Emotion detection deficits and changes in personality traits linked to loss of white matter integrity in primary progressive aphasia
Bibliographic record
Abstract
Non-cognitive features including personality changes are increasingly recognized in the three PPA variants (semantic-svPPA, non fluent-nfvPPA, and logopenic-lvPPA). However, differences in emotion processing among the PPA variants and its association with white matter tracts are unknown. We compared emotion detection across the three PPA variants and healthy controls (HC), and related them to white matter tract integrity and cortical degeneration. Personality traits in the PPA group were also examined in relation to white matter tracts. Thirty-three patients with svPPA, nfvPPA, lvPPA, and 32 HC underwent neuropsychological assessment, emotion evaluation task (EET), and MRI scan. Patients' study partners were interviewed on the Clinical Dementia Rating Scale (CDR) and completed an interpersonal traits assessment, the Interpersonal Adjective Scale (IAS). Diffusion tensor imaging of uncinate fasciculus (UF), superior longitudinal fasciculus (SLF) and inferior longitudinal fasciculus (ILF), and voxel-based morphometry to derive gray matter volumes for orbitofrontal cortex (OFC), anterior temporal lobe (ATL) regions were performed. In addition, gray matter volumes of white matter tract-associated regions were also calculated: inferior frontal gyrus (IFG), posterior temporal lobe (PTL), inferior parietal lobe (IPL) and occipital lobe (OL). ANCOVA was used to compare EET performance. Partial correlation and multivariate linear regression were conducted to examine association between EET and neuroanatomical regions affected in PPA. All three variants of PPA performed significantly worse than HC on EET, and the svPPA group was least accurate at recognizing emotions. Performance on EET was related to the right UF, SLF, and ILF integrity. Regression analysis revealed EET performance primarily relates to the right UF integrity. The IAS subdomain, cold-hearted, was also associated with right UF integrity. Disease-specific emotion recognition and personality changes occur in the three PPA variants and are likely associated with disease-specific neuroanatomical changes. Loss of white matter integrity contributes as significantly as focal atrophy in behavioral changes in PPA.
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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".