ArticleHuman brain mapping2019
Refined measure of functional connectomes for improved identifiability and prediction.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences.Nature neuroscience · 2025Article
- Connectome-based predictive modelling predicts frailty levels in older adults.bioRxiv : the preprint server for biology · 2025Article
- Connectome-based predictive modeling of grip strength: a marker of physical frailty.Frontiers in neuroscience · 2025Article
- Single scan, subject-specific component extraction in dynamic functional connectivity using dictionary learning.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data.GeroScience · 2024Article
- Explainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment.Applied sciences (Basel, Switzerland) · 2024Article
- Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.ArXiv · 2023Article
- Enhancing the network specific individual characteristics in rs-fMRI functional connectivity by dictionary learning.Human brain mapping · 2023Article
- Article
- Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies.IEEE transactions on bio-medical engineering · 2022Article
- Functional connectomes incorporating phase synchronization for the characterization and prediction of individual differences.Journal of neuroscience methods · 2022Article
- Refined prefrontal working memory network as a neuromarker for Alzheimer's disease.Biomedical optics express · 2021Article
- 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
- Refined measure of functional connectomes for improved identifiability and prediction.Human brain mapping · 2019Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Brain functional connectome analysis is commonly based on population-wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population-wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject-specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group-wise inferences and refined connectomes are indeed desirable for brain mapping.
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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.