A&O READING – BRAIN: “The Brain’s Dark Energy” (Raichle 2006)

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BRAIN: “The Brain’s Dark Energy”

Marcus E. Raichle[i] (2006)

Science 314; 24 Nov. pp. 1249-1250[ii]

 

Since  the  19th  century,  and  possibly longer, two perspectives on brain functions have existed (1). One view posits that the brain is primarily reflexive, driven by the momentary demands of the environment; the  other,  that  the  brain’s  operations  are mainly intrinsic, involving the maintenance of information for interpreting, responding to, and even predicting environmental demands. While neither view is dominant, the former  has  motivated  most  neuroscience research. But technological advances, particularly in neuroimaging,  have  provoked  a reassessment of these two perspectives.

 

Human functional neuroimaging, first with positron emission tomography (PET) and now largely  with  functional  magnetic  resonance imaging (fMRI), allows the brain’s responses to controlled stimuli to be studied by measuring changes in brain circulation and metabolism (energy  consumption).  Surprisingly,  these studies have revealed that the additional energy required for such brain responses is extremely small  compared  to  the  ongoing  amount  of energy that the brain normally and continuously expends (2). The brain apparently uses most of its energy for functions unaccounted for—dark energy, in astronomical terms. What do we know about this dark energy?

 

The adult human brain represents about 2% of the body weight, yet accounts for about 20% of the body’s total energy consumption, 10 times that predicted by its weight alone. What fraction of this energy is directly related to brain function? Depending on the approach used,  it  is  estimated  that  60  to  80%  of  the energy budget of the brain supports communication among neurons and their supporting cells (2). The additional energy burden associated with momentary demands of the environment may be as little as 0.5 to 1.0% of the total energy budget (2). This cost-based analysis implies that intrinsic activity may be far more significant than evoked activity in terms of overall brain function.

 

Consideration  of  brain  energy  may  thus provide new insights into questions that have long  puzzled  neuroscientists.  For  example, researchers have sought to explain the relative disproportion of connections (i.e., synapses) among neurons that appear to perform functions intrinsically within the cerebral cortex.

 

Take the visual cortex, whose primary function is to respond to external input to the retina. Less than 10% of all synapses carry incoming information from the external world (3)—a surprisingly  small  number.  From  a  brain energy perspective, however, the cortex may simply be more involved in intrinsic activities.

 

What is this intrinsic activity? One possibility is that it simply represents unconstrained, spontaneous  cognition—our  daydreams or,  more  technically,  stimulus-independent thoughts. But it is highly unlikely to account for more than that elicited by responding to controlled stimuli, which accounts for a very small fraction of total brain activity.

 

Another possibility is that the brain’s enormous intrinsic functional activity facilitates responses to stimuli. Neurons continuously receive both excitatory and inhibitory inputs. The “balance” of these stimuli determines the responsiveness (or gain) of neurons to correlated  inputs  and,  in  so  doing,  potentially sculpts communication pathways in the brain (4). Balance also manifests at a large systems level. For example, neurologists know that strokes that damage cortical centers that control eye movements lead to deviation of the eyes toward the side of the lesion, implying the preexisting presence of “balance.” It may be  that  in  the  normal  brain,  a  balance  of opposing forces enhances the precision of a wide  range  of  processes.  Thus,  “balance” might be viewed as a necessary enabling, but costly, element of brain function.  

 

A  more  expanded  view  is  that  intrinsic activity instantiates the maintenance of information for interpreting, responding to, and even predicting environmental demands. In this regard, a useful conceptual framework from theoretical neuroscience posits that the brain operates as a Bayesian inference engine, designed  to  generate  predictions  about  the future (5). Beginning with a set of “advance” predictions  at  birth  (genes),  the  brain  is then sculpted by worldly experience to represent intrinsically a “best guess” (“priors” in Bayesian  parlance)  about  the  environment and, in the case of humans at least, to make predictions about the future (6). It has long been thought that the ability to reflect on the past or contemplate the future has facilitated the development of unique human attributes such as imagination and creativity (7, 8). [more about Bayesian approach: Horgan’s (2016) Sci Amer blog; and see endnotes)  

