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Abstract

Directionality of signaling among brain regions provides essential information about human cognition and disease states. Assessing such effective connectivity (EC) across brain states using functional magnetic resonance imaging (fMRI) alone has proven difficult, however. We propose a novel measure of EC, termed metabolic connectivity mapping (MCM), that integrates undirected functional connectivity (FC) with local energy metabolism from fMRI and positron emission tomography (PET) data acquired simultaneously. This method is based on the concept that most energy required for neuronal communication is consumed postsynaptically, i.e., at the target neurons. We investigated MCM and possible changes in EC within the physiological range using "eyes open" versus "eyes closed" conditions in healthy subjects. Independent of condition, MCM reliably detected stable and bidirectional communication between early and higher visual regions. Moreover, we found stable top-down signaling from a frontoparietal network including frontal eye fields. In contrast, we found additional top-down signaling from all major clusters of the salience network to early visual cortex only in the eyes open condition. MCM revealed consistent bidirectional and unidirectional signaling across the entire cortex, along with prominent changes in network interactions across two simple brain states. We propose MCM as a novel approach for inferring EC from neuronal energy metabolism that is ideally suited to study signaling hierarchies in the brain and their defects in brain disorders. 

Author information

Riedl V1, Utz L2, Castrillón G3, Grimmer T4, Rauschecker JP5, Ploner M6, Friston KJ7, Drzezga A8, Sorg C9.

1Department of Neuroradiology, Klinikum rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Department of Nuclear Medicine, Klinikum rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Neuroimaging Center, Klinikum Rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; valentin.riedl@mytum.de.

2Department of Neuroradiology, Klinikum rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Neuroimaging Center, Klinikum Rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Institute of Advanced Studies, Technischen Universitaet Muenchen, 81675 Muenchen, Germany;

3Department of Neuroradiology, Klinikum rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Neuroimaging Center, Klinikum Rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Instituto de Alta Tecnologia Medica, 050026 Medellin, Colombia;

4Neuroimaging Center, Klinikum Rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Department of Psychiatry and Psychotherapy, Klinikum Rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany;

5Neuroimaging Center, Klinikum Rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Institute of Advanced Studies, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Laboratory of Integrative Neuroscience and Cognition, Georgetown University, Washington, DC 20057;

6Neuroimaging Center, Klinikum Rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Department of Neurology, Klinikum Rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany;

7The Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom;

8Department of Nuclear Medicine, Uniklinik Köln, 50937 Köln, Germany.

9Department of Neuroradiology, Klinikum rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Neuroimaging Center, Klinikum Rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany; Department of Psychiatry and Psychotherapy, Klinikum Rechts der Isar, Technischen Universitaet Muenchen, 81675 Muenchen, Germany;