SynthesisNeuroImage2017
Group-regularized individual prediction: theory and application to pain.
Synthesis in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed.
- Analgesic Effects Evoked by Real and Imagined Acupuncture: A Neuroimaging Study.Cerebral cortex (New York, N.Y. : 1991) · 2019Trial
- Machine learning and individual variability in electric field characteristics predict tDCS treatment response.Brain stimulationTrial
- Multi-voxel pattern analysis for developmental cognitive neuroscientists.Developmental cognitive neuroscience · 2025Review
- Graph-constrained Analysis for Multivariate Functional Data.Journal of multivariate analysis · 2025Article
- Decoding Mindfulness With Multivariate Predictive Models.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Review
- Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models.Pain · 2025Article
- Modulating subjective pain perception with decoded Montreal Neurological Institute-space neurofeedback: a proof-of-concept study.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Regulation of craving for real-time fMRI neurofeedback based on individual classification.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Machine learning-optimized non-invasive brain stimulation and treatment response classification for major depression.Bioelectronic medicine · 2024Article
- Does pain hurt more in Spanish? The neurobiology of pain among Spanish-English bilingual adults.Social cognitive and affective neuroscience · 2023Article
- Article
- The role of ascending arousal network in patients with chronic insomnia disorder.Human brain mapping · 2023Article
- Pain-related reorganization in the primary somatosensory cortex of patients with postherpetic neuralgia.Human brain mapping · 2022Article
- Limits of decoding mental states with fMRI.Cortex; a journal devoted to the study of the nervous system and behavior · 2022Article
- Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature.NeuroImage · 2022Article
- Brain Mechanism of Acupuncture Treatment of Chronic Pain: An Individual-Level Positron Emission Tomography Study.Frontiers in neurology · 2022Article
- Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain.Frontiers in neurology · 2022Article
- Multivariate spatial feature selection in fMRI.Social cognitive and affective neuroscience · 2021Article
- Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.Scientific reports · 2021Observational
- Recommendations for the Development of Socioeconomically-Situated and Clinically-Relevant Neuroimaging Models of Pain.Frontiers in neurology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.