Prediction of treatment outcomes in psychiatry--where do we stand ?

Comorbid anxiety in depression and rTMS treatment response: A retrospective study

INTRODUCTION

Few studies have examined the relationship between anxiety and response to repetitive transcranial magnetic stimulation (rTMS) in patients with unipolar depressive disorder (UDD) and bipolar depressive disorder (BDD). The primary aim of this study was to investigate the correlation between anxiety symptoms and the response to rTMS in individuals with comorbid anxiety, in both UDD and BDD patients. The secondary aim was to evaluate the efficacy of rTMS in reducing depressive symptoms, anxiety, and different subtypes of anxiety disorder.

METHODS

A total of 379 outpatients (214 UDD, 165 BDD) underwent 20 sessions of either right unilateral low-frequency rTMS or sequential bilateral rTMS targeting the dorsolateral prefrontal cortex (DLPFC). Beck Anxiety Inventory (BAI) and Beck Depression Inventory (BDI-II) were used for assessment.

RESULTS

Binary logistic regression analysis indicated that pre-treatment physical anxiety scores showed a negative correlation with rTMS response, while subjective anxiety scores were positively associated. Distinct and overlapping response profiles for UDD and BDD patients with comorbid anxiety were identified based on anxiety symptoms. Predictive values for the BDD group were more reliable than those for the UDD group (PPV: 75 %, NPV: 77 %). Moreover, both unilateral and bilateral rTMS significantly reduced depressive symptoms as well as physical, subjective, and panic-related anxiety symptoms.

CONCLUSION

The findings suggest that in both UDD and BDD, physical anxiety symptoms are linked to less response to rTMS, whereas subjective anxiety symptoms are associated with more response. Future prospective studies are required to confirm the predictive value of anxiety symptoms in UDD and BDD patients who suffer from comorbid anxiety.

Application of Near-Infrared Spectroscopy in Early Detection of Antidepressant Treatment Efficacy in Major Depressive Disorder: A Longitudinal Study

BACKGROUND

Major depressive disorder (MDD) is a prevalent and debilitating mental health condition, necessitating early detection and effective treatment strategies. Near-infrared spectroscopy (NIRS) is a promising neuroimaging technique for monitoring cerebral hemodynamics and may serve as an objective biomarker for MDD diagnosis and treatment efficacy. This study aimed to investigate the utility of NIRS in the early detection and longitudinal monitoring of antidepressant treatment efficacy in MDD patients.

METHODS

This longitudinal study, conducted from May 2022 to May 2024, included 138 participants. After propensity score matching analyses, 80 were included, including 40 MDD patients and 40 healthy controls matched for age, gender, race, education, height, weight, and body mass index (BMI). Participants underwent NIRS measurements during cognitive tasks, including verbal fluency, sustained attention (e-primer), and one-back memory tests. Clinical assessments were conducted using the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Clinical Global Impression (CGI), Continuous Performance Test (CPT), and one-back tests at baseline and after treatment at 4 weeks and 24 weeks. Statistical analyses were performed to evaluate changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) levels and their correlation with clinical outcomes.

RESULTS

At baseline, MDD patients had significantly lower HbO and higher HbR levels compared to controls (p < 0.01). After treatment, HbO increased (4.77 ± 1.23 to 5.37 ± 1.21 µmol/L, p < 0.05) while HbR decreased (3.46 ± 0.98 to 2.91 ± 0.96 µmol/L, p < 0.05) in the MDD group. However, these levels differed significantly from controls at 4 weeks (p < 0.01). By 24 weeks, HbO further increased (6.01 ± 1.08 µmol/L, p < 0.05), and HbR further decreased (2.19 ± 0.71 µmol/L, p < 0.05), with no significant differences from controls (p > 0.05). Clinically, MDD patients showed significant improvements in HAMD, HAMA, CGI, CPT, and one-back scores over 24 weeks (all p < 0.05). At 4 weeks, HAMD, HAMA, and CGI scores were higher, and CPT and one-back responses were lower than controls (p < 0.01). By 24 weeks, HAMD, HAMA, and CGI scores remained higher (p < 0.01), and CPT and one-back responses were lower than controls (p < 0.01).

CONCLUSION

This study underscores the potential of NIRS as a non-invasive, objective tool for early detection and monitoring of treatment efficacy in MDD. The significant correlations between NIRS findings and clinical improvements highlight its utility in personalized treatment strategies, paving the way for more effective management of MDD.

From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium and ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence of mental health disorders like depression and schizophrenia, which necessitate precise, innovative approaches. Emerging technologies like artificial intelligence, induced pluripotent stem cells, and multi-omics have the potential to transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies such as animal models and single-variable analyses continue to be used, frequently failing to capture the complexities of human neuropsychiatric conditions. Summary: This review critically evaluates the transition from serendipity to precision-based methodologies in neuropsychiatric research. It focuses on key innovations such as dynamic systems modeling and network-based approaches that use genetic, molecular, and environmental data to identify new therapeutic targets. Furthermore, it emphasizes the importance of interdisciplinary collaboration and human-specific models in overcoming the limitations of traditional approaches. Conclusions: We highlight precision psychiatry's transformative potential for revolutionizing mental health care. This paradigm shift, which combines cutting-edge technologies with systematic frameworks, promises increased diagnostic accuracy, reproducibility, and efficiency, paving the way for tailored treatments and better patient outcomes in neuropsychiatric care.

Functional brain connectivity changes associated with day-to-day fluctuations in affective states

Affective neuroscience has traditionally relied on cross-sectional studies to uncover the brain correlates of affects, emotions, and moods. Such findings obfuscate intraindividual variability that may reveal meaningful changing affect states. The few functional magnetic resonance imaging longitudinal studies that have linked changes in brain function to the ebbs and flows of affective states over time have mostly investigated a single individual. In this study, we explored how the functional connectivity of brain areas associated with affective processes can explain within-person fluctuations in self-reported positive and negative affects across several subjects. To do so, we leveraged the Day2day dataset that includes 40 to 50 resting-state functional magnetic resonance imaging scans along self-reported positive and negative affectivity from a sample of six healthy participants. Sparse multivariate mixed-effect linear models could explain 15% and 11% of the within-person variation in positive and negative affective states, respectively. Evaluation of these models' generalizability to new data demonstrated the ability to predict approximately 5% and 2% of positive and negative affect variation. The functional connectivity of limbic areas, such as the amygdala, hippocampus, and insula, appeared most important to explain the temporal dynamics of affects over days, weeks, and months.

