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. 2013 Aug 13;110(33):13642-7.
doi: 10.1073/pnas.1303346110. Epub 2013 Jul 29.

Energetic cost of brain functional connectivity

Dardo Tomasi  1 , Gene-Jack Wang, Nora D Volkow
Affiliations

Energetic cost of brain functional connectivity

Dardo Tomasi et al. Proc Natl Acad Sci U S A. .

Abstract

The brain's functional connectivity is complex, has high energetic cost, and requires efficient use of glucose, the brain's main energy source. It has been proposed that regions with a high degree of functional connectivity are energy efficient and can minimize consumption of glucose. However, the relationship between functional connectivity and energy consumption in the brain is poorly understood. To address this neglect, here we propose a simple model for the energy demands of brain functional connectivity, which we tested with positron emission tomography and MRI in 54 healthy volunteers at rest. Higher glucose metabolism was associated with proportionally larger MRI signal amplitudes, and a higher degree of connectivity was associated with nonlinear increases in metabolism, supporting our hypothesis for the energy efficiency of the connectivity hubs. Basal metabolism (in the absence of connectivity) accounted for 30% of brain glucose utilization, which suggests that the spontaneous brain activity accounts for 70% of the energy consumed by the brain. The energy efficiency of the connectivity hubs was higher for ventral precuneus, cerebellum, and subcortical hubs than for cortical hubs. The higher energy demands of brain communication that hinges upon higher connectivity could render brain hubs more vulnerable to deficits in energy delivery or utilization and help explain their sensitivity to neurodegenerative conditions, such as Alzheimer's disease.

Keywords: PET-FDG; allometric scaling; energy budget; fMRI connectivity; graph theory.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
(A) Schematic representation. Neuronal firing in the voxel x0 (red) stimulates secondary voxels (green) trough a sequence of m = 3 sequential stimulation cascades (primary red and secondary green arrows) with k = 2 stimulated voxels per cascade in average, and a total number of D = km stimulated voxels (degree of x0). (B) Absolute maps of CMRGlu computed from PET scans collected in resting conditions (eyes open) in 54 healthy subjects. Subjects were injected with 4–6 mCi of FDG i.v. and were asked to refrain from moving or speaking during the 30-min uptake period. Absolute CMRGlu maps were computed from 20-min emission scans that were acquired using standard PET procedures. (C) The amplitude of the low-frequency MRI signal fluctuations were computed for all 54 subjects from R-fMRI time series (40). (D) Global and local degree, graph theory measures of the number of functional connections per voxel, were mapped at 3-mm isotropic resolution from R-fMRI time series (21), to quantify the degree of the functional connectivity in the brain.
Fig. 2.
Fig. 2.
Statistical maps of the voxelwise correlations between CMRGlu and R-fMRI signal amplitude (A) and between CMRGlu global (B) and local (C) degree across 54 healthy subjects, superimposed on surface views of the cerebral cortex (medial and lateral) and the posterior cerebellum. The color bars indicate t-score values. Scatter plots exemplifying the linear association between CMRGlu and R-fMRI signal amplitudes (D) and the power scaling of CMRGlu and degree (E and F) across 54 healthy subjects for three different networks. The color lines are reduced-major axis regression fits to the data (0.96 < R2 < 0.99).
Fig. 3.
Fig. 3.
Average distribution of glucose efficiency (η) across subjects superimposed on the surface views of the Colin human brain.
See this image and copyright information in PMC

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