Sara Mora, Sumit Madan, Stephan Gebel, Mauro Giacomini
Proposal of an Architecture for Terminology Management in a Research Project
IOS Press https://doi.org/10.3233/SHTI200447

Sarah M. Carpentier, Andrea R. McCulloch, Tanya M. Brown, Sarah E. M. Faber, Petra Ritter, Zheng Wang, Valorie Salimpoor, Kelly Shen and Anthony R. McIntosh
Complexity Matching: Brain Signals Mirror Environment Information Patterns during Music Listening and Reward.
Journal of cognitive neurosceince doi: 10.1162/jocn_a_01508

Hannelore Aerts, Michael Schirner, Thijs Dhollander, Ben Jeurissen, Eric Achten, Dirk Van Roost, Petra Ritter, Daniele Marinazzo
Modeling brain dynamics after tumor resection using The Virtual Brain
Neuroimage doi: https://doi.org/10.1101/752931

Sabrina Chettouf, Laura M. Rueda-Delgado, Ralph de Vries, Petra Ritter, Andreas Daffertshofer
Are unimanual movements bilateral?
Neuroscience & Biobehavioral reviews doi: 10.1016/j.neubiorev.2020.03.002

Poldrack, Feingold, Frank, Gleeson, de Hollander, Huys, Love, Markiewitcz, Moran, Ritter, Turner, Yarkoni, Zhang, Cohen.
The importance of standards for sharing of computational models and data
May 2019, https://psyarxiv.com/q3rnx

N. A. Akar et. al.
Arbor – a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures.
arXiv e-prints, art. arXiv:1901.07454, Jan. 2019. https://arxiv.org/abs/1901.07454.S

Shen K, Bezgin G, Schirner M, Ritter P, Everling S, McIntosh AR (2019)
A macaque connectome for large-scale network simulations in TheVirtualBrain
Nature Scientific  https://doi.org/10.1038/s41597-019-0129-z

Stefanovski, Triebkorn, Spiegler, Diaz-Cortes, Solodkin, Jirsa, McIntosh, Ritter; for the Alzheimer’s Disease Neuroimaging Initiative (2019).
Linking molecular pathways and large-scale computational modeling to assess candidate disease mechanisms and pharmacodynamics in Alzheimer’s disease.
Frontiers Computational Neuroscience doi: 10.3389/fncom.2019.00054

Blundell et. al.
Code Generation in Computational Neuroscience: A Review of Tools and Techniques.
Frontiers in Neuroinformatics, 12:68, 2018. ISSN 1662-5196. doi:10.3389/fninf.2018.00068.

Nakamura A, Cuesta P, Fernández A, Arahata Y, Iwata K, Kuratsubo I, Bundo M, Hattori H, Sakurai T, Fukuda K, Washimi Y, Endo H, Takeda A, Diers K, Bajo R, Maestú F, Ito K, Kato T.
Electromagnetic signatures of the preclinical and prodromal stages of Alzheimer's disease.
Brain. 2018 May 1;141(5):1470-1485.

Oehrl et. al.
Streaming live neuronal simulation data into visualization and analysis.
In R. Yokota, M. Weiland, J. Shalf, and S. Alam, editors, High Performance Computing, pages 258–272, Cham, 2018. Springer International Publishing. ISBN 978-3-030-02465-9. doi:10.1007/978-3-030-02465-9_18.

Schirner, McIntosh, Jirsa, Deco, Ritter
Inferring multi-scale neural mechanisms with brain network modelling.
eLife https://doi.org/10.7554/eLife.28927.001
Video tutorial:  https://www.youtube.com/watch?v=rAyAiQ83B9U&list=PLVtblERyzDeIATvaoHp-bBtksF25zm8p1&index=7

Susi G, de Frutos Lucas J, Niso G, Ye Chen SM, Anton Toro L, Chino Vilca BN, and Maestu F.
Healthy and pathological neurocognitive aging: Spectral and functional connectivity analyses using magnetoencephalography.
In OXFORD RESEARCH ENCYCLOPEDIA OF PSYCHOLOGY AND AGING. Oxford University press, 2018.

