ArticleHuman brain mapping2021
Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease.
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 27 papers.
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Who cites it
27 citing papers in PubMed.
- In-scanner thoughts contribute to resting-state functional connectivity.Nature communications · 2026Article
- A connectome-based neural correlate of pediatric ADHD hyperactivity-impulsivity symptoms.Frontiers in psychiatry · 2026Article
- Integrating individualized connectome with amyloid pathology improves predictive modeling of future cognitive decline.Communications medicine · 2025Article
- Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences.Nature neuroscience · 2025Article
- Uncovering longitudinal changes in the brain functional connectome along the migraine cycle: a multilevel clinical connectome fingerprinting framework.The journal of headache and pain · 2025Article
- TR(Acking) Individuals Down: Exploring the Effect of Temporal Resolution in Resting-State Functional MRI Fingerprinting.Human brain mapping · 2025Article
- A whole-brain functional connectivity model of Alzheimer's disease pathology.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Tangent space functional reconfigurations in individuals at risk for alcohol use disorder.Network neuroscience (Cambridge, Mass.) · 2025Article
- Fingerprints of brain disease: connectome identifiability in Alzheimer's disease.Communications biology · 2024Article
- In-Scanner Thoughts shape Resting-state Functional Connectivity: how participants "rest" matters.bioRxiv : the preprint server for biology · 2024Article
- PRESERVING HUMAN LARGE-SCALE BRAIN CONNECTIVITY FINGERPRINT IDENTIFIABILITY WITH RANDOM PROJECTIONS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Edge time series components of functional connectivity and cognitive function in Alzheimer's disease.Brain imaging and behavior · 2024Article
- Brain fingerprint and subjective mood state across the menstrual cycle.Frontiers in neuroscience · 2024Article
- Edge Time Series Components of Functional Connectivity and Cognitive Function in Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2023Article
- Article
- Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.ArXiv · 2023Article
- Article
- Identifying vulnerable brain networks associated with Alzheimer's disease risk.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Uncovering individual differences in fine-scale dynamics of functional connectivity.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis.NeuroImage. Clinical · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
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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.