Pharmacological fMRI: Effects of subanesthetic ketamine on resting-state functional connectivity in the default mode network, salience network, dorsal attention network and executive control network

Abstract

BackgroundSubanesthetic dosages of the NMDAR antagonist, S-Ketamine, can cause changes in behavior in healthy subjects, which are similar to the state acute psychosis and are relevant in translational schizophrenia research. Functional magnetic resonance imaging (fMRI) can be used for non-hypothesis-driven analysis of brain connectivity. The correlation between clinical behavioral scores and neuroimaging can help to characterize ketamine effects on healthy brains in resting state.Methodseventeen healthy, male subjects (mean: 27.42 years, SD: 4.42) were administered an infusion with S-Ketamine (initial bolus 1 mg/kg and continuous infusion of 0.015625 mg/kg/min with dosage reduction −10%/10 min) or saline in a randomized, double-blind, cross-over study. During infusion, resting state connectivity was measured and analyzed with a seed-to-voxel fMRI analysis approach. The seed regions were located in the posterior cingulate cortex, intraparietal sulcus, dorsolateral prefrontal cortex and fronto-insular cortex. Receiver operating characteristics (ROC) were calculated to assess the accuracy of the ketamine-induced functional connectivity changes. Bivariate Pearson correlation was used for correlation testing of functional connectivity changes with changes of clinical scores (PANSS, 5D-ASC).ResultsIn the executive network (ECN), ketamine significantly increases the functional connectivity with parts of the anterior cingulum and superior frontal gyrus, but no significant correlations with clinical symptoms were found. Decreased connectivity between the salience network (SN) and the calcarine fissure was found, which is significantly correlated with negative symptoms (PANSS) (R2 > 0.4).ConclusionDecreased ketamine-induced functional connectivity in the salience network may qualify as accurate and highly predictive biomarkers for ketamine induced negative symptoms.

Modulation of simultaneously collected hemodynamic and electrophysiological functional connectivity by ketamine and midazolam

“…We assessed drug modulations of the RSNs for each pipeline. Ketamine reduced connectivity within all networks (Figures 4 and 6b), unlike some previous research where no changes within the VN (Niesters et al, 2012) and DMN (Mueller et al, 2018;Niesters et al, 2012) were found.…”

“…We assessed drug modulations of the RSNs for each pipeline. Ketamine reduced connectivity within all networks (Figures and b), unlike some previous research where no changes within the VN (Niesters et al, ) and DMN (Mueller et al, ; Niesters et al, ) were found. Reductions similar to those found with our SIMPLE pipeline within the DMN were also found by Bonhomme et al (): these authors used two dosing levels, with the lowest being most similar to ours, and their preprocessing like our SIMPLE pipeline.…”

“…electrophysiological signals (Schölvinck, Maier, Ye, Duyn, & Leopold, 2010;Wen & Liu, 2016). The most common approach to noise removal in pharmacological fMRI studies is a more indirect method of accessing these global fluctuations: regressing out average CSF and WM signals (Bonhomme et al, 2016;Grimm et al, 2015;Joules et al, 2015;Khalili-Mahani et al, 2015;Mueller et al, 2018;J. J. Wong, O'Daly, Mehta, Young, & Stone, 2016), like in our SIMPLE pipeline.…”

“…The literature displays several commonalities in fMRIderived connectivity metrics, such as increased connectivity between the prefrontal cortex and subcortical areas with ketamine (Anticevic et al, 2015;Dandash et al, 2015;Grimm et al, 2015), and disruption of higher cognitive networks and increased connectivity within lowlevel sensory networks with midazolam (M. Greicius, 2008;Kiviniemi et al, 2005;Liang et al, 2015). However, discrepancies between results for both drugs are also evident, especially with the default mode network (DMN), where ketamine has been shown to decrease connectivity (Bonhomme et al, 2016), increase connectivity (Fleming et al, 2019), or to have no effect (Mueller et al, 2018;Niesters et al, 2012), and midazolam to decrease (Liang et al, 2015), or have no effect . While these discrepancies could be driven by differences in analysis, such as using different seed locations, they could also be due in part to different preprocessing strategies.…”

Comparison of EEG source reconstructed functional networks in healthy subjects elicited during visual oddball task

“…However, this technique is unable to estimate the dynamic connectivity among the cortical regions. The functional Magnetic Resonance Imaging (fMRI) technique is widely utilized to characterize the cortical FC [10-12] as it provides excellent spatial resolution. However fMRI has relatively lower temporal resolution [13,14] (slow sampling rate; ∼1s).…”