ArticleBrain communications2021
Dynamic connectivity predicts acute motor impairment and recovery post-stroke.
Article in Brain communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 32 citations in OpenAlex.
- The relevance of rich club regions for functional outcome post-stroke is enhanced in women.Human brain mapping · 2023Trial
- Motor network reorganization after motor imagery training in stroke patients with moderate to severe upper limb impairment.CNS neuroscience & therapeutics · 2023Trial
- Motor-related reorganization in left inferior frontal gyrus after subcortical stroke: neurochemical basis and response to targeted neuromodulation.BMC medicine · 2026Article
- Circulating IncRNAs and Their Association With Ischemic Stroke Severity and Prognosis: A Systematic Review and Meta-Analysis.Health science reports · 2026Review
- Attenuated sensorimotor hemodynamic responses and altered cortical dynamics during passive proprioceptive-related stimulation after stroke: a multidimensional fNIRS study.Frontiers in neurology · 2026Article
- Intermittent theta burst stimulation vs. high-frequency repetitive transcranial magnetic stimulation for post-stroke dysfunction: a Bayesian model-based network meta-analysis of RCTs.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Identification of a cognitive network with effective connectivity to post-stroke cognitive impairment.Cognitive neurodynamics · 2024Article
- Exploring the Relationship between Abnormal Communication Efficiency of Cerebral Cortex and Multiple Cognitive Functions in Mild Subcortical Stroke: A Resting-State fMRI Study.Brain sciences · 2024Article
- Reorganization of Dynamic Network in Stroke Patients and Its Potential for Predicting Motor Recovery.Neural plasticity · 2024Article
- Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity.Brain communications · 2024Article
- Dynamic brain states in spatial neglect after stroke.Frontiers in systems neuroscience · 2023Article
- Prediction of post-stroke motor recovery benefits from measures of sub-acute widespread network damages.Brain communications · 2023Article
- Neuroimaging Techniques as Potential Tools for Assessment of Angiogenesis and Neuroplasticity Processes after Stroke and Their Clinical Implications for Rehabilitation and Stroke Recovery Prognosis.Journal of clinical medicine · 2022Review
- Regional homogeneity alterations in multifrequency bands in patients with basal ganglia stroke: A resting-state functional magnetic resonance imaging study.Frontiers in aging neuroscience · 2022Article
- Suboptimal states and frontoparietal network-centered incomplete compensation revealed by dynamic functional network connectivity in patients with post-stroke cognitive impairment.Frontiers in aging neuroscience · 2022Article
- Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke.Human brain mapping · 2021Article
- A review of the impact of circadian rhythm on motor function in stroke recovery patients.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We here built random forest classifier-based prediction models of acute motor impairment and recovery post-stroke. Predictions relied on structural and resting-state fMRI data from 54 stroke patients scanned within the first days of symptom onset. Functional connectivity was estimated via static and dynamic approaches. Motor performance was phenotyped in the acute phase and 6 months later. A model based on the time spent in specific dynamic connectivity configurations achieved the best discrimination between patients with and without motor impairments (out-of-sample area under the curve, 95% confidence interval: 0.67 ± 0.01). In contrast, patients with moderate-to-severe impairments could be differentiated from patients with mild deficits using a model based on the variability of dynamic connectivity (0.83 ± 0.01). Here, the variability of the connectivity between ipsilesional sensorimotor cortex and putamen discriminated the most between patients. Finally, motor recovery was best predicted by the time spent in specific connectivity configurations (0.89 ± 0.01) in combination with the initial impairment. Here, better recovery was linked to a shorter time spent in a functionally integrated configuration. Dynamic connectivity-derived parameters constitute potent predictors of acute impairment and recovery, which, in the future, might inform personalized therapy regimens to promote stroke recovery.
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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.