Open-source software and experimental materials from my projects, shared so that others can reproduce and build on this work.
Kosciessa, J. Q., Chetverikov, A., Cornelissen, M., Carpino, E., van der Zee, K., Wimmers, M., Adelhoefer, N., Meijer, S., & Verhagen, L. (2026). PRESTUS (Version v0.6.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.20392907
PRESTUS is an open-source MATLAB toolbox that aims to streamline imaging-informed simulations of Transcranial Ultrasound Stimulation (TUS): from the segmentation of T1-weighted MRI head scans and mapping of medium tissue properties (possibly informed for skull via (pseudo-)CT images) in a simulation grid, to the execution of acoustic and thermal simulations using the widely adopted k-Wave engine. High-performance computing (HPC) support via SLURM enables efficient parallelization and large-scale analyses. Output 3D NIfTI images are automatically mapped to a standard template (MNI) space to facilitate group reporting.
Code Poster Lecture: personalized simulations Course: TUS Foundations (NeuroTechEU)
This software was honored with the inaugural Donders Open Research Output Award (2026). The PRESTUS poster received a Gold Poster Award at the Focused Ultrasound Neuromodulation Conference (FUN25).
Kosciessa, J. Q., Grandy, T. H., Garrett, D. D., & Werkle-Bergner, M. (2020). Single-trial characterization of neural rhythms: Potential and challenges. NeuroImage, 206, 116331. doi:10.1016/j.neuroimage.2019.116331
eBOSC is a toolbox that detects the occurrence of rhythms in continuous signals (i.e., at the single trial level). It uses a static aperiodic ‘background’ spectrum as the basis to define a ‘power threshold’ that continuous signals have to exceed in order to qualify as ‘rhythmic’, and an optional ‘duration threshold’. The main goal of the code is to produce a list of rhythmic episodes, i.e., when and for how long rhythms occur.
Kosciessa, J. Q., Kloosterman, N. A., & Garrett, D. D. (2020). Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it? PLoS Computational Biology, 16(5), e1007885. doi:10.1371/journal.pcbi.1007885
Kosciessa, J. Q., Lindenberger, U., & Garrett, D. D. (2021). Thalamocortical excitability modulation guides human perception under uncertainty. Nature Communications, 12(1), 2430. doi:10.1038/s41467-021-22511-7
Kosciessa, J. Q., Mayr, U., Lindenberger, U. & Garrett, D. D. (2024) Broadscale dampening of uncertainty adjustment in the aging brain. Nat Commun 15, 10717. https://doi.org/10.1038/s41467-024-55416-2
The MAAT is an adaptation of the random dot motion paradigm that manipulates uncertainty about which stimulus features will be relevant for an upcoming choice. A dynamic dot display varies along four features (color, motion direction, size, and luminance), and a cue before each stimulus indicates which of these features may be probed. Increasing the number of cued features raises uncertainty about what to prioritize. In younger adults, higher uncertainty increased thalamic engagement and shifted cortical dynamics from a rhythmic to a more aperiodic regime (Kosciessa et al., 2021). In a sample of 100 younger and older adults, this integrated neural response to uncertainty was dampened in older age, and its maintenance was associated with higher cognitive flexibility (Kosciessa et al., 2024).
Voice is a naturalistic response mode in psychological experiments, but labeling thousands of word responses, sometimes from continuous recordings, requires time and patience. CARL has been designed to make the manual labeling process smoother and more time-efficient, as well as to provide measures of voice on- and offset times, e.g., by automatically pre-labeling word on- and offsets based on sound energy and allowing quick manual refinement via drag and drop.
Julian Q. Kosciessa