ArticleNeuroImage. Clinical2018
Dynamic functional network connectivity discriminates mild traumatic brain injury through machine learning.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 97 citations in OpenAlex.
- Functional Connectivity Changes on Resting-State fMRI after Mild Traumatic Brain Injury: A Systematic Review.AJNR. American journal of neuroradiology · 2024Pooled it
- Mild and repetitive mild traumatic brain injury: Changes in microglial cells and synapses.Neural regeneration research · 2026Article
- Impact frequency and interval modulate brain network outcomes in a rat model of repetitive mild traumatic brain injury.Scientific reports · 2026Article
- Time-resolved directional organization of brain networks: distinct strength and temporal signatures from neurophysiology to disease.bioRxiv : the preprint server for biology · 2026Article
- Dynamic Fusion of Genomics and Functional Network Connectivity in UK Biobank Reveals Schizophrenia-Related SNP Manifolds.Human brain mapping · 2026Article
- Channel Capacity for Time-Resolved Effective Connectivity in Functional Neuroimaging.bioRxiv : the preprint server for biology · 2026Article
- Time-Varying Directed Interactions in Functional Brain Networks: Modeling and Validation.ArXiv · 2026Article
- Aberrant dynamic functional network connectivity in patients with diffuse axonal injury.Scientific reports · 2024Article
- Potential Applications and Ethical Considerations for Artificial Intelligence in Traumatic Brain Injury Management.Biomedicines · 2024Review
- A Comprehensive and Broad Approach to Resting-State Functional Connectivity in Adult Patients with Mild Traumatic Brain Injury.AJNR. American journal of neuroradiology · 2024Article
- Aberrant dynamic properties of whole-brain functional connectivity in acute mild traumatic brain injury revealed by hidden Markov models.CNS neuroscience & therapeutics · 2024Article
- Discovering individual fingerprints in resting-state functional connectivity using deep neural networks.Human brain mapping · 2024Article
- Adaptive spatial-temporal neural network for ADHD identification using functional fMRI.Frontiers in neuroscience · 2024Article
- Predicting shock-induced cavitation using machine learning: implications for blast-injury models.Frontiers in bioengineering and biotechnology · 2024Article
- Article
- Traumatic Brain Injury Rehabilitation Outcome Prediction Using Machine Learning Methods.Archives of rehabilitation research and clinical translation · 2023Article
- How brain networks tic: Predicting tic severity through rs-fMRI dynamics in Tourette syndrome.Human brain mapping · 2023Article
- Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease.Brain connectivity · 2023Article
- Chronic Mild Traumatic Brain Injury: Aberrant Static and Dynamic Connectomic Features Identified Through Machine Learning Model Fusion.Neuroinformatics · 2023Article
- Dynamic phase-locking states and personality in sub-acute mild traumatic brain injury: An exploratory study.PloS one · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Mild traumatic brain injury (mTBI) can result in symptoms that affect a person's cognitive and social abilities. Improvements in diagnostic methodologies are necessary given that current clinical techniques have limited accuracy and are solely based on self-reports. Recently, resting state functional network connectivity (FNC) has shown potential as an important imaging modality for the development of mTBI biomarkers. The present work explores the use of dynamic functional network connectivity (dFNC) for mTBI detection. Forty eight mTBI patients (24 males) and age-gender matched healthy controls were recruited. We identified a set of dFNC states and looked at the possibility of using each state to classify subjects in mTBI patients and healthy controls. A linear support vector machine was used for classification and validated using leave-one-out cross validation. One of the dFNC states achieved a high classification performance of 92% using the area under the curve method. A series of
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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.