ArticleAnnals of biomedical engineering2026
Multimodal Graphical Network Analysis of Small-for-Gestational-Age in Preterm Infants: Integrating Neonatal Brain Volume, Structural Connectivity, and Early Neurodevelopmental Outcome.
Article in Annals of biomedical engineering, 2026. 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
purposeInfants born Small-for-gestational-age (SGA) face heightened risks for cognitive and language impairments. The neurobiological mechanisms underlying these deficits remain unclear. This study aimed to identify multimodal neuroimaging biomarkers associated with fetal growth restriction and to characterize network-level associations linking SGA to early neurodevelopmental vulnerability using a data-driven graph-based framework.
methodsIn this prospective cohort of 186 preterm infants, near-term brain magnetic resonance imaging (MRI) and Bayley-III developmental assessments were analyzed. Multimodal imaging features-including volumetric indices from T2-weighted MRI and diffusion metrics from diffusion tensor imaging-were integrated with perinatal data. A sparse partial-correlation network was estimated using the Graphical Lasso algorithm (λ optimized via cross-validation) to infer conditional dependencies among features. Variables directly connected to birthweight Z-scores were identified as candidate biomarkers and validated for SGA classification and developmental outcomes using logistic regression and correlation analyses.
resultsNetwork analysis identified eight neuroanatomical correlates of birthweight Z-scores, including increased cerebrospinal fluid (CSF) volume; elevated axial, mean, and radial diffusivity in the left inferior longitudinal fasciculus (ILFL); higher axial diffusivity in the inferior fronto-occipital fasciculus; and altered degree centrality in the right precentral and posterior cingulate cortices (PCC). Logistic regression revealed CSF volume and ILFL diffusivity as independent predictors of SGA. Infants with language delay showed trend-level increases in ILFL diffusivity and reduced PCC centrality after FDR correction, suggesting possible associations between microstructural and connectomic alterations and language vulnerability.
conclusionBy integrating volumetric and diffusion MRI with graph-based modeling, this study uncovers latent neurobiological markers of SGA and provides clinically interpretable biomarkers for early risk stratification and individualized intervention.
Indexed as
Identifiers
42043701What 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.