Altered intrinsic and extrinsic connectivity in schizophrenia

Abstract

Schizophrenia is a disorder characterized by functional dysconnectivity among distributed brain regions. However, it is unclear how causal influences among large-scale brain networks are disrupted in schizophrenia. In this study, we used dynamic causal modeling (DCM) to assess the hypothesis that there is aberrant directed (effective) connectivity within and between three key large-scale brain networks (the dorsal attention network, the salience network and the default mode network) in schizophrenia during a working memory task. Functional MRI data during an n-back task from 40 patients with schizophrenia and 62 healthy controls were analyzed. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes, we found that intrinsic (within-region) and extrinsic (between-region) effective connectivity involving prefrontal regions were abnormal in schizophrenia. Specifically, in patients (i) inhibitory self-connections in prefrontal regions of the dorsal attention network were decreased across task conditions; (ii) extrinsic connectivity between regions of the default mode network was increased; specifically, from posterior cingulate cortex to the medial prefrontal cortex; (iii) between-network extrinsic connections involving the prefrontal cortex were altered; (iv) connections within networks and between networks were correlated with the severity of clinical symptoms and impaired cognition beyond working memory. In short, this study revealed the predominance of reduced synaptic efficacy of prefrontal efferents and afferents in the pathophysiology of schizophrenia.

Large‐scale dynamic causal modeling of major depressive disorder based on resting‐state functional magnetic resonance imaging

“…Two reasons may underlie the sensitivity of the PEB method. Taken together, we suggest that NBS is suitable to use as an exploratory approach to detect the group differences with relatively larger effect size in a large-scale network that can be used to form specific testing hypotheses, while PEB has the main advantage of testing specific hypotheses and may be more tailored for DCM studies, as previously tested (Zhou et al, 2018). Second, in a large network, many parameters may work together to produce the observed group effects while individual contributions are more difficult to be confidently identified due to the small effect size with large individual variability.…”

“…However, it should be noted that the increased number of group differences found by PEB might also reduce the specificity (more false positives could be revealed). Taken together, we suggest that NBS is suitable to use as an exploratory approach to detect the group differences with relatively larger effect size in a large-scale network that can be used to form specific testing hypotheses, while PEB has the main advantage of testing specific hypotheses and may be more tailored for DCM studies, as previously tested (Zhou et al, 2018).…”

“…PEB is a between-subject hierarchical and empirical Bayesian model that estimates the effects of group mean and group differences on each EC link and it was specifically proposed for DCM studies (Friston et al, 2016). PEB has been recently used to identify altered EC in a small-scale DCM with nine ROIs (Zhou et al, 2018) and its applicability to test specific hypotheses in large EC networks (>25 ROIs) was evaluated in this study for the first time. The PEB model is characterized by the following equations (Friston et al, 2016):…”

“…PEB was recently used in DCM studies, showing increased sensitivity and robustness (Friston et al, 2016;Zhou et al, 2018) (HDRS-17) with a total score larger than 18 (Hamilton, 1967), two professional psychologists who have more than 10 years experience separately carried out the MDD diagnosis according to the Diagnostic and Statistical Manual (DSM-5, American Psychiatric Association, 2013). Such limitations reduced statistical power and hindered a comprehensive understanding of the large-scale EC changes in MDD.…”

“…Parametric empirical Bayes (PEB) is a two-stage empirical Bayesian model dedicated to DCM studies, which considers both the mean and uncertainty (variance) of the EC estimation to infer group differences. PEB was recently used in DCM studies, showing increased sensitivity and robustness (Friston et al, 2016;Zhou et al, 2018) (HDRS-17) with a total score larger than 18 (Hamilton, 1967), two professional psychologists who have more than 10 years experience separately carried out the MDD diagnosis according to the Diagnostic and Statistical Manual (DSM-5, American Psychiatric Association, 2013).…”