = .04), independent of emotional expression. Conclusions: The study provides novel electrophysiologic data showing that emotional faces are processed differently in patients with BN as compared with healthy controls. We suggest that deficits in early automatic emotion classification in BN are followed by an increased allocation of attentional resources to compensate for those deficits. These findings might contribute to a better understanding of the impaired social functioning in BN.