Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset
Bibliographic record
Abstract
ABSTRACT Deep brain stimulation targeting the posterior hypothalamus (pHyp-DBS) is being investigated as treatment for refractory aggressive behaviour, but its mechanisms of action remain elusive. We conducted an integrated imaging analysis of a large multi-centre dataset, incorporating volume of activated tissue modeling, probabilistic mapping, normative connectomics, and atlas-derived transcriptomics. 91% of the patients responded positively to treatment, with a more striking improvement recorded in the pediatric population. Probabilistic mapping revealed an optimized surgical target within the posterior-inferior-lateral posterior hypothalamic area and normative connectomic analyses identified fiber tracts and interconnected brain areas associated with sensorimotor function, emotional regulation, and monoamine production. Functional connectivity between the target, periaqueductal gray and the amygdala – together with patient age – was highly predictive of treatment outcome. Finally, transcriptomic analysis showed that genes involved in mechanisms of aggressive behaviour, neuronal communication, plasticity and neuroinflammation may underlie this functional network. SIGNIFICANCE STATEMENT This study investigated the brain mechanisms associated with symptom improvement following deep brain stimulation of the posterior hypothalamus for severe and refractory aggressive behavior. Conducting an integrated imaging analysis of a large international multi-center dataset of patients treated with hypothalamic deep brain stimulation, we were able to show for the first time that treatment is highly efficacious across various patients with an average improvement greater than 70%. Leveraging this unique dataset allowed us to demonstrate that some patient characteristics are important for treatment success, describe the optimal target zone for maximal benefit, that engagement of distinct fiber tracts and networks within the emotional neurocircuitry are key for positive outcome, and - using imaging transcriptomics - elucidate some potential molecular underpinnings. The provided optimal stimulation site allows for direct clinical application.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".