Work

Group-regularized individual prediction: theory and application to pain

Year: 2015

Type: article

Source: NeuroImage

DOI: 10.1016/j.neuroimage.2015.10.074

Authors: Martin A. Lindquist, Anjali Krishnan, Marina López-Solà, Marieke Jepma, Choong-Wan Woo, Leonie Koban, Mathieu Roy, Lauren Y. Atlas, Liane Schmidt, Luke J. Chang, Elizabeth A. Reynolds Losin, Hedwig Eisenbarth, Yoni K. Ashar, Elizabeth Delk, Tor D. Wager

Institutions: Johns Hopkins University, City University of New York, University of Colorado Boulder, Brooklyn College, National Institutes of Health, National Center for Complementary and Integrative Health, INSEAD, Inserm, École Normale Supérieure - PSL, INSEAD, University of Miami, University of Colorado System

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Institutes of Health, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of Biomedical Imaging and Bioengineering

Awards: G1420189321, G2139073708, Causal Inference for Neuroimaging, Learning to avoid pain: Computational mechanisms and application to methamphetami, The neural bases of placebo effects and their relation to regulatory processes, G9000000297, G9000025190, G9000057464

OpenAlex ID: W2211857764

FWCI: 3.127

Cites: 71

Cited by: 64

Related to: 10