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Die Arbeit befasst sich mit dem Phänomen „Systemsprenger*in“ und untersucht in diesem Zusammenhang die Lücken im System der Kinder-und Jugendhilfe. Dadurch das sogenannte Systemsprenger*innen in ihrer Kindheit traumatische und negative (Beziehungs-)Erfahrungen gemacht haben, entwickeln sie Bewältigungsstrategien, die den Kindern dabei helfen schwierige Lebenslagen zu meistern. Obwohl diese Überlebensstrategien subjektiv sinnvoll sind, werden sie von den betreuenden Einrichtungen als überfordernd wahrgenommen und Maßnahmen werden beendet, was erneute Beziehungsabbrüche für das Kind bedeutet. Durch mangelnde Kooperation unter Fachkräften und fehlende Passung der Angebote an die Bedürfnisse der Kinder, wird die Abbruchspirale von Seiten des Systems weiter gefördert. In der Arbeit werden ebenfalls die aktuell gängigsten Angebote für Systemsprenger*innen beleuchtet und abschließend wird sich der Frage nach passenden und fördernden Umgangsformen gewidmet. The thesis deals with the phenomenon of "system crashers" and examines in this context the gaps in child and youth services. Because so-called system crashers have had traumatic and negative (relationship) experiences throughout their childhood, they develop coping strategies that help them to master difficult life situations. Although these survival strategies are subjectively meaningful to the children, by welfare institutions they are perceived as overtaxing and thus measures are terminated, which results in renewed relationship breakups for the child. Due to a lack of cooperation among professionals and a lack of fit of the offers to the needs of the children, the cycle of breakups is furthered by the system. The paper also examines the most common services currently offered to system crashers and concludes by addressing the question of supportive ways of dealing with them.

This software provides the utility of creating a nnU-Net anatomical MRI segmentation and mask with a infant brain trained model for the purposes of circumventing JLF within Nibabies. Note: Audrey Houghton and Greg Conan are co-first authors on this project.

Monitoring infants’ cognitive development is a challenging endeavor. Up until now assessments are often restricted to those that can be done after a certain behavioral maturity months or years into development, even though it is known that newborn human infants possess remarkable cognitive skills (e.g., recognition of their native language (Mehler et al. 1988) or their mother’s voice (DeCasper and Fifer 1980)). As young infants have a limited behavioral repertoire, neuroimaging methods like electroencephalography (EEG) and magnetoencephalography (MEG) are helpful tools for the assessment of early cognitive abilities by looking at event-related brain responses. However, while source modeling of both EEG/MEG are improving, the ability to characterize the interplay and architecture of specific functional networks is still limited. fMRI, known for its ability to non-invasively measure whole brain functional systems in infancy is well suited for this goal (Graham et al., 2021). Both measures of event related brain responses and brain functional connectivity showed to be predictive for cognitive abilities tested behaviorally. For example Spann et al. (2018) showed that greater functional connectivity between the dorsal anterior cingulate cortex and medial prefrontal cortex in newborns (weeks 40-44 postmenstrual age) was associated with lower cognitive scores in the Bayley Scales of Infant Development at 14 months of age and Graham et al. (2016) demonstrated that amygdala functional connectivity measured at 1 month of age was associated with cognitive abilities at 6 months of age. Weber et al. (2016), who used EEG to measure habituation by a decrease in the event-related mismatch response following a repeating deviating stimulus in newborns at term age and found this measure to be predictive for the Mental Developmental Index of the Bayley Scales of Infant Development (1st version) at around 21 months of age. These examples pose the question in which way these different approaches to look at early brain behavior relationships are related. Previous work in adults and children have shown a close correspondence between task activation in various paradigms and network architecture as determined via functional connectivity (e.g. Kim et al. 2014, Gordon et al. 2017). It is unclear to which extent - in a time of heightened plasticity in the brain - these same principles extend to the newborn period. This work will focus on the interplay between brain organization and task-related brain activity measured with fMRI in infants. Task-related brain activity in newborns can for example be measured with an auditory oddball paradigm as demonstrated by Sylvester et al. (2021). Brain organization will be evaluated by looking at functional connectivity of brain networks derived from the fMRI data. Past research demonstrated that complex networks exist already in infants before term age (Doria et al., 2010). However, different networks show different levels of maturity. While auditory and visual networks did not change from preterm to term age and already show adult like patterns, higher order networks like the executive control, salience network (SN) or default mode network showed an increase in connectivity not only up to term age but over the first two years of life (Doria et al., 2010, Gao et al., 2015). More recent findings suggest that an adult-like pattern of all functional connectivity networks is detectable in newborns when accounting for the weaker strength of long-range connections (Sylvester et al., 2022). The main objective is to characterize how task-related brain activation in newborns is related to their network organization. Furthermore, we want to find out whether network maturity is related to the degree of task based activation and might therefore partly explain the link between evoked activity and later cognitive outcomes. The project will make use of an existing dataset, which consists of both resting state and task-based activity (responses towards auditory oddball stimuli) in newborns. These data allow us to characterize neonatal resting state functional connectivity networks onto which we can map neonatal novelty response. In addition, they allow us to calculate an index of habituation, which is characterized by a response decrement towards repeating stimuli, by comparing novelty responses between the beginning and the end of a functional run. As in previous work, we will calculate a maturity index (MI) for each neonate based on their pattern of functional connectivity. This index will be related to the magnitude of novelty responses and to the habituation index. The project will improve our understanding of neuronal markers of cognitive functioning and can increase their applicability as potential biomarkers for functional or dysfunctional cognitive development. This can ultimately lead to the possibility to assess deviations from normal cognitive developmental trajectories early on, allowing to design interventions in a time of high brain plasticity.

