↓Skip to main content
  1. Research projects/

B03: A process-based brain-computer interface to modulate aggressive behavior – a real-time fMRI neurofeedback study

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.

Contributors

Klaus Mathiak

B03: A process-based brain-computer interface to modulate aggressive behavior – a real-time fMRI neurofeedback study C02: Aggressive decisions in social conflicts: Neuro-cognitive models for healthy individuals and psychiatric patients with high scores of aggression Q02: Data management for computational modelling Principal investigator Research data committee member Equal opportunity committee member RWTH 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.

Publications

Characterizing the distribution of neural and non-neural components in multi-echo EPI data across echo times based on tensor-ICA

Klaus Mathiak Jana Zweerings B03: A process-based brain-computer interface to modulate aggressive behavior – a real-time fMRI neurofeedback study

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.