ArticleHuman brain mapping2015
Exploring the brain's structural connectome: A quantitative stroke lesion-dysfunction mapping study.
Article in Human brain mapping, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review.Journal of neuroengineering and rehabilitation · 2022Pooled it
- Infarct-related structural disconnection and delirium in surgical aortic valve replacement patients.Annals of clinical and translational neurology · 2024Trial
- Early effect of thrombolysis on structural brain network organisation after anterior-circulation stroke in the randomized WAKE-UP trial.Human brain mapping · 2022Trial
- Preserved structural connectivity mediates the clinical effect of thrombolysis in patients with anterior-circulation stroke.Nature communications · 2021Trial
- Resting-state connectivity and task-based cortical response in post-stroke executive dysfunction: A fNIRS study.Neuroimage. Reports · 2025Article
- Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies.Brain sciences · 2024Review
- Predicting Language Function Post-Stroke: A Model-Based Structural Connectivity Approach.Neurorehabilitation and neural repair · 2024Article
- Metric comparison of connectome-based lesion-symptom mapping in post-stroke aphasia.Brain communications · 2024Article
- Predicting Motor Outcomes Using Atlas-Based Voxel Features of Post-Stroke Neuroimaging: A Scoping Review.Neurorehabilitation and neural repair · 2023Article
- Disruptions in Structural and Functional Connectivity Relate to Poststroke Fatigue.Brain connectivity · 2023Article
- The sequence of regional structural disconnectivity due to multiple sclerosis lesions.Brain communications · 2023Article
- Preserved anatomical bypasses predict variance in language functions after stroke.Cortex; a journal devoted to the study of the nervous system and behavior · 2022Article
- Cross-validation of predictive models for functional recovery after post-stroke rehabilitation.Journal of neuroengineering and rehabilitation · 2022Article
- Effects of Optimized Acupuncture and Moxibustion Treatment on Depressive Symptoms and Executive Functions in Patients With Post-Stroke Depression: Study Protocol for a Randomized Controlled Trial.Frontiers in neurology · 2022Article
- Machine learning-based multimodal prediction of language outcomes in chronic aphasia.Human brain mapping · 2021Article
- Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis.Frontiers in neuroscience · 2021Article
- Predictors of Function, Activity, and Participation of Stroke Patients Undergoing Intensive Rehabilitation: A Multicenter Prospective Observational Study Protocol.Frontiers in neurology · 2021Article
- Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions.NeuroImage. Clinical · 2021Article
- Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups.NeuroImage. Clinical · 2021Article
- Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke.Neurorehabilitation and neural repair · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The aim of this work was to quantitatively model cross-sectional relationships between structural connectome disruptions caused by cerebral infarction and measures of clinical performance. Imaging biomarkers of 41 ischemic stroke patients (72.0 ± 12.0 years, 20 female) were related to their baseline performance in 18 cognitive, physical and daily life activity assessments. Individual estimates of structural connectivity disruption in gray matter regions were computed using the Change in Connectivity (ChaCo) score. ChaCo scores were utilized because they can be calculated using routinely collected clinical magnetic resonance imagings. Partial Least Squares Regression (PLSR) was used to predict various acute impairment and activity measures from ChaCo scores and patient demographics. Statistical methods of cross-validation, bootstrapping and multiple comparisons correction were implemented to minimize over-fitting and Type I errors. Multiple linear regression models based on lesion volume and lateralization information were constructed for comparison. All models based on connectivity disruption had lower Akaike Information Criterion and almost all had better goodness-of-fit values (R(2) : 0.26-0.92) than models based on lesion characteristics (R(2) : 0.06-0.50). Confidence intervals of PLSR coefficients identified brain regions important in predicting each clinical assessment. Appropriate mapping of eloquent functions, that is, language and motor, and replication of results across pathologies provided validation of this method. Models of complex functions provided new insights into brain-behavior relationships. In addition to the potential applications in prognostication and rehabilitation development, this quantitative approach provides insight into the structural networks underlying complex functions like activities of daily living and cognition. Quantitative analysis of big data will be invaluable in understanding complex brain-behavior relationships.
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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.