Precision psychiatry: predicting predictability.

Precision psychiatry is an emerging field that aims to provide individualized approaches to mental health care. An important strategy to achieve this precision is to reduce uncertainty about prognosis and treatment response. Multivariate analysis and machine learning are used to create outcome prediction models based on clinical data such as demographics, symptom assessments, genetic information, and brain imaging. While much emphasis has been placed on technical innovation, the complex and varied nature of mental health presents significant challenges to the successful implementation of these models. From this perspective, I review ten challenges in the field of precision psychiatry, including the need for studies on real-world populations and realistic clinical outcome definitions, and consideration of treatment-related factors such as placebo effects and non-adherence to prescriptions. Fairness, prospective validation in comparison to current practice and implementation studies of prediction models are other key issues that are currently understudied. A shift is proposed from retrospective studies based on linear and static concepts of disease towards prospective research that considers the importance of contextual factors and the dynamic and complex nature of mental health.

Advancing specificity in delirium: The delirium subtyping initiative.

BACKGROUND: Delirium, a common syndrome with heterogeneous etiologies and clinical presentations, is associated with poor long-term outcomes. Recording and analyzing all delirium equally could be hindering the field's understanding of pathophysiology and identification of targeted treatments. Current delirium subtyping methods reflect clinically evident features but likely do not account for underlying biology. METHODS: The Delirium Subtyping Initiative (DSI) held three sessions with an international panel of 25 experts. RESULTS: Meeting participants suggest further characterization of delirium features to complement the existing Diagnostic and Statistical Manual of Mental Disorders Fifth Edition Text Revision diagnostic criteria. These should span the range of delirium-spectrum syndromes and be measured consistently across studies. Clinical features should be recorded in conjunction with biospecimen collection, where feasible, in a standardized way, to determine temporal associations of biology coincident with clinical fluctuations. DISCUSSION: The DSI made recommendations spanning the breadth of delirium research including clinical features, study planning, data collection, and data analysis for characterization of candidate delirium subtypes. HIGHLIGHTS: Delirium features must be clearly defined, standardized, and operationalized. Large datasets incorporating both clinical and biomarker variables should be analyzed together. Delirium screening should incorporate communication and reasoning.

Childhood trauma is associated with reduced frontal gray matter volume: a large transdiagnostic structural MRI study.

BACKGROUND: Childhood trauma increases risk for psychopathology and cognitive impairment. Prior research mainly focused on the hippocampus and amygdala in single diagnostic categories. However, other brain regions may be impacted by trauma as well, and effects may be independent of diagnosis. This cross-sectional study investigated cortical and subcortical gray matter volume in relation to childhood trauma severity. METHODS: We included 554 participants: 250 bipolar-I patients, 84 schizophrenia-spectrum patients and 220 healthy individuals without a psychiatric history. Participants filled in the Childhood Trauma Questionnaire. Anatomical T1 MRI scans were acquired at 3T, regional brain morphology was assessed using Freesurfer. RESULTS: In the total sample, trauma-related gray matter reductions were found in the frontal lobe (ß = -0.049, p = 0.008; q = 0.048), this effect was driven by the right medial orbitofrontal, paracentral, superior frontal regions and the left precentral region. No trauma-related volume reductions were observed in any other (sub)cortical lobes nor the hippocampus or amygdala, trauma-by-group (i.e. both patient groups and healthy subjects) interaction effects were absent. A categorical approach confirmed a pattern of more pronounced frontal gray matter reductions in individuals reporting multiple forms of trauma and across quartiles of cumulative trauma scores. Similar dose-response patterns were revealed within the bipolar and healthy subgroups, but did not reach significance in schizophrenia-spectrum patients. CONCLUSIONS: Findings show that childhood trauma is linked to frontal gray matter reductions, independent of psychiatric morbidity. Our results indicate that childhood trauma importantly contributes to the neurobiological changes commonly observed across psychiatric disorders. Frontal volume alterations may underpin affective and cognitive disturbances observed in trauma-exposed individuals.

DeltaScan for the Assessment of Acute Encephalopathy and Delirium in ICU and non-ICU Patients, a Prospective Cross-Sectional Multicenter Validation Study.

