Task-State Functional MRI of Brain Regions with Reduced Activation in Aphasia Patients: A Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Language is one of the most celebrated hallmarks of human cognition. With the continuous improvement of medical technology, functional MRI (fMRI) has been used in aphasia. Although many related studies have been carried out, most studies have not extensively focused on brain regions with reduced activation in aphasic patients. The aim of this study was to identify brain regions normally activated in healthy controls but with reduced activation in aphasic patients during fMRI language tasks. METHODS: We collected all previous task-state fMRI studies of secondary aphasia. The brain regions showed normal activation in healthy controls and reduced activation in aphasic patients were conducted activation likelihood estimation (ALE) meta-analysis to obtain the brain regions with consistently reduced activation in aphasic patients. RESULTS: The ALE meta-analysis revealed that the left inferior frontal gyrus, left middle temporal gyrus, left superior temporal gyrus, left fusiform gyrus, left lentiform nucleus and the culmen of the cerebellum were the brain regions with reduced activation in aphasic patients. DISCUSSION: These findings from the ALE meta-analysis have significant implications for understanding the language network and the potential for recovery of language functions in individuals with aphasia.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.025 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".