Determinants of Treatment Response in Schizophrenia: Insights from a Cross-sectional Observational Study

Schizophrenia presents significant treatment challenges, particularly due to medication resistance observed in some patients receiving antipsychotics. Emerging research suggests a potential link between impaired reinforcement learning, the severity of psychotic symptoms, and dopamine system abnormalities. Exploring reinforcement learning in therapeutic settings could provide critical insights into the efficacy of antipsychotic treatments. This study aimed to investigate whether neurocognitive profiles, specifically choice strategies and model fitting parameters assessed using the Dynamic Reward Task (DRT), could provide insights into treatment response variability among patients with schizophrenia. We conducted a comprehensive neurocognitive assessment on chronic schizophrenia patients experiencing psychotic relapse, categorized by treatment response (high-response vs low-response). Participants underwent DRT, Wisconsin Card Sorting Test (WCST), and Continuous Performance Test (CPT) to evaluate reward processing, executive function, and sustained attention, respectively. We employed statistical analyses to compare task performance between groups and assess changes before and after antipsychotic treatment. We identified significant differences in treatment effects across different response groups in DRT scores, choice strategies, and model-fitting parameters. Conversely, all schizophrenia groups had consistent abnormalities on the WCST and CPT evaluations compared to controls. Our findings highlight the efficacy of DRT, WCST, and CPT in delineating neurocognitive profiles relevant to treatment response in schizophrenia. Specifically, the DRT effectively differentiated between high- and low-response patients. Distinct deficits in reward processing and executive function identified here may serve as potential indicators, informing personalized treatment strategies tailored to individual responses to antipsychotic medication.

Investigating the relationship between premorbid functioning and treatment response in schizophrenia is relevant to understanding the illness and predicting treatment outcomes. To examine the relationship between premorbid characteristics and treatment response of people with recent-onset schizophrenia. Data came from a large, double-blind trial of recent-onset psychosis treated with a flexible dose of risperidone or haloperidol. Median treatment length was 206 days. Premorbid functioning was categorised using the Cannon-Spoor Premorbid Adjustment Scale. There were significant differences between the premorbid groups on change on the Positive and Negative Syndrome Scale, Clinical Global Impression severity and cognitive functioning and Extrapyramidal Symptoms Rating Scale. Patients in the ;stable-good' premorbid group (n = 251) improved more than those in the'stable-poor' (n = 198) and 'declining' (n = 81) groups. The ;stable-good' group received the lowest doses of antipsychotic and had the least extrapyramidal symptoms. Patients in the 'declining' group had the highest dosages and the most extrapyramidal symptoms. In first-episode psychosis good premorbid functioning is associated with better response to treatment and fewer extrapyramidal symptoms.

Antipsychotics are thought to improve schizophrenia symptoms through the antagonism of dopamine D2 receptors, which are abundant mainly in subcortical regions. By introducing functional gradient, a novel approach to identify hierarchy alterations by capturing the similarity of whole brain fucntional connectivity (FC) profiles between two voxels, the present study aimed to characterize how the subcortical gradient is associated with treatment effects and response in first-episode schizophrenia in vivo. Two independent samples of first-episode schizophrenia (FES) patients with matched healthy controls (HC) were obtained: the discovery dataset included 71 patients (FES0W) and 64 HC at baseline, and patients were re-scanned after either 6 weeks (FES6W, N = 33) or 12 months (FES12M, N = 57) of antipsychotic treatment, of which 19 patients finished both 6-week and 12-month evaluation. The validation dataset included 22 patients and 24 HC at baseline and patients were re-scanned after 6 weeks. Gradient metrics were calculated using BrainSpace Toolbox. Voxel-based gradient values were generated and group-averaged gradient values were further extracted across all voxels (global), three systems (thalamus, limbic and striatum) and their subcortical subfields. The comparisons were conducted separately between FES0W and HC for investigating illness effects, and between FES6W/FES12M and FES0W for treatment effects. Correlational analyses were then conducted between the longitudinal gradient alterations and the improvement of clinical ratings. Before treatment, schizophrenia patients exhibited an expanded range of global gradient scores compared to HC which indicated functional segregation within subcortical systems. The increased gradient in limbic system and decreased gradient in thalamic and striatal system contributed to the baseline abnormalities and led to the disruption of the subcortical functional integration. After treatment, these disruptions were normalized and the longitudinal changes of gradient scores in limbic system were significantly associated with symptom improvement. Similar illness and treatment effects were also observed in the validation dataset. By measuring functional hierarchy of subcortical organization, our findings provide a novel imaging marker that is sensitive to treatment effects and may make a promising indicator of treatment response in schizophrenia.

