ArticleBrain communications2020
Evaluating the granularity and statistical structure of lesions and behaviour in post-stroke aphasia.
Article in Brain communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 36 citations in OpenAlex.
- The Influence of Contralesional Brain Age Gradients on Chronic Stroke Aphasia Severity and Treatment Response.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Abnormalities in brain white matter structural networks associated with language function of post-stroke aphasia.Brain imaging and behavior · 2026Article
- How object naming dissociates from repetition and comprehension impairments when post stroke aphasia is less severe.Scientific reports · 2026Article
- Semantic and Phonological Brain Networks in Older Adults: A Systematic Scoping Review.Brain sciences · 2026Review
- Using unsupervised dimensionality reduction to identify lesion patterns predictive of post-stroke aphasia severity.Cortex; a journal devoted to the study of the nervous system and behavior · 2025Article
- Data-driven classification of narrative speech characteristics in stroke aphasia distinguishes neurological and strategic contributions.Cortex; a journal devoted to the study of the nervous system and behavior · 2025Article
- Neurotransmitters' white matter mapping unveils the neurochemical fingerprints of stroke.Nature communications · 2025Article
- Protocol for detection and monitoring of post-stroke cognitive impairment through AI-powered speech analysis: a mixed methods pilot study.Frontiers in aging neuroscience · 2025Article
- Connected Speech Fluency in Poststroke and Progressive Aphasia: A Scoping Review of Quantitative Approaches and Features.American journal of speech-language pathology · 2024Article
- Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers.Brain communications · 2024Article
- Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes.bioRxiv : the preprint server for biology · 2023Article
- Interpreting and validating complexity and causality in lesion-symptom prognoses.Brain communications · 2023Review
- Brain Areas Critical for Picture Naming: A Systematic Review and Meta-Analysis of Lesion-Symptom Mapping Studies.Neurobiology of language (Cambridge, Mass.) · 2023Article
- Structural disconnections associated with language impairments in chronic post-stroke aphasia using disconnectome maps.Cortex; a journal devoted to the study of the nervous system and behavior · 2022Article
- Dissociable language and executive control deficits and recovery in post-stroke aphasia: An exploratory observational and case series study.Neuropsychologia · 2022Observational
- The multidimensional nature of aphasia recovery post-stroke.Brain : a journal of neurology · 2022Article
- Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia.Stroke · 2022Article
- The cognitive and neural underpinnings of discourse coherence in post-stroke aphasia.Brain communications · 2022Article
- Reclassifying stroke lesion anatomy.Cortex; a journal devoted to the study of the nervous system and behavior · 2021Article
- Content Word Production during Discourse in Aphasia: Deficits in Word Quantity, Not Lexical-Semantic Complexity.Journal of cognitive neuroscience · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The pursuit of relating the location of neural damage to the pattern of acquired language and general cognitive deficits post-stroke stems back to the 19th century behavioural neurology. While spatial specificity has improved dramatically over time, from the large areas of damage specified by post-mortem investigation to the millimetre precision of modern MRI, there is an underlying issue that is rarely addressed, which relates to the fact that damage to a given area of the brain is not random but constrained by the brain's vasculature. Accordingly, the aim of this study was to uncover the statistical structure underlying the lesion profile in chronic aphasia post-stroke. By applying varimax-rotated principal component analysis to the lesions of 70 patients with chronic post-stroke aphasia, we identified 17 interpretable clusters, largely reflecting the vascular supply of middle cerebral artery sub-branches and other sources of individual variation in vascular supply as shown in classical angiography studies. This vascular parcellation produced smaller displacement error in simulated lesion-symptom analysis compared with individual voxels and Brodmann regions. A second principal component analysis of the patients' detailed neuropsychological data revealed a four-factor solution reflecting phonological, semantic, executive-demand and speech fluency abilities. As a preliminary exploration, stepwise regression was used to relate behavioural factor scores to the lesion principal components. Phonological ability was related to two components, which covered the posterior temporal region including the posterior segment of the arcuate fasciculus, and the inferior frontal gyrus. Three components were linked to semantic ability and were located in the white matter underlying the anterior temporal lobe, the supramarginal gyrus and angular gyrus. Executive-demand related to two components covering the dorsal edge of the middle cerebral artery territory, while speech fluency was linked to two components that were located in the middle frontal gyrus, precentral gyrus and subcortical regions (putamen and thalamus). Future studies can explore in formal terms the utility of these principal component analysis-derived lesion components for relating post-stroke lesions and symptoms.
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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.