{"id":11370,"date":"2023-01-27T13:36:43","date_gmt":"2023-01-27T12:36:43","guid":{"rendered":"https:\/\/www.digitalfutures.kth.se\/?page_id=11370"},"modified":"2024-12-19T13:02:44","modified_gmt":"2024-12-19T12:02:44","slug":"publications","status":"publish","type":"page","link":"https:\/\/wpmu-tris.sys.kth.se\/digitalfutures\/research\/collaborative-projects\/completed-collaborative-projects\/dbrain\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

We like to inspire and share interesting knowledge!<\/strong><\/p>\n

Publications<\/strong><\/p>\n

    \n
  1. Sanna Persson, Rodrigo Moreno, Bounding tractogram redundancy, Frontiers in Neuroscience<\/em>. 18:1403804, 2024, https:\/\/doi.org\/10.3389\/fnins.2024.1403804<\/a><\/li>\n
  2. Malin Siegbahn, Daniel J\u00f6rgens, Filip Asp, Malou Hultcrantz, Rodrigo Moreno, Cecilia Engm\u00e9r Berglin. Asymmetry in cortical thickness of the Heschl\u2019s gyrus in unilateral ear canal atresia<\/em>. Otology & Neurotology. 45(4),e342-e350, 2024. https:\/\/doi.org\/10.1097\/MAO.0000000000004137<\/a><\/li>\n
  3. Caroline Dartora, Anna Marseglia, Gustav M\u00e5rtensson, Gull Rukh, Junhua Dang, J- Sebastian Muehlboeck, Lars-Olof Wahlund, Rodrigo Moreno, Jos\u00e9 Barroso, Daniel Ferreira, Helgi Schi\u00f6th, Eric Westman. A deep learning model for brain age prediction using minimally preprocessed T1w-images as input<\/em>. Frontiers in Aging Neuroscience 15, 1303036, 2024. https:\/\/doi.org\/10.3389\/fnagi.2023.1303036<\/a><\/li>\n
  4. Antonia Hain, Daniel J\u00f6rgens, Rodrigo Moreno. Randomized iterative spherical-deconvolution informed tractogram filtering. <\/em>Neuroimage 278, 120248, 2023. https:\/\/doi.org\/10.1016\/j.neuroimage.2023.120248<\/a><\/li>\n
  5. Jingru Fu, Antonios Tzortzakakis, Jos\u00e9 Barroso, Eric Westman, Daniel Ferreira, Rodrigo Moreno. Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration<\/em>. Human Brain Mapping 44 (4), 1289-1308, 2023. https:\/\/doi.org\/10.1002\/hbm.26165<\/a><\/li>\n
  6. Kajsa Ahlgren, Christoffer Olsson, Inna Ermilova, Jan Swenson. New insights into the protein stabilizing effects of trehalose by comparing with sucrose.<\/em> Physical Chemistry Chemical Physics 25 (32), 21215-21226, 2023. https:\/\/doi.org\/10.1039\/D3CP02639F<\/a><\/li>\n
  7. Zhou Zhou, Chirstoffer Olsson, T Christian Gasser, Xiaogai Li, Svein Kleiven. The White Matter Fiber Tract Deforms Most in the Perpendicular Direction During In Vivo Volunteer Impacts<\/em>. J Neurotrauma. 2024 41(23-24):2554-2570, 2024. https:\/\/doi.org\/10.1089\/neu.2024.0183<\/a><\/li>\n
  8. Jingru Fu, Daniel Ferreira, \u00d6rjan Smedby, Rodrigo Moreno. A deformation-based morphometry framework for disentangling Alzheimer\u2019s disease from normal aging using learned normal aging templates<\/em>. arXiv.2311.08176, 2023. https:\/\/doi.org\/10.48550\/arXiv.2311.08176<\/a><\/li>\n
  9. Daniel J\u00f6rgens, Pierre Marc Jodoin, Maxime Descoteaux, Rodrigo Moreno. Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering. <\/em>arXiv:2307.05786, 2023. https:\/\/doi.org\/10.48550\/arXiv.2307.05786<\/a><\/li>\n
  10. Jingru Fu, Simone Bendazzoli, \u00d6rjan Smedby, Rodrigo Moreno. Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation. <\/em>MICCAI 2024 Workshop on Advancing Data Solutions in Medical Imaging AI (ADSMI’24) (in press). https:\/\/doi.org\/10.48550\/arXiv.2406.16848<\/a><\/li>\n
  11. Sanna Persson, Xinyi Wan, and Rodrigo Moreno. Randomly COMMITting: Iterative Convex Optimization for Microstructure-Informed Tractography. <\/em>MICCAI 2024 Workshop on Computational Diffusion MRI (CDMRI\u201924) (in press)<\/li>\n
