Depression in Premanifest Huntington's Disease: Aberrant Effective Connectivity of Striatum and Default Mode Network
Bibliographic record
Abstract
BACKGROUND: Depression frequently precedes motor symptoms in Huntington's disease gene expansion carriers (HDGECs), yet the neural mechanisms remain poorly characterized. OBJECTIVE: We investigated effective connectivity between the default mode network (DMN) and striatal regions in HDGECs. METHODS: We analyzed 3-T resting-state functional magnetic resonance imaging data from 98 HDGECs (48.98% females; mean age, 42.82 years). Spectral dynamic causal modeling estimated subject-level connectivity, whereas parametric empirical Bayes determined group-level effective connectivity differences between participants with a diagnosed depression history and those without, across current, remitted, and never-depressed states. Brain-behavior associations with clinical depression measures were examined. RESULTS: Model estimation was excellent (89.82% variance-explained). HDGECs with depression history showed decreased inhibitory posterior cingulate cortex-to-hippocampal connectivity, increased hippocampus-to-posterior cingulate cortex inhibition, and increased inhibitory influence of striatum on DMN. HDGECs with a depression history showed increased inhibitory striatal influence on DMN, including left putamen, a propensity for right hippocampal involvement, and disinhibitory posterior cingulate-hippocampal connectivity. Current versus never-depressed comparisons showed more pronounced dysconnectivity, with stronger striatum-to-network connections. Current versus remitted depression exhibited distinct patterns with increased medial prefrontal cortex-to-posterior cingulate cortex connectivity, increased medial prefrontal cortex self-connectivity, and decreased posterior cingulate cortex-to-medial prefrontal cortex connectivity. CONCLUSIONS: These findings establish distinct striatal-network interaction patterns in depression for HDGECs that differ from non-neurological depression. Our findings suggested the posterior DMN-posterior cingulate and hippocampus-as drivers of depression for HDGECs and potential involvement of right DMN in keeping with compensatory patterns broadly in HD. These connectivity patterns could serve as functional biomarkers for depression in HDGECs. © 2025 International Parkinson and Movement Disorder Society.
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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 | 0.000 | 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 | 0.000 |
| 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".