Repetitive transcranial magnetic stimulation for major depressive disorder comorbid with huntington’s disease: a case report
Bibliographic record
Abstract
Huntington's disease (HD) is a rare genetic disorder causing progressive degeneration of nerve cells in parts of the basal ganglia leading to motor, cognitive and neuropsychiatric symptoms.Around 33 to 69% of HD patients suffer from comorbid major depressive disorder (MDD).Considering high risk of suicide, detection and treatment of MDD in people suffering from HD is essential.We report on the case of a patient suffering from neuropsychiatric symptoms since early 2000s and diagnosed with HD around 2013 who was referred to our service for repetitive transcranial magnetic stimulation treatments (rTMS) in the context of pharmaco-resistant MDD.Treatment consisted of a high-frequency (HF) 20 Hz protocol delivered over the left dorsolateral prefrontal cortex (DLPFC) at 120% of the resting motor threshold (rMT) using R30/X100 stimulators equipped with DB80 coils (MagVenture, Farum, Denmark).The DB80 coil was chosen given the light diffuse cerebral atrophy seen on MRI.Treatment course consisted of 3 daily sessions every weekday over 2 weeks, for a total of 30 sessions.Symptoms were assessed pre-and post-treatment using the Montgomery-Asberg Depression Rating Scale (MADRS).Remission was defined as a score <10.The patient underwent two courses of rTMS.After his first series in 2019, he achieved remission, with MADRS scores going from 24 at baseline down to 5 at follow-up.He remained stable until about 21 months later after which he experienced a depressive recurrence.The patient subsequently underwent a second course of treatment using the same protocol, after which he once again reached remission with MADRS scores decreasing from 18 at baseline down to 6 at follow-up.Treatment was well-tolerated without any significant side-effects.We report on the successful treatment of MDD in an HD patient with rTMS.rTMS should be considered as a valid option to treat MDD in the context of 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".