Schizophrenia is a severe mental disorder characterized by positive, negative and cognitive symptoms. It has a prevalence of approximately 1% (Howes and Kapur, 2014). The introduction of antipsychotic medications has revolutionized the treatment of schizophrenia. However, despite the availability of various antipsychotic therapies, nearly 20-30% of patients have minimal or no response to these medications (Howes et al., 2009; Kane, 2012). This group of patients is associated with longer hospital stays and higher treatment costs due to persistent symptoms compared to patients who respond to antipsychotics (Kennedy et al., 2014). It is also known that there are neuropsychological differences between treatment-resistant and treatment-responsive schizophrenia (Millgate et al, 2022). Based on these observations, the group of patients with minimal or no response to antipsychotic medications, with recurrent major hospitalizations and representing an enduring public health problem, is referred to as treatment-resistant schizophrenia (TRS) (Nucifora et al. 2018, Chan, S. K. W et al. 2019). Dopamine D2/3 receptor occupancy studies conducted in this patient population have reported similar D2 receptor occupancy rates in TRS and non-TRS patients. These findings suggest that failure to achieve adequate drug concentrations in the brain does not explain treatment resistance (Howes et al., 2012). These findings raise the question: what is the neurobiology underlying the minimal benefit of non-clozapine antipsychotics in these patients? The answer to this question is important for the development of new strategies for the treatment of TRS patients. It is also important for the early identification of TRS patients; so that they can be started on appropriate treatment (Howes et al., 2012). A potential biomarker for the early detection of treatment resistance in schizophrenia patients could eliminate the need for empirical use of different antipsychotics.

A study comparing patients with healthy controls on the neurobiology of schizophrenia reports reliable differences (Syeda WT et al., 2022). However, although the studies comparing TRS patients with non-TRS patients show differences in some neuroanatomical regions, a specific region consistently associated with treatment resistance has not yet been identified. (Barry et al., 2019 ; Assunção-Leme IB et al., 2021). Different results from the limited number of studies in the literature may be due to methodological differences. The MRICloud is a web-accessible, cloud-based platform for integrating image standardization, quantification and cross-variables. An integrative cloud platform is a technology that offers significant opportunities for volumetric estimation, as it requires a significant amount of atlas sources and intensive computing. In previous studies, one of the main barriers to generalizing results has been the use of different tools on different platforms by people with different skills to make an estimate based on a single image. The MRICloud enables sustainable integration of whole brain segmentation (Figure 1) (Sakamoto et al., 2019; Djamanakova et al., 2014). A number of studies indicate the accuracy of the segmentation developed by multi-atlas fusion in contrast to single-atlas approaches (Artaechevarria et al., 2009; Warfield et al., 2004). In the present study, we aimed to explore potential neuroanatomical regions that may be associated with treatment resistance in schizophrenia patients by comparing neuroanatomical regions of TRS and non-TRS patients using the MRICloud method. Understanding the underlying differences between different subtypes of response may provide a better understanding of schizophrenia.