ArticleIEEE transactions on bio-medical engineering2020
A Manifold Regularized Multi-Task Learning Model for IQ Prediction From Two fMRI Paradigms.
Article in IEEE transactions on bio-medical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Improving predictability, reliability, and generalizability of brain-wide associations for cognitive abilities via multimodal stacking.PNAS nexus · 2025Article
- Challenges in multi-task learning for fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would likely require thousands of patients.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Explainable Multimodal Deep Dictionary Learning to Capture Developmental Differences From Three fMRI Paradigms.IEEE transactions on bio-medical engineering · 2023Article
- An explainable autoencoder with multi-paradigm fMRI fusion for identifying differences in dynamic functional connectivity during brain development.Neural networks : the official journal of the International Neural Network Society · 2023Article
- Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics.Neural processing letters · 2023Article
- Individualized prediction of future cognition based on developmental changes in cortical anatomy.Neuroimage. Reports · 2022Article
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies.IEEE transactions on bio-medical engineering · 2022Article
- Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity.Translational psychiatry · 2022Article
- Multi-Modal Imaging Genetics Data Fusion via a Hypergraph-Based Manifold Regularization: Application to Schizophrenia Study.IEEE transactions on medical imaging · 2022Article
- Brain Functional Connectivity Analysis via Graphical Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- Article
- Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis.IEEE transactions on bio-medical engineering · 2021Article
- Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder.Human brain mapping · 2021Article
- Multi-Paradigm fMRI Fusion via Sparse Tensor Decomposition in Brain Functional Connectivity Study.IEEE journal of biomedical and health informatics · 2021Article
- Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns From fMRI Data.IEEE transactions on bio-medical engineering · 2021Article
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5 authors.
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Abstract
objectiveMulti-modal brain functional connectivity (FC) data have shown great potential for providing insights into individual variations in behavioral and cognitive traits. The joint learning of multi-modal data can utilize intrinsic association, and thus can boost learning performance. Although several multi-task based learning models have already been proposed by viewing feature learning on each modality as one task, most of them ignore the structural information inherent across the modalities, which may play an important role in extracting discriminative features.
methodsIn this paper, we propose a new manifold regularized multi-task learning model by simultaneously considering between-subject and between-modality relationships. Specifically, the l
resultsOur model is validated on the Philadelphia Neurodevelopmental Cohort dataset, where our modalities are regarded as two types of functional MRI (fMRI) data collected under two paradigms. We conduct experimental studies on fMRI-based FC network data in two task conditions for intelligence quotient (IQ) prediction. The results show that our proposed model can not only achieve improved prediction performance, but also yield a set of IQ-relevant biomarkers. CONCLUSION AND SIGNIFICANCE: This paper develops a new multi-task learning model, enabling the discovery of significant biomarkers that may account for a proportion of the variance in human intelligence.
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