Monitoring infants’ cognitive development is a challenging endeavor. Up until now assessments are often restricted to those that can be done after a certain behavioral maturity months or years into development, even though it is known that newborn human infants possess remarkable cognitive skills (e.g., recognition of their native language (Mehler et al. 1988) or their mother’s voice (DeCasper and Fifer 1980)). As young infants have a limited behavioral repertoire, neuroimaging methods like electroencephalography (EEG) and magnetoencephalography (MEG) are helpful tools for the assessment of early cognitive abilities by looking at event-related brain responses. However, while source modeling of both EEG/MEG are improving, the ability to characterize the interplay and architecture of specific functional networks is still limited. fMRI, known for its ability to non-invasively measure whole brain functional systems in infancy is well suited for this goal (Graham et al., 2021). Both measures of event related brain responses and brain functional connectivity showed to be predictive for cognitive abilities tested behaviorally. For example Spann et al. (2018) showed that greater functional connectivity between the dorsal anterior cingulate cortex and medial prefrontal cortex in newborns (weeks 40-44 postmenstrual age) was associated with lower cognitive scores in the Bayley Scales of Infant Development at 14 months of age and Graham et al. (2016) demonstrated that amygdala functional connectivity measured at 1 month of age was associated with cognitive abilities at 6 months of age. Weber et al. (2016), who used EEG to measure habituation by a decrease in the event-related mismatch response following a repeating deviating stimulus in newborns at term age and found this measure to be predictive for the Mental Developmental Index of the Bayley Scales of Infant Development (1st version) at around 21 months of age. These examples pose the question in which way these different approaches to look at early brain behavior relationships are related. Previous work in adults and children have shown a close correspondence between task activation in various paradigms and network architecture as determined via functional connectivity (e.g. Kim et al. 2014, Gordon et al. 2017). It is unclear to which extent - in a time of heightened plasticity in the brain - these same principles extend to the newborn period. This work will focus on the interplay between brain organization and task-related brain activity measured with fMRI. Task-related brain activity in newborns can for example be measured with an auditory oddball paradigm as demonstrated by Sylvester et al. (2021). Brain organization will be evaluated by looking at functional connectivity of brain networks derived from the fMRI data. Past research demonstrated that complex networks exist already in infants before term age (Doria et al., 2010). However, different networks show different levels of maturity. While auditory and visual networks did not change from preterm to term age and already show adult like patterns, higher order networks like the executive control, salience network (SN) or default mode network showed an increase in connectivity not only up to term age but over the first two years of life (Doria et al., 2010, Gao et al., 2015). More recent findings suggest that an adult-like pattern of all functional connectivity networks is detectable in newborns when accounting for the weaker strength of long-range connections (Sylvester et al., 2022). The main objective is to characterize how task-related brain activation in newborns is related to their network organization. Furthermore, we want to find out whether network maturity is related to the degree of task based activation and might therefore partly explain the link between evoked activity and later cognitive outcomes. Some of the correspondence (and maturation of this correspondence) between task evoked activity and network organization might be missed without considering individual variation. Gordon et al. (2017) demonstrated that the reconstruction of individual functional connectivity networks is feasible with a sufficient amount of data (several hours). With these so-called “precision neuroimaging” data from ten individual subjects, they showed that task-based fMRI activation in individual subjects corresponds to the boundaries of the individually defined resting state networks (e.g. scene/face contrast to contextual association network or contrast of hand/tongue movement to the respective sensorimotor networks). Whereas, even though, there is generally a high correspondence between task-evoked responses and network boundaries in individuals, this correspondence was higher within than across subjects (Gordon et al. 2017). For newborn infants, no such precision neuroimaging data exist. It is unclear whether newborns show similar, larger, or even smaller inter-individual differences and whether correspondence between states is accurate in ‘averaged’ data during this early maturational stage. For this project, precision neuroimaging data will be collected to evaluate individual networks in newborns and relate their individual network organization to their task-based activity. This approach will increase specificity compared to the analysis using an average adult or infant network definition. The project will improve our understanding of neuronal markers of cognitive functioning and can increase their applicability as potential biomarkers for functional or dysfunctional cognitive development. This can ultimately lead to the possibility to assess deviations from normal cognitive developmental trajectories early on, allowing to design interventions in a time of high brain plasticity.

Quickly and accurately segments an optimally-aligned T1 and T2 pair with a deep neural network trained via nnU-Net and SynthSeg with a large 0 to 8 month old infant MRI brain dataset.

The study aims to determine the effect of maternal emotional state on the fetal and neonatal nervous system. Maternal emotional states will be assessed psychologically via questionnaires, covering anxiety, depressive symptoms, chronic and pregnancy-related stress. The activity of the fetal and neonatal autonomous nervous system (heart activity) and central nervous system (brain activity) will be measured via fetal magnetoencephalography (fMEG), a non-invasive technique uniquely suited to these measurements. An auditory habituation paradigm will be employed to evaluate the maturation of the fetal nervous system. Groups with high values and low values based on a median split of an emotional state Z-scored scale will be compared in regard to their ability to habituate to auditory stimulation (brain activity) and their spontaneous heart acitivity. This will give us first insights as to the overall influence of maternal emotional state on the fetus and allow us to determine further research possibilities in this field.