David Kala, MSc, PhD
Post-doctoral fellow
Department of Patophysiology, Second Faculty of Medicine, Charles University
Scientific background
David Kala finished his master’s study in 2016 at the Faculty of Biomedical Engineering, Czech Technical University in Prague with a thesis called Metody pro adaptivní segmentaci EEG signálů. In 2016-2022 he was employed as a Ph.D. student and a neuroscientist at the Institute of Physiology, Czech Academy of Sciences. He finished his PhD in 2023 at the Faculty of Electrical Engineering, CTU in Prague, with the thesis called Image processing pipelines for analysis of clinical human MRI. Since 2020 he is a member of the Prague Epilepsy Research Centre as a Neuroimaging analyst. Since 2023 he is a postdoctoral scientist at Charles University, Second Faculty of Medicine and Motol University Hospital.
Research interests
His scientific field is experimental neurology with a focus on epileptology and neurovascular diseases. He is an expert in image analysis of human brain data (MRI, CT, microscopy), processing of MRI diffusion data, tractography, and brain connectivity. He is also experienced in in-vivo microscopy of living animals and subsequent processing of 2D and 3D microscopic images.
Selected publications
Kala, D., Šulc, V., Olšerová, A., Svoboda, J., Prysiazhniuk, Y., Pošusta, A., Kynčl, M., Šanda, J., Tomek, A., & Otáhal, J. (2022).
Evaluation of Blood-Brain Barrier Integrity by the Analysis of Dynamic Contrast-Enhanced MRI – a Comparison of Quantitative and Semi-Quantitative Methods. Physiological Research, 71. https://doi.org/10.33549/physiolres.934998
Kala, D., Svoboda, J., Litvinec, A., Pošusta, A., Lisý, J., Šulc, V., Tomek, A., Marusič, P., Jiruška, P., & Otáhal, J. (2017).
/GD-Tracker/ A software for blood-brain barrier permeability assessment. Lekar a Technika, 47(2).
Bučková, B. R., Kala, D., Kořenek, J., Matušková, V., Kumpošt, V., Svobodová, L., Otáhal, J., Škoch, A., Šulc, V., Olšerová, A., Vyhnálek, M., Janský, P., Tomek, A., Marusič, P., Jiruška, P., & Hlinka, J. (2023).
Structural connectivity-based predictors of cognitive impairment in stroke patients attributable to aging. PLoS ONE, 18(4 APRIL). https://doi.org/10.1371/journal.pone.0280892