First-rank symptoms, severity of illness, and treatment response in schizophrenia

Resting-state functional connectivity predictors of treatment response in schizophrenia – A systematic review and meta-analysis

Schizophrenia is associated with widespread cortical thinning and abnormality in the structural covariance network, which may reflect connectome alterations due to treatment effect or disease progression. Notably, patients with treatment-resistant schizophrenia (TRS) have stronger and more widespread cortical thinning, but it remains unclear whether structural covariance is associated with treatment response in schizophrenia. We organized a multicenter magnetic resonance imaging study to assess structural covariance in a large population of TRS and non-TRS, who had been resistant and responsive to non-clozapine antipsychotics, respectively. Whole-brain structural covariance for cortical thickness was assessed in 102 patients with TRS, 77 patients with non-TRS, and 79 healthy controls (HC). Network-based statistics were used to examine the difference in structural covariance networks among the 3 groups. Moreover, the relationship between altered individual differentiated structural covariance and clinico-demographics was also explored. Patients with non-TRS exhibited greater structural covariance compared with HC, mainly in the fronto-temporal and fronto-occipital regions, while there were no significant differences in structural covariance between TRS and non-TRS or HC. Higher individual differentiated structural covariance was associated with lower general scores of the Positive and Negative Syndrome Scale in the non-TRS group, but not in the TRS group. These findings suggest that reconfiguration of brain networks via coordinated cortical thinning is related to treatment response in schizophrenia. Further longitudinal studies are warranted to confirm if greater structural covariance could serve as a marker for treatment response in this disease.

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

A structured approach is used to review the rapid progress in neurobiology related to treatment response in schizophrenia. Findings are presented and discussed according to the molecular, cellular and regional levels of brain organization. The genotype-phenotype structure is used to consider potential interactions between genes, clinical manifestations of the illness and treatment response. The integration of neurobiological research into the future development of new treatment strategies for schizophrenia holds significant promise.

BackgroundNeuromelanin (NM), a byproduct of monoamine metabolism, reflects dopamine and norepinephrine activity in the brain. NM-sensitive MRI sequences enable in vivo quantification of NM levels in the substantia nigra (SN) and locus coeruleus (LC), which correspond to dopamine and norepinephrine neuron activity, respectively. Striatal dopamine dysfunction is a hallmark of schizophrenia: increased striatal dopamine synthesis has been associated with responsiveness to first-line antipsychotics (first-line responders [FLR]), whereas normal striatal dopamine synthesis characterizes treatment-resistant schizophrenia (TRS). Clozapine is the only approved treatment for TRS; however, its relationship with NM-MRI-derived dopamine markers remains unexplored. Additionally, norepinephrine dysregulation, originating in the LC, has been implicated in delusions and cognitive impairments in schizophrenia, yet its role in treatment response remains unvalidated using NM-MRI.Aims & ObjectivesThis study aims to elucidate the relationship between NM accumulation and treatment responsiveness in schizophrenia. Specifically, we sought to:Compare NM-MRI-derived signal intensities in the SN and LC among schizophrenia subgroups and healthy controls (HCs).Investigate differences in NM-related dopaminergic and noradrenergic activity between clozapine-nonresponsive TRS (ultra-resistant schizophrenia [URS]) and clozapine-responsive TRS (non-URS).Explore associations between NM-MRI findings and clinical characteristics, including symptom severity and treatment outcomes.MethodWe conducted a cross-sectional study involving four groups: URS (n = 16), non-URS (n = 16), FLR (n = 20), and HCs (n = 26). NM-MRI was used to measure NM signals in the SN and LC, quantified as contrast ratios (CR). Group comparisons were performed, controlling for age and sex, with Benjamini–Hochberg correction applied for multiple comparisons. Associations between CR and clinical characteristics were also analyzed.ResultsOf the 78 participants, two (1 URS, 1 FLR) were excluded due to insufficient data quality. Significant group differences were observed in CR for both the SN and LC (SN: F(3,70) = 4.45, η² = 0.16, p = 0.01; LC: F(3,70) = 2.87, η² = 0.11, p = 0.04). In the SN, both URS (Cohen’s d = 1.01, p = 0.01) and FLR (Cohen’s d = 0.94, p = 0.01) showed elevated CR compared to HCs. In the LC, URS demonstrated higher CR than FLR (Cohen’s d = 0.99, p = 0.04). No significant associations were found between CR and clinical characteristics or symptom severity.Discussion & ConclusionsThis study highlighted distinct dopaminergic and noradrenergic activity patterns in schizophrenia subgroups. Elevated dopaminergic activity (SN CR) was observed in both URS and FLR, whereas heightened noradrenergic activity (LC CR) differentiated URS from FLR. These findings suggest NM-MRI’s potential in predicting treatment response in schizophrenia, underscoring the need for longitudinal studies to establish its clinical utility.

