ArticleFrontiers in neuroscience2022
Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03241732 (Defining Neurobiological Signatures for Chronic Traumatic Brain Injury Using PET-MRI Technology), which is not on this map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Defining Neurobiological Signatures for Chronic Traumatic Brain Injury Using PET-MRI Technology
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Systematic Review of Artificial Intelligence Applications in Clinical Trials for Central Nervous System Injuries.Current neuropharmacology · 2026Pooled it
- Hybrid Evidence-Informed Synthesis of Resting-State Functional Connectivity Alterations in Mild Traumatic Brain Injury.Brain sciences · 2026Review
- Altered Functional Specialization and Interhemispheric Coordination in Rhegmatogenous Retinal Detachment: Associations With Gene Expression, Neurotransmitter Receptor Distribution, and SVM-SHAP ClassificationCNS neuroscience & therapeutics · 2026Article
- Cortical functional and structural alterations in rhegmatogenous retinal detachment: a multimodal analysis using surface-based independent component analysis, morphometry, and machine learning.Quantitative imaging in medicine and surgery · 2025Article
- Visualizing functional network connectivity differences using an explainable machine-learning method.Physiological measurement · 2025Article
- Animal Models of Traumatic Brain Injury and Their Relevance in Clinical Settings.CNS neuroscience & therapeutics · 2025Review
- Repetitive Mild Closed-Head Injury Induced Synapse Loss and Increased Local BOLD-fMRI Signal Homogeneity.Journal of neurotrauma · 2024Article
- Long Noncoding RNA VLDLR-AS1 Levels in Serum Correlate with Combat-Related Chronic Mild Traumatic Brain Injury and Depression Symptoms in US Veterans.International journal of molecular sciences · 2024Article
- Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging.Frontiers in neuroscience · 2024Article
- Treatment effects of N-acetyl cysteine on resting-state functional MRI and cognitive performance in patients with chronic mild traumatic brain injury: a longitudinal study.Frontiers in neurology · 2024Article
- Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging.Frontiers in neuroscience · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample Clinical trial registration: [clinicaltrials.gov], identifier [NCT03241732].
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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.