Identifying common and distinct subcortical volumetric abnormalities in 3 major psychiatric disorders: a single-site analysis of 640 participants
Bibliographic record
Abstract
BACKGROUND: Subcortical volumetric abnormalities in schizophrenia, bipolar disorder and major depressive disorder (MDD) have been consistently found on a single-diagnosis basis in previous studies. However, whether such volumetric abnormalities are specific to a particular disorder or shared by other disorders remains unclear. METHODS: We analyzed the structural MRIs of 160 patients with schizophrenia, 160 patients with bipolar disorder, 160 patients with MDD and 160 healthy controls. We calculated the volumes of the thalamus, hippocampus, amygdala, accumbens, putamen, caudate, pallidum and lateral ventricles using FreeSurfer 7.0 and compared them among the groups using general linear models. RESULTS: We found a significant group effect on the volumes of the thalamus, hippocampus, accumbens and pallidum. Further post hoc analysis revealed that thalamic volumes in patients with schizophrenia, bipolar disorder and MDD were significantly reduced compared to those in healthy controls, but did not differ from one another. Patients with schizophrenia and bipolar disorder also shared a significant reduction in hippocampal volumes. Among the 3 clinical groups, patients with schizophrenia showed significantly lower hippocampal volumes and higher pallidal volumes than patients with bipolar disorder and MDD. LIMITATIONS: Differences in psychotropic use and duration of illness among the patient groups may limit the interpretation of our findings. CONCLUSION: Our findings indicate that decreased thalamic volume is a common feature of schizophrenia, bipolar disorder and MDD. Smaller hippocampal and larger pallidal volumes differentiate schizophrenia from bipolar disorder and MDD and may provide clues to the biological basis for the Kraepelinian distinction between these illnesses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".