OBJECTIVES: To measure the diagnostic accuracy of DeltaScan: a portable real-time brain state monitor for identifying delirium, a manifestation of acute encephalopathy (AE) detectable by polymorphic delta activity (PDA) in single-channel electroencephalograms (EEGs). DESIGN: Prospective cross-sectional study. SETTING: Six Intensive Care Units (ICU's) and 17 non-ICU departments, including a psychiatric department across 10 Dutch hospitals. PARTICIPANTS: 494 patients, median age 75 (IQR:64-87), 53% male, 46% in ICUs, 29% delirious. MEASUREMENTS: DeltaScan recorded 4-minute EEGs, using an algorithm to select the first 96 seconds of artifact-free data for PDA detection. This algorithm was trained and calibrated on two independent datasets. METHODS: Initial validation of the algorithm for AE involved comparing its output with an expert EEG panel's visual inspection. The primary objective was to assess DeltaScan's accuracy in identifying delirium against a delirium expert panel's consensus. RESULTS: DeltaScan had a 99% success rate, rejecting 6 of the 494 EEG's due to artifacts. Performance showed and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.86 (95% CI: 0.83-0.90) for AE (sensitivity: 0.75, 95%CI=0.68-0.81, specificity: 0.87 95%CI=0.83-0.91. The AUC was 0.71 for delirium (95%CI=0.66-0.75, sensitivity: 0.61 95%CI=0.52-0.69, specificity: 72, 95%CI=0.67-0.77). Our validation aim was an NPV for delirium above 0.80 which proved to be 0.82 (95%CI: 0.77-0.86). Among 84 non-delirious psychiatric patients, DeltaScan differentiated delirium from other disorders with a 94% (95%CI: 87-98%) specificity. CONCLUSIONS: DeltaScan can diagnose AE at bedside and shows a clear relationship with clinical delirium. Further research is required to explore its role in predicting delirium-related outcomes.

Functional brain network and trail making test changes following major surgery and postoperative delirium: a prospective, multicentre, observational cohort study.

BACKGROUND: Delirium is a frequent complication after surgery in older adults and is associated with an increased risk of long-term cognitive impairment and dementia. Disturbances in functional brain networks were previously reported during delirium. We hypothesised that alterations in functional brain networks persist after remission of postoperative delirium and that functional brain network alterations are associated with long-term cognitive impairment. METHODS: In this prospective, multicentre, observational cohort study, we included older patients who underwent clinical assessments (including the Trail Making Test B [TMT-B]) and resting-state functional MRI (rs-fMRI) before and 3 months after elective surgery. Delirium was assessed on the first seven postoperative days. RESULTS: Of the 554 enrolled patients, 246 remained after strict motion correction, of whom 38 (16%) developed postoperative delirium. The rs-fMRI functional connectivity strength increased 3 months after surgery in the total study population (ß=0.006; 95% confidence interval [CI]: 0.001-0.011; P=0.013), but it decreased after postoperative delirium (ß=-0.015; 95% CI: -0.028 to 0.002; P=0.023). No difference in TMT-B scores was found at follow-up between patients with and without postoperative delirium. Patients with decreased functional connectivity strength declined in TMT-B scores compared with those who did not (ß=11.04; 95% CI: 0.85-21.2; P=0.034). CONCLUSIONS: Postoperative delirium was associated with decreased brain functional connectivity strength after 3 months, suggesting that delirium has a long-lasting impact on brain networks. The decreased connectivity strength was associated with significant cognitive deterioration after major surgery. CLINICAL TRIAL REGISTRATION: NCT02265263.

Clustering of antipsychotic-naïve patients with schizophrenia based on functional connectivity from resting-state electroencephalography.

Schizophrenia is associated with aberrations in the Default Mode Network (DMN), but the clinical implications remain unclear. We applied data-driven, unsupervised machine learning based on resting-state electroencephalography (rsEEG) functional connectivity within the DMN to cluster antipsychotic-naïve patients with first-episode schizophrenia. The identified clusters were investigated with respect to psychopathological profile and cognitive deficits. Thirty-seven antipsychotic-naïve, first-episode patients with schizophrenia (mean age 24.4 (5.4); 59.5% males) and 97 matched healthy controls (mean age 24.0 (5.1); 52.6% males) underwent assessments of rsEEG, psychopathology, and cognition. Source-localized, frequency-dependent functional connectivity was estimated using Phase Lag Index (PLI). The DMN-PLI was factorized for each frequency band using principal component analysis. Clusters of patients were identified using a Gaussian mixture model and neurocognitive and psychopathological profiles of identified clusters were explored. We identified two clusters of patients based on the theta band (4-8 Hz), and two clusters based on the beta band (12-30 Hz). Baseline psychopathology could predict theta clusters with an accuracy of 69.4% (p = 0.003), primarily driven by negative symptoms. Five a priori selected cognitive functions conjointly predicted the beta clusters with an accuracy of 63.6% (p = 0.034). The two beta clusters displayed higher and lower DMN connectivity, respectively, compared to healthy controls. In conclusion, the functional connectivity within the DMN provides a novel, data-driven means to stratify patients into clinically relevant clusters. The results support the notion of biological subgroups in schizophrenia and endorse the application of data-driven methods to recognize pathophysiological patterns at earliest stage of this syndrome.