 

fMRI provides one important experimental approach to understanding the nature of the brain’s intrinsic functional activity without direct recourse to controlled stimuli and observable behaviors. A prominent feature of fMRI is that the unaveraged signal is quite noisy, prompting researchers to average their data to reduce this “noise” and  increase  the  signals  they  seek.  In doing this, it turns out that a considerable fraction of the variance in the blood oxygen level–dependent (BOLD) signal of fMRI in the  frequency  range  below  0.1  Hz,  which reflects  fluctuating  neural  activity,  is  lost.  This  activity  exhibits  striking  patterns  of coherence within known networks of specific neurons in the human brain in the absence of observable behaviors (see the figure).

 

Future research should address the cellular  events  underlying  spontaneous  fMRI BOLD signal fluctuations. Studies likely will cover  a  broad  range  of  approaches  to  the study of spontaneous activity of neurons (9, 10). In this regard, descriptions of slow fluctuations  (nominally  <0.1  Hz)  in  neuronal membrane  polarization—so-called  up  and down states—are intriguing (4, 10). Not only does their temporal frequency correspond to that of the  spontaneous fluctuations in the fMRI BOLD signal, but their functional consequences may be relevant to an understanding  of  the  variability  in  task-evoked  brain activity as well as behavioral variability in human performance.

 

William  James  presciently  suggested  in 1890 (11) that “Enough has now been said to prove the general law of perception, which is this, that whilst part of what we perceive comes through our senses from the object before us, another  part  (and  it  may  be  the  larger  part) always comes (in Lazarus’s phrase) out of our own head.”

 

The brain’s energy consumption tells us that the brain is never at rest. The challenge of neuroscience is to understand the functions associated with this energy consumption.

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At rest, but active.    fMRI images of a normal human brain at rest. The images reveal the highly organized nature of intrinsic brain activity, represented by correlated spontaneous fluctuations in the fMRI signal. Correlations are depicted by an arbitrary color scale. Positive correlations reside in areas known to increase activity during responses to controlled stimuli; negative correlations reside in areas that decrease activity under the same conditions. (Left) Lateral and medial views of the left hemisphere; (center) dorsal view; (right) lateral and medial views of the right hemisphere. [Reprinted from (12)]

 

 

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References

1.  R. Llinas, I of the Vortex: From Neurons to Self (MIT Press, Cambridge, MA, 2001).

2.  M. E. Raichle, M. Mintun, Annu. Rev. Neurosci. 29, 449 (2006).

3.  A. Peters, B. R. Payne, J. Budd, Cereb. Cortex 4, 215 (1994).

4.  B. Haider, A. Duque, A. R. Hasenstaub, D. A. McCormick,

J. Neurosci. 26, 4535 (2006).

5.  B. A. Olshausen, in The Visual Neurosciences, L. M. Chalupa, J. S. Werner, Eds. (MIT Press, Cambridge, MA, 2003), pp. 1603–1615.

6.  D. H. Ingvar, Hum. Neurobiol. 4, 127 (1985).

7.  D. Gilbert, Stumbling on Happiness (Knopf, New York, 2006).

8.  J. Hawkins, S. Blakeslee, On Intelligence (Holt, New York, 2004).

9.  D. A. Leopold, Y. Murayama, N. K. Logothetis, Cereb. Cortex 13, 422 (2003).

10.  C. C. H. Petersen, T. T. G. Hahn, M. Mehta, A. Grinvald, B. Sakmann, Proc. Natl. Acad. Sci. U.S.A. 100, 13638 (2003).

11.  W. James, Principles of Psychology (Henry Holt & Company, New York, 1890), vol. 2, p. 103.

12.  M. D. Fox et al., Proc. Natl. Acad. Sci. U.S.A. 102 , 9673 (2005).  10.1126/science. 1134405