Shifting priorities: highly conserved behavioral and brain network adaptations to chronic stress across species
Bibliographic record
Abstract
Parallel clinical and preclinical research have begun to illuminate the biological basis of stress-related disorders, including major depression, but translational bridges informing discrete mechanistic targets for intervention are missing. To address this critical need, we used structural MRI in a mouse model and in a large human sample to examine stress effects on brain structure that may be conserved across species. Specifically, we focused on a previously unexplored approach, whole-brain structural covariance, as it reflects synchronized changes in neuroanatomy, potentially due to mutual trophic influences or shared plasticity across regions. Using the unpredictable chronic mild stress (UCMS) paradigm in mouse we first demonstrate that UCMS-induced elevated behavioral emotionality correlates with increased size of the amygdala and other corticolimbic regions. We further identify focal increases in the amygdala's 'hubness' (degree and strength) set against the background of a global stress-related loss of network clustering and modularity. These macroscopic changes are supported on the molecular level by increased postsynaptic density-95 protein in the amygdala, consistent with stress-induced plastic changes and synaptic strengthening. Finally, we provide clinical evidence that strikingly similar structural network reorganization patterns exist in young adults reporting high childhood trauma and increased mood symptoms. Collectively, we provide initial translational evidence for a conserved stress-related increase in amygdala-centered structural synchrony, as measured by enhanced structural covariance, which is paralleled by a decrease in global structural synchrony. This putative trade-off reflected in increased amygdala-centered plastic changes at the expense of global structural dedifferentiation may represent a mechanistic pathway for depression and related psychopathology.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".