Axons: The cost of communication in the brain
Imaging ATP in axons reveals that they rely on glucose from the blood and lactate produced by glial cells as sources of energy.
- University of British Columbia, Canada
Although metabolism is considered to be a dull subject by some, it is vital to life. The metabolic processes that convert food into energy are particularly important for the brain: although it accounts for just 2% of total body weight, the brain is responsible for 20% of the body’s total energy expenditure. Most of this energy comes from adenosine triphosphate (or ATP for short), which the body produces by metabolizing glucose and oxygen.
The neurons in the brain are made up of four distinct parts: the dendrites, which receive information from other neurons at special structures called synapses; the cell body, where the nucleus and genetic material are located; the axon, which carries information away from the cell; and the synapses at the end of the axon, where information is passed on to the dendrites of other neurons. The cell bodies, dendrites and synapses are located in the gray matter of the brain, while axons make up the white matter.
To use an analogy that may soon be redundant in the era of cell phones, axons are to neurons what telephone cables are to land-line phones. Like telephone cables, axons transmit information over long distances as electrical signals called action potentials (which are based on differences in the concentrations of certain ions inside and outside the neuron). There is usually a small voltage across the membrane of a neuron called a resting membrane potential. However, when a neuron is stimulated, various ions suddenly travel into or out of the neuron, changing this voltage and creating an action potential that travels along the axon. The efficiency with which information is communicated over long distances by complex networks is an impressive feature of our brains.
In humans, the white matter accounts for 50% of the brain volume (Laughlin and Sejnowski, 2003). The first signs of neurodegenerative diseases in the brain can often be seen in axons; a specific loss of axons in the white matter affects brain functions such as memory or vision, even though the cell bodies of neurons may still be intact. To better understand neurological diseases associated with degenerating axons and white matter lesions (Iadecola, 2013; Hirrlinger and Nave, 2014), it is important to quantify the amount of energy needed to fuel action potentials and axonal activity. Previous estimates based on glucose uptake measurements indicate that white matter consumes only one third of what gray matter requires.
Mathematical models suggest that the gray matter requires a lot of energy for synaptic transmission, which involves molecules called neurotransmitters traveling from a pre-synaptic neuron to a post-synaptic neuron (Attwell and Laughlin, 2001). Most of the energy in the white matter, however, is used for generating an action potential and subsequently reestablishing the resting membrane potential (Harris and Attwell, 2012). Yet, the energy use of the white matter is still poorly understood, as it has been difficult to measure the exact amount of ATP consumption during the generation of action potentials in the axons.
Now, in eLife, Johannes Hirrlinger, Klaus-Armin Nave and colleagues – including Andrea Trevisiol of the Max Planck Institute for Experimental Medicine as first author – report that they have used optical sensors to directly measure the ATP consumption required to power action potentials (Trevisiol et al., 2017) This new approach allowed them to do two things for the first time: to visualize the energy use of action potentials in real time, and to determine the metabolic source of the ATP. The experiments were performed on an adapted version of the mouse optic nerve, a well-established nerve model for white matter electrophysiology (Stys et al., 1991; Brown et al., 2003).
Trevisiol et al. first uncovered a remarkable correlation between ATP levels and the generation of action potentials. When action potentials were evoked more frequently, the ATP levels decreased, indicating that action potentials rapidly consume energy. On the other hand, when the ATP production was interrupted, action potentials in the axons declined and progressively failed, because they did not spread as effectively. However, the technique cannot determine the exact levels of energy consumption because their optical sensor cannot measure the absolute concentration of ATP.
Trevisiol et al. subsequently showed that axons rely on several sources of energy for ATP production. Although glucose from the blood is the principal source, the researchers were able to show that the axons also use lactate as an energy source. It is thought that glial cells called astrocytes, which are found in both white and gray matter, metabolize a form of glucose called glycogen to produce lactate (Brown et al., 2003; Suzuki et al., 2011). Other glial cells called oligodendrocytes can also supply lactate (which they produce by metabolizing glucose) (Fünfschilling et al., 2012; Lee et al., 2012). This suggests that a complex energy supply network in which multiple cell types and metabolic energy sources are used to maintain the ATP levels is crucial for axons to work properly.
