Friday, September 18, 2009

Energy and the Brain

The questions of how much energy is used by the brain, especially its various parts, and how it's used are important.  For one thing, our understanding of the brain depends strongly on functional magnetic resonance imaging (fMRI), which in turn has a number of built-in assumptions and open questions regarding how blood flow and nutrient concentrations relate to energy usage within the tiny regions (voxels) that it can resolve.[7] [8]  When dividing the brain into "parts" I'm talking not so much about areas or regions of the brain, as the microarchitectural constituents, such as axons, large and small dendrite branches, parts of the synapses on both sides of the synaptic cleft, and even astrocytes and other glial cells.  (There's considerable debate regarding how much and what types of energy transfers take place between glial cells and neurons.[8]

Thus, a very recent paper in Science,[1] Energy-Efficient Action Potentials in Hippocampal Mossy Fibers (by Henrik Alle, Arnd Roth, and Jörg R. P. Geiger) provides an important resolution to an open question regarding energy usage in unmyelinated axons.  They studied the current flows in axons of the Hippocampal Mossy Fibers, and demonstrated that the axons of these cells likely use about a third of the energy predicted by the standard notion, which is based on work going back to 1952.[13] [14]  The general applicability of this notion has been disputed, however, since at least 1975 based on early data[11] on unmyelinated axons of different species obtained with radiolabeled K+.[1] 

I'm going to start with the implications of this finding, followed by a discussion of what Alle et al. did and didn't discover, followed by a brief summary of what they did to perform this measurement.

Implications of the Lower Axonal Energy Usage

I've previously discussed the various functional aspects of the brain, in terms of performing the calculations (computations) leading to its function.  These include the general system of action potentials (APs) being fired in neurons, traveling along the axons to the pre-synaptic areas where they stimulate the release of neurotransmitters, which cross the synaptic cleft to stimulate currents in the post-synaptic areas in dendrites of other neurons, which currents in turn produce voltage changes that are transmitted to the soma (neural cell body), the axon hillock, and the Axon Initial Segment (AIS) which are the most common locations for the firing of new APs (primarily the AIS).  I've also discussed the ways in which many calculations can take place beyond the simple determination whether/when to fire an AP, as well as the non-linear ways in which the dendrite behaves as an "active cable", rather than the passive cable used in simpler models of neural activity.

Now, in order to behave as an "active cable", the dendritic membrane has to have some level of on-going current that can be modified in a non-linear fashion in response to voltage changes.  These currents, or rather the ion-pumping activity required to maintain (or recover) the concentration gradients that drive them, cost energy just as do the currents in the synapses and axons.  We have general ideas how much total energy any region of the brain uses during various activities, and the reduction of how much we think the small, local, unmyelinated axons are using means there's more left over for the other functions, including membranes with "active cable" characteristics.  ...

The Results of the Research

Let's start with the easy stuff.  This study was done in the hippocampus, which is one part of the brain out of more than a hundred.  We can't know for sure that similar energy-efficiency holds in any other regions of the brain until similar studies have been made for them.  Similarly, this study was done in rats, and in principle we don't even know if the findings hold true for mice, much less monkeys or humans.  Finally, these findings apply to only one kind of cell in the hippocampus.  In principal it might not hold for the other types of cell even there.

Realistically, however, it's reasonable to assume that what holds in one place holds in all, at least potentially.  Various studies of brain energy have suggested much lower values for axon energy usage,[11] and we can assume that what evolution has done in one place, it can do in others, assuming some sort of selective incentive to reduce energy expenditure.  And I think we can.  (Ideally, there should be some scattershot studies of other cell types and regions, to verify the general principle.  Hopefully this will offer opportunities for various researchers to get published, now that the cream has been skimmed off the discovery.)

Given the energy incentives for large-brained creatures, it seem likely that this energy efficiency evolved early in the lineages leading to mammals (and likely dinosaurs and birds as well, maybe independently).  However, the rapid early expansion of the brain in Hadrocodium wui, to a point large even for modern mammals,[15] may represent the first opportunistic use of some mutation allowing for this energy efficiency.  (Studies of monotreme, bird, crocodilian, and other reptilian (and perhaps amphibian, depending on reptilian results) axon current flows are strongly indicated.)

In general, then, unmyelinated axons in mammalian brains can probably be assumed to be as energy-efficient as their needs for high speed will allow.  Further research and modeling will probably give us a good idea what the trade-offs are, this can be expected to be a hot area of research for a while.

Now, let's take a look at what, specifically, was discovered.

I've included links to several discussions of how action potentials work, so I'm not going to try to cover everything here.  Basically, there are several ion flows involved in the action potential in the axon, but primarily they are sodium (Na+) and potassium (K+), with the Na+ concentration much higher outside the cell than inside, thus creating a current when it flows into the cell (INa), and the opposite for K+ (which currents are abbreviated Ik).  These two currents are in opposite directions, and if they occur simultaneously at any one spot along the axon they will cancel out, while taking up energy.

