Usefulness of language and memory testing during intracarotid amobarbital testing: observations from an fMRI study
Bibliographic record
Abstract
BACKGROUND: Several procedures for testing language lateralization and memory function exist during the intracarotid amobarbital test (IAT). The use of functional magnetic resonance imaging (fMRI) gives the opportunity to assess the validity of some of these procedures, or at least to inspect the neuronal correlates. A comprehensive fMRI protocol was tested, aimed at addressing aspects of lateralization of language, as well as testing memory in relation to activation of mesiotemporal regions. Here we report observations with possible consequences for the current IAT procedures. MATERIALS AND METHODS: The protocol consisted of three language tasks (overt naming, semantic decision and silent word generation) and two memory tasks (encoding and retrieving visual scenes). The paradigms used a block-related procedure in nine right-handed normal volunteers. During the procedure dynamic weighted full brain images were acquired which are sensitive to the blood oxygenation activation effect. RESULTS: Encoding showed symmetrical bilateral activation in the mesiotemporal regions, specifically the hippocampus, parahippocampal gyrus and fusiform gyrus. With a retrieval task activation of the mesiotemporal areas was restricted to the posterior hippocampal area. Overt object naming showed results, similar to encoding tasks with bilateral activation of hippocampal areas. Silent word generation showed much stronger ability to lateralize than the other two language-related tasks and especially object naming. CONCLUSION: Activation revealed by fMRI activation shows that IAT procedures, using active semantic language processing or comprehensive procedures with multiple language tasks have the highest guarantee for individual activation lateralization. Simple object naming does not guarantee a lateralized language fMRI activation pattern. Of the different memory procedures during IAT, the procedures (Interview and the Montreal) demanding encoding processing will be related to larger areas of bilateral hippocampal activation than procedures (Seattle) exclusively requiring retrieval. Moreover, tasks using recognition of previously presented language items (naming objects) are equally effective for assessing hippocampal activation compared with presenting separate memory items.
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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 | 0.002 |
| 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) | 0.001 | 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".