Martin Walter

This study investigates how empathy influences social conformity through physiological arousal, visual attention, and socio-emotional traits using a combination of behavioral and physiological measures.

The paper distinguishes pedohebephilic preference from child sexual offending using resting-state functional connectivity and network-based prediction, identifying distinct neural network alterations.

This study identifies grey matter coupling patterns with cholinergic nuclei Ch1-4 and their synaptic targets using neuroimaging, showing their correlation with cholinergic markers and potential as disease markers.

The paper establishes a normative reference for large-scale human brain dynamics using resting-state fMRI data from over 10,000 individuals, revealing systematic reorganization across development and ageing and disorder-specific deviations in mental health conditions.

The paper examines how childhood maltreatment affects brain structure in 3,711 individuals with MDD and PTSD, finding age- and sex-specific brain morphometric differences.

The paper identifies generalizable neural patterns of emotional engagement and disengagement using fMRI and predictive modeling across two studies.

This paper investigates the effects of ghrelin infusions on motivation and brain activity in humans, finding increased effort for rewards and enhanced striatal connectivity.

The paper introduces a standardized nomenclature for PET-based molecular connectivity and covariance to improve reproducibility and interpretation across imaging modalities and radiotracers.

This study investigates the effects of transcutaneous vagus nerve stimulation (tVNS) on brain signaling using fMRI, finding increased NTS activation but no changes in dynamic functional connectivity or gastric motility.

This study shows that non-invasive vagus nerve stimulation improves positive mood after physical and cognitive exertion, with effects dependent on baseline mood.

The paper examines how gray matter volume interacts with white matter connectivity to influence brain controllability, finding that gray matter has a significant impact in highly connected regions.

The paper uses machine learning to predict local glutamate levels from functional connectivity in the pregenual anterior cingulate cortex, showing better prediction for p32 than p24.

This study shows that EEG microstate parameters are associated with vigilance levels, with different effects on microstate class durations and transitions.

This study shows that transcutaneous auricular vagus nerve stimulation (taVNS) increases vigor for rewards, particularly for food, but not effort maintenance, with left-side stimulation having a stronger effect.

This study shows that vagus nerve stimulation impairs reinforcement learning by reducing the learning rate, particularly after punishment, in a go/no-go task.

This study identifies subcortical shape differences in MDD patients using ENIGMA data, highlighting region-specific changes in surface area and thickness.

This study shows that non-invasive vagus nerve stimulation reduces gastric frequency without affecting resting energy expenditure.

This study investigates how the anterior insula modulates prefrontal cortex responses to emotional stimuli based on expectancy signals using fMRI and cross-correlation analyses.

The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.

This study investigates how motion and physiological noise affect amygdala real-time fMRI neurofeedback learning and finds that these factors do not drive self-regulation learning.

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.