Background:Environmental modification of genetic information (epigenetics) is often invoked to explain interindividual differences in the phenotype of schizophrenia. In clinical practice, such variability is most prominent in the symptom profile and the treatment response. Epigenetic regulation of immune function is of particular interest, given the therapeutic relevance of this mechanism in schizophrenia.Methods:We analyzed the DNA methylation data of immune-relevant genes in patients with schizophrenia whose disease duration was less than 3 years, with previous lifetime antipsychotic treatment of no more than 2 weeks total.Results:A total of 441 patients met the inclusion criteria. Core symptoms were consistently associated with 206 methylation positions, many of which had previously been implicated in inflammatory responses. Of these, 24 methylation positions were located either in regulatory regions or near the CpG islands of 20 genes, including the SRC gene, which is a key player in glutamatergic signalling. These symptom-associated immune genes were enriched in neuronal development functions, such as neuronal migration and glutamatergic synapse. Compared with using only clinical information (including scores on the Positive and Negative Syndrome Scale), integrating methylation data into the model significantly improved the predictive ability (as indicated by area under the curve) for response to 8 weeks of antipsychotic treatment.Limitations:We focused on a small number of methylation probes (immune-centred search) and lacked nutritional data and direct brain-based measures.Conclusion:Epigenetic modifications of the immune system are associated with symptom severity at onset and subsequent treatment response in schizophrenia.

The first- and second-generation antipsychotic drugs have become mainstay drug treatment for schizophrenia. However, patients who receive antipsychotic drugs differ with respect to treatment response and drug-induced adverse events. The biological predictors of treatment response are being researched worldwide, with emphasis on molecular genetic predictors of treatment response. Because of the rapid and exciting developments in the field, we reviewed the recent studies of the molecular genetic basis of treatment response in schizophrenia. The accumulating data suggest that DNA information in the pathways for drug metabolism and drug target sites may be an important predictor of treatment response in schizophrenia. The data suggest that clinicians may soon be using a patient's genotype to decide initial choice of antipsychotic drug treatment in schizophrenia. The pharmacogenetics of schizophrenia can improve the prospects of individualized treatment and drug discovery. Pharmacogenetic investigations of schizophrenia susceptibility loci, and genes controlling drug target site receptors, drug-metabolizing enzymes, the blood-brain barrier systems, and epigenetic mechanisms could lead to a molecular classification of treatment response and adverse events of psychotropic drugs.

BackgroundTreatment 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.MethodsHealthy 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.ResultsDevelopmental 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).ConclusionsNeurodevelopmental features such as NSS and age of clinical onset provide a means to identify patients who may require rapid treatment adaptation.

