Anna Pidnebesna works at the intersection of network neuroscience, mathematical/topological methods, and clinical neuroimaging. The unifying drive across her work is the development and application of advanced mathematical tools — deconvolution, topological data analysis, graph-theoretic descriptors, simulation-based frameworks — to characterize the structure of connectivity in the brain, and to ask what that structure reveals about pathology, altered states, and individual differences.

Contact:

ICS CAS (office 222A)
Pod Vodarenskou vezi 271/2,
11800, Prague, Czech Republic

e-mail: pidnebesna[at]cs.cas.cz

Publications:

  • Dallmer-Zerbe, I., Pidnebesna, A., & Hlinka, J. (2026). Distinct synaptic excitation-inhibition mechanisms underlie clinically defined seizure onset patterns. medRxiv, 2026–03.
  • Caputi, L., Pidnebesna, A., & Hlinka, J. (2025). Integral Betti signatures of brain, climate and financial networks compared to hyperbolic, Euclidean and spherical models. Scientific Reports.
  • Hartman, D., Hlinka, J., Pidnebesna, A., & Szczepanik, F. (2025). Local iterative algorithms for approximate symmetry guided by network centralities. In International Conference on Learning and Intelligent Optimization (pp. 218–232). Springer Nature Switzerland.
  • Pidnebesna, A., Hartman, D., Pokorná, A., Straka, M., & Hlinka, J. (2025). Computing approximate global symmetry of complex networks with application to brain lateral symmetry. Information Systems Frontiers, 1–20.
  • Rádlová, S., Pidnebesna, A., Chomik, A., Tomeček, D., Hlinka, J., Frynta, D., & Landová, E. (2025). From ancient fears to airborne threats: fMRI insights into neural fear responses. Brain and Cognition, 191, 106371.
  • Dallmer-Zerbe, I., Kopal, J., Pidnebesna, A., Curot, J., Denuelle, M., De Barros, A., Sol, J.-C., Valton, L., Barbeau, E. J., & Hlinka, J. (2025). Pro-ictal, rather than pre-ictal, brain state marked by global critical slowing and local gamma power increase. Clinical Neurophysiology, 2110742.
  • Landová, E., Rádlová, S., Pidnebesna, A., Tomeček, D., Janovcová, M., Peléšková, Š., Sedláčková, K., Štolhoferová, I., Polák, J., Hlinka, J., et al. (2023). Toward a reliable detection of arachnophobia: subjective, behavioral, and neurophysiological measures of fear response. Frontiers in Psychiatry, 14, 1196785.
  • Pidnebesna, A., Fajnerová, I., Horáček, J., & Hlinka, J. (2023). Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD. Applied Mathematical Modelling, 116, 735-748. (link)
  • Landová, E., Rádlová, S., Pidnebesna, A., Tomeček, D., Janovcová, M., Peléšková, Š., Sedláčková, K., Stolhoferová, I., Polák, J., Hlinka, J. & Frynta, D. (2023). Toward a reliable detection of arachnophobia: subjective, behavioral, and neurophysiological measures of fear response. Frontiers in Psychiatry, 14, 1196785. (link)
  • Pidnebesna, A., Sanda, P., Kalina, A., Hammer, J., Marusic, P., Vlcek, K., & Hlinka, J. (2022). Tackling the challenges of group network inference from intracranial EEG data. Frontiers in Neuroscience, 16, 1061867. (link)
  • Caputi, L., Pidnebesna, A., & Hlinka, J. (2021). Promises and pitfalls of topological data analysis for brain connectivity analysis. NeuroImage, 238, 118245. (link)
  • Kopal, J., Pidnebesna, A., Tomeček, D., Tintěra, J., and Hlinka, J. (2020). Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines. Human Brain Mapping, 41:5325–5340. (link)
  • Pidnebesna, A., Tomeček, D., and Hlinka, J. (2018). BRAD: Software for brain activity detection from hemodynamic response. Computer Methods and Programs in Biomedicine, 156:113 – 119, (link)
  • Pidnebesna, A., Helisová, K., and Staněk, J. (2018). Statistical analysis of dependencies among submissions to municipalities in the Czech Republic. Information Bulletin of the Czech statistical society, 29(3):1–19.
  • Pidnebesna, A., Helisová, K., Dvořák, J., Lechnerová, R., and Lechner, T. (2016). Statistical analysis and modelling of submissions to municipalities in the Czech Republic. Information Bulletin of the Czech statistical society, 27(4):1–18.

Dissertation:

Pidnebesna, A. (2020). Statistical analysis of the spatiotemporal processes (link)