Muva physical activity intervention to improve social functioning in people with a severe mental illness: study protocol of a pragmatic stepped wedge cluster randomized trial.

BACKGROUND: People with severe mental illness (SMI) often suffer from long-lasting symptoms that negatively influence their social functioning, their ability to live a meaningful life, and participation in society. Interventions aimed at increasing physical activity can improve social functioning, but people with SMI experience multiple barriers to becoming physically active. Besides, the implementation of physical activity interventions in day-to-day practice is difficult. In this study, we aim to evaluate the effectiveness and implementation of a physical activity intervention to improve social functioning, mental and physical health. METHODS: In this pragmatic stepped wedge cluster randomized controlled trial we aim to include 100 people with SMI and their mental health workers from a supported housing organization. The intervention focuses on increasing physical activity by implementing group sports activities, active guidance meetings, and a serious game to set physical activity goals. We aim to decrease barriers to physical activity through active involvement of the mental health workers, lifestyle courses, and a medication review. Participating locations will be divided into four clusters and randomization will decide the start of the intervention. The primary outcome is social functioning. Secondary outcomes are quality of life, symptom severity, physical activity, cardiometabolic risk factors, cardiorespiratory fitness, and movement disturbances with specific attention to postural adjustment and movement sequencing in gait. In addition, we will assess the implementation by conducting semi-structured interviews with location managers and mental health workers and analyze them by direct content analysis. DISCUSSION: This trial is innovative since it aims to improve social functioning in people with SMI through a physical activity intervention which aims to lower barriers to becoming physically active in a real-life setting. The strength of this trial is that we will also evaluate the implementation of the intervention. Limitations of this study are the risk of poor implementation of the intervention, and bias due to the inclusion of a medication review in the intervention that might impact outcomes. TRIAL REGISTRATION: This trial was registered prospectively in The Netherlands Trial Register (NTR) as NTR NL9163 on December 20, 2020. As the The Netherlands Trial Register is no longer available, the trial can now be found in the International Clinical Trial Registry Platform via: https://trialsearch.who.int/Trial2.aspx?TrialID=NL9163 .

Direction of information flow in large-scale resting-state networks is frequency-dependent.

Normal brain function requires interactions between spatially separated, and functionally specialized, macroscopic regions, yet the directionality of these interactions in large-scale functional networks is unknown. Magnetoencephalography was used to determine the directionality of these interactions, where directionality was inferred from time series of beamformer-reconstructed estimates of neuronal activation, using a recently proposed measure of phase transfer entropy. We observed well-organized posterior-to-anterior patterns of information flow in the higher-frequency bands (alpha1, alpha2, and beta band), dominated by regions in the visual cortex and posterior default mode network. Opposite patterns of anterior-to-posterior flow were found in the theta band, involving mainly regions in the frontal lobe that were sending information to a more distributed network. Many strong information senders in the theta band were also frequent receivers in the alpha2 band, and vice versa. Our results provide evidence that large-scale resting-state patterns of information flow in the human brain form frequency-dependent reentry loops that are dominated by flow from parieto-occipital cortex to integrative frontal areas in the higher-frequency bands, which is mirrored by a theta band anterior-to-posterior flow.

Minimum spanning tree analysis of the human connectome.

