ArticleHuman brain mapping2021
Functional connectome fingerprinting: Identifying individuals and predicting cognitive functions via autoencoder.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 55 citations in OpenAlex.
- Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models.bioRxiv : the preprint server for biology · 2026Article
- Neurodiversity in the brain: More variable localization of face regions in autism.bioRxiv : the preprint server for biology · 2026Article
- VarCoNet: A Variability-Aware Self-Supervised Framework for Functional Connectome Extraction From Resting-State fMRI.Human brain mapping · 2026Article
- Unsupervised Representation Learning Generates Differentiable Neurophysiological Profiles.bioRxiv : the preprint server for biology · 2026Article
- Personalized functional network connectivity abnormalities in chronic insomnia disorder.Psychoradiology · 2026Article
- Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences.Nature neuroscience · 2025Article
- Reconstructing brain causal dynamics for subject and task fingerprints using fMRI time-series data.Health information science and systems · 2025Article
- Connectome-based predictive modelling predicts frailty levels in older adults.bioRxiv : the preprint server for biology · 2025Article
- Deep learning-based embedding of functional connectivity profiles for precision functional mapping.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Temporal variability of brain-behavior relationships in fine-scale dynamics of edge time series.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Connectome-based predictive modeling of grip strength: a marker of physical frailty.Frontiers in neuroscience · 2025Article
- A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis.PeerJ. Computer science · 2025Article
- Causality-based Subject and Task Fingerprints using fMRI Time-series Data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2024Article
- Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Integrative, segregative, and degenerate harmonics of the structural connectome.Communications biology · 2024Article
- Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.Statistics in medicine · 2024Article
- Using a deep generation network reveals neuroanatomical specificity in hemispheres.Patterns (New York, N.Y.) · 2024Article
- Discovering individual fingerprints in resting-state functional connectivity using deep neural networks.Human brain mapping · 2024Article
- Resting-state functional connectivity in children cooled for neonatal encephalopathy.Brain communications · 2024Article
- Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as "brain fingerprinting" to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter-subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest-rest pair). Furthermore, high-level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high-order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions.
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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.