Treatment resistance is a major challenge in psychiatric disorders. Early detection of potential future resistance would improve prognosis by reducing the delay to appropriate treatment adjustment and recovery. Here, we sought to determine whether neurodevelopmental markers can predict therapeutic response.

Healthy controls (N = 236), patients with schizophrenia (N = 280) or bipolar disorder (N = 78) with a known therapeutic outcome, were retrospectively included. Age, sex, education, early developmental abnormalities (obstetric complications, height, weight, and head circumference at birth, hyperactivity, dyslexia, epilepsy, enuresis, encopresis), neurological soft signs (NSS), and ages at first subjective impairment, clinical symptoms, treatment, and hospitalization, were recorded. A supervised algorithm leveraged NSS and age at first clinical signs to classify between resistance and response in schizophrenia.

Developmental abnormalities were more frequent in schizophrenia and bipolar disorder than in controls. NSS significantly differed between controls, responsive, and resistant participants with schizophrenia (5.5 ± 3.0, 7.0 ± 4.0, 15.0 ± 6.0 respectively, p = 3 × 10−10) and bipolar disorder (5.5 ± 3.0, 8.3 ± 3.0, 12.5 ± 6.0 respectively, p < 1 × 10−10). In schizophrenia, but not in bipolar disorder, age at first subjective impairment was three years lower, and age at first clinical signs two years lower, in resistant than responsive subjects (p = 2 × 10−4 and p = 9 × 10−3, respectively). Age at first clinical signs and NSS accurately predicted treatment response in schizophrenia (area-under-curve: 77 ± 8%, p = 1 × 10−14).

Neurodevelopmental features such as NSS and age of clinical onset provide a means to identify patients who may require rapid treatment adaptation.

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.

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).

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).

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

Approximately one-third of patients with psychotic disorders does not respond to standard antipsychotic treatments. Consensus criteria for treatment resistance (TR) may aid the identification of non-response and subsequent tailoring of treatments. Since consensus criteria require stability of clinical status, they are challenging to apply in first-episode psychosis (FEP). This study aims to investigate (a) if an adaptation of consensus criteria can be used to identify FEP patients with early signs of TR (no early clinical recovery—no-ECR) after 1 year in treatment and (b) to what extent differences in antipsychotic treatments differentiate between outcome groups.

Participants with FEP DSM-IV schizophrenia spectrum disorders were recruited during their first treatment. A total of 207 participated in the 1-year follow-up. Remission and recovery definitions were based on adaptations of the “Remission in Schizophrenia Working Group” criteria and TR on adaptations of the “Treatment Response and Resistance in Psychosis” (TRRIP) working group criteria.

97 participants (47%) could be classified as no-ECR, 61 (30%) as ECR, and 49 (23%) as with partial ECR (P-ECR). Statistically significant baseline predictors of no-ECR matched previously identified predictors of long-term TR. Only 35 no-ECR participants had two adequate treatment trials and met the full TRRIP criteria. 21 no-ECR participants were using the same medication over the follow-up year despite the lack of significant effects.

The difference in the percentage of FEP participants classified as no-ECR versus TR indicates that we may underestimate the prevalence of early TR when using consensus criteria.

There is substantial variation in patient symptoms following psychological therapy for depression and anxiety. However, reliance on endpoint outcomes ignores additional interindividual variation during therapy. Knowing a patient's likely symptom trajectories could guide clinical decisions. We aimed to identify latent classes of patients with similar symptom trajectories over the course of psychological therapy and explore associations between baseline variables and trajectory class.

Patients received high-intensity psychological treatment for common mental health problems at National Health Service Improving Access to Psychological Therapies services in South London (N = 16 258). To identify trajectories, we performed growth mixture modelling of depression and anxiety symptoms over 11 sessions. We then ran multinomial regressions to identify baseline variables associated with trajectory class membership.