Predicting anxiety treatment outcome in community mental health services using linked health administrative data

Anxiety disorders is ranked as the most common class of mental illness disorders globally, affecting hundreds of millions of people and significantly impacting daily life. Developing reliable predictive models for anxiety treatment outcomes holds immense potential to help guide the development of personalised care, optimise resource allocation and improve patient outcomes. This research investigates whether community mental health treatment for anxiety disorder is associated with reliable changes in Kessler psychological distress scale (K10) scores and whether pre-treatment K10 scores and past health service interactions can accurately predict reliable change (improvement). The K10 assessment was administered to 46,938 public patients in a community setting within the Western Australia dataset in 2005-2022; of whom 3794 in 4067 episodes of care were reassessed at least twice for anxiety disorders, obsessive-compulsive disorder, or reaction to severe stress and adjustment disorders (ICD-10 codes F40-F43). Reliable change on the K10 was calculated and used with the post-treatment score as the outcome variables. Machine learning models were developed using features from a large health service administrative linked dataset that includes the pre-treatment K10 assessment as well as community mental health episodes of care, emergency department presentations, and inpatient admissions for prediction. The classification model achieved an area under the receiver operating characteristic curve of 0.76 as well as an F1 score, precision and recall of 0.69, and the regression model achieved an R2 of 0.37 with mean absolute error of 5.58 on the test dataset. While the prediction models achieved moderate performance, they also underscore the necessity for regular patient monitoring and the collection of more clinically relevant and contextual patient data to further improve prediction of treatment outcomes.

Pharmacogenomic scores in psychiatry: systematic review of current evidence

In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.

Schizophrenia polygenic risk scores, clinical variables and genetic pathways as predictors of phenotypic traits of bipolar I disorder

AIM

We investigated the predictive value of polygenic risk scores (PRS) derived from the schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3) for phenotypic traits of bipolar disorder type-I (BP-I) in 1878 BP-I cases and 2751 controls from Romania and UK.

METHODS

We used PRSice-v2.3.3 and PRS-CS for computing SCZ3-PRS for testing the predictive power of SCZ3-PRS alone and in combination with clinical variables for several BP-I subphenotypes and for pathway analysis. Non-linear predictive models were also used.

RESULTS

SCZ3-PRS significantly predicted psychosis, incongruent and congruent psychosis, general age-of-onset (AO) of BP-I, AO-depression, AO-Mania, rapid cycling in univariate regressions. A negative correlation between the number of depressive episodes and psychosis, mainly incongruent and an inverse relationship between increased SCZ3-SNP loading and BP-I-rapid cycling were observed. In random forest models comparing the predictive power of SCZ3-PRS alone and in combination with nine clinical variables, the best predictions were provided by combinations of SCZ3-PRS-CS and clinical variables closely followed by models containing only clinical variables. SCZ3-PRS performed worst. Twenty-two significant pathways underlying psychosis were identified.

LIMITATIONS

The combined RO-UK sample had a certain degree of heterogeneity of the BP-I severity: only the RO sample and partially the UK sample included hospitalized BP-I cases. The hospitalization is an indicator of illness severity. Not all UK subjects had complete subphenotype information.

CONCLUSION

Our study shows that the SCZ3-PRS have a modest clinical value for predicting phenotypic traits of BP-I. For clinical use their best performance is in combination with clinical variables.

Neuromarkers in addiction: definitions, development strategies, and recent advances

Substance use disorders (SUDs) are the most costly and prevalent psychiatric conditions. Recent calls emphasize a need for biomarkers-measurable, stable indicators of normal and abnormal processes and response to treatment or environmental agents-and, in particular, brain-based neuromarkers that will advance understanding of the neurobiological basis of SUDs and clinical practice. To develop neuromarkers, researchers must be grounded in evidence that a putative marker (i) is sensitive and specific to the psychological phenomenon of interest, (ii) constitutes a predictive model, and (iii) generalizes to novel observations (e.g., through internal cross-validation and external application to novel data). These neuromarkers may be used to index risk of developing SUDs (susceptibility), classify individuals with SUDs (diagnostic), assess risk for progression to more severe pathology (prognostic) or index current severity of pathology (monitoring), detect response to treatment (response), and predict individualized treatment outcomes (predictive). Here, we outline guidelines for developing and assessing neuromarkers, we then review recent advances toward neuromarkers in addiction neuroscience centering our discussion around neuromarkers of craving-a core feature of SUDs. In doing so, we specifically focus on the Neurobiological Craving Signature (NCS), which show great promise for meeting the demand of neuromarkers.

Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression

BACKGROUND

Tightly connected symptom networks have previously been linked to treatment resistance, but most findings come from small-sample studies comparing single responder v. non-responder networks. We aimed to estimate the association between baseline network connectivity and treatment response in a large sample and benchmark its prognostic value against baseline symptom severity and variance.

METHODS

N = 40 518 patients receiving treatment for depression in routine care in England from 2015-2020 were analysed. Cross-sectional networks were constructed using the Patient Health Questionnaire-9 (PHQ-9) for responders and non-responders (N = 20 259 each). To conduct parametric tests investigating the contribution of PHQ-9 sum score mean and variance to connectivity differences, networks were constructed for 160 independent subsamples of responders and non-responders (80 each, n = 250 per sample).

RESULTS

The baseline non-responder network was more connected than responders (3.15 v. 2.70, S = 0.44, p < 0.001), but effects were small, requiring n = 750 per group to have 85% power. Parametric analyses revealed baseline network connectivity, PHQ-9 sum score mean, and PHQ-9 sum score variance were correlated (r = 0.20-0.58, all p < 0.001). Both PHQ-9 sum score mean (β = -1.79, s.e. = 0.07, p < 0.001), and PHQ-9 sum score variance (β = -1.67, s.e. = 0.09, p < 0.001) had larger effect sizes for predicting response than connectivity (β = -1.35, s.e. = 0.12, p < 0.001). The association between connectivity and response disappeared when PHQ-9 sum score variance was accounted for (β = -0.28, s.e. = 0.19, p = 0.14). We replicated these results in patients completing longer treatment (8-12 weeks, N = 22 952) and using anxiety symptom networks (N = 70 620).

CONCLUSIONS

The association between baseline network connectivity and treatment response may be largely due to differences in baseline score variance.

Resting-state electroencephalography theta predicts neurofeedback treatment 4-month follow-up response in nicotine addiction

Background

The high rate of long-term relapse is a major cause of smoking cessation failure. Recently, neurofeedback training has been widely used in the treatment of nicotine addiction; however, approximately 30% of subjects fail to benefit from this intervention. Our previous randomised clinical trial (RCT) examined cognition-guided neurofeedback and demonstrated a significant decrease in daily cigarette consumption at the 4-month follow-up. However, significant individual differences were observed in the 4-month follow-up effects of decreased cigarette consumption. Therefore, it is critical to identify who will benefit from pre-neurofeedback.

Aims

We examined whether the resting-state electroencephalography (EEG) characteristics from pre-neurofeedback predicted the 4-month follow-up effects and explored the possible mechanisms.

Methods

This was a double-blind RCT. A total of 60 participants with nicotine dependence were randomly assigned to either the real-feedback or yoked-feedback group. They underwent 6 min closed-eye resting EEG recordings both before and after two neurofeedback sessions. A follow-up assessment was conducted after 4 months.

Results

The frontal resting-state theta power spectral density (PSD) was significantly altered in the real-feedback group after two neurofeedback visits. Higher theta PSD in the real-feedback group before neurofeedback was the only predictor of decreased cigarette consumption at the 4-month follow-up. Further reliability analysis revealed a significant positive correlation between theta PSD pre-neurofeedback and post-neurofeedback. A leave-one-out cross-validated linear regression of the theta PSD pre-neurofeedback demonstrated a significant correlation between the predicted and observed reductions in cigarette consumption at the 4-month follow-up. Finally, source analysis revealed that the brain mechanisms of the theta PSD predictor were located in the orbital frontal cortex.

Conclusions

Our study demonstrated changes in the resting-state theta PSD following neurofeedback training. Moreover, the resting-state theta PSD may serve as a prognostic marker of neurofeedback effects. A higher resting-state theta PSD predicts a better long-term response to neurofeedback treatment, which may facilitate the selection of individualised interventions.

Trial registration number

ChiCTR-IPR-17011710.

A roadmap for applying machine learning when working with privacy-sensitive data: predicting non-response to treatment for eating disorders

OBJECTIVES

Applying machine-learning methodology to clinical data could present a promising avenue for predicting outcomes in patients receiving treatment for psychiatric disorders. However, preserving privacy when working with patient data remains a critical concern.

METHODS

In showcasing how machine-learning can be used to build a clinically relevant prediction model on clinical data, we apply two commonly used machine-learning algorithms (Random Forest and least absolute shrinkage and selection operator) to routine outcome monitoring data collected from 593 patients with eating disorders to predict absence of reliable improvement 12 months after entering outpatient treatment.

RESULTS

An RF model trained on data collected at baseline and after three months made 31.3% fewer errors in predicting lack of reliable improvement at 12 months, in comparison with chance. Adding data from a six-month follow-up resulted in only marginal improvements to accuracy.

CONCLUSION

We were able to build and validate a model that could aid clinicians and researchers in more accurately predicting treatment response in patients with EDs. We also demonstrated how this could be done without compromising privacy. ML presents a promising approach to developing accurate prediction models for psychiatric disorders such as ED.

Resting-state functional connectivity predicting clinical improvement following treatment in female adolescents with non-suicidal self-injury

BACKGROUND

Non-suicidal self-injury (NSSI) is highly prevalent among adolescents and predicts future psychopathology including suicide. To improve therapeutic decisions and clinical outcome of patients engaging in NSSI, it seems beneficial to determine neurobiological markers associated with treatment response. The present study investigated whether resting-state functional brain connectivity (RSFC) served to predict clinical improvements following treatment in adolescents engaging in NSSI.

METHODS

N = 27 female adolescents with NSSI took part in a baseline MRI exam and clinical outcome was assessed at follow-ups one, two and three years after baseline. During the follow-up period, patients received in- and/or outpatient treatment. Mixed-effects linear regression models were calculated to examine whether RSFC was associated with clinical improvement.

RESULTS

Patients' clinical outcome improved across time. Lower baseline RSFC between left paracentral gyrus and right anterior cingulate gyrus was associated with clinical improvement from baseline to one-year and from two-year to three-year follow-up. Lower and higher baseline RSFC in several inter- and intrahemispheric cortico-cortical and cortico-subcortical connections of interest were associated with clinical symptomatology and its severity, independent from time.

LIMITATIONS

A relatively small sample size constrains the generalizability of our findings. Further, no control group not receiving treatment was recruited, therefore clinical changes across time cannot solely be attributed to treatment.

CONCLUSIONS

While there was some evidence that RSFC was associated with clinical improvement following treatment, our findings suggest that functional connectivity is more predictive of severity of psychopathology and global functioning independent of time and treatment. We thereby add to the limited research on neurobiological markers as predictors of clinical outcome after treatment.