  12. Yu Zhou, Jingru Fu, \u00d6rjan Smedby, Rodrigo Moreno. Synthesis of Pediatric Brain Tumor Images with Mass Effect. <\/em>SPIE – Medical Imaging: Image Processing 12464, 699-707, 2023. San Diego, USA, February 2023. https:\/\/doi.org\/10.1117\/12.2654366<\/a><\/li>\n
  13. I Carannante, Y Johansson, G Silberberg, J Hellgren Kotaleski. Data-Driven Model of Postsynaptic Currents Mediated by NMDA or AMPA Receptors in Striatal Neurons<\/em>. Front Comput Neurosci. 2022; 16: 806086.<\/li>\n
  14. J Fu, A Tzortzakakis, J Barroso, E Westman, D Ferreira, R Moreno. Generative Aging of Brain Images with Diffeomorphic Registration<\/em>. arXiv preprint. 2022; arXiv:2205.15607. https:\/\/doi.org\/10.48550\/arXiv.2205.15607<\/a><\/li>\n
  15. A Hain, D Jo\u0308rgens, R Moreno. Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering<\/em>. arXiv preprint 2022; arXiv:2205.04843. https:\/\/doi.org\/10.48550\/arXiv.2205.04843<\/a><\/li>\n
  16. M Siegbahn, C Engme\u0301r Berglin, R Moreno. Automatic segmentation of the core of the acoustic radiation in humans<\/em>. Front Neurol. 2022 : 934650. https:\/\/doi.org\/10.3389\/fneur.2022.934650<\/a><\/li>\n
  17. C Dartora, A Marseglia, G Ma\u030artensson, G Rukh, J Dang, JS Muehlboeck, LO Wahlund, R Moreno, J Barroso, D Ferreira, HB Schio\u0308th, E Westman. Predicting the Age of the Brain with Minimally Processed T1-weighted MRI Data<\/em>. medRxiv preprint. 2022; medRxiv:2022.09.06.22279594. https:\/\/doi.org\/10.1101\/2022.09.06.22279594<\/a><\/li>\n
  18. 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<\/em>. eNeuro. 2022 Mar 18;9(2):ENEURO.0376-21.2022. doi: 10.1523\/ENEURO.0376-21.2022.<\/li>\n
  19. Hjorth, J.J.J., Hellgren Kotaleski, J. & Kozlov, A. Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using\u00a0Snudda<\/em>. Neuroinform 19, 685\u2013701 (2021). https:\/\/doi.org\/10.1007\/s12021-021-09531-w<\/a><\/li>\n
  20. G Colombo, R Cuber, L Kanari, A Venturino, R Schulz, M Scolamiero, J Agerberg, H Mathys, L Tsai, W Chacho\u0301lski,, K Hess, S Siegert. Microglial morphOMICs, a tool for mapping microglial morphology, reveals brain-region-and sex-dependent phenotypes<\/em>. Nature neuroscience, 2022.<\/li>\n
  21. W Chacho\u0301lski, A Guidolin, I Ren, M Scolamiero, F Tombari. Effective computation of relative homological invariants for functors over posets<\/em>. arXiv preprint 2022; arXiv:2209.05923<\/li>\n
  22. W Chacho\u0301lski, A Jin, F Tombari. Realisations of posets and tameness<\/em>. arXiv preprint 2022; arXiv:2112.12209<\/li>\n
  23. W Chacho\u0301lski, Rene\u0301 Corbet, Anna-Laura Sattelberger. The Shift-Dimension of Multipersistence Modules<\/em>. arXiv preprint 2022; arXiv:2112.06509.<\/li>\n
  24. W Chacho\u0301lski, B Gunti, C Landi, Decomposing filtered chain complexes: Geometry behind barcoding algorithms<\/em>. Computational Geometry, Volume 109, 2023.<\/li>\n
  25. Georgios Moschovis. NeuralDynamicsLab at ImageCLEF Medical 2022<\/em>. ImageCLEF, 2022.<\/li>\n
  26. Helson Pascal, Lundqvist Daniel, Svenningsson Per, Vinding Mikkel, Kumar Arvind (2023). Cortex-wide topography of 1\/f-exponent in Parkinson\u2019s disease<\/em>. Nature Parkinson’s Disease, 9, Article number: 109.<\/li>\n
  27. Zang, Jie, Liu Shenquan, Helson Pascal, Kumar Arvind (2024). Structural constraints on the emergence of oscillations in multi-population neural networks<\/em>. eLIFE, 12(Feb 5).<\/li>\n
  28. W\u00e4rnberg Emil, Kumar Arvind (2023). Feasibility of dopamine as a vector-valued feedback signal in the basal ganglia. <\/em>PNAS\u00a032(120):e2221994120<\/li>\n<\/ol>\n