This development of an imaging approach that can monitor changes in ATP levels is an important step in quantifying the metabolic costs of communication via white matter axons. It also gives us a clever insight into defining the important supporting roles of glia cells in maintaining the health of the white matter itself via the production of lactate.
References
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An energy budget for signaling in the grey matter of the brainJournal of Cerebral Blood Flow & Metabolism 21:1133–1145.https://doi.org/10.1097/00004647-200110000-00001
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Glycogen regulation and functional role in mouse white matterThe Journal of Physiology 549:501–512.https://doi.org/10.1113/jphysiol.2003.042416
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The energetics of CNS white matterJournal of Neuroscience 32:356–371.https://doi.org/10.1523/JNEUROSCI.3430-11.2012
Article and author information
Author details
Leigh Wicki-Stordeur
Louis-Philippe Bernier
Publication history
- Version of Record published: May 22, 2017 (version 1)
- Version of Record updated: May 23, 2017 (version 2)
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© 2017, MacVicar et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
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Axons: The cost of communication in the brain
eLife 6:e27894.
https://doi.org/10.7554/eLife.27894
Further reading
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- Neuroscience
The presence of global synchronization of vasomotion induced by oscillating visual stimuli was identified in the mouse brain. Endogenous autofluorescence was used and the vessel ‘shadow’ was quantified to evaluate the magnitude of the frequency-locked vasomotion. This method allows vasomotion to be easily quantified in non-transgenic wild-type mice using either the wide-field macro-zoom microscopy or the deep-brain fiber photometry methods. Vertical stripes horizontally oscillating at a low temporal frequency (0.25 Hz) were presented to the awake mouse, and oscillatory vasomotion locked to the temporal frequency of the visual stimulation was induced not only in the primary visual cortex but across a wide surface area of the cortex and the cerebellum. The visually induced vasomotion adapted to a wide range of stimulation parameters. Repeated trials of the visual stimulus presentations resulted in the plastic entrainment of vasomotion. Horizontally oscillating visual stimulus is known to induce horizontal optokinetic response (HOKR). The amplitude of the eye movement is known to increase with repeated training sessions, and the flocculus region of the cerebellum is known to be essential for this learning to occur. Here, we show a strong correlation between the average HOKR performance gain and the vasomotion entrainment magnitude in the cerebellar flocculus. Therefore, the plasticity of vasomotion and neuronal circuits appeared to occur in parallel. Efficient energy delivery by the entrained vasomotion may contribute to meeting the energy demand for increased coordinated neuronal activity and the subsequent neuronal circuit reorganization.
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- Medicine
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Background:
Ketamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamine’s molecular mechanisms connect to its neural and behavioral effects.
Methods:
We conducted a single-blind placebo-controlled study, with participants blinded to their treatment condition. 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hr). We quantified resting-state functional connectivity via data-driven global brain connectivity and related it to individual ketamine-induced symptom variation and cortical gene expression targets.
Results:
We found that: (i) both the neural and behavioral effects of acute ketamine are multi-dimensional, reflecting robust inter-individual variability; (ii) ketamine’s data-driven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, while the mean effect did not; and (iii) behavioral data-driven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the single-subject level.
Conclusions:
These results highlight the importance of considering individual behavioral and neural variation in response to ketamine. They also have implications for the development of individually precise pharmacological biomarkers for treatment selection in psychiatry.
Funding:
This study was supported by NIH grants DP5OD012109-01 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA012870-11 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1–190420) (FXV, KHP); Swiss Neuromatrix Foundation (Grant No. 2016–0111) (FXV, KHP); Swiss National Science Foundation under the framework of Neuron Cofund (Grant No. 01EW1908) (KHP); Usona Institute (2015 – 2056) (FXV).
Clinical trial number:
