Metabolic connectivity mapping reveals effective connectivity in the resting human brain

Riedl V, Utz L, Castrillon G, Grimmer T, Rauschecker JP, Ploner M, Friston KJ, Drzezga A, Sorg C (2016)

Publication Type: Journal article

Publication year: 2016

Journal

Proceedings of the National Academy of Sciences of the United States of America National Academy of Sciences

Book Volume: 113

Pages Range: 428-433

Journal Issue: 2

DOI: 10.1073/pnas.1513752113

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.

Authors with CRIS profile

Valentin Riedl

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) University College London (UCL) GB United Kingdom (GB) Universitätsklinikum Köln DE Germany (DE)

How to cite

APA:

Riedl, V., Utz, L., Castrillon, G., Grimmer, T., Rauschecker, J.P., Ploner, M.,... Sorg, C. (2016). Metabolic connectivity mapping reveals effective connectivity in the resting human brain. Proceedings of the National Academy of Sciences of the United States of America, 113(2), 428-433. https://doi.org/10.1073/pnas.1513752113

MLA:

Riedl, Valentin, et al. "Metabolic connectivity mapping reveals effective connectivity in the resting human brain." Proceedings of the National Academy of Sciences of the United States of America 113.2 (2016): 428-433.

BibTeX: Download