    Master School Theses<\/strong><\/p>\n

      \n
    1. \n
      Yuqi Zheng. Predictive MR Image Generation for Alzheimer\u2019s Disease and Normal Aging Using Diffeomorphic Registration<\/span><\/span><\/em>. MS thesis KTH 2023. Supervisors Rodrigo Moreno, Jingru Fu. Diva diva2:1833589<\/span><\/div>\n<\/li>\n
    2. \n
      Xinyi Wan. Assessing the Streamline Plausibility Through Convex Optimization for Microstructure Informed Tractography(COMMIT) with Deep Learning<\/span><\/span><\/em>. MS thesis KTH 2023. Supervisor Rodrigo Moreno. Diva\u00a0diva2:1754277<\/span><\/div>\n<\/li>\n
    3. \n
      Teodor Pstrusi\u0144ski. Impact of the autoencoder-based FINTA tractogram filtering method on brain networks in subjects with Mild Cognitive Impairment<\/em>. MS thesis KTH 2023. Supervisors Rodrigo Moreno, Fabian Sinzinger. Diva diva2:1799683<\/span><\/span><\/span><\/div>\n<\/li>\n
    4. \n
      Erika Bengtsdotter. Towards Anatomically Plausible Streamline Tractography with Deep Reinforcement Learning<\/span><\/span><\/em>. MS thesis KTH 2022. Supervisors Rodrigo Moreno, Fabian Sinzinger. Diva diva2:1695872<\/span><\/span><\/span><\/div>\n<\/li>\n
    5. \n
      Filippo Maschio. Influence of tractogram filtering in the analysis of tractography data in rat brains<\/em>. MS thesis University of Padua, Italy 2022. Supervisor Rodrigo Moreno.<\/div>\n<\/li>\n
    6. \n
      Yu Zhou. Synthesis of Pediatric Brain Tumor\u00a0Image With Mass Effect<\/span><\/span><\/em>. MS thesis KTH 2022. Supervisors Rodrigo Moreno, Jingru Fu. Diva diva2:1686799<\/span><\/span><\/span><\/div>\n<\/li>\n
    7. \n
      Antonia Hain. Assessing individual streamline plausibility through randomized spherical deconvolution-informed tractogram filtering<\/em>. MS thesis University of Saarland, Germany 2021. Supervisor Rodrigo Moreno.<\/div>\n<\/li>\n
    8. N Hulst, Exploring persistent homology as a method for capturing functional connectivity differences in Parkinson\u2019s Disease<\/em>. Master Thesis. DiVA, id: diva2:1687257.<\/li>\n
    9. \n
      Marvin K\u00f6pff. Impact of tractogram filtering and graph creation for structural connectomics in subjects with mild cognitive impairment<\/span><\/span><\/em>. MS thesis KTH 2020. Supervisors Rodrigo Moreno, <\/span><\/span><\/span>Joana Pereira. Diva: diva2:1463915<\/div>\n<\/li>\n
    10. \n
      Johannes Wennberg. Longitudinal assessment of functional connectivity impairment in rat brains<\/em><\/span><\/span>. MS thesis KTH 2019. Supervisor Rodrigo Moreno, <\/span><\/span><\/span>diva2:1327919<\/span><\/div>\n<\/li>\n
    11. \n
      Cristina Zanin. Graph Convolutional Networks for static and dynamic functional connectivity: an application to the Autism Spectrum Disorder<\/em>. MS thesis University of Padua, Italy 2020. Supervisor Rodrigo Moreno.<\/div>\n<\/li>\n
    12. \n
      Lucas Ho\u0308glund. Analysis of Eye Tracking Data from Parkinson\u2019s Patients using Machine Learning.<\/em>\u00a0MS thesis, KTH.<\/div>\n<\/li>\n
    13. Leo Bergman. Feature extraction with self-supervised learning on eye-tracking data from Parkinson\u2019s patients and healthy individuals.<\/em>\u00a0MS thesis, KTH.<\/li>\n
    14. Emma Lind. Analysis of Brain Signals from Patients with Parkinson\u2019s Disease using Self-Supervised Learning<\/em>. MS thesis, KTH.<\/li>\n
    15. Wilhelm A\u030agren. Feature extraction from MEG data using self-supervised learning<\/em>. MS thesis, KTH.<\/li>\n
    16. Giulia Tuccio. Parameter estimation in a cardiovascular computational model using numerical optimization<\/em>. MS thesis, KTH.<\/li>\n
    17. Paolo Calderaro. Patient simulation. Generation of a machine learning \u201cinverse\u201d digital twin<\/em>. MS thesis, KTH.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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