Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine RWTH Aachen University, Aachen
Klaus Mathiak is a professor at RWTH Aachen University, specializing in
psychiatry and psychotherapy. His research integrates neuroimaging,
psychophysiology, and clinical studies to understand the neural mechanisms
underlying social cognition, aggression, and media influence on behavior.
Mathiak’s work aims to enhance therapeutic interventions for psychiatric
disorders by elucidating the brain’s role in social and emotional processing.
Projects
Probe the self-regulation of CS networks in adults and adolescents
diagnosed with mental disorders related to frequent stress-associated affective outbursts and aggressive symptoms
in posttraumatic stress disorder (PTSD), and BPD. The patients will subsequently be trained to regulate the frontal
control network to varying acute threat in a double-blind, randomized, controlled design. An immersive, virtual brain-
computer-interface (BCI) will allow for a culture- and age-sensitive, personalized training approach. The aim of the present
investigation is to assess feasibility of the approach according to four clinical markers: Reduction of perceived threat
and aggressive behavior in daily life, improved control in the face of unfair provocation, and neurofeedback-specific
modulation of the neural networks.
Develop virtual scenarios to assess decision strategies in cartoon-like and naturalistic
contexts. The core question is how healthy individuals and patients make (mal-)adaptive aggressive decisions in social
conflicts given their threat sensitivity, cognitive functions, and learning experience. We plan to present mathematically
well-defined aggressive decision scenarios to healthy participants as well as patients across diagnostic categories with
high scores of aggressive behavior, threat sensitivity, and inference of hostile intent in others. Computational models that
accurately explain behavioral choices and neural responses (tested using fMRI and pupillometry) will be developed to
identify the aggressive decision strategies humans employ in approach-avoidance conflicts of increasing complexity and
ecological realism. The purpose will be to determine if patients use overly aggressive strategies that are not warranted by
the necessary defense of self-threats and underlying neural circuits.
Data management and training platform. A decentralized data management
infrastructure will help focus on developmental and therapeutic longitudinal
data, training all participating researchers in the necessary skills for future
use. This strategy will lay the foundations for further data-driven
computational modelling projects in the next funding period.
This is a distributed project, with representatives at all main TRR379 sites.
Publications
Perceptual changes in major depressive disorder (MDD) may extend beyond emotional content and include the processing of basic stimulus features. These alterations may ultimately contribute to perceptual bias and anhedonia. To characterize blood oxygen level-dependent (BOLD) signal of perceptual processing, we investigated temporally independent fMRI signal components related to naturalistic stimulus processing in 39 patients with MDD and 36 healthy subjects. Leveraging the capability of multi-echo data to detect BOLD activity changes, we extracted physiologically meaningful group temporal components. For each component that exhibited a significant correlation with the movie content, we localized its underlying brain network and assessed MDD-associated alterations. Two components exhibited significant group differences; one was associated with auditory features (sound pressure level) and one with visual features (temporal contrast of intensity). Notably, these deficits in MDD localized primarily to higher-order processing areas, such as the dorsal prefrontal cortex and insula, rather than primary sensory cortices. For the visual feature component, additional group differences emerged in non-visual primary sensory cortices (auditory and somatosensory) as well as major hubs of the motor system. Our findings support the hypothesis that basic sensory processing deficits represent an inherent feature of MDD which may contribute to anhedonia and negative perceptual bias. These deficits are primarily confined to higher-order processing units, as well as cross-modal primary sensory cortices indicating predominant dysfunction of top-down control and multisensory integration. Therapeutic effects of interventions targeting the prefrontal cortex may be partially mediated by restoring prefrontal control not only over emotional but also sensory processing hubs.
Multi-echo echo-planar imaging (ME-EPI) acquires images at multiple echo times (TEs), enabling the differentiation of BOLD and non-BOLD fluctuations through TE-dependent analysis of transverse relaxation time and initial intensity. Decomposing ME-EPI in tensor space is a promising approach to characterize the distribution of changes across TEs (TE patterns) directly and aid the classification of components by providing information from an additional domain. In this study, the tensorial extension of independent component analysis (tensor-ICA) is used to characterize the TE patterns of neural and non-neural components in ME-EPI data. With the constraints of independent spatial maps, an ME-EPI dataset was decomposed into spatial, temporal, and TE domains to understand the TE patterns of noise or signal-related independent components. Our analysis revealed three distinct groups of components based on their TE patterns. Motion-related and other non-BOLD origin components followed decreased TE patterns. While the long-TE-peak components showed a large overlay on grey matter and signal patterns, the components that peaked at short TEs reflected noise that may be related to the vascular system, respiration, or cardiac pulsation, amongst others. Accordingly, removing short-TE peak components as part of a denoising strategy significantly improved quality control metrics and revealed clearer, more interpretable activation patterns compared to non-denoised data. To our knowledge, this work is the first application of decomposing ME-EPI in a tensor way. Our findings demonstrate that tensor-ICA is efficient in decomposing ME-EPI and characterizing the neural and non-neural TE patterns aiding in classifying components which is important for denoising fMRI data.
Sites
RWTH Aachen University is one of Europe’s leading institutions for science and
engineering education. Renowned for its strong emphasis on research and
innovation, RWTH Aachen collaborates closely with industry and is part of the
prestigious IDEA League. The university offers a wide range of programs and is
known for its cutting-edge facilities and interdisciplinary approach to solving
global challenges.
