TMS–EEG Reveals Distinct Cortical Signatures in Non-Fluent PPA
Bibliographic record
Abstract
Primary Progressive Aphasias (PPA) are a group of neurodegenerative disorders characterized by the gradual decline of language abilities. They are typically divided into three major clinical variants: the non-fluent (nfvPPA), the semantic (svPPA) and the logopenic (lvPPA) variant. Even with an extensive clinical examination, a correct differential diagnosis among variants can be difficult due to the overlapping of dysfunctional language features. In this context, the combination of Transcranial Magnetic Stimulation and Electroencephalography (i.e., TMS-EEG) could extend our understanding of nfvPPA pathophysiology, given the possibility to non-invasively and directly measure cortical reactivity of brain speech networks to external perturbations. Twenty PPA patients (7 nfvPPA, 13 lvPPA) and 8 elderly controls underwent a TMS-EEG session targeting the left dorsal premotor cortex (Brodmann area 6). A subset of 9 patients (8 lvPPA, 1 nfvPPA) were additionally stimulated in the right homologous region. We automatically detected the EEG channel under the stimulator with the highest peak-to-peak amplitude of the early TMS-evoked response and computed the following measures: (i) natural frequency; (ii) normalized evoked spectral power in the alpha, low-beta, high-beta and gamma range. Non-fluent PPA patients showed a slower and simplified TMS-evoked response as compared to healthy elderly subjects, namely a reduction in high-beta power and natural frequency coupled with higher low frequencies (i.e., alpha) intrusion. No significant differences were detected between lvPPA and controls or nfvPPA and lvPPA. The speech rate was positively correlated with TMS-EEG measures (the high-beta power and the natural frequency). Furthermore, compared to the left side, the stimulation of the right hemisphere elicited TMS-evoked responses with higher natural frequency and high-beta power in both lvPPA and nfvPPA patients. This study first shows that TMS-EEG may provide useful neurophysiological biomarkers for characterizing the nfvPPA variant and monitor disease progression across variants. These findings might be employed in the future to stratify patients and eventually inform the application of variant-specific stimulation protocols tailored to individual neurophysiological profiles.
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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 | 0.000 |
| 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".