Cortical thickness, volume and surface area in patients with bipolar disorder types I and II
Bibliographic record
Abstract
BACKGROUND: Bipolar disorder (BD) is a common chronic psychiatric disorder mainly characterized by episodes of mania, hypomania and depression. The disorder is associated with cognitive impairments and structural brain abnormalities, such as lower cortical volumes in primarily frontal brain regions than healthy controls. Although bipolar disorder types I (BDI) and II (BDII) exhibit different symptoms and severity, previous studies have focused on BDI. Furthermore, the most frequently investigated measure in this population is cortical volume. The aim of our study was to investigate abnormalities in patients with BDI and BDII by simultaneously analyzing cortical volume, thickness and surface area, which yields more information about disease- and symptom-related neurobiology. METHODS: We used MRI to measure cortical volume, thickness and area in patients with BDI and BDII as well as in healthy controls. The large study cohort enabled us to adjust for important confounding factors. RESULTS: We included 81 patients with BDI, 59 with BDII and 85 controls in our analyses. Cortical volume, thickness and surface area abnormalities were present in frontal, temporal and medial occipital regions in patients with BD. Lithium and antiepileptic drug use had an effect on the observed differences in medial occipital regions. Patients with the subtypes BDI and BDII displayed common cortical abnormalities, such as lower volume, thickness and surface area than healthy controls in frontal brain regions but differed in temporal and medial prefrontal regions, where only those with BDI had abnormally low cortical volume and thickness. LIMITATIONS: The group differences can be explained by progressive changes, but also by premorbid conditions. They could also have been influenced by unknown factors, such as social, environmental or genetic factors. CONCLUSION: Our findings suggest diagnosis-related neurobiological differences between the BD subtypes, which could explain distinct symptoms and point to potential biomarkers that could inform the subtype diagnosis of BD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".