Brain Structures and Networks Underlying Treatment Response to Deep Brain Stimulation Targeting the Inferior Thalamic Peduncle in Obsessive-Compulsive Disorder
Bibliographic record
Abstract
BACKGROUND: Obsessive-compulsive disorder (OCD) is a debilitating disease with a lifetime prevalence of 2-3%. Neuromodulatory treatments have been successfully used in severe cases. Deep brain stimulation (DBS) targeting the inferior thalamic peduncle (ITP) has been shown to successfully alleviate symptoms in OCD patients; however, the brain circuits implicated remain unclear. Here, we investigate the efficacious neural substrates following ITP-DBS for OCD. METHODS: High-quality normative structural and functional connectomics and voxel-wise probabilistic mapping techniques were applied to assess the neural substrates of OCD symptom alleviation in a cohort of 5 ITP-DBS patients. RESULTS: The region of most efficacious stimulation was located in the regions of the ITP and bed nucleus of the stria terminalis. Both functional and structural connectomics analyses demonstrated that successful symptom alleviation involved a brain network encompassing the bilateral amygdala and prefrontal regions. LIMITATIONS: The main limitation is the small size of the ITP-DBS cohort. While the findings are highly consistent and significant, these should be validated in larger studies. CONCLUSIONS: These results identify a tripartite brain network - composed of the bilateral amygdala and prefrontal regions 24 and 46 - whose engagement is associated with greater symptom improvement. They also provide information for optimizing targeting and identifying network components critically involved in ITP-DBS treatment response. Amygdala engagement in particular seems to be a key component for clinical benefits and could constitute a biomarker for treatment optimization.
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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".