Malignant catatonia represents a severe and life-threatening neuropsychiatric syndrome that demands prompt recognition and intervention. This condition poses particular diagnostic and management challenges in adolescents, especially when genetic predispositions and neurodevelopmental vulnerabilities complicate the clinical picture.

This report examines a complex case of malignant catatonia in a 17-year-old female with developmental delay but no prior psychiatric diagnoses, who developed severe cognitive and behavioural deterioration. We explore the diagnostic complexities, therapeutic challenges and potential genetic contributions to her presentation.

We present a comprehensive case analysis documenting clinical progression, treatment responses and genetic findings through whole-exome sequencing. The patient’s journey spans from initial presentation to long-term follow-up, with systematic assessment using standardised catatonia rating scales.

The patient’s condition manifested as severe psychomotor impairment, mutism and autonomic instability, showing minimal response to initial treatment. Electroconvulsive therapy yielded significant but temporary amelioration of symptoms. Genetic analysis revealed a heterozygous mutation in the pogo transposable element derived with zinc finger domain (POGZ) gene – a gene implicated in neurodevelopmental disorders – suggesting this variant contributed to her neurobiological vulnerability. Concurrent features of functional neurological disorder further compounded the diagnostic complexity, illustrating the intricate interplay between genetic susceptibility and clinical presentation.

This case illuminates the challenges clinicians face when diagnosing and treating complex neuropsychiatric presentations in adolescents, particularly when genetic predispositions intersect with functional neurological symptoms. The findings emphasise how comprehensive, multidisciplinary approaches remain essential for optimal patient care. Moreover, this case highlights the selective utility of genetic investigation in elucidating potential underpinnings of complex, treatment-resistant malignant catatonia, whilst demonstrating that genetic variants may confer vulnerability rather than direct causation.

Electroconvulsive therapy (ECT) is often used to treat severe mental disorders in individuals with impaired capacity to consent to the treatment. Little is known about how different types of electrode placement are used in consensual and nonconsensual ECT.

To investigate whether there was an association between ECT consent status and electrode placement, given that ECT electrode placement affects efficacy and cognitive outcomes.

Using a statewide database across 3 years in Victoria, Australia, we performed chi-squared tests to determine whether consent status (consensual versus nonconsensual) was associated with particular electrode placements. A three-way log–linear analysis was then conducted to examine whether age, gender, level of education and psychiatric diagnosis influenced the relationship between consent status and electrode placement. Given the comparable cognitive outcomes of right unilateral and bifrontal ECT, these electrode placements were combined in the analysis.

In total, 3882 participants received ECT in the Victorian public health service during the study period. In the nonconsensual ECT group, 722 of 1576 individuals (45.81%) received bitemporal ECT, compared with 555 of 2306 (24.06%) in the consensual group (χ2 = 200.53; P < 0.0001; odds ratio: 2.6673, 95% CI: 2.3244–3.0608). This association remained significant after adjustment for gender, age, level of education and diagnosis.

Significantly more participants in the nonconsensual ECT group received bitemporal ECT rather than right unilateral or bifrontal ECT compared with those in the consensual group. As bitemporal ECT is associated with more cognitive impairment, this choice of electrode placement in vulnerable patients who lack capacity to consent raises ethical considerations in the practice of ECT.

Difficult-to-treat depression (DTD) is a common clinical challenge for major depressive disorder and bipolar disorders. Electro convulsive therapy (ECT) has proven to be one of the most effective treatments for this condition. Although several studies have investigated individually the clinical factors associated with the DTD response, the role of their interplay in the clinical response to ECT remains unknown. In the present study, we aimed to characterize the network of symptoms in DTD, evaluate the effects of ECT on the interrelationship of depressive symptoms, and identify the network characteristics that could predict the clinical response.

A network analysis of clinical and demographic data from 154 patients with DTD was performed to compare longitudinally the patterns of relationships among depressive symptoms after ECT treatment. Furthermore, we estimated the network structure at baseline associated with a greater clinical improvement (≥80% reduction at Montgomery–Åsberg Depression Rating Scale total score).

ECT modulated the network of depressive symptoms, with increased strength of the global network (p = 0.03, Cohen’s d = −0.98, 95% confidence interval = [−1.07, −0.88]). The strength of the edges between somatic symptoms (appetite and sleep) and cognitive-emotional symptoms (tension, lassitude, and pessimistic thoughts) was also increased. A stronger negative relationship between insomnia and pessimistic thoughts was associated with a greater improvement after ECT. Concentration difficulties and apparent sadness showed the greatest centrality.

In conclusion, ECT treatment may affect not only the severity of the symptoms but also their relationship; this may contribute to the response in DTD.

The antidepressant mechanism of electroconvulsive therapy (ECT) remains not clearly understood. This study aimed to detect the changes in gray matter volume (GMV) in patients with major depressive disorder (MDD) caused by ECT and exploratorily analyzed the potential functional mechanisms.

