Translational Perspectives for Computational Neuroimaging

• Published in: Neuron (Cambridge, Mass.) Vol. 87; no. 4; pp. 716 - 732
• Main Authors: Stephan, Klaas E., Iglesias, Sandra, Heinzle, Jakob, Diaconescu, Andreea O.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 19.08.2015; Elsevier Limited
• Subjects: Animals; Brain - physiology; Brain research; Clinical medicine; Computer Simulation - trends; Functional Neuroimaging - methods; Functional Neuroimaging - trends; Humans; Mathematical models; Medical imaging; Models, Neurological; Nerve Net - physiology; Neuroimaging - methods; Neuroimaging - trends; Patients; Physiology; Schizophrenia; Success; Translational Medical Research - methods; Translational Medical Research - trends
• ISSN: 0896-6273; 1097-4199; 1097-4199
• DOI: 10.1016/j.neuron.2015.07.008

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. Psychiatry lacks diagnostic tools that enable clinical predictions and treatment selection for individual patients. Computational models of neuroimaging data offer a promising new avenue. Focusing on schizophrenia, this article reviews clinical applications, methodological challenges, and future developments of computational neuroimaging.