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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.

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.