ArticleJournal of the American Statistical Association2020
Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
26 citing papers in PubMed.
- Go figure: transparency in neuroscience images preserves context and clarifies interpretation.Nature methods · 2026Review
- A general framework for investigating neurodevelopment of brain functional networks using multisite and longitudinal neuroimaging.The annals of applied statistics · 2026Article
- Leveraging population information in brain connectivity via Bayesian ICA with a novel informative prior for correlation matrices.Biostatistics (Oxford, England) · 2025Article
- Topologically Optimized Intrinsic Brain Networks.Human brain mapping · 2025Article
- Sex differences in response to violence: role of salience network expansion and connectivity on depression.Translational psychiatry · 2025Article
- The altered resting-state functional connectivity of thalamic subregions in patients with globus pharyngeus.Brain imaging and behavior · 2025Article
- Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- How much is "enough"? Considerations for functional connectivity reliability in pediatric naturalistic fMRI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Sparse Independent Component Analysis with an Application to Cortical Surface fMRI Data in Autism.Journal of the American Statistical Association · 2024Article
- Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks.Biometrics · 2023Article
- Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets.Human brain mapping · 2023Article
- LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY.The annals of applied statistics · 2023Article
- Controversies and progress on standardization of large-scale brain network nomenclature.Network neuroscience (Cambridge, Mass.) · 2023Article
- Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression.Advances in geriatric medicine and research · 2023Article
- Template independent component analysis with spatial priors for accurate subject-level brain network estimation and inference.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Extracting brain disease-related connectome subgraphs by adaptive dense subgraph discovery.Biometrics · 2022Article
- Psilocybin induces spatially constrained alterations in thalamic functional organizaton and connectivity.NeuroImage · 2022Article
- Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Group linear non-Gaussian component analysis with applications to neuroimaging.Computational statistics & data analysis · 2022Article
- Social cognitive network neuroscience.Social cognitive and affective neuroscience · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals-including known population brain networks and subject-specific signals-are represented as latent variables. For estimation, we derive an expectation maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75-250% higher intra-subject reliability.
Identifiers
What OpenQuestion holds
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.