Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine.

To examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram.

Data were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole.

Anhedonia severity significantly improved after treatment with adjunct aripiprazole.

There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus.

Eight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.

To examine patterns of cognitive function among a clinical sample of patients seeking treatment for Post-Acute Sequelae of COVID-19 (PASC).

One hundred nineteen patients each completed a baseline neuropsychological evaluation, including clinical diagnostic interview, cognitive assessments, and a comprehensive battery of self-report questionnaires. Patients had a mean age of 50 years (range:18 to 74, SD=10.1) and a mean of 15.5 years (SD=2.54) of formal education. Patients were primarily female (74%) and of White/Caucasian race (75%). Hierarchical agglomerative clustering was used to partition the data into groups based on cognitive performance. Euclidean distance was used as the similarity measure for the continuous variables and within-cluster variance was minimized using Ward’s method. The optimal number of clusters was determined empirically by fitting models with 1 to 15 clusters, with the best number of clusters selected using the silhouette index. All analyses were conducted using the NbClust package, an R package for determining the relevant number of clusters in a data set.

Clustering yielded two distinct clusters of cognitive performance. Group 1 (n=57) performed worse than Group 2 (n=62) on most cognitive variables (including a brief cognitive screener and tests of attention/working memory, executive function, processing speed, learning and delayed recall). Of note, there were no significant differences between groups on an infection severity scale, hospitalizations/ICU admissions, initial or current COVID-19 symptoms, or prior comorbidities. Groups did not differ in age or gender, but Group 1 had a lower education level than Group 2 (M=14.7, SD=2.45 vs. M=16.2, SD=2.42; p=.001). Group 1 also had significantly more minorities than Group 2 (40% vs. 8%; p<.001). No other demographic differences (income, living arrangement, or marital status) were observed. In comparison to Group 2 patients, Group 1 patients self-reported significantly higher levels of anxiety and depression and functional impairment (Functional Activities Questionnaire: M=11.3, SD=8.33 vs. M=7.65, SD=7.97), perceived stress (Perceived Stress Scale: M=24.7, SD=7.90 vs. M=20.3, SD=7.89), insomnia (Insomnia Severity Index: M=16.0, SD=6.50 vs. M=13.1, SD=6.76), and subjective cognitive functioning (Cognitive Failures Questionnaire: M=58.8, SD=16.9 vs. M=50.3, SD=18.6; p’s<.05).

Findings indicate two predominant subtypes of patients seeking treatment for PASC, with one group presenting as more cognitively impaired and reporting greater levels of anxiety, depression, insomnia, perceived stress, functional limitations, and subjective cognitive impairment. Future directions include follow-up assessments with these patients to determine cognitive trajectories over time and tailoring treatment adjuncts to address mood symptoms, insomnia, functional ability, and lifestyle variables. Understanding mechanisms of differences in cognitive and affective symptoms is needed in future work. Limitations to the study were that patients were referred for evaluation based on the complaint of “brain fog” and the sample was a homogenous, highly educated, younger group of individuals who experienced generally mild COVID-19 course.

Specialist Perinatal Mental Health Services (SPMHS) are a new development in Ireland. This service evaluation examined the impact of the introduction of a SPMHS multidisciplinary team (MDT) on prescribing practices and treatment pathways in an Irish maternity hospital.

Clinical charts were reviewed to collect data on all referrals, diagnoses, pharmacological and non-pharmacological interventions delivered in a SPMHS over a 3-week period in 2019. The findings were compared to the same 3-week period in 2020 following the expansion of the SPMHS MDT.

In 2019 (n = 32) and 2020 (n = 47), most (75 and 79%, respectively) assessments were antenatal. The proportion of patients prescribed psychotropic medication within the SPMHS was not significantly different from 2019 (31%) to 2020 (23%), though more patients were already prescribed psychotropic medications at the time of referral (22% in 2019 v. 36% in 2020). There was an increase in MDT interventions in 2020 with more input from psychology, clinical nurse specialist (CNS), and social work intervention. Adherence to prescribing standards improved from 2019 to 2020.

Prescribing patterns remained unchanged between 2019 and 2020. Improvement was observed in adherence to prescribing standards and there was increased provision of MDT interventions in 2020. Broader diagnostic categories were also used in 2020, possibly suggesting that the service is now providing more individualized care.

Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

Many clinical trials leverage real-world data. Typically, these data are manually abstracted from electronic health records (EHRs) and entered into electronic case report forms (CRFs), a time and labor-intensive process that is also error-prone and may miss information. Automated transfer of data from EHRs to eCRFs has the potential to reduce data abstraction and entry burden as well as improve data quality and safety.

We conducted a test of automated EHR-to-CRF data transfer for 40 participants in a clinical trial of hospitalized COVID-19 patients. We determined which coordinator-entered data could be automated from the EHR (coverage), and the frequency with which the values from the automated EHR feed and values entered by study personnel for the actual study matched exactly (concordance).

The automated EHR feed populated 10,081/11,952 (84%) coordinator-completed values. For fields where both the automation and study personnel provided data, the values matched exactly 89% of the time. Highest concordance was for daily lab results (94%), which also required the most personnel resources (30 minutes per participant). In a detailed analysis of 196 instances where personnel and automation entered values differed, both a study coordinator and a data analyst agreed that 152 (78%) instances were a result of data entry error.

