Frankfurt

Dieses translationale Projekt untersucht geschlechtsabhängige Verhaltenseffekte der fäkalen Mikrobiota-Transplantation von AMD-Patienten (ausgewählt basierend auf ihren aggressiven und impulsiven Merkmalen aus Q01) sowie gesunden Kontrollpersonen an mikrobiomdepletierte Mäuse. Impulsivität wird über den Continuous Performance Test und Reaktionen auf akute Bedrohung über den Escalated Resident Intruder Test erfasst. Das Ziel ist, die geschlechtsabhängigen Effekte der fäkalen Transplantation auf ausgewählte Parameter zu bestimmen, die am Transfer des Patientenphänotyps auf die Mäuse beteiligt sind, wie Immunparameter, Sexualhormone, neuronale Aktivität (und Morphologie, z.B. Neuritenwachstum, Spines usw.) und Genexpression (z.B. Rbfox1 aus früheren Studien und neue Kandidaten aus C01 und C04).

Beta-Hydroxy-Butyrat (BHB), ein Ketonkörper, ist negativ mit aggressivem Verhalten assoziiert. BHB ist ein Metabolit und ein aktives Signalsubstrat, das an der epigenetischen Regulation von z.B. neurotrophen Faktorgenen im Gehirn beteiligt ist. Von den drei Hauptketonkörpern Aceton, Acetoacetat und BHB ist Aceton eine sehr flüchtige Verbindung, die hauptsächlich über die Atmung ausgeschieden wird und daher nicht-invasiv in der Atemluft gemessen werden kann. Eine Reduktion von Aceton in der Atemluft korreliert nachweislich stark mit BHB im Blut und ist mit dem Schweregrad der Symptome bei Schizophrenie assoziiert (Jiang et al. 2022). Mittels MR-Spektroskopie zielt A08 darauf ab, (1) festzustellen, ob Aceton und andere flüchtige organische Verbindungen in der Atemluft mit Aggression und akuter Bedrohungsverarbeitung bei psychischen Erkrankungen assoziiert sind, (2) zu untersuchen, ob diese Atemmarker mit direkten metabolischen Hirnkorrelaten (wie BHB, Glutamat) und mit dem brain-derived neurotrophic factor (BDNF)-Spiegel im Plasma assoziiert sind. In einem translationalen Ansatz werden wir (3) testen, ob die Supplementierung von BHB aggressives Verhalten bei Mäusen reduziert.

TDCS will be used as an interventional tool to decrease aggression. Using a simultaneous tDCS – fMRI approach, the project aims to enhance cognitive control by repeated prefrontal brain stimulation, investigating its effect on aggression. In addition to gauging tDCS responsivity, identifying the role of individual factors such as genetic profiles in aggression will be a particular focus of this project. By examining brain activity at multiple time points (e.g., before, during multiple stimulation sessions and after tDCS), it will add to the understanding of mechanisms underlying neural tDCS effects and help to identify individual factors that predict responsiveness to the stimulation. To determine the therapeutic potential, we will include psychiatric patients with substance use problems, a group of criminal, violent offenders, and healthy matched controls.

Sex-dependent effects and gene-environment interactions will be investigated by applying escalating aggression paradigms. Specifically, the project will investigate the effects of early life stress on aggression in response to threat and hyperactivity as well as social decision-making in 32 BXD mouse strains, the progenitor strains (C057Bl/6J and DBA/2J), and the F1 BXD cross. The project aims to identify the quantitative trait loci (QTL) and putative candidate genes contained within the QTL and associate them with specific behavioral responses of stressed and unstressed cohorts of mice. The publicly available database GeneNetwork (www.genenetwork.org) will be used to validate the findings which include measurements of mRNA and protein expression, and methylation patterns in mouse brains

Address sex-specific NVS (reactive aggression) and CS (different dimensions of psychopathy, proactive aggression) associated risk factors, and risk factor-based biosignatures in young people. Considering the interacting genetic, environmental, and hormonal factors related to these specific aggressive behavior dimensions, C04 will identify specific and shared factors and mechanisms related to NVS and CS in female and male youth with and without pathological aggression. Implementing deep-learning algorithms, sex-specific, data-driven subgroups in relation to dimensions of aggressive behavior will be described and probed against the NVS and CS. Group-level risk factors of aggressive behavior dimensions, and individual risk factor-based subgrouping will be the basis of developing a biologically informed stratification strategy for tailored treatment. Models and classifiers will be established cross-sectionally in available data and replicated in the prospectively collected cross-sectional data (Q01). In addition, C04 will test the models and classifiers for predictive validity in the longitudinal data of the TRR Q01 cohort.

Link questionnaire measures of aggression to specific neural substrates using structural MRI. The resulting patterns of aggression-related neuroanatomical variability will be co- registered with the Allen Human Brain Atlas providing gene-expression data, to highlight genes with a spatial pattern of expression that matches the neuroimaging findings. Utilizing the neurotypical control data, a normative model of neuroanatomical diversity within the NVS and CS will be established to quantify neuroanatomical abnormalities within these systems in individual cases

Test the interaction of the CS and frustrative non-reward as part of the NVS. It will investigate the electrophysiological correlates of frustrative feedback in aggression-prone patients. In the aftermath of induced stress, an EEG task-battery including frustrative feedback will be applied for extraction of error-related negativity (ERN) and contingent negative variation to monitor electro-physiologic signaling of the relevant learning and frustration processes. In half of the participants, tDCS over the prefrontal cortex will be applied to enhance cognitive control, with participants being put into a stress context inducing frustration.

The central recruitment platform for collecting and curating a longitudinal dataset for studying individual aggression dynamics related to the neural, cognitive-emotional, neurobiological, psychopathological and environmental factors in patient groups.

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.

Bundle all training and mentoring activities in a research training group (RTG) for the young researchers participating in this collaborative research center.