Two views of brain function
2010; Elsevier BV; Volume: 14; Issue: 4 Linguagem: Inglês
10.1016/j.tics.2010.01.008
ISSN1879-307X
Autores Tópico(s)EEG and Brain-Computer Interfaces
ResumoTraditionally studies of brain function have focused on task-evoked responses. By their very nature, such experiments tacitly encourage a reflexive view of brain function. Although such an approach has been remarkably productive, it ignores the alternative possibility that brain functions are mainly intrinsic, involving information processing for interpreting, responding to and predicting environmental demands. Here I argue that the latter view best captures the essence of brain function, a position that accords well with the allocation of the brain's energy resources. Recognizing the importance of intrinsic activity will require integrating knowledge from cognitive and systems neuroscience with cellular and molecular neuroscience where ion channels, receptors, components of signal transduction and metabolic pathways are all in a constant state of flux. Traditionally studies of brain function have focused on task-evoked responses. By their very nature, such experiments tacitly encourage a reflexive view of brain function. Although such an approach has been remarkably productive, it ignores the alternative possibility that brain functions are mainly intrinsic, involving information processing for interpreting, responding to and predicting environmental demands. Here I argue that the latter view best captures the essence of brain function, a position that accords well with the allocation of the brain's energy resources. Recognizing the importance of intrinsic activity will require integrating knowledge from cognitive and systems neuroscience with cellular and molecular neuroscience where ion channels, receptors, components of signal transduction and metabolic pathways are all in a constant state of flux. 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 out of our own head. William James (1890) This prescient comment by William James, to be found in Volume 2 (p. 103) of his monumental work The Principles of Psychology [1James W. The Principles of Psychology. Henry Holt & Company, 1890Crossref Google Scholar], captures the essence of a debate ongoing in the 19th century, and possibly earlier, surrounding two views of brain function. One view, pioneered by the work of Sir Charles Sherrington [2Sherrington C.S. The Integrative Action of the Nervous System. Yale University Press, 1906Google Scholar], posits that the brain is primarily reflexive, driven by the momentary demands of the environment. The other view is that the brain's operations are mainly intrinsic involving the acquisition and maintenance of information for interpreting, responding to and even predicting environmental demands, a view espoused by one of Sherrington's disciples T. Graham Brown [3Brown T.G. On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system.J. Physiol. 1914; 48: 18-46Crossref PubMed Scopus (505) Google Scholar] (for more recent perspectives see [4Llinas R. I of the Vortex. The MIT Press, 2001Crossref Google Scholar, 5Yuste R. et al.The cortex as a central pattern generator.Nat. Rev. Neurosci. 2005; 6: 477-483Crossref PubMed Scopus (231) Google Scholar]). The view that the brain is primarily reflexive has motivated most neuroscience research including that with functional neuroimaging. This is not surprising because experiments designed to measure brain responses to controlled stimuli and carefully designed tasks can be rigorously controlled, whereas evaluating the behavioral relevance of intrinsic activity (i.e. ongoing neural and metabolic activity which is not directly associated with subjects’ performance of a task) can be an elusive enterprise. Unfortunately, the success of studying evoked activity has caused us to lose sight of the possibility that our experiments reveal only a small fraction of the actual functional activity performed by our brain. Two challenges face a consideration of the view that the brain's operations are mainly intrinsic. First, how do we adjudicate the merits of such a claim? The answer comes primarily from a consideration of the considerable cost of running the brain most of which is devoted to its ongoing, internal functional activity. And, second, if the claim is correct then how do we unlock the mysteries of intrinsic activity? The answer will come from a serious consideration of multiple levels of inquiry ranging from cognitive and systems neuroscience to cell biology and metabolism. One of the most persuasive arguments for the importance of intrinsic activity emerges from a consideration of its relative cost in terms of brain energy consumption. In the average adult human, the brain represents about 2% of the total body weight yet it accounts for 20% of all the energy consumed [6Clarke D.D. Sokoloff L. Circulation and energy metabolism of the brain.in: Agranoff B.W. Siegel G.J. Basic Neurochemistry. Molecular, Cellular and Medical Aspects. 