Functional neural correlates of emotional expression processing deficits in behavioural variant frontotemporal dementia
Bibliographic record
Abstract
BACKGROUND: Frontotemporal dementia (FTD) is a neurodegenerative disorder resulting in social-cognitive deficits partially attributed to abnormalities processing social cues, such as facial expressions. However, to our knowledge, the functional neuroanatomy of deficient social cue processing in individuals with FTD has not been examined. The objective of this study was to delineate the functional abnormalities under lying altered facial expression processing in individuals with FTD using functional magnetic resonance imaging (fMRI). METHODS: Patients meeting Neary criteria for behavioural variant FTD (bvFTD) with supportive neuroimaging and 18 age-matched healthy controls completed an implicit facial expression task during fMRI. We conducted volumetric brain morphometry to correct functional imaging data for volume differences. RESULTS: We included 20 patients with bvFTD and 18 controls in our study. The results demonstrate emotion-specific functional abnormalities in frontal and limbic regions in patients with bvFTD. Patients also showed decreased activity in posterior ventral visual regions, specifically the fusiform cortex, possibly reflecting reduced afferent input from limbic regions. Finally, bvFTD was associated with increased activity in posterior regions, including the inferior parietal cortex. LIMITATIONS: Autopsy validation of frontotemporal dementia is not yet available for this cohort. CONCLUSION: Together, these findings suggest that fMRI combined with tasks targeting social-cognitive deficits is a powerful technique to objectively measure neural systems involved in emotion processing in individuals with bvFTD. As viewing emotional expressions is known to engage many of the same neural systems that are active when experiencing the emotion itself, fMRI during expression processing provides a novel window into the emotions of patients with FTD.
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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) | 0.001 | 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".