Schizophrenia (SZ) is a severe psychiatric disorder with a mortality rate for those affected two to four times that of the general population (Crawford and Go, 2022). The typical symptoms of SZ can be classified as positive, negative, or general (Owen et al., 2016). Moreover, patients with SZ suffer from serious cognitive impairment that persists even after treatment (Kahn et al., 2015), severely reducing their quality of life and exacerbating their social burden (Caqueo-Urízar et al., 2017; Harvey, 2014). A meta-analysis showed that, as a chronic mental disorder, SZ has an acute relapse rate of 24% and a treatment failure rate of 18% within one year (Ceraso et al., 2020). Second-generation antipsychotics (SGAs) are the most commonly used therapy for SZ. SGAs work predominantly by decreasing dopaminergic tone (De Hert et al., 2011) and are effective in improving positive symptoms and agitation but show limited effects on cognitive impairments in patients with SZ (Nielsen et al., 2015). Studies have shown that approximately 30% of the patients with SZ treated with SGAs develop drug resistance (Potkin et al., 2020). There was also a percentage of patients with multiple episodes of SZ who reported that taking SGAs again was not as effective as initially (Emsley et al., 2013). Chronic SZ continues to garner attention, and evaluating its therapeutic efficacy is an important clinical goal; however, there is a dearth of validated prognostic markers for acute exacerbations of chronic SZ. Therefore, elucidating antipsychotic treatment-induced changes in brain function in patients with chronic SZ is critical for the development of effective prognostic markers.

The human brain uses approximately 95% of its energy for spontaneous activity during the resting state (Fox and Raichle, 2007). Using safe and convenient resting-state functional magnetic resonance imaging (rs-fMRI), we examined resting-state brain function in patients with psychiatric disorders by detecting blood oxygen level-dependent (BOLD) signals in brain regions and identifying potential pathogenic mechanisms. The temporal correlation between BOLD signals is known as the functional connectivity (FC) (van den Heuvel et al., 2018). Based on FC, the human brain is constructed as a complex network, with FC as the edges and anatomical regions as nodes. Nodes generally cluster into functionally related subsystems and densely connected nodes are often critical for functional integration in the brain (van den Heuvel et al., 2018). From a functional network perspective, SZ is often considered a disorder of abnormal connectivity (Narr and Leaver, 2015; Pettersson-Yeo et al., 2011), which means that SZ is not caused by lesions in individual brain regions but by abnormal interactions in the whole brain. Degree centrality (DC) is a common metric based on the brain network graph theory that reveals the centrality of a brain functional connective network of nodes at the voxel level and the degree of importance of that node in brain functional network connections (Zuo et al., 2012). The voxel-based DC approach takes each voxel of the brain as a node and then calculates the number of functional connectivity with other nodes, the size of the resulting DC value reflects the importance of the node in the brain region, with the larger DC value the node is considered to be in a more core role in the brain network (García-García et al., 2015; Zuo et al., 2012). Several previous validation studies have shown that the BOLD-based DC method has a robust performance whether across subjects (Sheng et al., 2021), across frequencies (Wang et al., 2022b), or across scanner (Zhao et al., 2018b). More importantly, the coupling of BOLD variability and DC may underlie cognitive functions in psychiatric disorders (Sheng et al., 2021), suggesting that DC is a reliable measurement for investigating cognitive impairments in patients with SZ.

Previous rs-fMRI studies have shown that patients with SZ have DC defects in multiple brain areas, such as the default mode network containing the medial prefrontal and precuneus (Fan et al., 2019; Orliac et al., 2013), insula and anterior cingulate gyrus (Moran et al., 2013; Yang et al., 2020), and dorsolateral prefrontal and posterior parietal cortex (Manoliu et al., 2014). Zhou et al. indicated that the DC values in the right inferior frontal and superior parietal gyri were higher in patients with SZ and were correlated with clinical symptoms, suggesting that the frontoparietal network may play a critical role in the neurophysiological mechanisms of SZ (Zhou et al., 2022). It has also been shown that DC in the anterior parietal and caudate nucleus can effectively discriminate patients with SZ from healthy controls (HCs) (Shi et al., 2022). In addition, a longitudinal study on SZ reported that hippocampal connectivity with the anterior cingulate gyrus and caudate nucleus predicted a 6-week antipsychotic treatment response (Kraguljac et al., 2016). Using a machine learning approach, Liu et al. found that DC in brain regions including the left inferior frontal gyrus, inferior temporal gyrus, and bilateral thalamus could predict antipsychotic treatment response (Liu et al., 2022), suggesting that DC is a potential predictor of SGA treatment response.

Cognitive impairment is a core symptom of SZ that primarily affects processing speed, verbal memory, and working memory (Gebreegziabhere et al., 2022). Recent studies have found that DC abnormalities in SZ are closely associated with cognitive impairments. Eryilmaz et al. reported that DC in some prefrontal and parietal regions in patients with SZ is associated with working memory and is an effective predictor of scores on working memory tests (Eryilmaz et al., 2022). A task-state fMRI study demonstrated decreased DC of the dorsal posterior cingulate cortex in patients with SZ during a working memory task, showing an abnormal pattern and relationship with delusional symptoms (Wang et al., 2022a). These studies suggest that brain node dysfunction may underlie cognitive impairment in SZ, emphasizing the importance of topological network analysis. However, few studies have focused on the relationship between cognitive improvement in response to SGA treatment for acute exacerbations of chronic SZ and DC.

In this study, we proposed the following hypotheses: (1) DC is abnormal in patients with acute exacerbation of chronic SZ; (2) DC is altered in SZ after treatment with SGAs; and (3) there may be a relationship between DC alteration in SZ and improvement in clinical symptoms and cognitive performance. We collected rs-fMRI, clinical data, and cognitive test scores of patients with SZ at baseline and follow-up (post-treatment) and calculated DC metrics for each participant, followed by cross-sectional comparisons of SZ and HCs and longitudinal comparisons of SZ. Patients with SZ were classified into responder and non-responder groups according to their treatment response to further analyze the correlation between DC changes, clinical data, and cognitive performance.