openHSU – Research Showcase

We investigated whether music experts and laypersons differ with regard to aesthetic evaluation of musical sequences. 16 music experts and 16 music laypersons judged the aesthetic value (beauty judgment task) as well as the harmonic correctness (correctness judgment task) of chord sequences. The sequences consisted of five chords with the final chord sounding congruous, ambiguous or incongruous relative to the harmonic context established by the preceding four chords. On behavioural measures, few differences were observed between experts and laypersons. However, several differences in event-related potential (ERP) parameters were observed in auditory, cognitive and aesthetic processing of chord cadences between experts and laypersons. First, established ERP effects known to reflect the processing of harmonic rule violation were investigated. Here, differences between the groups were observed in the processing of the mild violation — experts and laypersons differed in their early brain responses to the beginning of the chord sequence. Furthermore, ERP data indicated distinctions between experts and laypersons in aesthetic evaluation at three different stages. Firstly, during the interval of task-cue presentation, a stronger contingent negative variation (CNV) to the beauty judgment task was observed for experts, indicating that experts invest more effort into preparation for aesthetic processes than into correctness judgments. Secondly, during the first four chords, preparation for the correctness judgment required more exertion on the laypersons' side. Thirdly, during the last chord, laypersons showed a larger late and widespread positivity for the beauty compared to the correctness judgment, indicating a stronger reliance on internal affective states while forming a judgment.

Human aesthetic processing entails the sensation-based evaluation of an entity with respect to concepts like beauty, harmony or well-formedness. Aesthetic appreciation has many determinants ranging from evolutionary, anatomical or physiological constraints to influences of culture, history and individual differences. There are a vast number of dynamically configured neural networks underlying these multifaceted processes of aesthetic appreciation. In the current challenge of successfully bridging art and science, aesthetics and neuroanatomy, the neuro-cognitive psychology of aesthetics can approach this complex topic using a framework that postulates several perspectives, which are not mutually exclusive. In this empirical approach, objective physiological data from event-related brain potentials and functional magnetic resonance imaging are combined with subjective, individual self-reports.

In this human ERP study, effects of language-specific phonotactic restrictions on automatic auditory speech processing were investigated by means of the dorsal fricative assimilation (DFA) that is obligatory in German grammar. Using a multiple passive oddball paradigm, we studied the deviance-related processing of phonotactically ill-formed strings violating DFA. Eight VC-syllables were created by exhaustively combining the vowels inline image and the dorsal fricatives inline image, resulting in four well-formed and four ill-formed stimuli that were contrasted in oddball blocks with changing probabilities of occurrence. Only the ill-formed deviants elicited a negative ERP deflection maximal at about 100 msec after the onset of the fricative. This negativity is considered to reflect a phonotactic evaluation process requiring the activation of implicit phonotactic knowledge from long-term memory and resulting in the automatic detection of a DFA violation.

With about ten million deceased patients per year, the diagnosis and therapy of cancer represents one of the most important medical challenges to date. In the central nervous system, a rare yet very relevant tumor entity are ependymomas, which affect patients of all age groups and present unique challenges for their diagnosis. In particular, they exhibit heterogeneous histomorphological and molecular characteristics, which are used along with other properties to define 10 ependymoma types. These types are associated with variable prognosis and clinical outcome of patients and their accurate diagnosis is hence crucial for patient-specific treatment decisions. While diagnoses of ependymomas were traditionally based on histomorphological patterns, neuropathologists nowadays manually integrate these patterns with other sources of information, in particular DNA methylation profiles. However, such DNA methylation data was found to be inconsistent with histological assessment for a fraction of cases and is additionally too expensive for worldwide use in routine diagnostics. Thus, the field requires a unified view on molecular and morphological analyses of ependymomas, e.g., via prediction of DNA methylation types from histological images. Prospectively, further improvements for the diagnoses and treatment of ependymomas may arise from the additional consideration of protein profiles of the tumor. To date, however, measurement biases (batch-effects) and missing values prevent the integration and quantitative comparison of independently acquired proteome profiles and render novel and efficient data integration algorithms and classification algorithms necessary. In this work, an interpretable method for the prediction of ependymoma DNA methylation types from histological whole-slide images is developed using self-supervised and multiple-instance learning approaches. The approach is characterized on spinal cord ependymomas from the University Medical Center Hamburg-Eppendorf and is found to outperform the diagnoses of experienced neuropathologists. Moreover, the algorithm generalizes to data from other medical facilities with human-grade performance. Further characterization studies demonstrate that the approach can be applied to other common ependymoma types and that it scales to large datasets. Seizing the interpretability of the algorithm, novel, morphological evidence of major DNA methylation types of ependymomas is extracted. In comparison to other studies, the presented approach is the first to use neural networks in order to provide a unified view on the molecular and histomorphological landscape of clinically relevant ependymoma types from multiple anatomical compartments. With respect to the integration of proteomic datasets, a novel and computationally efficient algorithm for batch-effect correction of incomplete data is presented. In extensive parameter studies it is shown that, in comparison to existing approaches, the new algorithm offers improved tolerance to missing values as well as provides enhanced flexibility with respect to imbalanced data. It is demonstrated, that the method scales to large data integration tasks and can leverage the multi-core architecture of modern computers. The unique suitability of the method for the integration of proteomic and even transcriptomic data is demonstrated and the benefit of dataset integration for (diagnostic) classification algorithms is explored. Finally, this work investigates how incomplete molecular data (e.g., from proteome analyses) can be used to additionally improve classification performance. To this end, it introduces a novel classification method based on average pairwise correlations, which is found to yield improved classification results compared to other correlation-based approaches and to allow for the combination of the results from the aforementioned, newly developed algorithms into an integrated approach to ependymoma diagnostics. The benefit of this integrated method over the independent consideration of histological images or proteome data is demonstrated. In summary, this work is the first to present multiple, novel algorithmic approaches for the integrated classification of tumors. In particular, the presented methods allow to solve the unique diagnostic challenges of ependymomas by integration of proteomic and histological data. Prospectively, this work will allow researchers and clinical practitioners to obtain a better, integrated understanding of the histo-molecular characteristics for diseases under consideration and thus to improve their respective diagnoses and therapy.