Objective: The aim of the study is to compare the sexual functioning among men with and without severe mental illness.&#x0D; Materials and Methods: The study was conducted in Psychiatry Department, Sree Balaji Medical College and hospital, Chromepet, Chennai Tamilnadu, India from the period of August 2016 to February 2018 (18 months). Cross sectional analytical study: 1. Semi structured questionnaire to capture demographic variables, illness variable, type of sexual practice and their expectations from MHPs. 2. General health questionnaire (12) tamil and English, 3. CAGE screening tool for alcohol. 4. The Brief Male Sexual Function Inventory (BMSF) for males (70).&#x0D; Results: The study population covered 50% of men with severe mental illness and 50% of men without severe mental illness. Among the study population regarding the educational status it was inferred that 70% were literate and only 30% were illiterate. An attempt to identify the mental illness of the study population was made and it was found that among the total cases, the prevailing mental illness was Schizophrenia (27%). Bipolar affective disorder (17%) was ranked as second highest. Delusional disorder, major depressive disorder covered up to 4% and 1% respectively. The comparisons of quantitative variable between the groups were given in Table 7. The median (range) of the age in case was 40 (29, 58) and in controls was 39 (25, 55), which shows that the median value of age between the groups was not found to be statistically significant (P&gt;0.05). The median value of general health questionnaire among the patients with severe mental illness was 26 (18, 34) and in controls it was 1 (0, 4) which shows that this median difference was highly significant (P&lt;0.0001). The median value of CAGE among the cases was 1 (0, 4) and in normal males was 19 (15, 29) which shows that the median difference between the groups were found to be highly significant (P&lt;0.0001). Similarly, the median difference of variables such as BSFI sexual drive, ejection score, ejaculation score, overall satisfaction score, problem assessment score and total score was found to highly significant (P&lt;0.0001). This implies that the sexual function of men with severe mental illness differs significantly from the normal males.&#x0D; Conclusion: Sexual dysfunction is common among men with severe mental illness than persons without severe mental illness. The male patients with severe mental illness such as Schizophrenia, delusional disorders, BPAD, major depressive disorder have impaired sexual function compared with the persons without severe mental illness study group which is evident from the BFSI score (17 in persons with severe mental illness vs 34 in persons without severe mental illness) which is clinically significant. In our study, sexual dysfunction among the men with severe mental illness is not proportional to the duration of the illness &amp; the age of the patients. Recommendations based on gender identity development, attachment, non-sexual and sexual abuse, puberty/adolescence.

In this article, we will review the recent progress in pharmacogenetics of antipsychotic drugs in regard to treatment response in schizophrenia. Research yielded new frontiers in schizophrenia over the past decade: progress in characterizing the first episode, understanding treatment-resistant illness, deciphering the role of inflammation, and developing tools for treatment of neurocognitive deficits. Notwithstanding the new insights into pathogenesis and treatment targets, our ability to individualize the treatment of schizophrenia in clinical practice has not followed suit. Herein, we review the advances that have been made in understanding the genetic variation in traditional drug targets like dopamine, serotonin, and glutamate receptors, as well as genetic markers associated with response in treatment-resistant schizophrenia and neurocognition. Practitioners are still longing for a test that would inform the choice of antipsychotic treatment and guide medication dosing, such that patients avoid spending time on failed treatment trials. Treatment-resistant schizophrenia may be one of the first areas to benefit of prediction of patients’ response to antipsychotic medication.

Research supporting the "early-onset" theory of antipsychotic activity is reviewed, with an emphasis on psychometric assessment of early response to antipsychotic agents as a tool for optimizing schizophrenia treatment outcomes. A growing body of evidence indicates that a poor response to antipsychotic therapy in the first weeks of schizophrenia treatment may justify a prompt switch to alternative medication in some cases. In placebo-controlled trials of both first- and second-generation antipsychotics, nonresponse at week 1 or 2, as determined with assessment instruments such as the Brief Psychiatric Rating Scale (BPRS) and the Positive and Negative Syndrome Scale (PANSS), was found highly predictive of nonresponse at week 4 or later; however, an early favorable response to a particular antipsychotic agent does not appear to be a similarly strong predictor of continued responsiveness. While the available evidence indicates that the BPRS, PANSS, and other scoring tools can be useful in guiding schizophrenia treatment decisions, it also emphasizes the importance of patient-specific factors (e.g., severity of illness at diagnosis, age at symptom onset, premorbid adolescent functioning) as determinants of both initial and longer-term antipsychotic response. The current evidence suggests that early nonresponse to antipsychotic treatment may predict subsequent non-response, though early response is not necessarily indicative of future response. If patients do not respond to treatment within the first two weeks of an acute exacerbation, clinicians (being cognizant of patient-specific factors) should consider switching antipsychotic agents, except in patients with first-episode psychosis, for whom a longer trial of the initially prescribed therapy appears to be appropriate.