ReviewNeuroscience and biobehavioral reviews2019
Neuroimaging of individual differences: A latent variable modeling perspective.
Review in Neuroscience and biobehavioral reviews, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- What Is the Test-Retest Reliability of Common Task-Functional MRI Measures? New Empirical Evidence and a Meta-Analysis.Psychological science · 2020Pooled it
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- Capturing individual variation in children's electroencephalograms during nREM sleep.PLoS computational biology · 2026Article
- Generative Principal Component Regression via Variational Inference.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2026Article
- Associations Between Steep Delay Discounting and Punishment Learning.Computational psychiatry (Cambridge, Mass.) · 2026Article
- Pulse Pressure Impairs Cognition via White Matter Disruption.Hypertension (Dallas, Tex. : 1979) · 2025Article
- Neural Topologies of Reinforcement Sensitivity Theory: A Latent Variable Approach to Magnetic Resonance Imaging Data.Biological psychiatry global open science · 2025Article
- Latent Growth Models of Longitudinal Changes in Functional Connectivity during Early Stage Psychosis.Neuroinformatics · 2025Article
- Pathways to adolescent social anxiety: Testing interactions between neural social reward function and perceived social threat in daily life.Development and psychopathology · 2025Article
- Social pain is associated with altered developmental trajectories of connectivity among the triple network model of psychopathology.Social cognitive and affective neuroscience · 2025Article
- Trait state occasion (TSO) modeling of event-related potentials (ERPs).Biological psychology · 2025Article
- Reliability of structural brain change in cognitively healthy adult samples.bioRxiv : the preprint server for biology · 2025Article
- Modelling nonlinear moderation effects with local structural equation modelling (LSEM): A non-technical introduction.International journal of psychology : Journal international de psychologie · 2025Article
- Enhancing task fMRI individual difference research with neural signatures.medRxiv : the preprint server for health sciences · 2025Article
- Reliability of structural brain change in cognitively healthy adult samples.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Events in context-The HED framework for the study of brain, experience and behavior.Frontiers in neuroinformatics · 2024Article
- Enhancing experimental design through Bayes factor design analysis: insights from multi-armed bandit tasks.Wellcome open research · 2024Article
- Impact of brain segmentation methods on regional metabolism quantification inEJNMMI research · 2023Article
- Neurobehavioral indices of gaze perception are associated with social cognition across schizophrenia patients and healthy controls.Journal of psychopathology and clinical science · 2023Article
- Reliability of task-evoked neural activation during face-emotion paradigms: Effects of scanner and psychological processes.Human brain mapping · 2022Article
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4 authors.
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Abstract
Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.
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