Cognitive and brain micro-structural correlates of alexithymia in essential tremor patients
Bibliographic record
Abstract
Essential tremor (ET) is the most common movement disorder which has both motor and non-motor findings such as neuropsychiatic symptoms. Alexithymia is defined as inability to identify and describe emotions experienced by one's self or others. In our study, we aimed to evaluate the neurocognitive and brain micro-structural correlates of alexithymia in ET. 40 ET patients (mean age = 53.05 ± 19.74 years), were included. Fahn-Tolosa-Marin Tremor Rating Scale, Toronto Alexithymia Scale (TAS), Beck Depression Inventory, Beck Anxiety Inventory and detailed neurocognitive evaluation were applied to all patients. The patients were divided into three groups based on their TAS scores: no alexithymia, probable alexithymia, definite alexithymia. Diffusion Tensor Imaging (DTI) was performed in all patients. The mean TAS score was 50.05 ± 10.06. Depressive symptoms and anxiety levels were higher in definite alexithymia (p < 0.001, p < 0.01). Partial correlation controlling for age, gender and educational level between alexithymia scores and each cognitive test showed significant association between similarities (p < 0.001) and phonemic verbal fluency (p = 0.04). Left orbitofrontal cortex average diffusion coefficient (ADC) value (p = 0.05), left anterior cingulate cortex fractional anisotropy (FA) value (p = 0.04), right cuneus FA value (p = 0.04), left amygdala ADC value (p = 0.01) and left insula ADC value (p = 0.02) were differed between groups. TAS and DTImetrics were not found to be independently associated with the level of anxiety (p < 0.001) and depressive symptoms (p < 0.01). As a conclusion, impairments in executive function and complex attention were correlated with higher levels of alexithymia in ET. Many micro-structural alterations were determined to be correlated with alexithymia levels.
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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".