ArticleIEEE transactions on bio-medical engineering2019
Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction.
Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Complexity in disguise: a systematic review of fractal analysis in psychiatric neuroimaging.European radiology · 2026Pooled it
- Improving predictability, reliability, and generalizability of brain-wide associations for cognitive abilities via multimodal stacking.PNAS nexus · 2025Article
- Multimodal analysis of mother-child interaction using hyperscanning and diffusion maps.Scientific reports · 2025Article
- MJNMF-GAT: Multi-Task Joint Non-Negative Matrix Factorization Graph Attention Network for Understanding Adolescent Neurodevelopment.IEEE access : practical innovations, open solutions · 2025Article
- Parent-child couples display shared neural fingerprints while listening to stories.Scientific reports · 2024Article
- Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity.Translational psychiatry · 2022Article
- Brain Functional Connectivity Analysis via Graphical Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis.IEEE transactions on bio-medical engineering · 2021Article
- Multi-Paradigm fMRI Fusion via Sparse Tensor Decomposition in Brain Functional Connectivity Study.IEEE journal of biomedical and health informatics · 2021Article
- Causality-Based Feature Fusion for Brain Neuro-Developmental Analysis.IEEE transactions on medical imaging · 2020Article
- Bootstrapping promotes the RSFC-behavior associations: An application of individual cognitive traits prediction.Human brain mapping · 2020Article
- A Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms.IEEE transactions on bio-medical engineering · 2020Article
- Multimodal Sparse Classifier for Adolescent Brain Age Prediction.IEEE journal of biomedical and health informatics · 2020Article
- The Relevance of Ecological Transitions to Intelligence in Marine Mammals.Frontiers in psychology · 2020Article
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5 authors.
Funding
Abstract
objectiveTo explain individual differences in development, behavior, and cognition, most previous studies focused on projecting resting-state functional MRI (fMRI) based functional connectivity (FC) data into a low-dimensional space via linear dimensionality reduction techniques, followed by executing analysis operations. However, linear dimensionality analysis techniques may fail to capture the nonlinearity of brain neuroactivity. Moreover, besides resting-state FC, the FC based on task fMRI can be expected to provide complementary information. Motivated by these considerations, we nonlinearly fuse resting-state and task-based FC networks (FCNs) to seek a better representation in this paper.
methodsWe propose a framework based on alternating diffusion map (ADM), which extracts geometry-preserving low-dimensional embeddings that successfully parameterize the intrinsic variables driving the phenomenon of interest. Specifically, we first separately build resting-state and task-based FCNs by symmetric positive definite matrices using sparse inverse covariance estimation for each subject, and then utilize the ADM to fuse them in order to extract significant low-dimensional embeddings, which are used as fingerprints to identify individuals.
resultsThe proposed framework is validated on the Philadelphia Neurodevelopmental Cohort data, where we conduct extensive experimental study on resting-state and fractal n-back task fMRI for the classification of intelligence quotient (IQ). The fusion of resting-state and n-back task fMRI by the proposed framework achieves better classification accuracy than any single fMRI, and the proposed framework is shown to outperform several other data fusion methods. CONCLUSION AND SIGNIFICANCE: To our knowledge, this paper is the first to demonstrate a successful extension of the ADM to fuse resting-state and task-based fMRI data for accurate prediction of IQ.
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