Jargonaphasia as a disconnection syndrome: A study combining white matter electrical stimulation and disconnectome mapping
Bibliographic record
Abstract
BACKGROUND: In jargonaphasia, speech is fluent but meaningless. While neuropsychological evaluation may distinguish a neologistic component characterised by non-word production and a semantic component where pronounced words are real but speech is senseless, how this relates to the underlying white matter anatomy is debated. OBJECTIVE: To identify white matter pathways causally involved in jargonaphasia. METHODS: We retrospectively screened the intraoperative brain mapping data of 571 awake oncological resections using direct cortico-subcortical electrostimulation. Jargonaphasia was induced in 17 patients (19 sites) during a naming task. Stimulation sites were normalized to the Montreal Neurological Institute template space and used to generate individual disconnectome maps. Non-parametric voxelwise one and two sample t-tests were performed to identify the underlying white matter anatomy. RESULTS: Jargonaphasia was induced only during stimulation of the left hemisphere. No cortical stimulation generated jargonaphasia. Subcortical sites causally associated with jargonaphasia clustered in 3 regions: in the temporal lobe (middle to inferior temporal gyri; n = 12), in the parietal lobe (supramarginal gyrus; n = 3) and in the temporal stem (n = 4). Disconnectome analysis indicated the inferior-fronto-occipital fasciculus (IFOF) was damaged in both neologistic and semantic jargonaphasia, while the involvement of the arcuate fasciculus was specific to neologistic jargonaphasia. CONCLUSION: For the first time, we show that jargonaphasia is induced by white matter stimulation, hinting at disconnection. As IFOF disconnection unites both variants, these may represent a continuum of disorders distinguished by semantic impairment. Conversely, damage to the arcuate fasciculus in addition to the IFOF is specific to neologistic jargonaphasia, thus suggesting a dual-disconnection syndrome.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".