6th edn. Lippincott-Raven, 1999: 637-670Google Scholar], 10 times that predicted by its weight alone. Relative to this very high rate of ongoing energy consumption in the resting state (Box 1), the additional energy consumption associated with changes in brain activity is remarkably small, often less than 5% (Figure 1). From these data it is clear that the brain's enormous energy consumption is little affected by task performance, an observation first made more than 50 years ago by Louis Sokoloff, Seymour Kety and their colleagues [7Sokoloff L. et al.The effect of mental arithmetic on cerebral circulation and metabolism.J. Clin. Invest. 1955; 34: 1101-1108Crossref PubMed Google Scholar] but rarely cited.Box 1The Resting StateThe resting state is here viewed as a behavioral state characterized by quiet repose usually with eyes closed but occasionally, in the experimental setting, with eyes open with or without visual fixation (visual fixation as a resting state proxy probably only applies to humans where maintaining visual fixation is near effortless compared to monkeys who must be coerced). We presume that during the resting state subjects experience an ongoing state of conscious awareness largely filled with stimulus-independent thoughts (SITs;[148Antrobus J.S. Information theory and stimulus-independent thought.Br. J. Psychol. 1968; 59: 423-430Crossref Google Scholar]) or, more popularly, day dreaming or mind wandering [47Christoff K. et al.Experience sampling during fMRI reveals default network and executive system contributions to mind wandering.Proc. Natl. Acad. Sci. U. S. A. 2009; 106: 8719-8724Crossref PubMed Scopus (1204) Google Scholar, 149Mason M.F. et al.Wandering minds: the default network and stimulus-independent thought.Science. 2007; 315: 393-395Crossref PubMed Scopus (2059) Google Scholar]. It is important to distinguish between the resting state, defined behaviorally, and the state of the brain that accompanies the resting state. The brain is never physiologically at rest as evidenced by ongoing intrinsic activity and a very high energy consumption that varies little between the resting state and engagement in attention-demanding tasks (Figure 1). The resting state is here viewed as a behavioral state characterized by quiet repose usually with eyes closed but occasionally, in the experimental setting, with eyes open with or without visual fixation (visual fixation as a resting state proxy probably only applies to humans where maintaining visual fixation is near effortless compared to monkeys who must be coerced). We presume that during the resting state subjects experience an ongoing state of conscious awareness largely filled with stimulus-independent thoughts (SITs;[148Antrobus J.S. Information theory and stimulus-independent thought.Br. J. Psychol. 1968; 59: 423-430Crossref Google Scholar]) or, more popularly, day dreaming or mind wandering [47Christoff K. et al.Experience sampling during fMRI reveals default network and executive system contributions to mind wandering.Proc. Natl. Acad. Sci. U. S. A. 2009; 106: 8719-8724Crossref PubMed Scopus (1204) Google Scholar, 149Mason M.F. et al.Wandering minds: the default network and stimulus-independent thought.Science. 2007; 315: 393-395Crossref PubMed Scopus (2059) Google Scholar]. It is important to distinguish between the resting state, defined behaviorally, and the state of the brain that accompanies the resting state. The brain is never physiologically at rest as evidenced by ongoing intrinsic activity and a very high energy consumption that varies little between the resting state and engagement in attention-demanding tasks (Figure 1). What is the nature of this ongoing intrinsic activity that commands such a large amount of the brain's energy resources? Assessments of brain energy metabolism using a variety of approaches ([8Attwell D. Laughlin S.B. An energy budget for signaling in the grey matter of the brain.J. Cereb. Blood Flow Metab. 2001; 21: 1133-1145Crossref PubMed Google Scholar, 9Ames III, A. 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Such estimates leave for future consideration the demands placed on the brain's energy budget by the activity of inhibitory interneurons [14Ackermann R.F. et al.Increased glucose metabolism during long-duration recurrent inhibition of hippocampal pyramidal cells.J. Neurosci. 1984; 4: 251-264Crossref PubMed Google Scholar, 15McCasland J.S. Hibbard L.S. GABAergic neurons in barrel cortex show strong, whisker-dependent metabolic activation during normal behavior.J. Neurosci. 1997; 17: 5509-5527Crossref PubMed Google Scholar, 16Waldvogel D. et al.The relative metabolic demand of inhibition and excitation.Nature. 