Altered Global Signal Topography and Its Different Regional Localization in Motor Cortex and Hippocampus in Mania and Depression
Bibliographic record
Abstract
Bipolar disorder (BD) is a complex psychiatric disorder characterized by dominant symptom swings across different phases (manic, depressive, and euthymic). Different symptoms in BD such as abnormal episodic memory recall and psychomotor activity have been related to alterations in different regions, ie, hippocampus and motor cortex. How the abnormal regional distribution of neuronal activity relates to specific symptoms remains unclear, however. One possible neuronal mechanism of the relationship is the alteration of the global distribution of neuronal activity manifested in specific local regions; this can be measured as the correlation between the global signal (GS) and local regions. To understand the GS and its relationship to psychopathological symptoms, we here investigated the alteration of both GS variance and its regional topography in healthy controls and 3 phases of BD. We found that the variance of GS showed no significant difference between the 4 groups. In contrast, the GS topography was significantly altered in the different phases of BD, ie, the regions showing abnormally strong topographical GS contribution changed from hippocampus (and parahippocampus/fusiform gyrus) in depression to motor cortex in mania. Importantly, topographical GS changes in these regions correlated with psychopathological measures in both depression and mania. Taken together, our findings demonstrate the central importance of GS topography for psychopathological symptoms. This sheds lights on the neuronal mechanisms of specific psychopathological symptoms in BD, and its relevance in the relationship between global and local neuronal activities for behavior in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".