In the earliest research into such currents, which were done in the giant axon of the squid,[13] [14] there appears to be considerable overlap.  (This type of axon was used because its large size allowed researchers "to insert voltage clamp electrodes inside the lumen of the axon", even at this comparatively primitive stage of the technology.) The assumption was made that this overlap was general, even in mammals, although (as mentioned above) other research on unmyelinated axons suggested otherwise.[11] 

As it turns out, Alle et al. have discovered that there's much less overlap of currents than previously assumed because the IK came mostly after the INa was complete.  They also determined, through simulations, that
the observed degrees of charge separation are accompanied by comparatively low peak conductance densities, suggesting low numbers of channel proteins per area, which would minimize infrastructural costs for AP conduction.
Thus, not only are APs cheaper in energy costs than has been assumed, but the cost of producing the infrastructure is also lower.

How the Research Was Done

Alle et al. used a technique called patch-clamp recording to measure the currents found in the membrane of rat hippocampal mossy fiber boutons (MFBs).  In patch-clamp recording, a small section of cell membrane is removed with a pipette, in this case from boutons, which are small enlargements of the axon containing the pre-synaptic portion of synapses.  A voltage command was applied that duplicated "a previously-recorded AP wave", and the currents were measured. 
The onset of K+ currents (IK; Fig. 1, B and C, blue traces; n = 8) was significantly delayed compared to that of INa (106 ± 5 µs; P < 0.001), similar to results obtained from whole-bouton recordings (Fig. 1D, 115 ± 7 µs; P < 0.001, n = 8; P > 0.5 for patch versus whole-bouton recording).  The resulting small overlap of inward and outward currents [Fig. 1, B (inset) and C] indicated a high Na+ efficiency and, accordingly, energy efficiency in hippocampal mossy fibers, contrasting with previous simulations of axonal APs and their underlying currents ([refs]).[1]
Untangling the technical language, we see that the cell membrane of these particular axons responds to the voltage regime found in the AP with currents that barely overlap.  This is the core finding.

There were also simulations: 
To complement these results by a quantitative assessment of the Na+ influx as well as peak Na+ and K+ conductance densities (GNa and GK) underlying an AP propagating along an axon, we performed numerical simulations of APs.  We used conductance functions (Fig. 2A) derived from recorded currents (Fig. 1) in a compartmental model of the mossy fiber ([ref]) to reconstitute propagating APs ([ref to supporting data]).  Simulations resulted in AP waveforms and underlying currents closely resembling recorded APs and currents (Fig. 2B and fig. S1, A to D).  The validity of our approach was further tested with independent predictions of the model, such as INa onset potential and AP propagation velocity, which both complied with experimental data (Fig. 2C and fig. S2).[1]
These demonstrate that the values and timings of the currents involved, when incorporated into simulations, match the observed data.

They also analyzed the energy costs of the activity at the synapse that results from arrival of an AP, estimating that
the cost ratio of the mossy fiber AP itself to the downstream events (Fig. 4) has an upper limit of about 0.15 ([ref to supporting data]), shifting the emphasis of activity-dependent energy demand to downstream processes elicited by transmitter release, as suggested by in vivo work ([refs]).
IOW the APs require less energy, so there's more for other processes.

Alle, H., Roth, A., & Geiger, J. (2009). Energy-Efficient Action Potentials in Hippocampal Mossy Fibers Science, 325 (5946), 1405-1408 DOI: 10.1126/science.1174331

Links:  I've included only the links called out in this leader. Not all of these links are called out in the text.  Many are references taken from the featured paper.  Use the back key if you came via clicking a footnote. 

1.  Energy-Efficient Action Potentials in Hippocampal Mossy Fibers paywall

2.  An Energy Budget for Signaling in the Grey Matter of the Brain Open Access

3.  The neural basis of functional brain imaging signals

4.  The Cost of Cortical Computation Open Access

5.  Hemodynamic Signals Correlate Tightly with Synchronized Gamma Oscillations Free Registration Required

6.  Coupling Between Neuronal Firing, Field Potentials, and fMRI in Human Auditory Cortex Free Registration Required

7.  What we can do and what we cannot do with fMRI

8.  Metabolic and hemodynamic events after changes in neuronal activity:  current hypotheses, theoretical predictions and in vivo NMR experimental findings Open Access Author manuscript

9.  An Energy Budget for the Olfactory Glomerulus Open Access

10.  Functional Trade-Offs in White Matter Axonal Scaling Open Access

11.  Energetic aspects of nerve conduction:  The relationships between heat production, electrical activity and metabolism paywall

12.  Cortical Action Potential Backpropagation Explains Spike Threshold Variability and Rapid-Onset Kinetics Open Access

13.  The Optimum Density of Sodium Channels in an Unmyelinated Nerve paywall


15.  A New Mammaliaform from the Early Jurassic and Evolution of Mammalian Characteristics Free registration required


  1. Thank you so much for sharing such a great article here. I also found that the human brain is only 2% of the weight of the body, but it consumes about 20% of the total energy in the body at rest.

    materiel informatique

  2. Our understanding of the brain depends strongly on functional magnetic resonance imaging which in turn has a number of built-in assumptions and open questions regarding how blood flow and nutrient concentrations relate to energy usage within the tiny regions that it can resolve.Good luck with your writing!