An automated EHR feed has the potential to significantly decrease study personnel effort while improving the accuracy of CRF data.

Blast related characteristics may contribute to the diversity of findings on whether mild traumatic brain injury sustained during war zone deployment has lasting cognitive effects. This study aims to evaluate whether a history of blast exposure at close proximity, defined as exposure within 30 feet, has long-term or lasting influences on cognitive outcomes among current and former military personnel.

One hundred participants were assigned to one of three groups based on a self-report history of blast exposure during combat deployments: 47 close blast, 14 non-close blast, and 39 comparison participants without blast exposure. Working memory, processing speed, verbal learning/memory, and cognitive flexibility were evaluated using standard neuropsychological tests. In addition, assessment of combat exposure and current post-concussive, posttraumatic stress, and depressive symptoms, and headache was performed via self-report measures. Variables that differed between groups were controlled as covariates.

No group differences survived Bonferroni correction for family-wise error rate; the close blast group did not differ from non-close blast and comparison groups on measures of working memory, processing speed, verbal learning/memory, or cognitive flexibility. Controlling for covariates did not alter these results.

No evidence emerged to suggest that a history of close blast exposure was associated with decreased cognitive performance when comparisons were made with the other groups. Limited characterization of blast contexts experienced, self-report of blast distance, and heterogeneity of injury severity within the groups are the main limitations of this study.

Patients with schizophrenia suffer from increased mortality rates equivalent to 15-20 years shorter life expectancy. Up to 60% of this excess mortality can be explained by preventable, somatic conditions like cardiovascular, metabolic, and respiratory comorbidities. As forensic psychiatric (FP) patients often experience the triple stigmatization of mental illness, substance misuse and criminal conviction, the risk of suboptimal diagnosis and treatment may be high. Although benefits from the addition of general practitioner (GP) services to non-FP wards have been shown elsewhere, this cross-sectoral approach has never been attempted in a Danish FP ward.

One purpose of this project is to evaluate the associations between self-reported quality of life and objective measures of somatic health.

A clinical intervention in which a GP consults patients in all medium secure wards in the Central Denmark Region (N=72). The consultation includes a physical examination, medication review, and evaluation of blood samples. Data is collected from: electronic patient files and questionnaires regarding quality of life (SF-12), lifestyle, and attitude towards GP services.

The population will be described in regards to socio-demographic, clinical, and forensic characteristics. Associations will be made between quality of life (SF-12), metabolic syndrome, blood markers, and heart-SCORE risk. Risk profiles for endocrinologic and coronary illness will be examined.

Results may guide future health interventions and will be used as a basis for adjustments to the current project.

No significant relationships.

Impairment in decision-making capacity is a serious consequence of executive dysfunction secondary to serious mental disorders like schizophrenia. Functional mental capacity (FMC) refers to an individual’s ability to make and communicate legally competent decisions autonomously. Studies have shown that FMC is dependent on severity of psychosis and can improve with treatment.

To ascertain the correlation between the scores on a structured judgement tool, namely the Dundrum Capacity Ladders (DCL) with level of acuity of treatment setting and length of stay in a secure forensic hospital.

Sixty-two patients were interviewed using the DCL across three domains – healthcare, welfare and finances. Correlation between DCL scores, length of hospital stay and level of acuity of treatment setting was assessed.

As patients moved from higher to lower dependency wards, mean DCL score increased, indicating a higher level of capacity. Patients in high dependency wards were most impaired while those in the low dependency wards performed significantly better (rs=0.472, p<0.001). The longer the patients stayed in the hospital, up until five years, the higher the mean welfare domain score (rs=0.402, p=0.011) and mean DCL score (rs=0.376, p=0.018). Beyond five years of hospital stay, those who had lower DCL scores and did not improve had longer length of stay.

Patients’ FMC improve as they progress from high to low level of acuity of treatment setting. However, this is dependent on the length of hospital stay. FMC may be a measure of recovery in the forensic setting.

No significant relationships.

Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.

In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.

A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.

A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional genome-wide association studies (GWAS) produce principal evidence for the association of genetic variants with disease. Transcriptomic imputation (TI) allows for the translation of those variants into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a broader setting through the use of phenome-wide association studies (pheWASs) in large and diverse clinical biobank populations with electronic health record phenotypes.

Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai BioMe™ Biobank and performed pheWASs on over 2000 outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of body mass index (BMI) and sex on AN-GReX clinical associations.

Our S-PrediXcan analysis identified 53 genes associated with AN, including what is, to our knowledge, the first-genetic association of AN with the major histocompatibility complex. AN-GReX was associated with autoimmune, metabolic, and gastrointestinal diagnoses in our biobank cohort, as well as measures of cholesterol, medications, substance use, and pain. Additionally, our analyses showed moderation of AN-GReX associations with measures of cholesterol and substance use by BMI, and moderation of AN-GReX associations with celiac disease by sex.

Our BMI-stratified results provide potential avenues of functional mechanism for AN-genes to investigate further.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.