Parashkev Nachev

This study compares univariate and multivariate lesion inference methods using patient data, finding multivariate approaches more accurate and less prone to mis-inference.

This paper uses deep learning to generate disconnectomes from stroke lesion masks, achieving faster computation and better prediction of post-stroke cognitive and behavioral outcomes.

This study uses a disconnectome method to identify white matter networks critical for reading and predicts lesion locations causing reading deficits.

This study uses lesion-deficit mapping to identify the neural substrates of fluid intelligence, finding that right frontal regions, not distributed networks, are critical.

This study maps the human Disconnectome using 1333 stroke lesions, revealing how white matter disconnections relate to brain function and behavior.

This paper demonstrates that full-waveform inversion can generate high-resolution 3D brain images using transcranial ultrasound, overcoming limitations of existing neuroimaging methods.