One of the challenges of brain network analysis is to directly compare network organization between subjects, irrespective of the number or strength of connections. In this study, we used minimum spanning tree (MST; a unique, acyclic subnetwork with a fixed number of connections) analysis to characterize the human brain network to create an empirical reference network. Such a reference network could be used as a null model of connections that form the backbone structure of the human brain. We analyzed the MST in three diffusion-weighted imaging datasets of healthy adults. The MST of the group mean connectivity matrix was used as the empirical null-model. The MST of individual subjects matched this reference MST for a mean 58%-88% of connections, depending on the analysis pipeline. Hub nodes in the MST matched with previously reported locations of hub regions, including the so-called rich club nodes (a subset of high-degree, highly interconnected nodes). Although most brain network studies have focused primarily on cortical connections, cortical-subcortical connections were consistently present in the MST across subjects. Brain network efficiency was higher when these connections were included in the analysis, suggesting that these tracts may be utilized as the major neural communication routes. Finally, we confirmed that MST characteristics index the effects of brain aging. We conclude that the MST provides an elegant and straightforward approach to analyze structural brain networks, and to test network topological features of individual subjects in comparison to empirical null models.

Oscillatory brain activity associates with neuroligin-3 expression and predicts progression free survival in patients with diffuse glioma.

INTRODUCTION: Diffuse gliomas have local and global effects on neurophysiological brain functioning, which are often seen as 'passive' consequences of the tumor. However, seminal preclinical work has shown a prominent role for neuronal activity in glioma growth: mediated by neuroligin-3 (NLGN3), increased neuronal activity causes faster glioma growth. It is unclear whether the same holds true in patients. Here, we investigate whether lower levels of oscillatory brain activity relate to lower NLGN3 expression and predict longer progression free survival (PFS) in diffuse glioma patients. METHODS: Twenty-four newly diagnosed patients with diffuse glioma underwent magnetoencephalography and subsequent tumor resection. Oscillatory brain activity was approximated by calculating broadband power (0.5-48 Hz) of the magnetoencephalography. NLGN3 expression in glioma tissue was semi-quantitatively assessed by immunohistochemistry. Peritumor and global oscillatory brain activity was then compared between different levels of NLGN3 expression with Kruskal-Wallis tests. Cox proportional hazards analyses were performed to estimate the predictive value of oscillatory brain activity for PFS. RESULTS: Patients with low expression of NLGN3 had lower levels of global oscillatory brain activity than patients with higher NLGN3 expression (P < 0.001). Moreover, lower peritumor (hazard ratio 2.17, P = 0.008) and global oscillatory brain activity (hazard ratio 2.10, P = 0.008) predicted longer PFS. CONCLUSIONS: Lower levels of peritumor and global oscillatory brain activity are related to lower NLGN3 expression and longer PFS, corroborating preclinical research. This study highlights the important interplay between macroscopically measured brain activity and glioma progression, and may lead to new therapeutic interventions in diffuse glioma patients.

Meditation is associated with increased brain network integration.

INTRODUCTION: This study aims to identify novel quantitative EEG measures associated with mindfulness meditation. As there is some evidence that meditation is associated with higher integration of brain networks, we focused on EEG measures of network integration. METHODS: Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators performed a basic meditation practice that supported effortless awareness, which is an important quality of experience related to mindfulness practices, while their EEG was recorded. Experienced meditators performed a self-selected meditation practice that supported effortless awareness. Network integration was analyzed with maximum betweenness centrality and leaf fraction (which both correlate positively with network integration) as well as with diameter and average eccentricity (which both correlate negatively with network integration), based on a phase-lag index (PLI) and minimum spanning tree (MST) approach. Differences between groups were assessed using repeated-measures ANOVA for the theta (4-8 Hz), alpha (8-13 Hz) and lower beta (13-20 Hz) frequency bands. RESULTS: Maximum betweenness centrality was significantly higher in experienced meditators than in novices (P = 0.012) in the alpha band. In the same frequency band, leaf fraction showed a trend toward being significantly higher in experienced meditators than in novices (P = 0.056), while diameter and average eccentricity were significantly lower in experienced meditators than in novices (P = 0.016 and P = 0.028 respectively). No significant differences between groups were observed for the theta and beta frequency bands. CONCLUSION: These results show that alpha band functional network topology is better integrated in experienced meditators than in novice meditators during meditation. This novel finding provides the rationale to investigate the temporal relation between measures of functional connectivity network integration and meditation quality, for example using neurophenomenology experiments.

Loss of EEG Network Efficiency Is Related to Cognitive Impairment in Dementia With Lewy Bodies.

