Network Dysconnection of Striatum and Default Mode Network Underpins Temporal Dynamics of Depression in Premanifest Huntington’s Disease
Bibliographic record
Abstract
Abstract Background and objectives Depression emerges years before motor signs in Huntington”s disease gene expansion carriers (HDGECs), yet how directional connectivity patterns evolve over time and relate to depression trajectories remains unknown. We hypothesized HDGECs with depression history would show accelerated grey matter atrophy and disinhibition of striatal-to-default mode network connections over time. Methods The study utilised the largest longitudinal study of premanifest HD, TrackOn-HD, including structural and resting-state brain scans. HDGECs were stratified by depression history and followed over three annual assessments. Analyses focused on 8 predefined regions spanning striatal and default mode network regions. Here, we applied spectral dynamic causal modelling to examine effective connectivity, alongside voxel-based morphometry of grey matter atrophy and linear mixed models of depressive symptoms. Spectral DCM employed Bayesian model reduction with free energy thresholds ≥0.95 for strong evidence of connectivity changes. Results The study included 105 HDGECs (53 females, mean age = 43, mean CAG = 43). Depression-related network dysconnection operated independently of regional grey matter atrophy, with HDGECs exhibiting widespread striatal volume loss but no differential atrophy patterns between depression groups. Larger posterior cingulate cortex volumes predicted increased depression severity specifically in HDGECs with depression history, across both Beck Depression Inventory-II (β = 36.79, 95% CI = 2.33-71.25, p =.041) and Hospital Anxiety and Depression Scale-Depression Subscale (β = 27.31, 95% CI = 11.12-43.50, p =.002). Spectral dynamic causal models explained 88.6% of variance in depression groups and 90.4% in non-depression groups. Longitudinal effective connectivity analyses revealed distinct dysconnection profiles: HDGECs with depression history showed widespread interhemispheric alterations including progressive inhibitory changes of striatal-DMN circuits and aberrant hippocampal regulatory control, while those without depression exhibited more focal dysconnection. These patterns occurred despite no group differences in grey matter atrophy trajectories over 24 months. Discussion Depression in HDGECs emerges through network dysconnection that operates independently of regional atrophy. Functional alterations may precede detectable structural breakdown and represent pathological responses to subclinical neurodegenerative processes, suggesting functional connectivity markers may serve as early indicators of depression vulnerability in premanifest HD.
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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) | 0.000 | 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".