Associations of psychiatric disease and ageing with FKBP5 expression converge on superficial layer neurons of the neocortex

Identification and characterisation of novel targets for treatment is a priority in the field of psychiatry. FKBP5 is a gene with decades of evidence suggesting its pathogenic role in a subset of psychiatric patients, with potential to be leveraged as a therapeutic target for these individuals. While it is widely reported that FKBP5/FKBP51 mRNA/protein (FKBP5/1) expression is impacted by psychiatric disease state, risk genotype and age, it is not known in which cell types and sub-anatomical areas of the human brain this occurs. This knowledge is critical to propel FKBP5/1-targeted treatment development. Here, we performed an extensive, large-scale postmortem study (n = 1024) of FKBP5/1, examining neocortical areas (BA9, BA11 and ventral BA24/BA24a) derived from subjects that lived with schizophrenia, major depression or bipolar disorder. With an extensive battery of RNA (bulk RNA sequencing, single-nucleus RNA sequencing, microarray, qPCR, RNAscope) and protein (immunoblot, immunohistochemistry) analysis approaches, we thoroughly investigated the effects of disease state, ageing and genotype on cortical FKBP5/1 expression including in a cell type-specific manner. We identified consistently heightened FKBP5/1 levels in psychopathology and with age, but not genotype, with these effects strongest in schizophrenia. Using single-nucleus RNA sequencing (snRNAseq; BA9 and BA11) and targeted histology (BA9, BA24a), we established that these disease and ageing effects on FKBP5/1 expression were most pronounced in excitatory superficial layer neurons of the neocortex, and this effect appeared to be consistent in both the granular and agranular areas examined. We then found that this increase in FKBP5 levels may impact on synaptic plasticity, as FKBP5 gex levels strongly and inversely correlated with dendritic mushroom spine density and brain-derived neurotrophic factor (BDNF) levels in superficial layer neurons in BA11. These findings pinpoint a novel cellular and molecular mechanism that has potential to open a new avenue of FKBP51 drug development to treat cognitive symptoms in psychiatric disorders.

The therapeutic role of ketamine and esketamine in treating psychopathological domains of depression

Over the past two decades, ketamine has emerged as a novel effective and rapid-acting antidepressant. While the vast majority of studies on ketamine have focused on its ability to reduce the severity of depression broadly, its effectiveness in specific domains such as cognition, anhedonia, suicidality, and workplace/social/scholastic functionality has been neglected. Similarly, current treatments (e.g., SSRIs and SNRIs) aim to improve overall depression severity, which often results in the persistence of one or more residual symptom domains and prevents full recovery to premorbid functionality. In this review, we narratively synthesize the literature pertaining to the effectiveness of ketamine in treating key domains of depressive symptomatology (i.e., cognition, anhedonia, suicidality, and psychosocial functionality). Our findings suggest that ketamine is effective across domains varyingly, with the strongest evidence being for its ability to reduce suicidality. The rapid acting nature of ketamine further supports its use in treating suicidality and potentially preventing the completion of suicide. Evidence for the effectiveness of ketamine in other domains is weak, primarily due to a lack of robust studies specifically designed to assess these domains as primary outcomes. Future studies should scrutinize the effects of ketamine on specific domains of depression to optimize its implementation.

The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation

BACKGROUND

Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field.

METHODS

A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation.

RESULTS

Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97).

CONCLUSIONS

An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.

Neurite Orientation Dispersion and Density Imaging in Psychiatric Disorders: A Systematic Literature Review and a Technical Note

While major psychiatric disorders lack signature diagnostic neuropathologies akin to dementias, classic postmortem studies have established microstructural involvement, i.e., cellular changes in neurons and glia, as a key pathophysiological finding. Advanced magnetic resonance imaging techniques allow mapping of cellular tissue architecture and microstructural abnormalities in vivo, which holds promise for advancing our understanding of the pathophysiology underlying psychiatric disorders. Here, we performed a systematic review of case-control studies using neurite orientation dispersion and density imaging (NODDI) to assess brain microstructure in psychiatric disorders and a selective review of technical considerations in NODDI. Of the 584 potentially relevant articles, 18 studies met the criteria to be included in this systematic review. We found a general theme of abnormal gray and white matter microstructure across the diagnostic spectrum. We also noted significant variability in patterns of neurite density and fiber orientation within and across diagnostic groups, as well as associations between brain microstructure and phenotypical variables. NODDI has been successfully used to detect subtle microstructure abnormalities in patients with psychiatric disorders. Given that NODDI indices may provide a more direct link to pathophysiological processes, this method may not only contribute to advancing our mechanistic understanding of disease processes, it may also be well positioned for next-generation biomarker development studies.

Approaches to Medication Administration in Patients With Lack of Insight

Lack of insight typically complicates psychiatric presentations, necessitating careful thought and planning to choose the best course of treatment. Exploring methods of medication administration techniques in the context of a lack of insight is crucial to achieving the ultimate goal of overcoming the insight barrier as rapidly as possible, which will result in therapeutic benefit. This study's objective was to systematically review the evidence on medication administration techniques in a backdrop of lack of insight and how that evidence was curated in the scientific literature. This study used the literature search strategy, which entails retrieving and analyzing the existing scientific literature pertinent to medication administration techniques for individuals with no insight between 2010 and 2022. Accessing online databases, such as PubMed, Google Scholar, and Medline was utilized in this study's literature search strategy. In our findings, in the primary evidence search, no randomized control trial (RCT) comparing the various models of medication administration with a lack of insight was found. No study provided data on the superiority of utility, quality of life, or efficacy outcome. Some 17 scientific papers were identified that cited various trials about lack of insight and medication use and met the inclusion criteria. We concluded that it could be challenging to administer medication to patients who lack insight.

Nonetheless, progress has been made to mitigate this obstacle. Common moral values, common sense, medicolegal support, person-centered integrated care, and cutting-edge medication techniques may play a role. However, these models of medication administration are still evolving, along with the ethical concerns accompanying them. Hopefully, the available models discussed in this analysis will serve as a foundation for future developments. Nonetheless, much remains to be done. We encourage contemporary research to investigate safer and more dynamic methods that can alleviate this condition.

