Neural Correlates of Syntactic Processing in the Nonfluent Variant of Primary Progressive Aphasia
Bibliographic record
Abstract
The left posterior inferior frontal cortex (IFC) is important for syntactic processing, and has been shown in many functional imaging studies to be differentially recruited for the processing of syntactically complex sentences relative to simpler ones. In the nonfluent variant of primary progressive aphasia (PPA), degeneration of the posterior IFC is associated with expressive and receptive agrammatism; however, the functional status of this region in nonfluent PPA is not well understood. Our objective was to determine whether the atrophic posterior IFC is differentially recruited for the processing of syntactically complex sentences in nonfluent PPA. Using structural and functional magnetic resonance imaging, we quantified tissue volumes and functional responses to a syntactic comprehension task in eight patients with nonfluent PPA, compared to healthy age-matched controls. In controls, the posterior IFC showed more activity for syntactically complex sentences than simpler ones, as expected. In nonfluent PPA patients, the posterior IFC was atrophic and, unlike controls, showed an equivalent level of functional activity for syntactically complex and simpler sentences. This abnormal pattern of functional activity was specific to the posterior IFC: the mid-superior temporal sulcus, another region modulated by syntactic complexity in controls, showed normal modulation by complexity in patients. A more anterior inferior frontal region was recruited by patients, but did not support successful syntactic processing. We conclude that in nonfluent PPA, the posterior IFC is not only structurally damaged, but also functionally abnormal, suggesting a critical role for this region in the breakdown of syntactic processing in this 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".