ArticleNeuroImage. Clinical2020
Structural and functional connectivity of motor circuits after perinatal stroke: A machine learning study.
Article in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Exploring the potential of machine learning and magnetic resonance imaging in early stroke diagnosis: a bibliometric analysis (2004-2023).Frontiers in neurology · 2025Pooled it
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- Research progress on the mechanisms and clinical applications of neuromodulation techniques for intervening in interhemispheric imbalance after stroke.Frontiers in neurology · 2026Review
- Functional Connectivity of Hippocampal Circuits and Visual Memory Function in Children and Adolescents With Perinatal Stroke.Human brain mapping · 2025Article
- Thalamic oscillatory dysrhythmia and disrupted functional connectivity in thalamocortical loops in perinatal stroke.Scientific reports · 2025Article
- Effects of tactile feedback in post-stroke hand rehabilitation on functional connectivity and cortical activation: an fNIRS study.Biomedical optics express · 2025Article
- Somatosensory-Thalamic Functional Dysconnectivity Associated With Poststroke Motor Function Rehabilitation: A Resting-State fMRI Study.Brain and behavior · 2025Article
- The mouse motor system contains multiple premotor areas and partially follows human organizational principles.Cell reports · 2024Article
- Executive functioning, ADHD symptoms and resting state functional connectivity in children with perinatal stroke.Brain imaging and behavior · 2024Article
- Artificial intelligence applied in acute ischemic stroke: from child to elderly.La Radiologia medica · 2024Review
- Alterations in cortical morphometry of the contralesional hemisphere in children, adolescents, and young adults with perinatal stroke.Scientific reports · 2023Article
- Structural connectivity of the sensorimotor network within the non-lesioned hemisphere of children with perinatal stroke.Scientific reports · 2022Article
- Cognitive outcome is related to functional thalamo-cortical connectivity after paediatric stroke.Brain communications · 2022Article
- Hemodynamic MRI parameters to predict asymptomatic unilateral carotid artery stenosis with random forest machine learning.Frontiers in neuroimaging · 2022Article
- Bihemispheric developmental alterations in basal ganglia volumes following unilateral perinatal stroke.NeuroImage. Clinical · 2022Article
- Task-State Cortical Motor Network Characteristics by Functional Near-Infrared Spectroscopy in Subacute Stroke Show Hemispheric Dominance.Frontiers in aging neuroscience · 2022Article
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Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Developmental neuroplasticity allows young brains to adapt via experiences early in life and also to compensate after injury. Why certain individuals are more adaptable remains underexplored. Perinatal stroke is an ideal human model of neuroplasticity with focal lesions acquired near birth in a healthy brain. Machine learning can identify complex patterns in multi-dimensional datasets. We used machine learning to identify structural and functional connectivity biomarkers most predictive of motor function. Forty-nine children with perinatal stroke and 27 controls were studied. Functional connectivity was quantified by fluctuations in blood oxygen-level dependent (BOLD) signal between regions. White matter tractography of corticospinal tracts quantified structural connectivity. Motor function was assessed using validated bimanual and unimanual tests. RELIEFF feature selection and random forest regression models identified predictors of each motor outcome using neuroimaging and demographic features. Unilateral motor outcomes were predicted with highest accuracy (8/54 features r = 0.58, 11/54 features, r = 0.34) but bimanual function required more features (51/54 features, r = 0.38). Connectivity of both hemispheres had important roles as did cortical and subcortical regions. Lesion size, age at scan, and type of stroke were predictive but not highly ranked. Machine learning regression models may represent a powerful tool in identifying neuroimaging biomarkers associated with clinical motor function in perinatal stroke and may inform personalized targets for neuromodulation.
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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.