A follow-up study on treatment effects of cognitive-behavioral therapy on social anxiety disorder: Impact of COVID-19 fear during post-lockdown period

There is a paucity of research on the role of COVID-19 related fear and lockdown on social anxiety disorder (SAD). In a follow-up study during post-lockdown period, we compared social anxiety of individuals with SAD who received cognitive-behavioral therapy (CBT) versus psychoeducational-supportive therapy (PST) before the COVID-19 pandemic, and the impact of COVID-19 related fear. Social anxiety severity was rated by the Social Phobia Inventory (SPIN) at pre-intervention, post-intervention, and post-lockdown periods. Fear of COVID-19 was assessed during the post-lockdown period. The treatment effects in the CBT group (n = 33) were significantly better than the PST group (n = 32) at post-intervention; this was maintained at 14-months following intervention despite COVID-related lockdown. In the PST group, there was no change following the intervention; and the social phobia increased after lockdown. The CBT group had significantly less COVID-19 related fear than the PST group. Social anxiety was positively correlated with fear of COVID-19; and individuals with comorbidities had significantly more fear. Using the hierarchical multiple regression, SPIN post-intervention, COVID-19 fear, and duration of SAD predicted social anxiety severity during the post-lockdown period. In conclusion, the effect of CBT for SAD was maintained through lockdown and was associated with significantly less COVID-19 related fear.

Cardiac autonomic functioning and clinical outcome in adolescent borderline personality disorder over two years

The present study aimed to expand on previous findings that pre-treatment autonomic nervous system (ANS) functioning serves as a predictor of clinical outcome in adolescent borderline personality disorder (BPD), while examining whether the relationship between ANS functioning and treatment outcome may vary as a function of early life maltreatment (ELM). ANS stress response was examined considering changes in heart rate (HR) and vagally-mediated heart rate variability (vmHRV) over different conditions of the Montreal Imaging Stress Task (MIST) in a clinical sample of N = 27 adolescents across the spectrum of BPD severity. Participants received in- and/or outpatient treatment, while clinical data was assessed at routine follow-ups. Clinical outcome was defined by change in the number of fulfilled BPD criteria (as measured using the SCID-II), severity of psychopathology (CGI-S), and global level of functioning (GAF), measured 12 and 24 months after baseline assessments. Mixed-effects (random-intercept/random slope) linear regression models were calculated to examine markers of ANS function as potential predictors of clinical outcome. Irrespective of the presence of ELM exposure, both vmHRV resting-state and stress recovery measures were identified as significant predictors of clinical outcome over time. This study adds to the existing literature by replicating and expanding on preliminary findings, considering also physiological reactivity and recovery in addition to resting-state measures of ANS functioning. The present results further highlight the potential of markers of ANS functioning to serve as objective measures in the process of monitoring patient progress and to make predictions regarding treatment outcome in psychiatry research.

Schizophrenia Detection Using Machine Learning Approach from Social Media Content

Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.

Pre-treatment allostatic load and metabolic dysregulation predict SSRI response in major depressive disorder: a preliminary report

BACKGROUND

Major depressive disorder (MDD) is associated with increased allostatic load (AL; a measure of physiological costs of repeated/chronic stress-responding) and metabolic dysregulation (MetD; a measure of metabolic health and precursor to many medical illnesses). Though AL and MetD are associated with poor somatic health outcomes, little is known regarding their relationship with antidepressant-treatment outcomes.

METHODS

We determined pre-treatment AL and MetD in 67 healthy controls and 34 unmedicated, medically healthy MDD subjects. Following this, MDD subjects completed 8-weeks of open-label selective serotonin reuptake inhibitor (SSRI) antidepressant treatment and were categorized as 'Responders' (⩾50% improvement in depression severity ratings) or 'Non-responders' (<50% improvement). Logistic and linear regressions were performed to determine if pre-treatment AL or MetD scores predicted SSRI-response. Secondary analyses examined cross-sectional differences between MDD and control groups.

RESULTS

Pre-treatment AL and MetD scores significantly predicted continuous antidepressant response (i.e. absolute decreases in depression severity ratings) (p = 0.012 and 0.014, respectively), as well as post-treatment status as a Responder or Non-responder (p = 0.022 and 0.040, respectively), such that higher pre-treatment AL and MetD were associated with poorer SSRI-treatment outcomes. Pre-treatment AL and MetD of Responders were similar to Controls, while those of Non-responders were significantly higher than both Responders (p = 0.025 and 0.033, respectively) and Controls (p = 0.039 and 0.001, respectively).

CONCLUSIONS

These preliminary findings suggest that indices of metabolic and hypothalamic-pituitary-adrenal-axis dysregulation are associated with poorer SSRI-treatment response. To our knowledge, this is the first study to demonstrate that these markers of medical disease risk also predict poorer antidepressant outcomes.

Machine learning enhances prediction of illness course: a longitudinal study in eating disorders

BACKGROUND

Psychiatric disorders, including eating disorders (EDs), have clinical outcomes that range widely in severity and chronicity. The ability to predict such outcomes is extremely limited. Machine-learning (ML) approaches that model complexity may optimize the prediction of multifaceted psychiatric behaviors. However, the investigations of many psychiatric concerns have not capitalized on ML to improve prognosis. This study conducted the first comparison of an ML approach (elastic net regularized logistic regression) to traditional regression to longitudinally predict ED outcomes.

METHODS

Females with heterogeneous ED diagnoses completed demographic and psychiatric assessments at baseline (n = 415) and Year 1 (n = 320) and 2 (n = 277) follow-ups. Elastic net and traditional logistic regression models comprising the same baseline variables were compared in ability to longitudinally predict ED diagnosis, binge eating, compensatory behavior, and underweight BMI at Years 1 and 2.

RESULTS

Elastic net models had higher accuracy for all outcomes at Years 1 and 2 [average Area Under the Receiving Operating Characteristics Curve (AUC) = 0.78] compared to logistic regression (average AUC = 0.67). Model performance did not deteriorate when the most important predictor was removed or an alternative ML algorithm (random forests) was applied. Baseline ED (e.g. diagnosis), psychiatric (e.g. hospitalization), and demographic (e.g. ethnicity) characteristics emerged as important predictors in exploratory predictor importance analyses.

CONCLUSIONS

ML algorithms can enhance the prediction of ED symptoms for 2 years and may identify important risk markers. The superior accuracy of ML for predicting complex outcomes suggests that these approaches may ultimately aid in advancing precision medicine for serious psychiatric disorders.

