Creativity and the Brain's Default Mode Network

5 Creativity and the Brain’s Default Mode Network

Marcus E. Raichle

The purpose of this chapter is to explore some new ways of thinking about how the brain instantiates creativity. The route taken begins with an appraisal of the brain’s intrinsic or ongoing activity which, as I will explain, is the dominant mode of brain activity. A productive discussion of creativity must take into account the role of intrinsic activity. An important feature of this approach is to highlight the critical role of a recently discovered brain network, the default mode network (DMN), which serves in the organization of intrinsic activity and among whose component operations (i.e., memory and the adjudication of risk-taking) are important elements of creativity. I begin with some general background material on intrinsic activity.

Intrinsic Brain Activity

Intrinsic activity refers to activity in all regions of the brain that is always present regardless of one’s state. It is present in conditions as varied as quiet repose with daydreaming to the full range of human behaviors. Intrinsic activity is also prominent during sleep. Intrinsic activity is easily observed with electrical recordings of brain activity obtained, for example, with electroencephalography (EEG). For those not familiar with EEG, it is performed with a large array of electrodes placed over the scalp. A noteworthy observation concerning this activity was made in 1929, by the German psychiatrist Hans Berger who performed the first EEGs on human subjects.1 Introducing his new technique, he rhetorically asked “Is it possible to demonstrate the influence of intellectual work upon the human electroencephalogram, insofar as it has been reported here? 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.”2

Marcus E. Raichle, Creativity and the Brain’s Default Mode Network. In: Secrets of Creativity: What Neuroscience, the Arts, and Our Minds Reveal. Edited by Suzanne Nalbantian and Paul M. Matthews, Oxford University Press (2019). © Oxford University Press. DOI: 10.1093/oso/9780190462321.003.0006 108 Creativity and the Brain

Berger’s prescient insight is strongly supported by an examination of the cost of brain function. 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,3 which is ten times that predicted by its weight alone. The energy cost of the developing human brain, which peaks at approximately the end of the first decade of life, approaches 50% of the total body energy consumption,4 which calls attention to the role of metabolism in brain development and plasticity across the lifespan, a subject relevant to creativity and one to which I will return later in this chapter. To put the high cost of intrinsic activity in perspective, it is important to note that the additional energy consumption associated with task-induced changes in brain activity is remarkably small, often less than 5% of a baseline level of activity locally.5 From these data it is clear that the brain’s enormous energy consumption is largely devoted to its intrinsic activity and little affected by task performance, an observation first made more than fifty years ago by Louis Sokoloff, Seymour Kety, and their colleagues6 but rarely cited. What other operational features attest to the functional importance of intrinsic activity? The processing of sensory information immediately comes to mind. As I sit before my computer composing this chapter, occasionally gazing out the window or surveying my office with its many interesting objects, I have no doubt about the richness of the details in the scene before me. Alas, it is an illu- sion! William James was one of the first to call attention to this fact,7 remarking 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.” More recently, the late Vernon Mountcastle, one of the twentieth century’s preeminent neurophysiologists, summed up the situation nicely: “Each of us believes himself to live directly within the world that surrounds him, to sense its objects and events precisely, and to live in real and current time. I assert that these are perceptual illusions. Sensation is an abstraction, not a replication, of the real world.”8 What facts sup- port such counterintuitive assertions? Complementary information comes from a consideration of the amount of sensory information made available to the brain. For example, it may surprise some to learn that visual information is significantly compressed as it passes from the eye to the visual cortex.9 Thus, of the information available from the environment, only about 1010 bits/s (i.e., 10 billion bits/s) are deposited in the retina. Yet, only 104 bits/s (i.e., 0.001% of that which was deposited on the retina) make it to primary visual cortex. These data make it clear that visual cortex receives a very compressed representation of the world, a subject of more than passing interest to those seeking an understanding of visual information processing.10 Creativity and the Brain’s Default Mode Network 109

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/s or less.11 As Arthur Schopenhauer noted some time ago, “Consciousness is the mere surface of our mind, of which, as of the earthly globe, we do not know the interior, but only the crust.”12 One might add that from within the reservoir of the uncon- scious comes the makings of the “aha” moments of creativity.13 Given this striking perspective, it is interesting to look at research on the visual cortex of experimental animals during a nonstimulated state. What emerges is the appearance of a highly organized preparatory state. In a series of papers on the cat visual cortex using a combination of electrode recording and voltage sensitive dyes from the Weizmann Institute beginning in 1995,14 it was shown that the magnitude of ongoing intrinsic activity was the same as evoked activity and that the two interacted strongly, with the intrinsic activity contributing significantly to the variability in evoked activity, thus confirming an observation made many years before by George Bishop and Karl Lashley.15 And, even in the absence of stimuli, cortical representations of visual attributes emerged from the ongoing spontaneous activity.16 Elegant replications and extensions of this work have been contributed by others.17 Conceptual work on how these “simulations” might actually function has been provided by Schroeder and colleagues.18 Thus, combining a variety of perspectives on intrinsic activity reveals common themes at the cellular level and the full-brain, human systems level. At a very local level, at least in sensory cortices, intrinsic activity is functionally organized into the cortical representations of anticipated sensory attributes. At the human full-brain level, intrinsic activity is organized into systems well known for their participation in the full range of overt behaviors19 and prone, as well, to antici- pate incoming sensory information in preparation for responding.20 All of this work echoes prescient views of earlier scholars of brain function such as Thomas Huxley, Henry Maudsley, and William James.21 One of the pivotal events in moving us forward along these lines was the discovery of a large-scale, functional network organized within and potentially dominant over the brain’s intrinsic activity. I am referring to the brain’s DMN, whose discovery was an event of pure serendipity.

