Enhanced temporal variability of amygdala-frontal functional connectivity in patients with schizophrenia

Abstract

BackgroundThe “dysconnectivity hypothesis” was proposed 20 years ago. It characterized schizophrenia as a disorder with dysfunctional connectivity across a large range of distributed brain areas. Resting-state functional magnetic resonance imaging (rsfMRI) data have supported this theory. Previous studies revealed that the amygdala might be responsible for the emotion regulation-related symptoms of schizophrenia. However, conventional methods oversimplified brain activities by assuming that it remained static throughout the entire scan duration, which may explain why inconsistent results have been reported for the same brain region.MethodsAn emerging technique is sliding time window analysis, which is used to describe functional connectivity based on the temporal variability of regions of interest (e.g., amygdala) in patients with schizophrenia. Conventional analysis of the static functional connectivity between the amygdala and whole brain was also conducted.ResultsStatic functional connectivity between the amygdala and orbitofrontal region was impaired in patients with schizophrenia. The variability of connectivity between the amygdala and medial prefrontal cortex was enhanced (i.e., greater dynamics) in patients with schizophrenia. A negative relationship was found between the variability of connectivity and information processing efficiency. A positive correlation was found between the variability of connectivity and symptom severity.ConclusionThe findings suggest that schizophrenia was related to abnormal patterns of fluctuating communication among brain areas that are involved in emotion regulations. Unveiling the temporal properties of functional connectivity could disentangle the inconsistent results of previous functional connectivity studies.

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

“…The atypical dFC patterns in patients with SZ may be related to disease traits such as reduced cognitive performance. The amygdala-medial prefrontal cortex (mPFC) dFC variability is shown to be positively correlated with the total PANNS (positive and negative symptom scale) score and negatively correlated with information processing efficiency indexed by the digit symbol coding task (Yue et al, 2018). The correlation between the energy index (spatiotemporal uniformity) of the subcortical domain and the attention/vigilance computerized multiphasic interactive neurocognitive dualdisplay system (CMINDS) cognitive domains was disrupted in SZ patients (Iraji et al, 2019a).…”

“…Spatial dynamics studies also have revealed weaker dFC strength (transient hypoconnectivity) within particular networks (Iraji et al, 2019a;Iraji et al, 2019b) in SZ. It has been shown that the decrease in dFC strength is accompanied by higher fluctuations in dFC between brain regions (Yue et al, 2018) and within and between certain brain networks (Ma et al, 2014;Iraji et al, 2019a) in patients with SZ.…”

Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis

“…Schizophrenia patients spent less time and made fewer transitions between states characterized by weak correlations between the thalami and sense-related brain regions (Damaraju et al, 2014). They also showed more lagged correlations between the DMN and sensory networks (Yaesoubi et al, 2017a) and a higher occupancy rate of globally disconnected states (Yu et al, 2015; Cetin et al, 2016; Rashid et al, 2016; Gazula et al, 2018; Yue et al, 2018; Zhang W. et al, 2018). In children with autism spectrum disorders, TVC was mainly decreased in DMN and insular areas (Falahpour et al, 2016; Guo et al, 2018; He et al, 2018; Rashid et al, 2018a).…”

“…Several studies tried to characterize TVC abnormalities present in different psychiatric and neurological diseases, sometimes looking for an early diagnostic biomarker (Du et al, 2018; Mennigen et al, 2018). Modification of TVC strength, dwell time or number of transitions between states varied according to the disease status in patients affected by bipolar disorder (Rashid et al, 2014, 2016), schizophrenia (Yu et al, 2015; Cetin et al, 2016; Rashid et al, 2016; Gazula et al, 2018; Yue et al, 2018; Zhang W. et al, 2018), depression (Liao et al, 2018; Qiu et al, 2018; Zhi et al, 2018), autism (He et al, 2018; Rashid et al, 2018a), stroke (Chen et al, 2018), mild traumatic brain injury (Vergara et al, 2018), epilepsy (Ridley et al, 2017; Klugah-Brown et al, 2018), Alzheimer's disease (Quevenco et al, 2017; Jie et al, 2018), and Parkinson's disease (Engels et al, 2018).…”

MIR137 polygenic risk is associated with schizophrenia and affects functional connectivity of the dorsolateral prefrontal cortex

“…In addition, Mothersill and his colleagues also found that MIR137 gene influenced the FCs between the right amygdala and frontal regions (Mothersill et al ., 2014). Moreover, previous studies consistently reported that the FCs of the right amygdala could be impaired in schizophrenia patients (Bjorkquist et al ., 2016; Park et al ., 2018; Yue et al ., 2018). Therefore, we further explored the relationship between the right amygdala FCs and MIR137 PRS in our two independent cohorts with healthy individuals.…”