Influence of childhood trauma on the treatment outcomes of pharmacological and/or psychological interventions for adolescents and adults with bipolar disorder: protocol for a systematic review and meta-analysis

INTRODUCTION

Despite available pharmacological and psychological treatments, remission rates for bipolar disorder remain relatively low. Current research implicates the experience of childhood trauma as a potential moderator of poor treatment outcomes among individuals with bipolar disorder. To date, the evidence reporting the influence of childhood trauma on the treatment outcomes of pharmacological and/or psychological interventions for adolescents and adults with bipolar disorder has not been systematically reviewed.

METHOD AND ANALYSIS

MEDLINE Complete, Embase, PsycINFO and the Cochrane Central Register of Controlled Trials will be searched to identify randomised and nonrandomised studies of pharmacological and/or psychological interventions for bipolar disorder, which also assessed childhood trauma. To be eligible for inclusion, studies must have been conducted with adolescents or adults (≥10 years). Data will be screened and extracted by two independent reviewers. The methodological quality of the included studies will be assessed with the Cochrane Collaboration's Risk of Bias tool and the Newcastle-Ottawa Scale. If deemed viable, a meta-analysis will be conducted using a random effects model. Heterogeneity of evidence will be estimated with the I² statistics.

ETHICS AND DISSEMINATION

This systematic review will use only previously published data. Therefore, ethical approval is not required. The results will be written in concordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines, published in peer-reviewed journals and presented at relevant conferences.

PROSPERO REGISTRATION NUMBER

CRD42020201891.

Individual Variability in Reaction Time and Prediction of Clinical Response to Methylphenidate in Adult ADHD: A Prospective Open Label Study Using Conners' Continuous Performance Test II

Objective: The aim of this study was to examine whether reaction time parameters in adult patients with ADHD could predict their response to methylphenidate (MPH). Method: Previously unmedicated patients (N = 123) were administered the Conners' Continuous Performance Test II (CPT II) at baseline and after 6 weeks of treatment with immediate-release MPH. In addition to traditional CPT measures, we extracted intraindividual raw data and analyzed time series using linear and nonlinear mathematical models. Results: Clinical responders, assessed with the Clinical Global Impression-Improvement scale, showed significant normalization of target failures, reduced variability and skewness, and increased complexity of reaction time series after 6 weeks of treatment, while nonresponders showed no significant changes. Prior to treatment, responders had significantly higher variability and skewness, combined with lower complexity, compared with nonresponders. Conclusion: These results show that the CPT test is useful in the evaluation of treatment response to MPH.

A learning algorithm for predicting mental health symptoms and substance use

Learning health systems use data to generate knowledge that informs clinical care, but few studies have evaluated how to leverage patient-reported mental health symptoms and substance use data to make patient-specific predictions. We developed a general Bayesian prediction algorithm that uses self-reported psychiatric symptoms and substance use within a population to predict future symptoms and substance use for individuals in that population. We validated our approach in 2444 participants from two clinical cohorts - the National Network of Depression Centers and the Johns Hopkins HIV Clinical Cohort - by predicting symptoms of depression, anxiety, and mania as well as alcohol, heroin, and cocaine use and comparing our predictions to observed symptoms and substance use. When we dichotomized mental health symptoms as moderate-severe vs. none-mild, individual predictions yielded areas under the ROC curve (AUCs) of 0.84 [95% confidence interval 0.80-0.88] and 0.85 [0.82-0.88] for symptoms of depression in the two cohorts, AUCs of 0.84 [0.79-0.88] and 0.85 [0.82-0.88] for symptoms of anxiety, and an AUC of 0.77 [0.72-0.82] for manic symptoms. Predictions of substance use yielded an AUC of 0.92 [0.88-0.97] for heroin use, 0.90 [0.82-0.97] for cocaine use, and 0.90 [0.88-092] for alcohol misuse. This rigorous, mathematically grounded approach could provide patient-specific predictions at the point of care. It can be applied to other psychiatric symptoms and substance use indicators, and is customizable to specific health systems. Such approaches can realize the potential of a learning health system to transform ever-increasing quantities of data into tangible guidance for patient care.

Testing Robustness of Child STEPs Effects with Children and Adolescents: A Randomized Controlled Effectiveness Trial

A critical task in psychotherapy research is identifying the conditions within which treatment benefits can be replicated and outside of which those benefits are reduced. We tested the robustness of beneficial effects found in two previous trials of the modular Child STEPs treatment program for youth anxiety, depression, trauma, and conduct problems. We conducted a randomized trial, with two significant methodological changes from previous trials: (a) shifting from cluster- to person-level randomization, and (b) shifting from individual to more clinically feasible group-based consultation with STEPs therapists. Fifty community clinicians from multiple outpatient clinics were randomly assigned to receive training and consultation in STEPs (n= 25) or to provide usual care (UC; n= 25). There were 156 referred youths-ages 6-16 (M= 10.52, SD = 2.53); 48.1% male; 79.5% Caucasian, 12.8% multiracial, 4.5% Black, 1.9% Latino, 1.3% Other-who were randomized to STEPs (n= 77) or UC (n= 79). Following previous STEPs trials, outcome measures included parent- and youth-reported internalizing, externalizing, total, and idiographic top problems, with repeated measures collected weekly during treatment and longer term over 2 years. Participants in both groups showed statistically significant improvement on all measures, leading to clinically meaningful problem reductions. However, in contrast to previous trials, STEPs was not superior to UC on any measure. As with virtually all treatments, the benefits of STEPs may depend on the conditions-for example, of study design and implementation support-in which it is tested. Identifying those conditions may help guide appropriate use of STEPs, and other treatments, in the future.

Modelling and forecasting Positive and Negative Syndrome Scale scores to achieve remission using time series analysis

OBJECTIVES

Schizophrenia is a chronic mental condition. The objective of this study is to apply time series modelling to Positive and Negative Syndrome Scale scores of outpatients with schizophrenia, observed at regular intervals of time, and hence forecast the number of visits required to reach remission.

METHODS

A retrospective data of outpatients diagnosed with chronic paranoid-type schizophrenia were extracted from the records of outpatient department of a tertiary hospital in New Delhi, India. Autoregressive integrated moving average (ARIMA) and ARIMAX models (ARIMA with explanatory variable as Clinical Global Impression Severity scale) are fitted to the data. The best fit models are employed to forecast the number of visits required to reach remission for the outpatients who did not achieve remission by the end of study. Prediction accuracy of the two models is compared using mean absolute percentage error and mean absolute deviation.