OBJECTIVE: The aim of this study was to test whether disturbed EEG resting-state functional connectivity and network organization are a potential neurophysiological substrate of cognitive impairment in dementia with Lewy bodies. METHODS: EEG recordings were obtained in dementia with Lewy bodies patients, Alzheimer's disease patients and controls, matched for age and sex (N = 66 for each group; 14 [21%] female; mean age: 70 years). We analyzed functional connectivity of band-filtered EEG time series using the phase lag index. Functional brain network topology was analyzed with the minimum spanning tree. Mini-Mental State Examination, Trail Making Test A, and Visual Association Test were used as cognitive measures. RESULTS: Dementia with Lewy bodies patients showed lower connectivity strength in the alpha frequency band, compared to both controls and Alzheimer's disease patients (P < 0.001). Functional network topology in dementia with Lewy bodies patients was less efficient and contained less hubs (P < 0.01). Network characteristics in Alzheimer's disease patients were in between (but did not differ from) those of the other two groups. In dementia with Lewy bodies patients, lower alpha band phase lag index correlated with Visual Association Test scores and Trail Making Test scores (ρ = 0.33 and ρ = 0.31, respectively), whereas leaf fraction (a measure of 'network efficiency') correlated with Visual Association Test scores (ρ = 0.29) and Mini-Mental State Examination scores (ρ = 0.27). CONCLUSION: Functional networks of dementia with Lewy bodies patients are characterized by decreased connectivity strength and a loss of network efficiency and hubs. Severity of these disturbances is related to cognitive impairment, suggesting that network disturbances mediate between neuropathology and the clinical syndrome in dementia with Lewy bodies.

Epilepsy surgery outcome and functional network alterations in longitudinal MEG: a minimum spanning tree analysis.

Seizure freedom after resective epilepsy surgery is not obtained in a substantial number of patients with medically intractable epilepsy. Functional neural network analysis is a promising technique for more accurate identification of the target areas for epilepsy surgery, but a better understanding of the correlations between changes in functional network organization due to surgery and postoperative seizure status is required. We explored these correlations in longitudinal magnetoencephalography (MEG) recordings of 20 lesional epilepsy patients. Resting-state MEG recordings were obtained at baseline (preoperatively; T0) and at 3-7 (T1) and 9-15months after resection (T2). We assessed frequency-specific functional connectivity and performed a minimum spanning tree (MST) network analysis. The MST captures the most important connections in the network. We found a significant positive correlation between functional connectivity in the lower alpha band and seizure frequency at T0, especially in regions where lesions were located. MST leaf fraction, a measure of integration of information in the network, was significantly increased between T0 and T2, only for the seizure-free patients. This is in line with previous work, which showed that lower functional network integration in lesional epilepsy patients is related to higher epilepsy burden. Finally, eccentricity and betweenness centrality, which are measures of hub-status, decreased between T0 and T2 in seizure free patients, also in regions that were anatomically close to resection cavities. Our results increase insight into functional network changes in successful epilepsy surgery and might eventually be utilized for optimization of neurosurgical approaches.

Decreased functional connectivity and disturbed directionality of information flow in the electroencephalography of intensive care unit patients with delirium after cardiac surgery.

BACKGROUND: In this article, the authors explore functional connectivity and network topology in electroencephalography recordings of patients with delirium after cardiac surgery, aiming to improve the understanding of the pathophysiology and phenomenology of delirium. The authors hypothesize that disturbances in attention and consciousness in delirium may be related to alterations in functional neural interactions. METHODS: Electroencephalography recordings were obtained in postcardiac surgery patients with delirium (N = 25) and without delirium (N = 24). The authors analyzed unbiased functional connectivity of electroencephalography time series using the phase lag index, directed phase lag index, and functional brain network topology using graph analysis. RESULTS: The mean phase lag index was lower in the α band (8 to 13 Hz) in patients with delirium (median, 0.120; interquartile range, 0.113 to 0.138) than in patients without delirium (median, 0.140; interquartile range, 0.129 to 0.168; P < 0.01). Network topology in delirium patients was characterized by lower normalized weighted shortest path lengths in the α band (t = -2.65; P = 0.01). δ Band-directed phase lag index was lower in anterior regions and higher in central regions in delirium patients than in nondelirium patients (F = 4.53; P = 0.04, and F = 7.65; P < 0.01, respectively). CONCLUSIONS: Loss of α band functional connectivity, decreased path length, and increased δ band connectivity directed to frontal regions characterize the electroencephalography during delirium after cardiac surgery. These findings may explain why information processing is disturbed in delirium.

