Anna Katharina Bonkhoff

The paper models sex differences in white matter substrates of aphasia using Bayesian analysis in 1,401 stroke patients, identifying sex-specific lesion effects in language networks.

This study uses a Siamese CNN framework to predict individual identity from resting-state fMRI data, achieving 88% accuracy in subject discrimination using spatial network pairs.

This paper develops a radiomic signature from T2-FLAIR MRI images to predict white matter hyperintensities burden and links it to clinical phenotypes.

The paper uses machine learning and Bayesian modeling to analyze lesion patterns in stroke patients, showing how left and right hemisphere damage relates to specific cognitive impairments.

This study identifies sex-specific lesion patterns in acute ischemic stroke that correlate with stroke severity, highlighting differences in lesion distribution between men and women.