Roopa Kalsank Pai
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Roopa Kalsank Pai has a total of 53 co-authors. Here are the top co-authors by number of shared publications:
- Agnes Flöel
Department of Neurology, University Medicine Greifswald, Greifswald, Germany
1 shared publication - Alex Upton
CSCS Swiss National Supercomputing Centre, Lugano, Switzerland
1 shared publication Alexander Peyser
SimLab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
1 shared publicationAna Solodkin
1 shared publication- André Blickensdörfer
Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany; Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Neurology with Experimental Neurology, Charitéplatz 1, 10117 Berlin, Germany
1 shared publication Anne Nahm
Jülich Supercomputer Centre, Forschungszentrum Jülich, Jülich, Germany
1 shared publication
Latest Publications
- Top Cited PapersOpen Access
- 4 February 2022
- Published by Elsevier in NeuroImage
- Vol. 251, 118973
Brain simulation as a cloud service: The Virtual Brain on EBRAINS
- 4 February 2022
- Published by Elsevier in NeuroImage
- Vol. 251, 118973
Top Cited PapersOpen AccessBrain simulation as a cloud service: The Virtual Brain on EBRAINS
- Open Access
- 18 November 2020
- Published by Center for Open Science in PsyArXiv
From genes to network models of Alzheimer's disease: Biological framework for multi-scale brain simulation with The Virtual Brain
While the knowledge in neuroscience and the possibilities in clinical neurology have improved for many years, neurogenerative diseases and associated dementia remain a growing problem. Alzheimer’s Disease (AD) is the most common cause of dementia and also represents the most prevalent type of neurodegenerative diseases. For AD, disease-modifying treatments are presently lacking and the understanding of disease mechanisms remain incomplete. In the present review, we consider candidate contributing factors leading to AD and we evaluate novel computational brain simulation methods to further disentangle their potential roles. We first discuss existing computational models of AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (TVB, www.thevirtualbrain.org), an open-source, multi- scale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics and treatment of AD.
- 18 November 2020
- Published by Center for Open Science in PsyArXiv
Open AccessFrom genes to network models of Alzheimer's disease: Biological framework for multi-scale brain simulation with The Virtual Brain
While the knowledge in neuroscience and the possibilities in clinical neurology have improved for many years, neurogenerative diseases and associated dementia remain a growing problem. Alzheimer’s Disease (AD) is the most common cause of dementia and also represents the most prevalent type of neurodegenerative diseases. For AD, disease-modifying treatments are presently lacking and the understanding of disease mechanisms remain incomplete. In the present review, we consider candidate contributing factors leading to AD and we evaluate novel computational brain simulation methods to further disentangle their potential roles. We first discuss existing computational models of AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (TVB, www.thevirtualbrain.org), an open-source, multi- scale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics and treatment of AD.