Trajectories of depression and anxiety symptoms were highly similar and best modelled by four classes. Three classes started with moderate-severe symptoms and showed (1) no change, (2) gradual improvement, and (3) fast improvement. A final class (4) showed initially mild symptoms and minimal improvement. Within the moderate-severe baseline symptom classes, patients in the two showing improvement as opposed to no change tended not to be prescribed psychotropic medication or report a disability and were in employment. Patients showing fast improvement additionally reported lower baseline functional impairment on average.

Multiple trajectory classes of depression and anxiety symptoms were associated with baseline characteristics. Identifying the most likely trajectory for a patient at the start of treatment could inform decisions about the suitability and continuation of therapy, ultimately improving patient outcomes.

Chronic Hepatitis C infection is considered a systemic disease with extrahepatic manifestations, mainly neuropsychiatric symptoms, which is associated with a chronic low-grade inflammatory state. Hepatitis C virus (HCV) eradication is currently achieved in >98% of cases with oral direct-acting antivirals (DAA).

To study potential clinical neuropsychiatric changes (mood, cognition, sleep, gastrointestinal, sickness, and motion) in HCV-infected patients after HCV eradication with DAA.

Design: Cohort study. Subjects: 37 HCV-infected patients, aged<55 years old, with non-advanced liver disease receiving DAA; free of current mental disorder. 24 healthy controls were included at baseline. Assessment: -Baseline (BL) (socio-demographic and clinical variables, MINI-DSM-IV, and Neurotoxicity Scale (NRS), (mood, cognitive, sleep, gastrointestinal, sickness and motor dimensions). Follow-up: End-of-treatment, 12weeks-after and 48weeks-after DAA: NRS. Analysis: Descriptive and bivariate non-parametrical analysis.

NRS total score and dimensions where different between cases and controls (.000) at baseline. NRS total score (.000) and mood (.000), cognition (.000), sleep (.002), gastrointestinal (.017), and sickness (.003), except motor dimension score (.130) showed significant longitudinal improvement.

HCV-infected patients with mild liver disease presented significantly worse scores for neurotoxicity symptomatology in all dimensions compared to healthy individuals. After HCV eradication with DAA, both at short and long follow-up a significant improvement of the NRS total score and each of the dimensions (except motor) were observed. However, they did not reach the values of healthy individuals, suggesting a not complete neuropsychiatric restoration in the period studied. Grant: ICIII-FIS:PI17/02297.(One way to make Europe) (RMS) and Gilead Fellowship-GLD17/00273 (ZM); and the support of SGR17/1798 (RMS)

No significant relationships.

Identifying the optimal treatment for individuals with major depressive disorder (MDD) is often a long and complicated process. Functional magnetic resonance imaging (fMRI) studies have been used to help predict and explain differences in treatment response among individuals with MDD.

We conducted a comprehensive meta-analysis of treatment prediction studies utilizing fMRI in patients with MDD to provide evidence that neural activity can be used to predict response to antidepressant treatment.

A multi-level kernel density analysis was applied to these primary fMRI studies, in which we analyzed brain activation patterns of depressed patients (N= 364) before receiving antidepressant treatment.

The results of this analysis demonstrated that hyperactivity in six brain regions significantly predicted treatment response in patients with MDD: the right anterior cingulate, right cuneus, left fusiform gyrus, left middle frontal gyrus, right cingulate gyrus, and left superior frontal gyrus.

This study provides evidence that neural activity, as measured by standard fMRI paradigms, can be used to successfully predict response to antidepressant treatment. This may be used in the future clinically to improve decision-making processes and treatment outcomes for patients.

No significant relationships.

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.

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.

Treatment response in schizophrenia can be influenced by cultural and ethno-biological factors. However, in delusional disorder (DD), these potential influences have been poorly investigated.

This review aims to synthesize what is known about the influence that cultural and biological factors may have on treatment response in DD.

A systematic review was performed on PubMed from inception to 2020 in keeping with PRISMA directives. Search terms: [(cultural OR ethnic* OR ethno*) AND (treatment OR therap* OR antipsychotic response) AND (delusional disorder)]. We included all studies whose objective was to explore ethno-psychopharmacological aspects of treatment response in DD.

