The Neurobiology of Treatment-Resistant Schizophrenia: Paths to Antipsychotic Resistance and A Roadmap for Future Research

Associations Between Structural Covariance Network and Antipsychotic Treatment Response in Schizophrenia

BACKGROUND AND HYPOTHESIS

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.

STUDY DESIGN

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.

STUDY RESULTS

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.

CONCLUSIONS

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.

Reduced cortical cerebral blood flow in antipsychotic-free first-episode psychosis and relationship to treatment response

BACKGROUND

Altered cerebral blood flow (CBF) has been found in people at risk for psychosis, with first-episode psychosis (FEP) and with chronic schizophrenia (SCZ). Studies using arterial spin labelling (ASL) have shown reduction of cortical CBF and increased subcortical CBF in SCZ. Previous studies have investigated CBF using ASL in FEP, reporting increased CBF in striatum and reduced CBF in frontal cortex. However, as these people were taking antipsychotics, it is unclear whether these changes are related to the disorder or antipsychotic treatment and how they relate to treatment response.

METHODS

We examined CBF in FEP free from antipsychotic medication (N = 21), compared to healthy controls (N = 22). Both absolute and relative-to-global CBF were assessed. We also investigated the association between baseline CBF and treatment response in a partially nested follow-up study (N = 14).

RESULTS

There was significantly lower absolute CBF in frontal cortex (Cohen's d = 0.84, p = 0.009) and no differences in striatum or hippocampus. Whole brain voxel-wise analysis revealed widespread cortical reductions in absolute CBF in large cortical clusters that encompassed occipital, parietal and frontal cortices (Threshold-Free Cluster Enhancement (TFCE)-corrected <0.05). No differences were found in relative-to-global CBF in the selected region of interests and in voxel-wise analysis. Relative-to-global frontal CBF was correlated with percentage change in total Positive and Negative Syndrome Scale after antipsychotic treatment (r = 0.67, p = 0.008).

CONCLUSIONS

These results show lower cortical absolute perfusion in FEP prior to starting antipsychotic treatment and suggest relative-to-global frontal CBF as assessed with magnetic resonance imaging could potentially serve as a biomarker for antipsychotic response.

Treatment resistance in psychiatry: state of the art and new directions

Treatment resistance affects 20-60% of patients with psychiatric disorders; and is associated with increased healthcare burden and costs up to ten-fold higher relative to patients in general. Whilst there has been a recent increase in the proportion of psychiatric research focussing on treatment resistance (R² = 0.71, p < 0.0001), in absolute terms this is less than 1% of the total output and grossly out of proportion to its prevalence and impact. Here, we provide an overview of treatment resistance, considering its conceptualisation, assessment, epidemiology, impact, and common neurobiological models. We also review new treatments in development and future directions. We identify 23 consensus guidelines on its definition, covering schizophrenia, major depressive disorder, bipolar affective disorder, and obsessive compulsive disorder (OCD). This shows three core components to its definition, but also identifies heterogeneity and lack of criteria for a number of disorders, including panic disorder, post-traumatic stress disorder, and substance dependence. We provide a reporting check-list to aid comparisons across studies. We consider the concept of pseudo-resistance, linked to poor adherence or other factors, and provide an algorithm for the clinical assessment of treatment resistance. We identify nine drugs and a number of non-pharmacological approaches being developed for treatment resistance across schizophrenia, major depressive disorder, bipolar affective disorder, and OCD. Key outstanding issues for treatment resistance include heterogeneity and absence of consensus criteria, poor understanding of neurobiology, under-investment, and lack of treatments. We make recommendations to address these issues, including harmonisation of definitions, and research into the mechanisms and novel interventions to enable targeted and personalised therapeutic approaches.

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping

Background: Psychiatric disorders have been historically classified using symptom information alone. Recently, there has been a dramatic increase in research interest not only in identifying the mechanisms underlying defined pathologies but also in redefining their etiology. This is particularly relevant for the field of personalized medicine, which searches for data-driven approaches to improve diagnosis, prognosis, and treatment selection for individual patients. Methods: This review aims to provide a high-level overview of the rapidly growing field of functional magnetic resonance imaging (fMRI) from the perspective of unsupervised machine learning applications for disease subtyping. Following the PRISMA guidelines for protocol reproducibility, we searched the PubMed database for articles describing functional MRI applications used to obtain, interpret, or validate psychiatric disease subtypes. We also employed the active learning framework ASReview to prioritize publications in a machine learning-guided way. Results: From the 20 studies that met the inclusion criteria, five used functional MRI data to interpret symptom-derived disease clusters, four used it to interpret clusters derived from biomarker data other than fMRI itself, and 11 applied clustering techniques involving fMRI directly. Major depression disorder and schizophrenia were the two most frequently studied pathologies (35% and 30% of the retrieved studies, respectively), followed by ADHD (15%), psychosis as a whole (10%), autism disorder (5%), and the consequences of early exposure to violence (5%). Conclusions: The increased interest in personalized medicine and data-driven disease subtyping also extends to psychiatric disorders. However, to date, this subfield is at an incipient exploratory stage, and all retrieved studies were mostly proofs of principle where further validation and increased sample sizes are craved for. Whereas results for all explored diseases are inconsistent, we believe this reflects the need for concerted, multisite data collection efforts with a strong focus on measuring the generalizability of results. Finally, whereas functional MRI is the best way of measuring brain function available to date, its low signal-to-noise ratio and elevated monetary cost make it a poor clinical alternative. Even with technology progressing and costs decreasing, this might incentivize the search for more accessible, clinically ready functional proxies in the future.