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. 2018 Mar 6;8(1):4074.
doi: 10.1038/s41598-018-22444-0.

Cerebral blood flow predicts differential neurotransmitter activity

Juergen Dukart  1 , Štefan Holiga  2 , Christopher Chatham  2 , Peter Hawkins  3 , Anna Forsyth  4 , Rebecca McMillan  4 , Jim Myers  5 , Anne R Lingford-Hughes  5 , David J Nutt  6 , Emilio Merlo-Pich  2 , Celine Risterucci  2 , Lauren Boak  2 , Daniel Umbricht  2 , Scott Schobel  2 , Thomas Liu  7   8 , Mitul A Mehta  3 , Fernando O Zelaya  3 , Steve C Williams  3 , Gregory Brown  9   6 , Martin Paulus  9   6 , Garry D Honey  2 , Suresh Muthukumaraswamy  4 , Joerg Hipp  2 , Alessandro Bertolino  2   10 , Fabio Sambataro  2   11
Affiliations

Cerebral blood flow predicts differential neurotransmitter activity

Juergen Dukart et al. Sci Rep. .

Abstract

Application of metabolic magnetic resonance imaging measures such as cerebral blood flow in translational medicine is limited by the unknown link of observed alterations to specific neurophysiological processes. In particular, the sensitivity of cerebral blood flow to activity changes in specific neurotransmitter systems remains unclear. We address this question by probing cerebral blood flow in healthy volunteers using seven established drugs with known dopaminergic, serotonergic, glutamatergic and GABAergic mechanisms of action. We use a novel framework aimed at disentangling the observed effects to contribution from underlying neurotransmitter systems. We find for all evaluated compounds a reliable spatial link of respective cerebral blood flow changes with underlying neurotransmitter receptor densities corresponding to their primary mechanisms of action. The strength of these associations with receptor density is mediated by respective drug affinities. These findings suggest that cerebral blood flow is a sensitive brain-wide in-vivo assay of metabolic demands across a variety of neurotransmitter systems in humans.

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Conflict of interest statement

J.D., S.H., C.C., C.R., D.U., E.M., L.B., S.S., G.D.H., J.H., A.B. and F.S. are current or former full-time employees of F.Hoffmann-La Roche, Basel Switzerland. The authors received no specific funding for this work. F.Hoffmann-La Roche provided financial contribution in the form of salary for all authors listed above but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Figures

Figure 1
Figure 1
Schematic overview of the proposed mapping of cerebral blood flow (CBF) changes to underlying receptor densities, activity and affinities.
Figure 2
Figure 2
Results of Pearson correlation, multiple linear regression and effect size analyses. (a) Results of Pearson correlation (left) and multiple linear regression analyses between receptor densities and CBF changes are displayed as bar plots. For drugs with only one evaluated dose the drug profiles are colored as “high dose”. Red line for Pearson correlation plots indicates significance at an uncorrected two-sided p < 0.05 and yellow star indicates significant Bonferroni corrected findings, For multiple linear regressions a plus indicates a marginally significant (p < 0.1) and red star a significant (p < 0.05) effect of the corresponding regressor. (b) Voxel-wise effect size maps (Cohen’s d) are displayed for drug treatments matching the order of drugs displayed in (a). For risperidone the outcomes for the high dose are displayed.
Figure 3
Figure 3
Results of correlational analyses with molecular imaging based receptor density estimates and affinities. (a) Correlational plots between regional cerebral blood flow (CBF) changes and respective dopamine transporter (DAT) density profiles are displayed for each drug with dopaminergic mechanism of action. (b) DAT density estimates obtained from a healthy volunteer cohort provided by the Parkinson’s Progression Marker Initiative. (c) Correlational plot between midazolam induced CBF changes and GABAa density estimates obtained from flumazenil positron emission tomography. (d) Correlations of cerebral blood flow (CBF) changes to receptor density profiles with drug affinities. Colors indicate different receptors. Shapes indicate different drugs. Solid line in all plots indicates the linear regression fit.
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