ART & ORGANISM
Excerpted, glossed, and annotated
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.
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)]
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
[i] “The brain’s dark energy” by M. E. Raichle (24 Nov. 2006, p. 1249). The author’s affiliation should be Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA. E-mail: email@example.com
[ii] The Brain’s Dark Energy Marcus E. Raichle (2006) Science 24 Nov., Vol. 314(5803):1249-1250 DOI: 10.1126/science. 1134405 https://science.sciencemag.org/content/314/5803/1249.abstract
- Neuroenergetics and the kinetic design of excitatory synapses. Attwell D, Gibb A.Nat Rev Neurosci. 2005 Nov;6(11):841-9. doi: 10.1038/nrn1784.PMID: 16261178 Review.
- Two views of brain function. Raichle ME.Trends Cogn Sci. 2010 Apr;14(4):180-90. doi: 10.1016/j.tics.2010.01.008. Epub 2010 Mar 4.PMID: 20206576 Review.
- Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Fox MD, Raichle ME.Nat Rev Neurosci. 2007 Sep;8(9):700-11. doi: 10.1038/nrn2201.PMID: 17704812 Review.
- Cognitive neuroscience. Bold insights. Raichle ME.Nature. 2001 Jul 12;412(6843):128-30. doi: 10.1038/35084300.PMID: 11449247 No abstract available.
- An energy budget for signaling in the grey matter of the brain. Attwell D, Laughlin SB.J Cereb Blood Flow Metab. 2001 Oct;21(10):1133-45. doi: 10.1097/00004647-200110000-00001.PMID: 11598490 Review.
The Bayesian response to hypothesis testing is to introduce the notion of degrees of belief that an investigator has for a hypothesis, relative to others. Then, as new information emerges, one reevaluates the likelihood of that information being compatible with each original possibility. (John F. DeCarlo[i]. (2019) https://junkyardofthemind.com/blog/2019/1/28/creative-thought-emotion-and-imagination
[i] John F. DeCarlo’s (2019) https://junkyardofthemind.com/blog/2019/1/28/creative-thought-emotion-and-imagination DeCarlo is a Visiting Professor in the Science, Technology & Society Department at SUNY Farmingdale, and has recently received research grants to Harvard University from Hofstra University, exploring the intermingling of poetic imagination, abductive logic, and scientific methodology. He is particularly interested in the philosophy of cancer research.
Relate to the DEFAULT mode of brain activity: Raichle et al 2001: https://www.pnas.org/content/98/2/676 and https://www.sciencedirect.com/topics/neuroscience/default-mode-network including: “Core Network Principles” by Warren W. Tryon, in Cognitive Neuroscience and Psychotherapy, 2014:
Default Mode Network fMRI Studies
Hans Berger discovered that the brain was constantly active in 1929, even during sleep, when he recorded the first electroencephalogram (EEG; Haas, 2003). Somehow this fact was minimized by fMRI investigators who considered that the brain was completely at rest during the control condition when participants lie quietly in an fMRI machine with eyes closed or eyes open fixed on a cross. Images taken under these conditions were considered to be just noise. The active experimental condition typically entailed the presentation of a stimulus; participation in a cognitive task. Only then was the brain expected to become active. There are two main approaches to analyzing the resulting data. The classic approach is to subtract the control image from the experimental image to see what brain networks were activated, turned on, by the stimulus or task. This approach assumes that all higher brain networks are quiet unless externally stimulated. No unconscious processing is thought to occur. Activity in lower brain structures responsible for autonomic functions such as respiration, heart rate, body temperature, blood pressure, and other autonomic functions is considered to be noise that is measured during the control condition. In short, this approach assumes a Pavlovian reflex perspective where the brain is passive until externally stimulated. This remains the dominant cognitive science research perspective.
The Default Mode Network (DMN) was discovered by Raichle et al. (2001). It was discovered by reversing the data subtraction procedure. The experimental image was subtracted from the control image to see what brain networks were deactivated, turned off when the brain attends to an external stimulus. This approach is based on the inverse perspective that the brain is always very busy doing its own thing while in the so-called ‘resting’ state, and interrupts itself when it needs to attend to an external stimulus or engage in a particular task.25 These interactive neural networks constitute the Default Mode Network (DMN). Upwards of 90% of the energy consumed by the brain is used to support the DMN (Raichle & Snyder, 2007). The DMN continues to be active during sleep. DMN activity persists even during light anesthesia (Raichle, 2009). The supporting evidence for the DMN has now been sufficiently well replicated that it is a neuroscience fact.
2001: Raichle et al “… posit that when an individual is awake and alert and yet not actively engaged in an attention-demanding task, a default state of brain activity exists that involves, among other areas, the MPFC and the posterior cingulate and precuneus. Information broadly arising in the external and internal milieu is gathered and evaluated. When focused attention is required, particularly if this activity is novel, activity within these areas may be attenuated. This attenuation in activity reflects a necessary reduction in resources devoted to general information gathering and evaluation. The MPFC with the posterior cingulate and medial parietal cortices may well be the “sentinels” to which William James referred (see ref. 48, p. 73), which, ‘when beams of light move over them, cry ‘who goes there’ and call the fovea to the spot. Most parts of the skin do but perform the same office for the fingertips.'”