2000; 406: 995-998Crossref PubMed Scopus (244) Google Scholar, 17Chatton J.Y. et al.GABA uptake into astrocytes is not associated with significant metabolic cost: implications for brain imaging of inhibitory transmission.Proc. Natl. Acad. Sci. U. S. 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That evidence notwithstanding it is probable that the majority of brain energy consumption is devoted to functionally significant intrinsic activity. Complementary information on the importance of intrinsic activity comes from consideration of sensory information. It might surprise some to learn that visual information is significantly degraded as it passes from the eye to the visual cortex. Thus, of the unlimited information available from the environment, only about 1010 bits/sec are deposited in the retina. Because of a limited number of axons in the optic nerves (approximately 1 million axons in each) only ∼6 x 106 bits/sec leave the retina and only 104 make it to layer IV of V1 [22Anderson C.H. et al.Directed visual attention and the dynamic control of information flow.in: Itti L. Neurobiology of Attention. Elsevier, 2005: 11-17Crossref Scopus (23) Google Scholar, 23Norretranders T. The User Illusion. Viking, 1998Google Scholar]. These data clearly leave the impression that visual cortex receives an impoverished representation of the world, a subject of more than passing interest to those interested in the processing of visual information [24Olshausen B.A. Field D.J. How close are we to understanding v1?.Neural. Comput. 2005; 17: 1665-1699Crossref PubMed Scopus (293) Google Scholar]. Parenthetically, it should be noted that estimates of the bandwidth of conscious awareness itself (i.e. what we ‘see’) are in the range of 100 bits/sec or less [22Anderson C.H. et al.Directed visual attention and the dynamic control of information flow.in: Itti L. Neurobiology of Attention. Elsevier, 2005: 11-17Crossref Scopus (23) Google Scholar, 23Norretranders T. The User Illusion. Viking, 1998Google Scholar]. Reinforcing this impression of the brain's ‘isolation’ is the fact that the number of synapses in the lateral geniculate nucleus of the thalamus and in layer IV of primary visual cortex devoted to incoming visual information is less than 10% of the total number of synapses in both locations [25Sillito A.M. Jones H.E. Corticothalamic interactions in the transfer of visual information.Philos. Trans. R Soc. Lond. B Biol. Sci. 2002; 357: 1739-1752Crossref PubMed Scopus (138) Google Scholar]. Various proposals have been made concerning the interpretation of these anatomical data [26Bruno R.M. Sakmann B. Cortex is driven by weak but synchronously active thalamocortical synapses.Science. 2006; 312: 1622-1627Crossref PubMed Scopus (520) Google Scholar, 27Douglas R.J. Martin K.A. Recurrent neuronal circuits in the neocortex.Curr. Biol. 2007; 17: R496-500Abstract Full Text Full Text PDF PubMed Scopus (202) Google Scholar] but the fact remains that the brain must interpret, respond to and even predict environmental demands from seemingly impoverished data. An explanation for its success in doing so must lie in significant measure with intrinsic brain processes that link representations residing broadly within brain systems to incoming sensory information [28Fiser J. et al.Small modulation of ongoing cortical dynamics by sensory input during natural vision.Nature. 2004; 431: 573-578Crossref PubMed Scopus (320) Google Scholar]. The challenge, of course, is how to study these intrinsic brain processes at the appropriate spatial and temporal scales. Since the introduction of electroencephalography (EEG) in humans by Hans Berger in 1929 [29Berger H. Uber des Elektrenkephalogramm des Menschen.Archiv fur Psychiatrie und Nervenkrankheiten. 1929; 87: 527-580Crossref Scopus (0) Google Scholar] (for an English translation of this important work see [30Gloor, P. (1969) Hans Berger on the Electroencephalogram of Man. The Fourteen Original Reports on the Human Electroencephalogram. Electroencephalogr Clin Neurophysiol Supplement 28, 1–350Google Scholar]) it has been clear that ongoing spontaneous electrical activity is a prominent feature in EEG. In referring to the spontaneous activity Berger rhetorically asked [29Berger H. Uber des Elektrenkephalogramm des Menschen.Archiv fur Psychiatrie und Nervenkrankheiten. 1929; 87: 527-580Crossref Scopus (0) Google Scholar] ‘Is it possible to demonstrate the influence of intellectual work upon the human electroencephalogram, insofar as it has been reported here?’ He then concluded that ‘Of course, one should not at first entertain too high hopes with regard to this, because mental work, as I explained elsewhere, adds only a small increment to the cortical work which is going on continuously and not only in the waking state’. As has been demonstrated in subsequent research, extensive averaging of the EEG has been used to significantly attenuate if not eliminate this seemingly random, ongoing activity leaving only predictably occurring, task-induced changes or event-related potentials (ERPs) as they are known generally. This strategy is analogous to image subtraction and averaging (Figure 1) with similar, unfortunate consequences for ongoing intrinsic activity. It was a chance observation in neuroimaging first with positron emission topography (PET) and later with functional magnetic resonance imaging (fMRI) that actually provided a new perspective on what to look for in studying the brain's intrinsic activity. This was the occurrence of activity decreases during the performance of goal-directed tasks compared with the resting state. The first formal characterization of task-induced activity decreases from a resting state was a large meta-analysis of published PET data from our group [31Shulman G.L. et al.Common blood flow changes across visual tasks: II. Decreases in cerebral cortex.J. Cogn. Neurosci. 1997; 9: 648-663Crossref PubMed Google Scholar]. This study generated a set of iconic images of a constellation of brain regions now generally referred to as the default mode network (DMN) (Figure 2A) after our later paper on a default mode of brain function [32Raichle M.E. et al.A default mode of brain function.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 676-682Crossref PubMed Scopus (9076) Google Scholar] (Box 2). Jeffery Binder and colleagues [33Binder J.R. et al.Conceptual processing during the conscious resting state. A functional MRI study.J. Cogn. Neurosci. 1999; 11: 80-95Crossref PubMed Scopus (860) Google Scholar] and Bernard Mazoyer and colleagues confirmed the unique identity of this group of brain regions in later meta-analyses. Similar observations are now an everyday occurrence in numerous laboratories, leaving little doubt that a specific set of brain areas decrease their activity across a remarkably wide array of task conditions when compared with a passive control condition such as visual fixation.Box 2Questions for future research:•To what extent does phase resetting of SCPs account for the evoked BOLD response?•How are the many functions of glucose allocated during development and how is this regulated?•What functions of glucose contribute to the persistent increase in aerobic glycolysis following learning and are these the same functions deployed during development?•Are spontaneous fluctuations in cortical excitability related to aerobic glycolysis and, if so, how?•How is the striking overall increase in brain energy consumption from waking distributed among brain systems?•Why does the DMN succumb to Alzheimer's disease and what, if any, is the role of aerobic glycolysis in this unique vulnerability? •To what extent does phase resetting of SCPs account for the evoked BOLD response?•How are the many functions of glucose allocated during development and how is this regulated?•What functions of glucose contribute to the persistent increase in aerobic glycolysis following learning and are these the same functions deployed during development?•Are spontaneous fluctuations in cortical excitability related to aerobic glycolysis and, if so, how?•How is the striking overall increase in brain energy consumption from waking distributed among brain systems?•Why does the DMN succumb to Alzheimer's disease and what, if any, is the role of aerobic glycolysis in this unique vulnerability? The finding of a network of brain areas frequently seen to decrease its activity from a resting state during goal-directed tasks (Figure 2A) was both surprising and challenging. Surprising because the areas involved had not previously been recognized as a system in the same way we might think of the motor or visual system. And, challenging because initially it was unclear how to characterize their activity arising as it did in a passive or resting condition. It was conceivable that these activity decreases were simply activations present in a poorly constrained resting state. It was clear that we needed a way to determine whether or not task-induced activity decreases were simply ‘activations’ in the absence of an externally directed task. To initiate our inquiry we employed quantitative PET measurements of regional brain blood flow and oxygen consumption to define a physiologic baseline. The details of this work have been recounted on several occasions [13Raichle M.E. Mintun M.A. Brain work and brain imaging.Annu. Rev. Neurosci. 2006; 29: 449-476Crossref PubMed Scopus (1134) Google Scholar, 32Raichle M.E. et al.A default mode of brain function.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 676-682Crossref PubMed Scopus (9076) Google Scholar, 35Gusnard D.A. Raichle M.E. Searching for a baseline: functional imaging and the resting human brain.Nat. Rev. Neurosci. 