ArticleJournal of neuroscience methods2020
Examining brain maturation during adolescence using graph Laplacian learning based Fourier transform.
Article in Journal of neuroscience methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Graph Laplacian Learning with Exponential Family Noise.IEEE transactions on signal and information processing over networks · 2025Article
- DFC-Igloo: A dynamic functional connectome learning framework for identifying neurodevelopmental biomarkers in very preterm infants.Computer methods and programs in biomedicine · 2024Article
- Machine Learning and Graph Signal Processing Applied to Healthcare: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Dynamic weighted hypergraph convolutional network for brain functional connectome analysis.Medical image analysis · 2023Article
- Front and center: Maturational dysregulation of frontal lobe functional neuroanatomic connections in attention deficit hyperactivity disorder.Frontiers in neuroanatomy · 2022Article
- Functional network estimation using multigraph learning with application to brain maturation study.Human brain mapping · 2021Article
- Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns From fMRI Data.IEEE transactions on bio-medical engineering · 2021Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundLongitudinal neuroimaging studies have demonstrated that adolescence is a crucial developmental period of continued brain growth and change. Motivated by both achievements in graph signal processing and recent evidence that some brain areas act as hubs connecting functionally specialized systems, we propose an approach to detect these regions from a spectral analysis perspective. In particular, as the human brain undergoes substantial development throughout adolescence, we evaluate functional network difference among age groups from functional magnetic resonance imaging (fMRI) measurements. NEW
methodsWe treated these measurements as graph signals defined on the parcellated functional brain regions and proposed a graph Laplacian learning based Fourier transform (GLFT) to transform the original graph signals into the frequency domain. Eigen-analysis was conducted afterwards to study the behaviors of the corresponding brain regions, which enabled the characterization of brain maturation.
resultWe first evaluated our method on the synthetic data and then applied it to resting state and task fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) dataset, comprised of normally developing adolescents from 8 to 22 years of age. The method provided an accuracy of 94.9% in distinguishing different adolescent stages and we detected 13 hubs from resting state fMRI and 16 hubs from task fMRI related to brain maturation. COMPARISON WITH EXISTING
methodsThe proposed GLFT demonstrated its superiority over conventional graph Fourier transform and alternative graph Fourier transform with high predictive power.
conclusionThe method provides a powerful approach for extracting brain connectivity patterns and identifying hub regions.
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