ArticleJournal of neurosurgery2023
Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.
Article in Journal of neurosurgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 11 citations in OpenAlex.
- Resting-State vs. Task-Based Functional Magnetic Resonance Imaging in Neurosurgical Planning: A Narrative Review of Clinical Applications.Biomedicines · 2026Review
- Predicting progression-free survival in glioblastoma with neuroimaging and machine learning.Journal of neuro-oncology · 2026Article
- Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances.Surgical neurology international · 2026Review
- Decision Support Systems in Neurosurgery: Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Diagnostic, Therapeutic, and Prognostic Applications of Artificial Intelligence (AI) in the Clinical Management of Brain Metastases (BMs).Brain sciences · 2025Review
- Longitudinal changes in brain connectivity correlate with neuropsychological testing in brain tumor resection patients.Frontiers in neuroscience · 2025Article
- The promise of precision functional mapping for neuroimaging in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Preoperative mapping techniques for brain tumor surgery: a systematic review.Frontiers in oncology · 2024Review
- Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice.Frontiers in neurology · 2024Article
- Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.Journal of neuro-oncology · 2023Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
Abstract
objectiveResting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data.
methodsImaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme.
resultsThe trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations.
conclusionsFunctional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.
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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.