2001; 2: 685-694Crossref PubMed Scopus (2716) Google Scholar, 36Raichle M. Snyder A.Z. A default mode of brain function: a brief history of an evolving idea.NeuroImage. 2007; 37: 1083-1090Crossref PubMed Scopus (1658) Google Scholar] and, thus, will not be repeated here. Suffice to say that this work allowed us to move forward on the assumption that activity within the DMN did not represent conventional activations in the resting state but, rather, a new view of the organization of the brain's intrinsic activity that we dubbed ‘a default mode of brain function’ [32Raichle M.E. et al.A default mode of brain function.Proc. Natl. Acad. Sci. U. S. A. 2001; 98: 676-682Crossref PubMed Scopus (9076) Google Scholar]. It is important to note that the DMN is not unique in exhibiting both high levels of baseline metabolic activity and organized functional activity in the resting state. It is a property of all brain systems and their subcortical connections as I will detail later. The discovery of the DMN made apparent the need for additional ways to study the large-scale intrinsic organization of the brain. A major step forward was the discovery that this large-scale network organization, including but not limited to the DMN, could be revealed by the study of patterns of spatial coherence in the spontaneous fluctuations (i.e. noise) in the fMRI blood oxygen level dependent (BOLD) signal. A prominent feature of fMRI is the noise in the raw BOLD signal. This has prompted researchers to average their data to increase signal and reduce noise. As first shown by Bharat Biswal and colleagues in the human somatomotor system [37Biswal B. et al.Hypercapnia reversibly suppresses low-frequency fluctuations in the human motor cortex during rest using echo-planar MRI.J. Cereb. Blood Flow Metab. 1997; 17: 301-308Crossref PubMed Google Scholar], a considerable fraction of this noise exhibits striking patterns of coherence within known brain systems. The significance of this observation was brought forcefully to our attention when Michael Greicius and colleagues looked at the patterns of coherence in the DMN [38Greicius M.D. et al.Functional connectivity in the resting brain: a network analysis of the default mode hypothesis.Proc. Natl. Acad. Sci. U. S. A. 2003; 100: 253-258Crossref PubMed Scopus (4890) Google Scholar] elicited by placing a region of interest in either the posterior cingulate cortex (yellow arrow, Figure 2A) or the ventral medial prefrontal cortex (orange arrow, Figure 2A). The resulting time-activity curves (Figure 2B) reflected a pattern of coherence within the entire DMN (Figure 2C). Similar patterns of resting state coherence have now been documented in most cortical systems in the human brain (Figure 3; for a recent review see [39Fox M.D. Raichle M. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging.Nat. Rev. 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These observations make it unlikely that the patterns of coherence and the intrinsic activity they represent are primarily the result of unconstrained, conscious cognition (i.e. mind-wandering or day dreaming [47Christoff K. et al.Experience sampling during fMRI reveals default network and executive system contributions to mind wandering.Proc. Natl. Acad. Sci. U. S. A. 2009; 106: 8719-8724Crossref PubMed Scopus (1204) Google Scholar]). Second, although resting state patterns of coherence do respect patterns of anatomical connectivity in both the monkey [43Vincent J.L. et al.Intrinsic functional architecture in the anaesthetized monkey brain.Nature. 2007; 447: 83-86Crossref PubMed Scopus (1393) Google Scholar] and human brain [41Zhang D. et al.Intrinsic functional relations between human cerebral cortex and thalamus.J. Neurophysiol. 2008; 100: 1740-1748Crossref PubMed Scopus (336) Google Scholar], it is clear that they are not constrained by these anatomical connections. Thus, the absence of monosynaptic connections between brain areas (e.g. right and left primary visual cortex [43Vincent J.L. et al.Intrinsic functional architecture in the anaesthetized monkey brain.Nature. 2007; 447: 83-86Crossref PubMed Scopus (1393) Google Scholar]) does not preclude the existence of functional connectivity as expressed in the maps of resting state coherence. Third, the strength of coherence between nodes within systems varies with age and disease. Developmental changes have been particularly well demonstrated in the DMN [48Fair D.A. et al.The maturing architecture of the brain's default network.Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 4028-4032Crossref PubMed Scopus (988) Google Scholar]. Such observations are consistent with the role of experience and, possibly, spontaneous activity itself in sculpting and maintaining these functional relationships in the human brain [49Yuste R. 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