Functional MRI Study of Verbal Fluency in a Patient with Subcortical Laminar Heterotopia
Bibliographic record
Abstract
RATIONALE: Double cortex syndrome is a malformation in which there is a band of subcortical heterotopic grey matter separated from the cortex by white matter. The functional activity of the heterotopic neurons is unclear. PATIENT: A 13-year-old female was evaluated for seizures. The EEG showed bifrontal spike wave disturbance. Band heterotopia, in association with mild reduction of sulcation of the cerebral hemispheres, was found on MRI. Psychological assessment indicated the presence of variable cognitive abilities, with verbal IQ [82] generally better than nonverbal IQ [59], and specific difficulties in language comprehension and mathematics. METHOD: Functional MRI was used to localize the areas of language and motor activation. The language activation paradigm was a visual verb generation task with a visual fixation baseline. The motor paradigm consisted of alternating blocks of sequential finger tapping and rest. Coronal functional and anatomical images were obtained. RESULTS: The motor paradigm produced activation of the primary motor cortex, the band heterotopia and the supplementary motor cortex. The language paradigm produced activation of the left inferior frontal gyrus and left supplementary motor area, but not of the band heterotopia. CONCLUSIONS: The activation of heterotopic grey matter during a motor task demonstrates a hemodynamic association with motor activity and suggests that this tissue may be functional. Such association was not seen with the language task. We speculate that later maturing functions such as language are restricted in their development to the normal situated superficial cortex in our patient.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".