A total of 182 papers were retrieved. Four studies tested ethno-biological factors and 10 reported cultural aspects of treatment response in DD. 1. Cultural hypothesis: 3 studies reported cultural differences in diagnostic practices; in 2 studies, culturally-determined long durations of untreated psychosis (DUP) and comorbidity with mood disorders was associated with response to both antipsychotics (AP) and antidepressants (AD); 3 studies reported that response and AP dose were similar among cultures and that culturally-sensitive psychotherapy improved adherence; 2 studies showed that, where women had poor access to health care, mortality rates were high. 2. Ethno-biological hypothesis: 1 study reviewed moderators and mediators of ethno-specific treatment response; 1 study presented a culture-bound syndrome (Taijin kyofusho) for which AD were found effective; 2 studies in diverse populations found that DD and schizophrenia were both significantly linked to HLA genes.

The sociodemographic profile of DD is consistent across various cultures and, when treated appropriately, responds, but in an ethno-culturally-specific manner.

No significant relationships.

Electroconvulsive therapy (ECT) is considered a gold-standart treatment of severe and treatment-resistant depression. Lack of response to ECT often causes distress in psychiatrists regarding the next therapeutic decisions.

To present a case report of a patient with psychotic depression with partial response to ECT.

Clinical interviews and review of literature using the Pubmed platform.

The authors present a case of a 60 year-old woman admitted for severe depressive episode with psychotic symptoms. Due to lack of response to multiple antidepressive and antipsychotic treatments, 15 sessions of ECT were performed with improvement of behavioral and psychotic symptoms. However, endogenous depressive symptoms with functional impairment persisted. It was then initiated Bupropion 300mg/day resulting in vast improvements on drive, energy and activity levels with restored functionality. Previously to ECT, Bupropion was not considered a valid option due to the psychomotor restlessness that was present. This case exposes the limitations of ECT and the therapeutic conundrums that arise when there is partial response. The symptoms expressed in the patient after ECT course correlate with deficits in noradrenergic and dopaminergic pathways that are involved in endogenous depression. The use of Bupropion, with its effect on noradrenaline and dopamine receptors, may offer a therapeutic lifeline in these cases.

ECT still stands as a gold-standart for severe depressive disorder, especially when several psychopharmacological therapies have failed. In cases of partial response to ECT, the neurobiological correlates of clinical presentation can guide the therapeutic management towards improved outcomes.

No significant relationships.

Depression is commonly associated with fronto-amygdala dysfunction during the processing of emotional face expressions. Interactions between these regions are hypothesized to contribute to negative emotional processing biases and as such have been highlighted as potential biomarkers of treatment response. This study aimed to investigate depression associated alterations to directional connectivity and assess the utility of these parameters as predictors of treatment response.

Ninety-two unmedicated adolescents and young adults (mean age 20.1; 56.5% female) with moderate-to-severe major depressive disorder and 88 healthy controls (mean age 19.8; 61.4% female) completed an implicit emotional face processing fMRI task. Patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or placebo. Using dynamic causal modelling, we examined functional relationships between six brain regions implicated in emotional face processing, comparing both patients and controls and treatment responders and non-responders.

Depressed patients demonstrated reduced inhibition from the dlPFC to vmPFC and reduced excitation from the dlPFC to amygdala during sad expression processing. During fearful expression processing patients showed reduced inhibition from the vmPFC to amygdala and reduced excitation from the amygdala to dlPFC. Response was associated with connectivity from the amygdala to dlPFC during sad expression processing and amygdala to vmPFC connectivity during fearful expression processing.

Our study clarifies the nature of face processing network alterations in adolescents and young adults with depression, highlighting key interactions between the amygdala and prefrontal cortex. Moreover, these findings highlight the potential utility of these interactions in predicting treatment response.

Treatment for major depressive disorder (MDD) is imprecise and often involves trial-and-error to determine the most effective approach. To facilitate optimal treatment selection and inform timely adjustment, the current study investigated whether neurocognitive variables could predict an antidepressant response in a treatment-specific manner.

