ArticleStatistics and data science in imaging2025
A Novel, Variance Component-Based Method for Detecting Brain-Behavior Associations in Neuroimaging Data.
Article in Statistics and data science in imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The sequence kernel association test (SKAT) is a widely used and computationally efficient method in high-dimensional studies that tests for the joint effect of multiple predictors while accommodating covariates. However, the omnibus nature of the test hinders interpretation. We develop a new method called LaxKAT (linear maximum kernel association test) that can identify both global and local signal in high-dimensional data. The LaxKAT statistic maximizes the SKAT statistic over a pre-specified subspace of linear kernels. We demonstrate via simulations that it exhibits improved global and local power compared to previous methods while controlling the family-wise error rate (FWER). We also apply LaxKAT to neuroimaging data from cognitively normal controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify brain regions exhibiting sex-specific differences in cortical thickness patterns.
Indexed as
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.