Zimmermann, Perry, Breakspear, Schirner, Sachdev, Wen, Kochan, Mapstone, Ritter, McIntosh, Solodkin (2018)
Differentiation of Alzheimer’s disease based on local and global parameters in personalized Virtual Brain models.
Neuroimage Clinical https://doi.org/10.1016/j.nicl.2018.04.017

Koval, Bone, Lois, Bottani, Marcoux, Samper-Gonzalez, Burgos, Charlier, Bertrand, Epelbaum, Colliot, Allassonniere, Durrleman for the Alzheimer’s Disease Neuroimaging Innitiative.
Simulating Alzheimer’s disease progression with personalised digital brain models (preprint)
https://who.rocq.inria.fr/Stanley.Durrleman/papers/DigitalBrainModels.pdf

Fluck, Senger, Ziegler, Claus, Schwichtenberg.
The cloud4health Project: Secondary Use of Clinical Data with Secure Cloud-Based Text Mining Services 2017

Griebel M., Schüller A., Schweitzer M. (eds)
Scientific Computing and Algorithms in Industrial Simulations.
Springer, Cham https://doi.org/10.1007/978-3-319-62458-7_15

Nakamura A, Cuesta P, Kato T, Arahata Y, Iwata K, Yamagishi M, Kuratsubo I, Kato K, Bundo M, Diers K, Fernández A, Maestú F, Ito K.
Early functional network alterations in asymptomatic elders at risk for Alzheimer's disease.
Sci Rep. 2017 Jul 26;7(1):6517. doi: 10.1038/s41598-017-06876-8.

Solodkin, Zimmermann, McIntosh, Stefanovski, Ritter (2017)
Neurological biomarkers and Neuroinformatics: The role of The Virtual Brain.
Book: in Molecular-Genetic and Statistical Techniques for Behavioral and Neural Research https://doi.org/10.1016/B978-0-12-804078-2.00001-5

Anandhi Iyappan, Michaela Gündel, Mohammad Shahid, Jiali Wang, Hui Li, Heinz-Theodor Mevissen, Bernd Müller, Juliane Fluck, Viktor Jirsa, Lia Domide, Erfan Younesi, Martin Hofmann-Apitius.
Toward a Pathway Inventory of the Human Brain for Modeling Disease Mechanisms Underlying Neurodegeneration.
J Alzheimers Dis. 2016 Apr 12;52(4):1343-60. doi: 10.3233/JAD-151178.

Matzke, Schirner, Vollbrecht, Rothmeier, Llarena, Rojas, Triebkorn, Domide, Mersmann, Solodkin, Jirsa, McIntosh, Ritter.
TVB-EduPack—An Interactive Learning and Scripting Platform for The Virtual Brain.
Frontiers in Neuroinformatics, 25 November 2015, https://doi.org/10.3389/fninf.2015.00027

Canuet L, Pusil S, López ME, Bajo R, Pineda-Pardo JÁ, Cuesta P, Gálvez G, Gaztelu JM, Lourido D, García-Ribas G, Maestú F.
Network Disruption and Cerebrospinal Fluid Amyloid-Beta and Phospho-Tau Levels in Mild Cognitive Impairment.
J Neurosci. 2015 Jul 15;35(28):10325-30.

Maestú F, Peña JM, Garcés P, González S, Bajo R, Bagic A, Cuesta P, Funke M, Mäkelä JP, Menasalvas E, Nakamura A, Parkkonen L, López ME, Del Pozo F, Sudre G, Zamrini E, Pekkonen E, Henson RN, Becker JT; Magnetoencephalography International Consortium of Alzheimer's Disease.
A multicenter study of the early detection of synaptic dysfunction in Mild Cognitive Impairment using Magnetoencephalography-derived functional connectivity.
Neuroimage Clin. 2015 Aug 1;9:103-9. doi: 10.1016/j.nicl.2015.07.011. eCollection 2015.

Schirner, M., S. Rothmeier, V. Jirsa, A. R. McIntosh and Ritter, P. (2015).
An automated pipeline for constructing personalised virtual brains from multimodal neuroimaging data.
Neuroimage. doi: 10.1016/j.neuroimage.2015.03.055.
Video tutorial: https://www.youtube.com/watch?v=j7r8WZ5ls64&list=PLVtblERyzDeIATvaoHp-bBtksF25zm8p1&index=6

Woodman, Pezard, Domide, Knock, Sanz-Leon, Mersmann, Mcintosh, Jirsa.
Integrating neuroinformatics tools in TheVirtualBrain.
Frontiers in Neuroinformatics, 2014; 8: 36. doi: 10.3389/fninf.2014.00036

López ME, Bruna R, Aurtenetxe S, Pineda-Pardo JA, Marcos A, Arrazola J, Reinoso AI, Montejo P, Bajo R, Maestú F.
Alpha-band hypersynchronization in progressive mild cognitive impairment: a magnetoencephalography study.
J Neurosci. 2014 Oct 29;34(44):14551-9.

Ritter, P., M. Schirner, A. R. McIntosh and V. K. Jirsa (2013).
The virtual brain integrates computational modeling and multimodal neuroimaging.
Brain Connect 3(2): 121-145.

Sanz Leon, Knock, Woodman, Domide, Mersmann, Mcintosh, Jirsa.
The Virtual Brain: a simulator of primate brain network dynamics.
Frontiers in Neuroinformatics, 2013 Jun 11;7:10. doi: 10.3389/fninf.2013.00010. eCollection 2013.