Genetics of catatonia: a systematic review of case reports and a gene pathway analysis
Bibliographic record
Abstract
BACKGROUND: Neurodevelopmental conditions are crucial risk factors for catatonia in pediatric and adult populations. Recent case reports and studies have identified an increasing number of genetic abnormalities likely contributing to catatonia. Catatonia associated with genetic abnormalities is challenging in terms of identification, chronicity, and resistance to treatment. In addition, understanding these genetic abnormalities through identifying rare single nucleotide and copy number variants may offer valuable insights into the underlying pathophysiology. METHODS: We conducted a systematic review of all genetic abnormalities reported with catatonia and performed a gene-set enrichment analysis. Our systematic literature search for relevant articles published through July 15, 2024, using combinations of "catatonia," "catatonic syndrome," "genetic," and "genes" in PubMed, yielded 317 articles. Of these, 94 were included, covering 374 cases of catatonia and 78 distinct genetic abnormalities. RESULTS: This review discusses the clinical presentation of catatonia for each genetic disorder, the treatment strategies, and the putative underlying mechanisms. CONCLUSIONS: The review highlights that catatonia underpinned by genetic abnormalities presents specific clinical and treatment-response features. Therefore, we propose genetic testing guidelines for catatonia and advocate for systematically investigating catatonia in several genetic diseases. Regarding the pathophysiology of catatonia, the gene ontology of biological processes reveals significant enrichment of variants in synaptic and post-synaptic regulatory genes, particularly within GABAergic neurons, reinforcing the implication of the excitatory/inhibitory imbalance. Finally, genetic variants are enriched in microglial cells, highlighting the role of brain inflammation in triggering catatonia. This comprehensive insight could pave the way for more effective management strategies for this condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".