Pre-treatment predictors of cognitive side-effects after treatment with electroconvulsive therapy in patients with depression: A multicenter study.

BACKGROUND: Electroconvulsive therapy (ECT) is a highly effective treatment for major depressive episodes (MDE). However, ECT-induced cognitive side-effects remain a concern. Identification of pre-treatment predictors that contribute to these side-effects remain unclear. We examined cognitive performance and individual cognitive profiles over time (up to six months) following ECT and investigated possible pre-treatment clinical and demographic predictors of cognitive decline shortly after ECT. METHODS: 634 patients with MDE from five sites were included with recruitment periods between 2001 and 2020. Linear mixed models were used to examine how cognitive performance, assessed with an extensive neuropsychological test battery, evolved over time following ECT. Next, possible pre-treatment predictors of cognitive side-effects directly after ECT were examined using linear regression. RESULTS: Directly after ECT, only verbal fluency (animal and letter; p < 0.0001; Cohen's d: -0.25 and -0.29 respectively) and verbal recall (p < 0.0001; Cohen's d: -0.26) significantly declined. However, during three and six months of follow-up, cognitive performance across all domains significantly improved, even outperforming baseline levels. No other pre-treatment factor than a younger age predicted a larger deterioration in cognitive performance shortly after ECT. LIMITATIONS: There was a substantial amount of missing data especially at 6 months follow-up. CONCLUSIONS: Our findings show that verbal fluency and memory retention are temporarily affected immediately after ECT. Younger patients may be more susceptible to experiencing these acute cognitive side-effects, which seems to be mostly due to a more intact cognitive functioning prior to ECT. These findings could contribute to decision-making regarding treatment selection, psychoeducation, and guidance during an ECT course.

Whole blood thiamine, intravenous thiamine supplementation and delirium occurrence in the intensive care unit: retrospective cohort analyses.

BACKGROUND: Thiamine di-phosphate is an essential cofactor in glucose metabolism, glutamate transformation and acetylcholinesterase activity, pathways associated with delirium occurrence. We hypothesised that a deficiency in whole blood thiamine and intravenous thiamine supplementation could impact delirium occurrence. AIM: To establish whether a deficiency in whole blood thiamine and/or intravenous thiamine supplementation within 72 h of intensive care admission is associated with delirium occurrence. METHOD: The first dataset was secondary analysis of a previous study in an intensive care unit in the Netherlands, reported in 2017. The second dataset contained consecutive intensive care admissions 2 years before (period 1: October 2014 to October 2016) and after (period 2: April 2017 to April 2019) routine thiamine supplementation was introduced within 72 h of admission. Delirium was defined as a positive Confusion Assessment Method-Intensive Care Unit score(s) in 24 h. RESULTS: Analysis of the first dataset (n = 57) using logistic regression showed no relationship between delirium and sepsis or whole blood thiamine, but a significant association with age (p = 0.014). In the second dataset (n = 3074), 15.1% received IV thiamine in period 1 and 62.6% during period 2. Hierarchical regression analysis reported reduction in delirium occurrence in the second period; this did not reach statistical significance, OR = 0.81 (95% CI 0.652-1.002); p = 0.052. CONCLUSION: No relationship was detected between whole blood thiamine and delirium occurrence on admission, at 24 and 48 h. It remains unclear whether routine intravenous thiamine supplementation during intensive care admission impacts delirium occurrence. Further prospective randomised clinical trials are needed.

Associations between psychotropic drugs and rsEEG connectivity and network characteristics: a cross-sectional study in hospital-admitted psychiatric patients.