A total of 24 patients with MDD who underwent eight ECT sessions were included in the study. Clinical symptom assessments and MRI scans were conducted and compared. Using whole-brain micro-array measurements provided by the Allen Human Brain Atlas (AHBA), regional gene expression profiles were calculated. The differential gene PLS1 was obtained through Partial Least Squares (PLS) regression analysis, and PLS1 was divided into positive contribution (PLS1+) and negative contribution (PLS1−) genes. Through gene function enrichment analysis, the functional pathways and cell types of PLS1 enrichment were identified.

Gray matter volume (GMV) in the somatosensory and motor cortices, occipital cortex, prefrontal cortex, and insula showed an increasing trend after ECT, while GMV in the temporal cortex, posterior cingulate cortex, and orbitofrontal cortex decreased. PLS1 genes were enriched in synapse- and cell-related biological processes and cellular components (such as ‘pre- and post-synapse’, ‘synapse organization’ etc.). A large number of genes in the PLS1+ list were involved in neurons (inhibitory and excitatory), whereas PLS1− genes were significantly involved in Astrocytes (Astro) and Microglia (Micro).

This study established a link between treatment-induced GMV changes and specific functional pathways and cell types, which suggests that ECT may exert its effects through synapse-associated functional and affect neurons and glial cells.

Older people with depression exhibit better response to electroconvulsive therapy (ECT). We aimed to measure the total effect of age on ECT response and investigate whether this effect is mediated by psychotic features, psychomotor retardation, psychomotor agitation, age of onset, and episode duration.

We pooled data from four prospective Irish studies where ECT was administered for a major depressive episode (unipolar or bipolar) with baseline score ≥21 on the 24-item Hamilton Depression Rating Scale (HAM-D). The primary outcome was change in HAM-D between baseline and end of treatment. The estimands were total effect of age, estimated using linear regression, and the indirect effects for each putative mediator, estimated using causal mediation analyses.

A total of 256 patients (mean age 57.8 [SD = 14.6], 60.2% female) were included. For every additional 10 years of age, HAM-D was estimated to decrease by a further 1.74 points over the ECT period (p < 0.001). Age acted on all putative mediators. Mechanistic theories, whereby a mediator drives treatment response, were confirmed for all putative mediators except age of onset. Consequently, mediation of the effect of age on change in HAM-D could be demonstrated for psychotic features, psychomotor retardation, psychomotor agitation, and episode duration but not for age of onset.

A total of 43.1% of the effect of older age on increased ECT response was explained by the mediators. Treatment planning could be improved by preferentially offering ECT to older adults, especially if presenting with psychotic features, greater severity of psychomotor disturbance, and earlier in the episode.

Electroconvulsive therapy (ECT) is a safe and effective treatment for several major psychiatric conditions, including treatment-resistant depression, mania, and schizophrenia; nevertheless, its use remains controversial. Despite its availability in some European countries, ECT is still rarely used in others. This study aims to investigate the experiences and attitudes of early career psychiatrists (ECPs) across Europe towards ECT and to examine how their exposure to ECT influences their perceptions.

In Europe, a cross-sectional survey was conducted among ECPs, including psychiatric trainees and recently fully qualified psychiatrists.

A total of 573 participants from 30 European countries were included in the study, of whom more than half (N = 312; 54.5%) received ECT training. Overall, ECPs had a positive attitude towards ECT, with the vast majority agreeing or strongly agreeing that ECT is an effective (N = 509; 88.8%) and safe (N = 464; 81.0%) treatment and disagreeing or strongly disagreeing that ECT was used as a form of control or punishment (N = 545; 95.1%). Those who had received ECT training during their psychiatry training were more likely to recommend ECT to their patients (p < 0.001, r = 0.34), and held more positive views on its safety (p < 0.001, r = 0.31) and effectiveness (p < 0.001, r = 0.33). Interest in further education about ECT was moderately high (modal rating on Likert scale: 4, agree), irrespective of prior training exposure.

ECT training is associated with more favorable perceptions of its safety and effectiveness among ECPs. There is a general willingness among ECPs to expand their knowledge and training on ECT, which could enhance patients’ access to this treatment.

Involuntary admissions to psychiatric hospitals are on the rise. If patients at elevated risk of involuntary admission could be identified, prevention may be possible. Our aim was to develop and validate a prediction model for involuntary admission of patients receiving care within a psychiatric service system using machine learning trained on routine clinical data from electronic health records (EHRs).

EHR data from all adult patients who had been in contact with the Psychiatric Services of the Central Denmark Region between 2013 and 2021 were retrieved. We derived 694 patient predictors (covering e.g. diagnoses, medication, and coercive measures) and 1134 predictors from free text using term frequency-inverse document frequency and sentence transformers. At every voluntary inpatient discharge (prediction time), without an involuntary admission in the 2 years prior, we predicted involuntary admission 180 days ahead. XGBoost and elastic net models were trained on 85% of the dataset. The models with the highest area under the receiver operating characteristic curve (AUROC) were tested on the remaining 15% of the data.

