Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Abstract

Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized.Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects.Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity.Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

“…Using a data-driven method of analyzing dFNC in CCN and DMN, we demonstrated that these brain networks are highly dynamic in both pre- and post-ECT states. This finding agrees with previous studies on MDD that have provided evidence of dynamism in CCN and DMN (Repple et al, 2020 ; Yang et al, 2020 ; Chen et al, 2021 ; Sendi et al, 2021a ). Previous studies differentiated MDD from the HC group, focusing on within-DMN and within-CCN functional connectivity.…”

“…Calculated dFNC for each window was concatenated for each subject as a form of C × C × T array (where C is the number of ICs and equals 276, and T represents total windows and equals 610). Finally, all arrays for all subjects were concatenated to show brain connectivity changes between ICs as a function of time ( Figure 1 , Step 2) (Allen et al, 2014 ; Sendi et al, 2021a , b ).…”

“…Dynamic FNC refers to brain connectivity within subintervals of the time series, as opposed to static FNC, which reflects averaged brain connections over an entire scan (Calhoun et al, 2014 ). In recent years, dFNC estimated from rs_fMRI time series has been highly informative about the underlying different brain regions connectivity patterns in various brain disorders, including schizophrenia, MDD, and Alzheimer's disease (Sendi et al, 2020 , 2021a , c ). As a result, we hypothesized that looking at the effect of ECT on dFNC might reveal how and to what extent ECT affects dynamic connectivity changes in the DMN and CCN.…”

Dynamic Alterations in Functional Connectivity Density in Amyotrophic Lateral Sclerosis: A Resting-State Functional Magnetic Resonance Imaging Study

“…Resting-state fMRI-based dynamic FC analysis may be employed to reflect the dynamic properties of brain function as well as improve our understanding of brain development/maturation/aging ( Chen et al, 2018 ) and cognitive-behavioral ( Nomi et al, 2017 ) mechanisms. Furthermore, the utilization of dynamic FC analysis enables us to elucidate the mechanisms underlying several psychiatric and neurological disorders such as schizophrenia ( Sendi et al, 2021a ) and Alzheimer’s disease ( Sendi et al, 2021b ). Several studies have assessed FC changes in ALS using a dynamic point of view.…”

iSparse kmeans: a two-step clustering approach for big dynamic functional network connectivity data

“…In recent years, a new line of research called dynamic functional network connectivity (dFNC) have moved beyond studying the strength of connectivity among brain regions and studied the temporal properties of the FNC (Allen et al, 2014). dFNC has shown promise as a biomarker for schizophrenia (Sendi et al, 2021a, 2021b), Alzheimer’s disease (Sendi et al, 2021c), major depressive disorder (Sendi et al, 2021e), and autism spectrum disorder (Harlalka et al, 2019). It has been shown that dFNC improves classification between disordered and healthy conditions (Rashid et al, 2015; Saha et al, 2021) and provides more information about the pathology of neurological and neuropsychiatric disorders than its static counterpart (Menon and Krishnamurthy, 2019).…”