Neural signatures of bipolar disorder subtypes: A comprehensive systematic review of neuroimaging studies
Bibliographic record
Abstract
The neurobiological mechanisms differentiating bipolar disorder type I (BD-I) from type II (BD-II) remain poorly understood. A comprehensive synthesis systematically comparing neuroimaging findings between BD subtypes is lacking. We conducted a systematic review (PubMed, Scopus, up to March 2024), including structural MRI, functional MRI, and diffusion tensor imaging studies, to provide a comprehensive overview of the common and distinct candidate neural signatures that differentiate BD subtypes. Out of the initial 5334 references, 38 MRI studies (41 experiments) were included. Structural MRI studies showed mixed results regarding volumetric and cortical surface differences between BD subtypes. BD-I exhibited widespread gray matter (GM) volume reductions, larger lateral ventricles, and decreases in cortical thickness. Hippocampal and cerebellar volume reductions were observed in both BD subtypes but did not differentiate BD-I from BD-II. While white matter (WM) abnormalities across BD subtypes remain heterogeneous and lack consistent replication, BD-I showed a tendency toward more disrupted WM microstructure and higher WM hyperintensities rates than BD-II. Functional MRI studies revealed distinct differences in task-based and resting-state activity, suggesting differential neural patterns in reward processing and emotion regulation. BD-I displayed a greater disconnection in emotion regulation circuits. While both BD-I and BD-II share some neuroimaging characteristics, the findings suggest BD-I is characterized by more pronounced WM disruptions and emotion dysregulation. In contrast, BD-II shows more remarkable subcortical volume preservation but with distinct connectivity alterations. These results offer insights into the different and shared neurobiological mechanisms of BD subtypes, which may help refine their pathophysiology and inform tailored interventions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".