Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging
Kragel, P. A., Koban, L., Feldman Barrett, L., & Wager, T. D.
In one sentence. Reviewed the history and theory of multivariate predictive brain models and brain signatures, arguing that distributed, falsifiable, cross-validated models like the Neurologic Pain Signature better link brain activity to mental constructs than traditional univariate mapping.
Abstract
Human neuroimaging research has transitioned from mapping local effects to developing predictive models of mental events that integrate information distributed across multiple brain systems. Here we review work demonstrating how multivariate predictive models have been utilized to provide quantitative, falsifiable predictions; establish mappings between brain and mind with larger effects than traditional approaches; and help explain how the brain represents mental constructs and processes. Although there is increasing progress toward the first two of these goals, models are only beginning to address the latter objective. By explicitly identifying gaps in knowledge, research programs can move deliberately and programmatically toward the goal of identifying brain representations underlying mental states and processes.
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Citation: Kragel, P. A., Koban, L., Feldman Barrett, L., & Wager, T. D. (2018). Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging. Neuron, 99(2), 257–273. https://doi.org/10.1016/j.neuron.2018.06.009