Distinct intrinsic functional brain network abnormalities in methamphetamine‐dependent patients with and without a history of psychosis
Ipser, Jonathan C.
Harvey, Brian H.
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Chronic methamphetamine use is associated with executive functioning deficits that suggest dysfunctional cognitive control networks (CCNs) in the brain. Likewise, abnormal connectivity between intrinsic CCNs and default mode networks (DMNs) has also been associated with poor cognitive function in clinical populations. Accordingly, we tested the extent to which methamphetamine use predicts abnormal connectivity between these networks, and whether, as predicted, these abnormalities are compounded in patients with a history of methamphetamine‐associated psychosis (MAP). Resting‐state fMRI data were acquired from 46 methamphetamine‐dependent patients [19 with MAP, 27 without (MD)], as well as 26 healthy controls (CTRL). Multivariate network modelling and whole‐brain voxel‐wise connectivity analyses were conducted to identify group differences in intrinsic connectivity across four cognitive control and three DMN networks identified using an independent components analysis approach (meta‐ICA). The relationship of network connectivity and psychotic symptom severity, as well as antipsychotic treatment and methamphetamine use variables, was also investigated. Robust evidence of hyper‐connectivity was observed between the right frontoparietal and anterior DMN networks in MAP patients, and ‘normalized’ with increased duration of treatment with antipsychotics. Attenuation of anticorrelated anterior DMN–dorsal attention network activity was also restricted to this group. Elevated coupling detected in MD participants between anterior and posterior DMN networks became less apparent with increasing duration of abstinence from methamphetamine. In summary, we observed both alterations of RSN connectivity between DMN networks with chronic methamphetamine exposure, as well as DMN‐CCN coupling abnormalities consistent with possible MAP‐specific frontoparietal deficits in the biasing of task‐appropriate network activity
- Faculty of Health Sciences