Translational Perspectives for Computational Neuroimaging.
- Stephan KE Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck Institute for Metabolism Research, 50931 Cologne, Germany. Electronic address: stephan@biomed.ee.ethz.ch.
- Iglesias S Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zurich, Switzerland.
- Heinzle J Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zurich, Switzerland.
- Diaconescu AO Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zurich, Switzerland.
- 2015-08-21
Published in:
- Neuron. - 2015
Animals
Brain
Computer Simulation
Functional Neuroimaging
Humans
Models, Neurological
Nerve Net
Neuroimaging
Translational Medical Research
English
Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. Promising new avenues for translation are provided by computational modeling of neuroimaging data. This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. These approaches-biophysical network models, generative models, and model-based fMRI analyses of neuromodulation-strive to move beyond statistical characterizations and toward mechanistic explanations of neuroimaging data. Focusing on schizophrenia as a paradigmatic spectrum disease, we review applications of these models to psychiatric questions, identify methodological challenges, and highlight trends of convergence among computational neuroimaging approaches. We conclude by outlining a translational neuromodeling strategy, highlighting the importance of openly available datasets from prospective patient studies for evaluating the clinical utility of computational models.
- Language
-
- English
- Open access status
- bronze
- Identifiers
-
- DOI 10.1016/j.neuron.2015.07.008
- PMID 26291157
- Persistent URL
- https://sonar.ch/global/documents/293634
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