Abstract
We report the first stochastic dynamic causal modeling (sDCM) study of effective connectivity within the default mode network (DMN) in schizophrenia. Thirty-three patients (9 women, mean age = 25.0 years, SD = 5) with a first episode of psychosis and diagnosis of schizophrenia—according to the Diagnostic and Statistic Manual of Mental Disorders, 4th edition, revised criteria—were studied. Fifteen healthy control subjects (4 women, mean age = 24.6 years, SD = 4) were included for comparison. All subjects underwent resting state functional magnetic resonance imaging (fMRI) interspersed with 2 periods of continuous picture viewing. The anterior frontal (AF), posterior cingulate (PC), and the left and right parietal nodes of the DMN were localized in an unbiased fashion using data from 16 independent healthy volunteers (using an identical fMRI protocol). We used sDCM to estimate directed connections between and within nodes of the DMN, which were subsequently compared with t tests at the between subject level. The excitatory effect of the PC node on the AF node and the inhibitory self-connection of the AF node were significantly weaker in patients (mean values = 0.013 and −0.048 Hz, SD = 0.09 and 0.05, respectively) relative to healthy subjects (mean values = 0.084 and −0.088 Hz, SD = 0.15 and 0.77, respectively; P < .05). In summary, sDCM revealed reduced effective connectivity to the AF node of the DMN—reflecting a reduced postsynaptic efficacy of prefrontal afferents—in patients with first-episode schizophrenia.
Highlights
Schizophrenia is a complex psychiatric disorder of unknown etiology, with significant clinical and pathophysiological heterogeneity, for which biomarkers are still lacking.[1]
Our results show weaker posterior cingulate (PC)-anterior frontal (AF) extrinsic connectivity and reduced AF self-inhibition in the default mode network (DMN) of patients with first-episode schizophrenia, relative to healthy control subjects
Patients with schizophrenia show a reduced sensitivity to both extrinsic and intrinsic afferents to the AF node. This is in agreement with theoretical accounts of the dysconnection hypothesis that appeal to predictive coding to explain false inference in schizophrenia.[18,33]
Summary
Schizophrenia is a complex psychiatric disorder of unknown etiology, with significant clinical and pathophysiological heterogeneity, for which biomarkers are still lacking.[1] Schizophrenia is generally thought to result from pathological interactions among gray matter structures. There are 2 versions of this hypothesis. One is implied by Wernicke’s “sejunction” hypothesis, which postulated an anatomical disruption or “disconnection” of association fibers between regions. The other postulates abnormalities at the level of synaptic efficacy and plasticity, leading to “dysfunctional” integration or connectivity among cortical and subcortical systems.[2,3] Neuroimaging studies of effective connectivity—defined as the causal influence of one neural system (eg, a network node) over another (or itself)—may, help to identify abnormalities in neural circuits whose dysfunction contributes to schizophrenia
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