Clinical and neuroimaging predictors of benzodiazepine response in catatonia: A machine learning approach.

Catatonia is a well characterized psychomotor syndrome combining motor, behavioural and neurovegetative signs. Benzodiazepines are the first-choice treatment, effective in 70 % of cases. Currently, the factors associated with benzodiazepine resistance remain unknown. We aimed to develop machine learning models using clinical and neuroimaging data to predict benzodiazepine response in catatonic patients. This study examined a cohort of catatonic patients who underwent standardized clinical evaluation, 3 T brain MRI, and benzodiazepine trial. Based on clinical response, patients were classified as benzodiazepine responders or non-responders. Cortical thickness and regional brain volumes were measured. Two machine learning models (linear model and gradient boosting tree model) were developed to identify predictors of treatment response using clinical, demographic, and neuroimaging data. The cohort included 65 catatonic patients, comprising 30 benzodiazepine responders and 35 non-responders. Using clinical data alone, the linear model achieved 63% precision, 51% recall, a specificity of 61%, and 58% AUC, while the gradient boosting tree (GBT) model attained 46% precision, 60% recall, a specificity of 62% and 64% AUC. Incorporating neuroimaging data improved model performance, with the linear model achieving 66% precision, 57% recall, a specificity of 67%, and 70% AUC, and the GBT model attaining 50% precision, 50% recall, a specificity of 62% and 70% AUC. The integration of imaging data with demographic and clinical information significantly enhanced the predictive performance of the models. The duration of the catatonic syndrome, along with the presence of mitgehen (passive obedience) and immobility/stupor, and the volume of the right medial orbito-frontal cortex emerged as important factors in predicting non-response to benzodiazepines.

Exploring seasonality in catatonia diagnosis: Evidence from a large-scale population study.

Catatonia is a severe psychomotor syndrome mainly associated with psychiatric disorders, such as mood disorders and schizophrenia. Seasonal patterns have been described for these psychiatric disorders, and a previous study conducted in South London showed for the first time a seasonal pattern in the onset of catatonia. In this study, we aim to extend those findings to a larger national sample of patients admitted to French metropolitan hospitals, between 2015 and 2022, and to perform subgroup analyses by the main associated psychiatric disorder. A total of 6225 patients diagnosed with catatonia were included. A seasonal pattern for catatonia diagnosis was described, using cosinor models. Two peaks of diagnoses for catatonic cases were described in March and around September-October. Depending on the associated psychiatric disorder, the seasonality of catatonia diagnosis differed. In patients suffering with mood disorders, peaks of catatonia diagnosis were found in March and July. For patients suffering with schizophrenia, no seasonal pattern was found.

Seasonality of presentation and birth in catatonia.

BACKGROUND: Catatonia is a neuropsychiatric syndrome associated with both psychiatric disorders and medical conditions. Understanding of the pathophysiology of catatonia remains limited, and the role of the environment is unclear. Although seasonal variations have been shown for many of the disorders underlying catatonia, the seasonality of this syndrome has not yet been adequately explored. METHODS: Clinical records were screened to identify a cohort of patients suffering from catatonia and a control group of psychiatric inpatients, from 2007 to 2016 in South London. In a cohort study, the seasonality of presentation was explored fitting regression models with harmonic terms, while the effect of season of birth on subsequent development of catatonia was analyzed using regression models for count data. In a case-control study, the association between month of birth and catatonia was studied fitting logistic regression models. RESULTS: In total, 955 patients suffering from catatonia and 23,409 controls were included. The number of catatonic episodes increased during winter, with a peak in February. Similarly, an increasing number of cases was observed during summer, with a second peak in August. However, no evidence for an association between month of birth and catatonia was found. CONCLUSIONS: The presentation of catatonia showed seasonal variation in accordance with patterns described for many of the disorders underlying catatonia, such as mood disorders and infections. We found no evidence for an association between season of birth and risk of developing catatonia. This may imply that recent triggers may underpin catatonia, rather than distal events.

Feasibility and usefulness of brain imaging in catatonia.

Catatonia is a well characterized psychomotor syndrome that has recognizable motor, affective, behavioural and vegetative manifestations. Despite recent demonstration that catatonia is often associated with brain imaging abnormalities, there is currently no consensus or guidelines about the role of brain imaging. In this study, we assessed the feasibility of brain imaging in a series of patients with catatonia in a routine clinical setting and estimated the prevalence of clinically relevant radiological abnormalities. Sixty patients with catatonia were evaluated against sixty non-healthy controls subjects with headache. The MRI reports were reviewed, and MRI scans were also interpreted by neuroradiologists using a standardised MRI assessment. In this cohort, more than 85% of brain scans of patients with catatonia revealed abnormalities. The most frequently reported abnormalities in the catatonic group were white matter abnormalities (n = 44), followed by brain atrophy (n = 27). There was no evidence for significant differences in the frequency of abnormalities found in radiology reports and standardised neuroradiological assessments. The frequency of abnormalities was similar to that found in a population of non-healthy controls subjects with headache. This study shows that MRI is feasible in patients with catatonia and that brain imaging abnormalities are common findings in these patients. Most frequently, white matter abnormalities and diffuse brain atrophy are observed.