The model was trained on 50 634 voluntary inpatient discharges among 17 968 patients. The cohort comprised of 1672 voluntary inpatient discharges followed by an involuntary admission. The best XGBoost and elastic net model from the training phase obtained an AUROC of 0.84 and 0.83, respectively, in the test phase.

A machine learning model using routine clinical EHR data can accurately predict involuntary admission. If implemented as a clinical decision support tool, this model may guide interventions aimed at reducing the risk of involuntary admission.

This study aimed to investigate changes in mRNA expression of the kynurenine pathway (KP) enzymes tryptophan 2, 3-dioxygenase (TDO), indoleamine 2, 3-dioxygenase 1 and 2 (IDO1, IDO2), kynurenine aminotransferase 1 and 2 (KAT1, KAT2), kynurenine monooxygenase (KMO) and kynureninase (KYNU) in medicated patients with depression (n = 74) compared to age- and sex-matched healthy controls (n = 55) and in patients with depression after electroconvulsive therapy (ECT). Associations with mood score (24-item Hamilton Depression Rating Scale, HAM-D24), plasma KP metabolites and selected glucocorticoid and inflammatory immune markers known to regulate KP enzyme expression were also explored.

HAM-D24 was used to evaluate depression severity. Whole blood mRNA expression was assessed using quantitative real-time polymerase chain reaction.

KAT1, KYNU and IDO2 were significantly reduced in patient samples compared to control samples, though results did not survive statistical adjustment for covariates or multiple comparisons. ECT did not alter KP enzyme mRNA expression. Changes in IDO1 and KMO and change in HAM-D24 score post-ECT were negatively correlated in subgroups of patients with unipolar depression (IDO1 only), psychotic depression and ECT responders and remitters. Further exploratory correlative analyses revealed altered association patterns between KP enzyme expression, KP metabolites, NR3C1 and IL-6 in depressed patients pre- and post-ECT.

Further studies are warranted to determine if KP measures have sufficient sensitivity, specificity and predictive value to be integrated into stress and immune associated biomarker panels to aid patient stratification at diagnosis and in predicting treatment response to antidepressant therapy.

Despite strong evidence of efficacy of electroconvulsive therapy (ECT) in the treatment of depression, no sensitive and specific predictors of ECT response have been identified. Previous meta-analyses have suggested some pre-treatment associations with response at a population level.

Using 10 years (2009–2018) of routinely collected Scottish data of people with moderate to severe depression (n = 2074) receiving ECT we tested two hypotheses: (a) that there were significant group-level associations between post-ECT clinical outcomes and pre-ECT clinical variables and (b) that it was possible to develop a method for predicting illness remission for individual patients using machine learning.

Data were analysed on a group level using descriptive statistics and association analyses as well as using individual patient prediction with machine learning methodologies, including cross-validation.

ECT is highly effective for moderate to severe depression, with a response rate of 73% and remission rate of 51%. ECT response is associated with older age, psychotic symptoms, necessity for urgent intervention, severe distress, psychomotor retardation, previous good response, lack of medication resistance, and consent status. Remission has the same associations except for necessity for urgent intervention and, in addition, history of recurrent depression and low suicide risk. It is possible to predict remission with ECT with an accuracy of 61%.

Pre-ECT clinical variables are associated with both response and remission and can help predict individual response to ECT. This predictive tool could inform shared decision-making, prevent the unnecessary use of ECT when it is unlikely to be beneficial and ensure prompt use of ECT when it is likely to be effective.

Most patients show temporary impairments in clinical orientation after electroconvulsive therapy (ECT)-induced seizures. It is unclear how postictal reorientation relates to electroencephalography (EEG) restoration. This relationship may provide additional measures to quantify postictal recovery and shed light on neurophysiological aspects of reorientation after ECT.

We analyzed prospectively collected clinical and continuous ictal and postictal EEG data from ECT patients. Postictal EEG restoration up to 1 h was estimated by the evolution of the normalized alpha–delta ratio (ADR). Times to reorientation in the cognitive domains of person, place, and time were assessed postictally. In each cognitive domain, a linear mixed model was fitted to investigate the relationships between time to reorientation and postictal EEG restoration.

In total, 272 pairs of ictal-postictal EEG and reorientation times of 32 patients were included. In all domains, longer time to reorientation was associated with slower postictal EEG recovery. Longer seizure duration and postictal administration of midazolam were related to longer time to reorientation in all domains. At 1-hour post-seizure, most patients were clinically reoriented, while their EEG had only partly restored.

We show a relationship between postictal EEG restoration and clinical reorientation after ECT-induced seizures. EEG was more sensitive than reorientation time in all domains to detect postictal recovery beyond 1-hour post-seizure. Our findings indicate that clinical reorientation probably depends on gradual cortical synaptic recovery, with longer seizure duration leading to longer postsynaptic suppression after ECT seizures.