RESULTS

The ARIMA (1, 2, 1) and ARIMAX (1, 2, 1) models are identified to be suitable models after a series of statistical tests.

CONCLUSIONS

ARIMA and ARIMAX models are suitable to predict number of visits required to reach remission. Further, ARIMAX model performed better than ARIMA model.

Rate of and time to symptomatic remission in first-episode psychosis in Northern Malawi: A STROBE-compliant article

Although longer duration of untreated psychosis (DUP) is associated with poor response to antipsychotic treatment, it remains unclear whether it independently influences time to symptomatic remission in first-episode psychosis (FEP). This study examined rate of symptomatic remission, and explored if DUP, premorbid functioning, global functioning, insight and socio-demographic characteristics were independently associated with time to symptomatic remission in FEP.This prospective study enrolled 126 FEP patients (aged 18-65) between June 2009 and September 2012. Subjects were followed-up monthly over 18 months after they had received antipsychotic medication. Remission in positive and negative symptoms was defined as in the Remission in Schizophrenia Working Group (RSWG) criteria. Subjects were defined as "in symptomatic remission" if they remitted in both negative and positive symptoms. At baseline, the following explanatory variables were measured: socio-demographic characteristics; DUP as short (≤5 months) and long (>5 months); premorbid functioning as deteriorating, stable poor, and stable good according to Cannon-Spoor Premorbid Adjustment Scale; global functioning as "worst (1-10) to serious (41-50)" and "moderate (51-60) to superior (91-100)," according to the Global Assessment of Functioning Scale; and insight as poor (≤8) and good (≥9) according to the Insight Scale (Birchwood). Univariate and multivariable analyses were used to generate results.Out of 126 subjects, 98 (78%) completed follow-up, of which 70 (71.4%) achieved symptomatic remission within mean duration of 8.05 (4.54) months. Besides, having long DUP and separated/divorced/widowed (adjusted hazard ratio [aHR] = 0.07, 95%CI = [0.01, 0.46]), long DUP and poor insight (aHR = 0.18, 95%CI = [0.04, 0.89]), poor insight and separated/divorced/widowed (aHR = 0.09, 95%CI = [0.01, 0.70]), deteriorating premorbid functioning (aHR = 0.47, 95%CI = [0.23, 0.97]), family history of psychiatric disorders (aHR = 0.52, 95%CI = (0.30, 0.93]), and being male (aHR = 0.47, 95%CI = [0.24, 0.92]) delayed symptomatic remission.These results propose that psychological interventions and social support for mental health problems are warranted and may enhance better response to antipsychotic medications among separated/divorced/widowed patients with long DUP or poor insight, and poor insight patients with long DUP. Deteriorating premorbid functioning, family history of psychiatric disorders and being male continue being important risk factors for poor odds of remission.

Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis

Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not effective for all patients and the factors predicting a good outcome remain poorly understood. Machine learning is a powerful approach that allows new predictors to be identified in a data-driven way, which can inform understanding of the mechanisms underlying therapeutic interventions, and ultimately make predictions about symptom improvement at the individual patient level. Thirty-eight patients with a diagnosis of schizophrenia completed a social affect task during functional MRI. Multivariate pattern analysis assessed whether treatment response in those receiving CBTp (n = 22) could be predicted by pre-therapy neural responses to facial affect that was either threat-related (ambiguous ‘neutral’ faces perceived as threatening in psychosis, in addition to angry and fearful faces) or prosocial (happy faces). The models predicted improvement in psychotic (r = 0.63, p = 0.003) and affective (r = 0.31, p = 0.05) symptoms following CBTp, but not in the treatment-as-usual group (n = 16). Psychotic symptom improvement was predicted by neural responses to threat-related affect across sensorimotor and frontal-limbic regions, whereas affective symptom improvement was predicted by neural responses to fearful faces only as well as prosocial affect across sensorimotor and frontal regions. These findings suggest that CBTp most likely improves psychotic and affective symptoms in those endorsing more threatening appraisals and mood-congruent processing biases, respectively, which are explored and reframed as part of the therapy. This study improves our understanding of the neurobiology of treatment response and provides a foundation that will hopefully lead to greater precision and tailoring of the interventions offered to patients.

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Machine learning models using neuroimaging data can predict response to CBTp.

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Neural responses to social threat predicted improvement in psychotic symptoms.

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Activation related to different social stimuli predicted distinct symptom domains.

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Predictors included activity in the hippocampus, frontal, and sensorimotor regions.

Translational research in psychiatry: The Research Domain Criteria Project (RDoC)

Despite the consensus achieved in the homogenization of clinical criteria by categorical psychiatric classification systems (DEM and CIE), they are criticized for a lack of validity and inability to guide clinical treatment and research. In this review article we introduce the Research Domain Criteria (RDoC) framework as an alternative framework for translational research in psychiatry. The RDOC framework systematizes both research targets and methodology for research in psychiatry. RDoC is based on a catalogue of neurobiological and neurocognitive evidence of behaviour, and conceives psychopathology as the phenotypic expression of alterations of functional domains that are classified into 5psychobiological systems. The RdoC framework also proposes that domains must be validated with evidence in 7levels of analysis: genes, molecules, cells, nerve circuits, physiology, behaviour and self-reports. As opposed to categorical systems focused on diagnosis, RDoC focuses on the study of psychopathology as a correlate of detectable functional, biological and behavioural disruption of normal processes. In order to build a useful psychiatric nosology for guiding clinical interventions, the RDoC research framework links the neurobiological basis of mental processes with phenotypical manifestations. Although the RDoC findings have not yet been articulated into a specific model for guiding clinical practice, they provide a useful transition system for creating clinical, basic and epidemiological research hypotheses.

