Specific disruption of the ventral anterior temporo-frontal network reveals key implications for language comprehension and cognition
Bibliographic record
Abstract
Recent investigations have raised the question of the role of the anterior lateral temporal cortex in language processing (ventral language network). Here we present the language and overall cognitive performance of a rare male patient with chronic middle cerebral artery cerebrovascular accident with a well-documented lesion restricted to the anterior temporal cortex and its connections via the extreme capsule with the pars triangularis of the inferior frontal gyrus (i.e. Broca's region). The performance of this unique patient is compared with that of two chronic middle cerebral artery cerebrovascular accident male patients with damage to the classic dorsal posterior temporo-parietal language system. Diffusion tensor imaging is used to reconstruct the relevant white matter tracts of the three patients, which are also compared with those of 10 healthy individuals. The patient with the anterior temporo-frontal lesion presents with flawless and fluent speech, but selective impairment in accessing lexico-semantic information, in sharp contrast to the impairments in speech, sentence comprehension and repetition observed after lesions to the classic dorsal language system. The present results underline the contribution of the ventral language stream in lexico-semantic processing and higher cognitive functions, such as active selective controlled retrieval.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".