ArticleScientific reports2017
An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparison between inferior frontal gyrus intrinsic connectivity network and verb-generation task fMRI network for presurgical language mapping in healthy controls and in glioma patients.Brain imaging and behavior · 2022Pooled it
- Dysconnectivity of Multiple Brain Networks in Schizophrenia: A Meta-Analysis of Resting-State Functional Connectivity.Frontiers in psychiatry · 2019Pooled it
- Spatially constrained ICA from a language task enables sensorimotor network extraction in patients with space-occupying brain lesions.Brain imaging and behavior · 2026Article
- Resting-State vs. Task-Based Functional Magnetic Resonance Imaging in Neurosurgical Planning: A Narrative Review of Clinical Applications.Biomedicines · 2026Review
- Utilizing connectome fingerprinting functional MRI models for motor activity prediction in presurgical planning: A feasibility study.Human brain mapping · 2024Article
- A comparison of fMRI presurgical mapping techniques with intraoperative brain mapping-based validation.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Bipolar and Laplacian montages are suitable for high-gamma modulation language mapping with stereoelectroencephalography.Frontiers in neurology · 2024Article
- Resting-state functional MRI for motor cortex mapping in childhood-onset focal epilepsy.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2022Article
- What Can Resting-State fMRI Data Analysis Explain about the Functional Brain Connectivity in Glioma Patients?Tomography (Ann Arbor, Mich.) · 2022Review
- A precise language network revealed by the independent component-based lesion mapping in post-stroke aphasia.Frontiers in neurology · 2022Article
- Resting-State Functional Magnetic Resonance Imaging for Surgical Neuro-Oncology Planning: Towards a Standardization in Clinical Settings.Brain sciences · 2021Review
- Multivariate machine learning-based language mapping in glioma patients based on lesion topography.Brain imaging and behavior · 2021Article
- Preliminary Evidence of Improvement in Adolescent and Young Adult Cancer Survivors' Brain Health Following Physical Activity: A Proof-of-Concept Sub-Study.Brain plasticity (Amsterdam, Netherlands) · 2021Article
- Canonical Correlation Analysis of Imaging Genetics Data Based on Statistical Independence and Structural Sparsity.IEEE journal of biomedical and health informatics · 2020Article
- Development of Brain Networks In Utero: Relevance for Common Neural Disorders.Biological psychiatry · 2020Review
- Mapping cognitive and emotional networks in neurosurgical patients using resting-state functional magnetic resonance imaging.Neurosurgical focus · 2020Review
- Article
- Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts.NeuroImage · 2019Review
- Resting-state functional connectivity predicts individual language impairment of patients with left hemispheric gliomas involving language network.NeuroImage. Clinical · 2019Article
- A comparison of resting state functional magnetic resonance imaging to invasive electrocortical stimulation for sensorimotor mapping in pediatric patients.NeuroImage. Clinical · 2019Article
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8 authors.
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Abstract
As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.
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