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  1. I Carannante, Y Johansson, G Silberberg, J Hellgren Kotaleski. Data-Driven Model of Postsynaptic Currents Mediated by NMDA or AMPA Receptors in Striatal Neurons. Front Comput Neurosci. 2022; 16: 806086.
  2. J Fu, A Tzortzakakis, J Barroso, E Westman, D Ferreira, R Moreno. Generative Aging of Brain Images with Diffeomorphic Registration. arXiv preprint. 2022; arXiv:2205.15607.
  3. A Hain, D Jörgens, R Moreno. Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering. arXiv preprint 2022; arXiv:2205.04843.
  4. M Siegbahn, C Engmér Berglin, R Moreno. Automatic segmentation of the core of the acoustic radiation in humans. Front Neurol. 2022 : 934650.
  5. C Dartora, A Marseglia, G Mårtensson, G Rukh, J Dang, JS Muehlboeck, LO Wahlund, R Moreno, J Barroso, D Ferreira, HB Schiöth, E Westman. Predicting the Age of the Brain with Minimally Processed T1-weighted MRI Data. medRxiv preprint. 2022; medRxiv:2022.09.06.22279594.
  6. Chakravarty K, Roy S, Sinha A, Nambu A, Chiken S, Hellgren Kotaleski J, Kumar A. Transient Response of Basal Ganglia Network in Healthy and Low-Dopamine State. eNeuro. 2022 Mar 18;9(2):ENEURO.0376-21.2022. doi: 10.1523/ENEURO.0376-21.2022.
  7. Hjorth, J.J.J., Hellgren Kotaleski, J. & Kozlov, A. Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda. Neuroinform 19, 685–701 (2021).
  8. G Colombo, R Cuber, L Kanari, A Venturino, R Schulz, M Scolamiero, J Agerberg, H Mathys, L Tsai, W Chachólski,, K Hess, S Siegert. Microglial morphOMICs, a tool for mapping microglial morphology, reveals brain-region-and sex-dependent phenotypes. Nature neuroscience, 2022.
  9. W Chachólski, A Guidolin, I Ren, M Scolamiero, F Tombari. Effective computation of relative homological invariants for functors over posets. arXiv preprint 2022; arXiv:2209.05923
  10. W Chachólski, A Jin, F Tombari. Realisations of posets and tameness. arXiv preprint 2022; arXiv:2112.12209
  11. W Chachólski, René Corbet, Anna-Laura Sattelberger. The Shift-Dimension of Multipersistence Modules. arXiv preprint 2022; arXiv:2112.06509.
  12. W Chachólski, B Gunti, C Landi, Decomposing filtered chain complexes: Geometry behind barcoding algorithms. Computational Geometry, Volume 109, 2023.
  13. N Hulst, Exploring persistent homology as a method for capturing functional connectivity differences in Parkinson’s Disease. Master Thesis. DiVA, id: diva2:1687257.
  14. Lucas Höglund, Analysis of Eye Tracking Data from Parkinson’s Patients using Machine Learning, MS thesis, KTH.
  15. Leo Bergman, Feature extraction with self-supervised learning on eye-tracking data from Parkinson’s patients and healthy individuals, MS thesis, KTH.
  16. Emma Lind. Analysis of Brain Signals from Patients with Parkinson’s Disease using Self-Supervised Learning. MS thesis, KTH.
  17. Wilhelm Ågren, Feature extraction from MEG data using self-supervised learning. MS thesis, KTH.
  18. Giulia Tuccio, Parameter estimation in a cardiovascular computational model using numerical optimization. MS thesis, KTH.
  19. Paolo Calderaro, Patient simulation. Generation of a machine learning “inverse” digital twin. MS thesis, KTH.
  20. Georgios Moschovis. NeuralDynamicsLab at ImageCLEF Medical 2022. ImageCLEF, 2022.