ArticleBrain imaging and behavior2019
The utility of lesion classification in predicting language and treatment outcomes in chronic stroke-induced aphasia.
Article in Brain imaging and behavior, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01927302 (The Center for the Neurobiology of Language Recovery), which is not on this map. Cited by 38 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.
The Center for the Neurobiology of Language Recovery (CNLR)
Who cites it
38 citing papers in PubMed, 53 citations in OpenAlex.
- Predicting Outcomes of Language Rehabilitation: Prognostic Factors for Immediate and Long-Term Outcomes After Aphasia Therapy.Journal of speech, language, and hearing research : JSLHR · 2023Trial
- Language Recovery after Brain Injury: A Structural Network Control Theory Study.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Trial
- Scalp acupuncture coupled with speech-language therapy modulates the ventral-dorsal structural brain network in patients with post-stroke anomia: a randomized clinical and DTI neuroimaging trial.Chinese medicine · 2026Article
- Diffusion Tensor Imaging and Advanced Diffusion Imaging in Post-Stroke Aphasia Recovery.Tomography (Ann Arbor, Mich.) · 2026Review
- Gene Expression-Based Lesion-Symptom Mapping:The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Network flexibility facilitates treatment-induced recovery in post-stroke aphasia.Brain communications · 2026Article
- The association between nonverbal fluency and anomia treatment outcomes in people with post-stroke aphasia.Frontiers in human neuroscience · 2026Article
- Article
- Comparison of Large Language Model with Aphasia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Aphasia rehabilitation: a narrative review of adjuvant techniques.Frontiers in human neuroscience · 2025Review
- Neural Predictors for the Generalization of Semantic and Phonological Treatment to Discourse Performance in Chronic Post-Stroke Aphasia.Neurobiology of language (Cambridge, Mass.) · 2025Article
- The role of language-related functional brain regions and white matter tracts in network plasticity of post-stroke aphasia.Journal of neurology · 2024Review
- Resting-state brain network connectivity is an independent predictor of responsiveness to language therapy in chronic post-stroke aphasia.Cortex; a journal devoted to the study of the nervous system and behavior · 2024Article
- Investigating silent pauses in connected speech: integrating linguistic, neuropsychological, and neuroanatomical perspectives across narrative tasks in post-stroke aphasia.Frontiers in neurology · 2024Article
- Metric comparison of connectome-based lesion-symptom mapping in post-stroke aphasia.Brain communications · 2024Article
- Pars Opercularis Underlies Efferent Predictions and Successful Auditory Feedback Processing in Speech: Evidence From Left-Hemisphere Stroke.Neurobiology of language (Cambridge, Mass.) · 2024Article
- Cerebral Small Vessel Disease Burden: An Independent Biomarker for Anomia Treatment Responsiveness in Chronic Stroke Patients With Aphasia.Archives of physical medicine and rehabilitation · 2023Article
- Improved prediction of glioma-related aphasia by diffusion MRI metrics, machine learning, and automated fiber bundle segmentation.Human brain mapping · 2023Article
- Dynamic network properties of the superior temporal gyrus mediate the impact of brain age gap on chronic aphasia severity.Communications biology · 2023Article
- Corticocerebellar White Matter Integrity Is Related to Naming Outcome in Post-Stroke Aphasia.Neurobiology of language (Cambridge, Mass.) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Stroke recovery models can improve prognostication of therapy response in patients with chronic aphasia, yet quantifying the effect of lesion on recovery is challenging. This study aimed to evaluate the utility of lesion classification via gray matter (GM)-only versus combined GM plus white matter (WM) metrics and to determine structural measures associated with aphasia severity, naming skills, and treatment outcomes. Thirty-four patients with chronic aphasia due to left hemisphere infarct completed T1-weighted and DTI scans and language assessments prior to receiving a 12-week naming treatment. GM metrics included the amount of spared tissue within five cortical masks. WM integrity was indexed by spared tissue and fractional anisotropy (FA) from four homologous left and right association tracts. Clustering of GM-only and GM + WM metrics via k-medoids yielded four patient clusters that captured two lesion characteristics, size and location. Linear regression models revealed that both GM-only and GM + WM clustering predicted baseline aphasia severity and naming skills, but only GM + WM clustering predicted treatment outcomes. Spearman correlations revealed that without controlling for lesion volume, the majority of left hemisphere metrics were related to language measures. However, adjusting for lesion volume, no relationships with aphasia severity remained significant. FA from two ventral left WM tracts was related to naming and treatment success, independent of lesion size. In sum, lesion volume and GM metrics are sufficient predictors of overall aphasia severity in patients with chronic stroke, whereas diffusion metrics reflecting WM tract integrity may add predictive power to language recovery outcomes after rehabilitation.
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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.