The 2018 ISHLT/APM/AST/ICCAC/STSW recommendations for the psychosocial evaluation of adult cardiothoracic transplant candidates and candidates for long-term mechanical circulatory support

The psychosocial evaluation is well-recognized as an important component of the multifaceted assessment process to determine candidacy for heart transplantation, lung transplantation, and long-term mechanical circulatory support (MCS). However, there is no consensus-based set of recommendations for either the full range of psychosocial domains to be assessed during the evaluation, or the set of processes and procedures to be used to conduct the evaluation, report its findings, and monitor patients' receipt of and response to interventions for any problems identified. This document provides recommendations on both evaluation content and process. It represents a collaborative effort of the International Society for Heart and Lung Transplantation (ISHLT) and the Academy of Psychosomatic Medicine, American Society of Transplantation, International Consortium of Circulatory Assist Clinicians, and Society for Transplant Social Workers. The Nursing, Health Science and Allied Health Council of the ISHLT organized a Writing Committee composed of international experts representing the ISHLT and the collaborating societies. This Committee synthesized expert opinion and conducted a comprehensive literature review to support the psychosocial evaluation content and process recommendations that were developed. The recommendations are intended to dovetail with current ISHLT guidelines and consensus statements for the selection of candidates for cardiothoracic transplantation and MCS implantation. Moreover, the recommendations are designed to promote consistency across programs in the performance of the psychosocial evaluation by proposing a core set of content domains and processes that can be expanded as needed to meet programs' unique needs and goals.

The revolution of personalized psychiatry: will technology make it happen sooner?

Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.

Translational Identification of Transcriptional Signatures of Major Depression and Antidepressant Response

Major depressive disorder (MDD) is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS) model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice) as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice) with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG), and the anterior cingulate cortex (ACC). Hierarchical clustering and rank-rank hypergeometric overlap (RRHO) procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE) patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C), whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1), as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1). These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD.

Religiosity in Acute Psychiatric Inpatients: Relationship With Demographics, Clinical Features, and Length of Stay

This study examined the relationship between religiosity in 175 psychiatric inpatients as measured by the subscales of the Duke University Religion Index (DUREL) and sociodemographic (age, sex, and race), clinical (primary diagnosis, suicidality, and psychotic symptoms), and outcome (length of stay [LOS] and readmission rates) measures. Psychosis was assessed by Clinician-Rated Dimensions of Psychosis Symptom Severity (CRDPSS) scale. Bivariate and multivariate analyses were used to examine the association between the DUREL subscales and the outcome measures. High scorers on the nonorganized religiosity subscale were less likely to have psychosis (47% vs. 52%; p < 0.05) but had greater psychosis severity (mean ± SD, 14.5 ± 5 vs.12.4 ± 6; p < 0.05), as measured by the CRDPSS scale, and significantly longer LOS (mean ± SD, 8.3 ± 3.8 vs. 6.9 ± 3.4; p < 0.05). Conversely, they were less likely to report previous suicide attempts than low scorers (p < 0.05). These results suggest that a brief measure of religious activities may identify psychiatric inpatients at greater risk for psychosis, suicidality, and longer hospitalizations.

The Use of Zonisamide for the Treatment of Psychiatric Disorders: A Systematic Review

OBJECTIVE

Traditional pharmacotherapy has undoubtedly improved the outcome of patients with psychiatric disorders, but partial efficacy or poor tolerability persists in a number of these subjects. Among different compounds, zonisamide has been used to address unmet needs of standard pharmacotherapy. The purpose of the present article is to provide a review about the use of zonisamide for the treatment of psychiatric conditions.

METHODS

A research in the main database sources has been conducted to obtain an overview of the use of zonisamide in psychiatric disorders or associated conditions (obesity and smoking cessation).

RESULTS

Most available data indicate the possible effectiveness of zonisamide for the treatment of acute phases of bipolar disorder, binge-eating disorder (BED), alcohol misuse, and obesity. A further assessment of the safety and tolerability of zonisamide is made necessary by the fact that, with the exception of BED, for all other disorders at least some data come from studies with combined pharmacological therapies.

CONCLUSIONS

Zonisamide may have some utility, especially as an adjunctive therapy, for the management of acute phases and weight gain in bipolar disorder and for prevention of alcohol misuse. Preliminary evidence indicates zonisamide as a candidate compound for the treatment of BED and obesity. However, open-label design and small sample sizes of most available studies prevent from drawing sound conclusions about the utility of this compound in psychiatry.

Predictive analytics in mental health: applications, guidelines, challenges and perspectives

The emerging field of 'predictive analytics in mental health' has recently generated tremendous interest with the bold promise to revolutionize clinical practice in psychiatry paralleling similar developments in personalized and precision medicine. Here, we provide an overview of the key questions and challenges in the field, aiming to (1) propose general guidelines for predictive analytics projects in psychiatry, (2) provide a conceptual introduction to core aspects of predictive modeling technology, and (3) foster a broad and informed discussion involving all stakeholders including researchers, clinicians, patients, funding bodies and policymakers.

Isolating biomarkers for symptomatic states: considering symptom-substrate chronometry

A long-standing goal of psychopathology research is to develop objective markers of symptomatic states, yet progress has been far slower than expected. Although prior reviews have attributed this state of affairs to diagnostic heterogeneity, symptom comorbidity and phenotypic complexity, little attention has been paid to the implications of intra-individual symptom dynamics and inter-relatedness for biomarker study designs. In this critical review, we consider the impact of short-term symptom fluctuations on widely used study designs that regress the 'average level' of a given symptom against biological data collected at a single time point, and summarize findings from ambulatory assessment studies suggesting that such designs may be sub-optimal to detect symptom-substrate relationships. Although such designs have a crucial role in advancing our understanding of biological substrates related to more stable, longer-term changes (for example, gray matter thinning during a depressive episode), they may be less optimal for the detection of symptoms that exhibit high frequency fluctuations, are susceptible to common reporting biases, or may be heavily influenced by the presence of other symptoms. We propose that a greater emphasis on intra-individual symptom chronometry may be useful for identifying subgroups of patients with common, proximal pathological indicators. Taken together, these three recent developments in the areas of symptom conceptualization and measurement raise important considerations for future studies attempting to identify reliable biomarkers in psychiatry.