In the two-stage Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) trial, outpatients with non-psychotic recurrent MDD were first randomized to an 8-week course of sertraline selective serotonin reuptake inhibitor or placebo. Behavioral measures of reward responsiveness, cognitive control, verbal fluency, psychomotor, and cognitive processing speeds were collected at baseline and week 1. Treatment responders then continued on another 8-week course of the same medication, whereas non-responders to sertraline or placebo were crossed-over under double-blinded conditions to bupropion noradrenaline/dopamine reuptake inhibitor or sertraline, respectively. Hamilton Rating for Depression scores were also assessed at baseline, weeks 8, and 16.

Greater improvements in psychomotor and cognitive processing speeds within the first week, as well as better pretreatment performance in these domains, were specifically associated with higher likelihood of response to placebo. Moreover, better reward responsiveness, poorer cognitive control and greater verbal fluency were associated with greater likelihood of response to bupropion in patients who previously failed to respond to sertraline.

These exploratory results warrant further scrutiny, but demonstrate that quick and non-invasive behavioral tests may have substantial clinical value in predicting antidepressant treatment response.

Understanding the patterns of treatment response is critical for the treatment of patients with schizophrenia; one way to achieve this is through using a longitudinal dynamic process study design.

This study aims to explore the response trajectory of antipsychotics and compare the treatment responses of seven different antipsychotics over 6 weeks in patients with schizoprenia (trial registration: Chinese Clinical Trials Registry Identifier: ChiCTR-TRC-10000934).

Data were collected from a multicentre, randomised open-label clinical trial. Patients were evaluated with the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up at weeks 2, 4 and 6. Trajectory groups were classified by the method of k-means cluster modelling for longitudinal data. Trajectory analyses were also employed for the seven antipsychotic groups.

The early treatment response trajectories were classified into a high-trajectory group of better responders and a low-trajectory group of worse responders. The results of trajectory analysis showed differences compared with the classification method characterised by a 50% reduction in PANSS scores at week 6. A total of 349 patients were inconsistently grouped by the two methods, with a significant difference in the composition ratio of treatment response groups using these two methods (χ2 = 43.37, P < 0.001). There was no differential contribution of high- and low trajectories to different drugs (χ2 = 12.52, P = 0.051); olanzapine and risperidone, which had a larger proportion in the >50% reduction at week 6, performed better than aripiprazole, quetiapine, ziprasidone and perphenazine.

The trajectory analysis of treatment response to schizophrenia revealed two distinct trajectories. Comparing the treatment responses to different antipsychotics through longitudinal analysis may offer a new perspective for evaluating antipsychotics.

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.

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.

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).

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.

Premorbid adjustment (PA) abnormalities in psychotic disorders are associated with an earlier age at onset (AAO) and unfavorable clinical outcomes, including treatment resistance. Prior family studies suggest that familial liability, likely reflecting increased genetic risk, and socioeconomic status (SES) contribute to premorbid maladjustment. However, their joint effect possibly indicating gene–environment interaction has not been evaluated.

We examined whether family history of psychosis (FHP) and parental SES may predict PA and AAO in unrelated cases with first-episode psychosis (n = 108) and schizophrenia (n = 104). Premorbid academic and social functioning domains during childhood and early adolescence were retrospectively assessed. Regression analyses were performed to investigate main effects of FHP and parental SES, as well as their interaction. The relationships between PA, AAO, and response to antipsychotic medication were also explored.

Positive FHP associated with academic PA difficulties and importantly interacted with parental SES to moderate social PA during childhood (interaction p = 0.024). Positive FHP and parental SES did not predict differences in AAO. Nevertheless, an earlier AAO was observed among cases with worse social PA in childhood (β = −0.20; p = 0.005) and early adolescence (β = −0.19; p = 0.007). Further, confirming evidence emerged for an association between deficient childhood social PA and poor treatment response (p = 0.04).

Familial risk for psychosis may interact with parental socioeconomic position influencing social PA in childhood. In addition, this study supports the link between social PA deviations, early psychosis onset, and treatment resistance, which highlights premorbid social functioning as a promising clinical indicator.