The pathophysiology of post-stroke aphasia: A network approach
Bibliographic record
Abstract
BACKGROUND: Post-stroke aphasia syndromes as a clinical entity arise from the disruption of brain networks specialized in language production and comprehension due to permanent focal ischemia. This approach to post-stroke aphasia is based on two pathophysiological concepts: 1) Understanding language processing in terms of distributed networks rather than language centers and 2) understanding the molecular pathophysiology of ischemic brain injury as a dynamic process beyond the direct destruction of network centers and their connections. While considerable progress has been made in the past 10 years to develop such models on a systems as well as a molecular level, the influence of these approaches on understanding and treating clinical aphasia syndromes has been limited. OBJECTIVE & METHODS: In this article, we review current pathophysiological concepts of ischemic brain injury, their relationship to altered information processing in language networks after ischemic stroke and how these mechanisms may be influenced therapeutically to improve treatment of post-stroke aphasia. CONCLUSION: Understanding the pathophysiological mechanism of post-stroke aphasia on a neurophysiological systems level as well as on the molecular level becomes more and more important for aphasia treatment, as the field moves from standardized therapies towards more targeted individualized treatment strategies comprising behavioural therapies as well as non-invasive brain stimulation (NIBS).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".