Identification of changes in grey matter volume using an evolutionary approach: an MRI study of schizophrenia

Cerebral Computed Tomographic Findings in Schizophrenia: Relationship to Second-Generation Antipsychotics and Hyperprolactinemia

Antipsychotic medications are essential for managing severe mental illnesses like schizophrenia, which impacts about 1% of the global population. Despite efficacy, in some cases, they can induce hyperprolactinemia, affecting roughly half of the patients. The prevalence of this condition varies with the specific medication used. Although prolactinomas are rare among schizophrenia patients, treating them with dopamine agonists poses conflicts with antipsychotic medication, necessitating careful monitoring and adjustments. The aim of this study was to explore the presence of brain tumors, prolactinomas, and other structural brain changes in schizophrenia patients treated with second-generation antipsychotics using cerebral computed tomography (CT) scans. We conducted a cross-sectional study involving 152 hospitalized patients diagnosed between 1 January 2020 and 31 March 2024. Evaluations included cerebral CT scans, prolactin level assessments, and the monitoring of side effects. Patients, with an average age of 42.79 years and an illness duration of 17.89 years, predominantly received olanzapine (46.05%) and risperidone (36.84%). Side effects, reported by 61.78% of patients, included tremors, dizziness, and weight gain. Abnormal prolactin levels were observed in 53.95% of patients, more prevalent in females on risperidone and in both genders on olanzapine. No prolactinomas were detected on CT scans. Managing hyperprolactinemia in schizophrenia patients undergoing antipsychotic therapy is essential to prevent long-term complications and to ensure treatment compliance.

Modular-level alterations of single-subject gray matter networks in schizophrenia

Schizophrenia is often regarded as a psychiatric disorder caused by disrupted connections in the brain. Evidence suggests that the gray matter of schizophrenia patients is damaged in a modular pattern. Recently, abnormal topological organization was observed in the gray matter networks of patients with schizophrenia. However, the modular-level alteration of gray matter networks in schizophrenia remains unclear. In this study, single-subject gray matter networks were constructed for a total of 217 subjects (116 patients with schizophrenia and 101 controls). We analyzed the topological characteristics of the brain network and the strengths of connections between and within modules. Compared with the outcomes in the control group, the global efficiency and participation coefficient values of the single-subject gray matter networks in schizophrenic patients were significantly reduced. The nodal participation coefficient of the regions involving the frontoparietal attention network, default mode network and subcortical network were significantly decreased in subjects with schizophrenia. The intermodule connections between the frontoparietal attention network and visual network and between the default mode network and subcortical network, in the frontoparietal attention network were significantly reduced in the patient group. In the frontoparietal attention network, the intramodule nodal connection strength of the left orbital inferior frontal gyrus and right inferior parietal gyrus was significantly decreased in schizophrenia patients. Reduced intermodule nodal connection strength between the frontoparietal attention network and visual network was associated with the severity of schizophrenia symptoms. These findings suggest that abnormal intramodule and intermodule connections in the structural brain network may a biomarker of schizophrenia symptoms.

Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study

Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.