Introduction: Resting-state EEG (rsEEG) characteristics, such as functional connectivity and network topology, are studied as potential biomarkers in psychiatric research. However, the presence of psychopharmacological treatment in study participants poses a potential confounding factor in biomarker research. To address this concern, our study aims to explore the impact of both single and multi-class psychotropic treatments on aforementioned rsEEG characteristics in a psychiatric population. Methods: RsEEG was analyzed in a real-world cross-sectional sample of 900 hospital-admitted psychiatric patients. Patients were clustered into eight psychopharmacological groups: unmedicated, single-class treatment with antipsychotics (AP), antidepressants (AD) or benzodiazepines (BDZ), and multi-class combinations of these treatments. To assess the associations between psychotropic treatments and the macroscale rsEEG characteristics mentioned above, we employed a general linear model with post-hoc tests. Additionally, Spearman's rank correlation analyses were performed to explore potential dosage effects. Results: Compared to unmedicated patients, single-class use of AD was associated with lower functional connectivity in the delta band, while AP was associated with lower functional connectivity in both the delta and alpha bands. Single-class use of BDZ was associated with widespread rsEEG differences, including lower functional connectivity across frequency bands and a different network topology within the beta band relative to unmedicated patients. All of the multi-class groups showed associations with functional connectivity or topology measures, but effects were most pronounced for concomitant use of all three classes of psychotropics. Differences were not only observed in comparison with unmedicated patients, but were also evident in comparisons between single-class, multi-class, and single/multi-class groups. Importantly, multi-class associations with rsEEG characteristics were found even in the absence of single-class associations, suggesting potential cumulative or interaction effects of different classes of psychotropics. Dosage correlations were only found for antipsychotics. Conclusion: Our exploratory, cross-sectional study suggests small but significant associations between single and multi-class use of antidepressants, antipsychotics and benzodiazepines and macroscale rsEEG functional connectivity and network topology characteristics. These findings highlight the importance of considering the effects of specific psychotropics, as well as their interactions, when investigating rsEEG biomarkers in a medicated psychiatric population.

Mental, cognitive and physical outcomes after intensive care unit treatment during the COVID-19 pandemic: a comparison between COVID-19 and non-COVID-19 patients.

To compare mental, cognitive and physical outcomes between COVID-19 and non-COVID-19 patients, 3-6 months after Intensive Care Unit (ICU) treatment during the COVID-19 pandemic and to compare mental outcomes between relatives of these patients. This retrospective cohort study included 209 ICU survivors (141 COVID-19 patients and 68 non-COVID-19 patients) and 168 of their relatives (maximum one per patient) during the COVID-19 pandemic. Primary outcomes were self-reported occurrence of mental, cognitive and/or physical symptoms 3-6 months after ICU discharge. The occurrence of mental symptoms did not differ between former COVID-19 patients (34.7% [43/124]) and non-COVID-19 patients (43.5% [27/62]) (p = 0.309), neither between relatives of COVID-19 patients (37.6% [38/101]) and relatives of non-COVID-19 patients (39.6% [21/53]) (p = 0.946). Depression scores on the Hospital Anxiety and Depression Scale were lower in former COVID-19 patients, compared to non-COVID-19 patients (p = 0.025). We found no differences between COVID-19 and non-COVID-19 patients in cognitive and physical outcomes. Mental, cognitive and physical outcomes in COVID-19 ICU survivors were similar to non-COVID-19 ICU survivors. Mental symptoms in relatives of COVID-19 ICU survivors did not differ from relatives of non-COVID-19 ICU survivors, within the same time frame.

Outcome prediction of electroconvulsive therapy for depression.

INTRODUCTION: We developed and tested a Bayesian network(BN) model to predict ECT remission for depression, with non-response as a secondary outcome. METHODS: We performed a systematic literature search on clinically available predictors. We combined these predictors with variables from a dataset of clinical ECT trajectories (performed in the University Medical Center Utrecht) to create priors and train the BN. Temporal validation was performed in an independent sample. RESULTS: The systematic literature search yielded three meta-analyses, which provided prior knowledge on outcome predictors. The clinical dataset consisted of 248 treatment trajectories in the training set and 44 trajectories in the test set at the same medical center. The AUC for the primary outcome remission estimated on an independent validation set was 0.686 (95%CI 0.513-0.859) (AUC values of 0.505 - 0.763 observed in 5-fold cross validation of the model within the train set). Accuracy 0.73 (balanced accuracy 0.67), sensitivity 0.55, specificity 0.79, after temporal validation in the independent sample. Prior literature information marginally reduced CI width. DISCUSSION: A BN model comprised of prior knowledge and clinical data can predict remission of depression after ECT with reasonable performance. This approach can be used to make outcome predictions in psychiatry, and offers a methodological framework to weigh additional information, such as patient characteristics, symptoms and biomarkers. In time, it may be used to improve shared decision-making in clinical practice.