No abstract

Acknowledgements:We would like to thank our 54 test supervisors who helped to conduct study 2 as well as all research assistants and interns involved in data collection.

Objective: 
Studying motor units (MUs) is essential for understanding motor control, the detection of neuromuscular disorders and the control of human-machine interfaces. Individual motor unit firings are currently identified in vivo by decomposing electromyographic (EMG) signals. Due to our body’s properties and anatomy, individual motor units can only be separated to a limited extent with surface EMG. Unlike electrical signals, magnetic fields do not interact with human tissues. This physical property and the emerging technology of quantum sensors make magnetomyography (MMG) a highly promising methodology. However, the full potential of MMG to study neuromuscular physiology has not yet been explored.
Approach:
In this work, we perform in silico trials that combine a biophysical model of EMG and MMG with state-of-the-art algorithms for the decomposition of motor units. This allows the prediction of an upper-bound for the motor unit decomposition accuracy.
Main results:
It is shown that non-invasive high-density MMG data is superior over comparable high-density surface EMG data for the robust identification of the discharge patterns of individual motor units. Decomposing MMG instead of EMG increased the number of identifiable motor units by 76%. Notably, MMG exhibits a less pronounced bias to detect superficial motor units.
Significance:
The presented simulations provide insights into methods to study the neuromuscular system non-invasively and in vivo that would not be easily feasible by other means. Hence, this study provides guidance for the development of novel biomedical technologies.

Given that the normative search for identity and belonging, as well as political socialization, plays an important role during adolescence, this life stage is characterized by high vulnerability to radicalization processes. When investigating the influence of different factors on radicalization processes, latent profile analysis can identify and analyze groups of adolescents with different vulnerabilities. Based on a sample of 6,715 ninth-graders from Germany, we identified six latent classes with specific vulnerabilities to right-wing attitudes as one possible outcome of radicalization. The results show that the class with the highest approval of right-wing statements mainly consists of male adolescents with a high sense of relative disadvantage and social deprivation. Specific family ties constitute a unique feature among those who are indifferent in their attitudes.

Studying motor units (MUs) is essential for understanding motor control, the detection of neuromuscular disorders and the control of humanmachine interfaces. Individual motor unit firings are currently identified in vivo by decomposing electromyographic (EMG) signals. Due to our body's electric properties, individual motor units can only be separated to a limited extent with surface EMG. Unlike electrical signals, magnetic fields pass through biological tissues without distortion. This physical property and emerging technology of quantum sensors make magnetomyography (MMG) a highly promising methodology. However, the full potential of MMG to study neuromuscular physiology has not yet been explored. In this work, we perform in silico trials that combine a biophysical model of EMG and MMG with state-of-the-art algorithms for the decomposition of motor units. This allows the prediction of an upper-bound for the motor unit decomposition accuracy. It is shown that non-invasive MMG is superior over surface EMG for the robust identification of the discharge patterns of individual motor units. Decomposing MMG instead of EMG increased the number of identifiable motor units by 71 %. Notably, MMG exhibits a less pronounced bias to detect superficial motor units. The presented simulations provide insights into methods to study the neuromuscular system non-invasively and in vivo that would not be easily feasible by other means. Hence, this study provides guidance for the development of novel biomedical technologies.