A Perfusion MRI Study of Emotional Valence and Arousal in Parkinson’s Disease
Bibliographic record
Abstract
Background. Brain regions subserving emotion have mostly been studied using functional magnetic resonance imaging (fMRI) during emotion provocation procedures in healthy participants. Objective. To identify neuroanatomical regions associated with spontaneous changes in emotional state over time. Methods. Self-rated emotional valence and arousal scores, and regional cerebral blood flow (rCBF) measured by perfusion MRI, were measured 4 or 8 times spanning at least 2 weeks in each of 21 subjects with Parkinson's disease (PD). A random-effects SPM analysis, corrected for multiple comparisons, identified significant clusters of contiguous voxels in which rCBF varied with valence or arousal. Results. Emotional valence correlated positively with rCBF in several brain regions, including medial globus pallidus, orbital prefrontal cortex (PFC), and white matter near putamen, thalamus, insula, and medial PFC. Valence correlated negatively with rCBF in striatum, subgenual cingulate cortex, ventrolateral PFC, and precuneus-posterior cingulate cortex (PCC). Arousal correlated positively with rCBF in clusters including claustrum-thalamus-ventral striatum and inferior parietal lobule and correlated negatively in clusters including posterior insula-mediodorsal thalamus and midbrain. Conclusion. This study demonstrates that the temporal stability of perfusion MRI allows within-subject investigations of spontaneous fluctuations in mental state, such as mood, over relatively long-time intervals.
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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 | 0.000 | 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".