title: Group-regularized individual prediction: theory and application to pain.
abstract: Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study.
authors.affiliation: Johns Hopkins University, USA.
authors.forename: Martin A
authors.initials: MA
authors.lastname: Lindquist
authors.affiliation: University of Colorado Boulder, USA; Brooklyn College of the City University of New York, USA.
authors.forename: Anjali
authors.initials: A
authors.lastname: Krishnan
authors.affiliation: University of Colorado Boulder, USA.
authors.forename: Marina
authors.initials: M
authors.lastname: López-Solà
authors.affiliation: University of Colorado Boulder, USA.
authors.forename: Marieke
authors.initials: M
authors.lastname: Jepma
authors.affiliation: University of Colorado Boulder, USA.
authors.forename: Choong-Wan
authors.initials: CW
authors.lastname: Woo
authors.affiliation: University of Colorado Boulder, USA.
authors.forename: Leonie
authors.initials: L
authors.lastname: Koban
authors.affiliation: Concordia University, USA.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Roy
authors.affiliation: National Center for Complementary and Integrative Health, National Institutes of Health, USA.
authors.forename: Lauren Y
authors.initials: LY
authors.lastname: Atlas
authors.affiliation: INSEAD, France; Cognitive Neuroscience Laboratory, INSERM U960, Department of Cognitive Sciences, Ecole Normale Supérieure, Paris, France.
authors.forename: Liane
authors.initials: L
authors.lastname: Schmidt
authors.affiliation: University of Colorado Boulder, USA.
authors.forename: Luke J
authors.initials: LJ
authors.lastname: Chang
authors.affiliation: University of Colorado Boulder, USA; University of Miami, USA.
authors.forename: Elizabeth A
authors.initials: EA
authors.lastname: Reynolds Losin
authors.affiliation: University of Colorado Boulder, USA.
authors.forename: Hedwig
authors.initials: H
authors.lastname: Eisenbarth
authors.affiliation: University of Colorado Boulder, USA.
authors.forename: Yoni K
authors.initials: YK
authors.lastname: Ashar
authors.affiliation: University of Colorado Boulder, USA.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Delk
authors.affiliation: University of Colorado Boulder, USA. Electronic address: tor.wager@colorado.edu.
authors.forename: Tor D
authors.initials: TD
authors.lastname: Wager
mesh_terms: D000328:Adult; D015415:Biomarkers; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D058748:Pain Perception / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D055815:Young Adult
chemical_list: D015415:Biomarkers
keywords: Empirical Bayes; MVPA; Machine learning; Mega-analysis; Meta-analysis; Pain; Prediction; Shrinkage; Statistical learning; fMRI