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There is a growing consensus on brain networks that it is not immutable but rather a dynamic complex system for adapting environment. The neuroimaging research studying how brain regions work collaboratively with dynamic methods had demonstrated its effectiveness in revealing the neural mechanisms of schizophrenia.
To investigate altered dynamic brain functional topology in first-episode untreated schizophrenia patients (SZs) and establish classification models to find objective brain imaging biomarkers.
Resting-state-functional magnetic resonance data for SZs and matched healthy controls were obtained(Table1).
Power-264-template was used to extract nodes and sliding-window approach was carried out to establish functional connectivity matrices. Functional topology was assessed by eigenvector centrality(EC) and node efficiency and its time-fluctuating was evaluated with coefficient of variation(CV). Group differences of dynamic topology and correlation analysis between Positive and Negative Syndrome Scale(PANSS) scores and topology indices showing group differences, which also were used in establishing classification models, was examed.
The CV of node efficiency in angular and paracingulate gyrus was larger in SZs. There are 13 nodes assigned into several brain networks displaying altered CV of EC between groups(Figure1.A). Fluctuation of EC of the node in DMN, which was lower in SZs, showed negative correlation with PANSS total scores(Figure1.B). Dynamic functional topology of above nodes was used to train classification models and demonstrated 80% and 71% accuracy for support vector classification(SVC) and random forest(RF), respectively(Figure2).
Dynamic functional topology illustrated a capability in identifying SZs. Aberrated dynamics of DMN relevant to severity of patient’s symptoms could reveal the reason why it contributed to classification.
No significant relationships.
One of the most perplexing and characteristic symptoms of the schizophrenia (SZ) patients is hallucination. The occurrence of hallucinations to be associated with altered activity in the auditory and visual cortex but is not well understood from the brain functional network dynamics in SZ.
To explore the brain abnormal basis of hallucinations in SZ with the dynamic functional connectivity (dFC).
Using magnetic resonance imaging for 83 SZ patients and 83 matched healthy controls and independent component analysis, 52 independent components (ICs) were identified as nodes and assigned into eight intrinsic connectivity networks (Figure 1A). Subsequently, we established dFC matrices and clustered them into four discrete states (Figure 1B) and three state transition metrics were obtained. To further explore the changes in the centrality of each component, eigenvector centrality (EC) was calculated and its time-varying was evaluated.
Compared to controls with FDR correction, we found that patients had more mean dwell times and fractional time in state 1 (P=0.0081 and P=0.0018), mainly with hypoconnectivity between auditory and visual network and other networks and hyperconnectivity between language and default-mode network (DMN). While, patients had less dwell times and fractional time in state 3 (P=0.0018 and P=0.0009), and decreased FC between visual network and executive control network (ECN) and increased FC between ECN and DMN than controls (Figure 2).
EC statistics showed that SZs displayed increased temporal dynamics in visual-related regions (Figure 3).
SZ was mainly manifested as altered dFC and temporal variability of nodal centrality in auditory and visual networks.
No significant relationships.
Though schizophrenia (SZ) and obsessive-compulsive disorder (OCD) are conceptualized as distinct clinical entities, they do have notable symptom overlap and a tight association. Graph-theoretical analysis of the brain connectome provides more indicators to describe the functional organization of the brain, which may help us understand the shared and disorder-specific neural basis of the two disorders.
To explore the static and dynamic topological organization of OCD and SZ as well as the relationship between topological metrics and clinical variables.
Resting state functional magnetic resonance imaging data of 31 OCD patients, 49 SZ patients, and 45 healthy controls (HC) were involved in this study (Table 1). Using independent component analysis to obtain independent components (ICs) (Figure 1), which were defined as nodes for static and dynamic topological analysis.
Static analysis showed the global efficiency of SZ was higher than HC. For nodal degree centrality, OCD exhibited decreased degree centrality in IC59 (located in visiual network) (P = 0.03) and increased degree centrality in IC38 (located in salience network) (P = 0.002) compared with HC. Dynamic analysis showed OCD exhibited decreased dynamics of degree centrality in IC38 (P = 0.003) compared with HC, which showed a negative correlation with clinical scores in OCD. While SZ showed decreased dynamics of degree centrality in IC76 (located in sensory motor network) compared with OCD (P=0.009), which showed a positive correlation with clinical scores in SZ (Figure 2).
These changes are suggestive of disorder-specific alternation of static and dynamic brain topological organization in OCD and SZ.
Obsessive-compulsive disorder (OCD) and schizophrenia (SZ) are both severe psychiatric disorders. Though these two disorders have distinct typical symptoms, there are partial polygenic overlap and comorbidity between the two disorders. However, few studies have explored the shared and disorder-specific brain function underlying the neural pathophysiology of the two disorders, especially in the aspect of dynamics.
To explore the abnormal characteristics of the dynamic functional connectivity (dFC) in OCD and SZ as well as the association between dFC metrics and symptom severity.
The resting state functional magnetic resonance imaging data of 31 patients with OCD, 49 patients with SZ, and 45 healthy controls were analyzed using independent component analysis to obtain independent components (ICs) and assigned them into eight brain networks (Figure 1), then used the sliding-window approach to generate dFC matrices. Using k-means clustering, we obtained three reoccurring dFC states (Figure 2), and state transition metrics were obtained
In a sparsely connected state (state 1), SZ showed both increased fractional time and mean dwell time than controls (P=0.047 and P=0.033) and OCD (P=0.001 and P=0.003). In a state characterized by negative FC between networks (state 2), OCD showed both increased fractional time and mean dwell time than controls (P=0.032 and P=0.013) and SZ (P=0.005 and P=0.003). Moreover, the fractional time of state 2 was positively correlated with anxiety scores in OCD (r=0.535, P=0.021, FDR corrected) (Figure 3).
OCD and SZ patients showed distinct alternations of brain functional dynamics.
No significant relationships.
