ArticleHuman brain mapping2020
Multi-scale network regression for brain-phenotype associations.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 28 citations in OpenAlex.
- Connectivity Regression.Biostatistics (Oxford, England) · 2025Article
- AN INTEGRATIVE NETWORK-BASED MEDIATION MODEL (NMM) TO ESTIMATE MULTIPLE GENETIC EFFECTS ON OUTCOMES MEDIATED BY FUNCTIONAL CONNECTIVITY.The annals of applied statistics · 2024Article
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- Article
- 3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics.Network neuroscience (Cambridge, Mass.) · 2023Article
- Sex differences in the functional topography of association networks in youth.Proceedings of the National Academy of Sciences of the United States of America · 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
- Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Article
- A regression framework for brain network distance metrics.Network neuroscience (Cambridge, Mass.) · 2022Article
- Effects of docosahexaenoic acid and eicosapentaoic acid supplementation on white matter integrity after repetitive sub-concussive head impacts during American football: Exploratory neuroimaging findings from a pilot RCT.Frontiers in neurology · 2022Article
- Exploratory imaging outcomes of a phase 1b/2a clinical trial of allopregnanolone as a regenerative therapeutic for Alzheimer's disease: Structural effects and functional connectivity outcomes.Alzheimer's & dementia (New York, N. Y.) · 2022Article
- A Hierarchical Graph Learning Model for Brain Network Regression Analysis.Frontiers in neuroscience · 2022Article
- Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition.NeuroImage · 2021Article
- Inferring network properties from time series using transfer entropy and mutual information: Validation of multivariate versus bivariate approaches.Network neuroscience (Cambridge, Mass.) · 2021Article
- Multiscale Risk Factors of Cardiovascular Disease: CLSA Analysis of Genetic and Psychosocial Factors.Frontiers in cardiovascular medicine · 2021Article
- Multi-scale network regression for brain-phenotype associations.Human brain mapping · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 4 countries.
Funding
Abstract
Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain-phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi-Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge- and community-level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion-related artifacts. Compared to single-scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge- and community-level information, MSNR has the potential to yield novel insights into brain-behavior relationships.
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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.