The Default Mode Network

The brain’s DMN was probably the most unexpected discovery of early functional imaging of the human brain. Anatomically, the DMN consists of a group of rather widely separated areas of the cerebral cortex (Figure 5.1A and 5.1C). 110 Creativity and the Brain

(D) (A)

(B)

(E) (C)

Figure 5.1 The group of brain areas (A) that decrease their activity during performance of a wide variety of tasks are often referred together as the brain’s default mode network (DMN). The spontaneous functional magnetic resonance image (fMRI) blood oxygen level dependent (BOLD) signal activity in the resting state (arrows, A) shows a remarkable similarity between regions of the DMN (B). For example, asking the general question of what areas in the brain are correlated with the activity in the back of the DMN (light arrow in [A],‌ light curve in [B]) reveals the entire DMN network (C). Analyses of other brain systems reveal similar levels of functional organization (D). Additional analyses suggest how resting-state activity propagates within and among brain networks. The image (E) shows anticorrelated relationships between the DMN and other systems collectively referred to as the task positive networks (TPNs), mediated by connectivity between the retrosplenial cortex (RSC) and the frontal eye fields (FEF). A–D adapted from Marcus Raichle, “The Restless Brain, How Intrinsic Activity Organizes Brain Function,”PhilosTrans R Soc B 370 (2015): 1–11. E adapted from Michael Fox, et al., “The Human Brain Is Organized into Dynamic, Anticorrelated Functional Networks,” PNAS 102 (2005): 9673–9678.

Yet, despite their anatomical separation, the components of the DMN behave as a unit. Many readers of this chapter, especially those not working in the field of cognitive neuroscience, will immediately wonder what functionality the DMN instantiates and what it has to do with creativity. After all, its name does not evoke an easily understood sense of brain function, as do categories like vision, Creativity and the Brain’s Default Mode Network 111 memory, attention, emotion, and language. For those who have not followed the development of human brain functional imaging, a brief review of these techniques may help in understanding why the discovery of the DMN was so unexpected and launch us on to a discussion of its function and importance in creativity. Not surprisingly, the initial focus of functional brain imaging was exclusively on defining components of the brain that increased their activity during the performance of specific tasks.22 To this day, these changes in activity are referred to as “activations” despite the fact that they arise from a background of significant ongoing, intrinsic activity—but more about that later. Mapping brain activations was made more effective by using experimental strategies borrowed from cognitive psychology. This strategy sought to isolate very specific task elements (e.g., areas of the brain concerned with reading a word aloud) by devising control states containing all but those task elements of interest. In this example, the control state might consist of passively viewing the word. This subtractive methodology proved to be ex- tremely effective in identifying brain components exhibiting task-specific activity increases.23 However, the exclusive focus on activity increases obscured the fact that intrinsic activity was eliminated from the images by the subtraction process. Venturing into this literature, one is usually confronted with images consisting of bright spots (i.e., activations) surrounded by total darkness. The message conveyed is that areas of the brain have been “turned on.” However, nothing could be further from the truth. But that truth was obscured by an imaging strategy that eliminated the background or baseline level of brain function. Furthermore, it was not at first realized that regional differences in either blood flow or metabolism could be used to delineate functions associated with intrinsic activity. A single event changed that perspective dramatically. Not only did task-relevant areas increase their activity during task performance, but, at the same time, a remarkably consistent group of areas (Figure 5.1A) also decreased their activity.24 This was observed when the task, usually attention-demanding and non–self- referential, was compared with a control state of resting quietly but awake with eyes open or closed. Employing such a control state, in the minds of many, violated one of the canons of cognitive psychology in not controlling our research participants’ thoughts! Thus, what we were observing were merely localized activations.25 We realized that we could not argue our viewpoint from a behavioral perspective, although we were du- bious that all our participants were spontaneously thinking alike. We elected to approach the question of distinguishing task-induced activations from deactivations by establishing a biological baseline level of brain activity 112 Creativity and the Brain from which changes in activity could confidently be assigned to either increases or decreases. A corollary of this approach is that task-induced decreases would, therefore, constitute evidence of an organized level of baseline activity. Our approach was based on work we had done analyzing task-induced changes in blood flow and oxygen consumption, in which blood flow and oxygen consumption become uncoupled during task-induced changes in brain activity.26 Based on this observation, it became a straightforward matter to ask whether the task-induced areas of decreased activity were activated during the resting or baseline state. The answer was no, they were not.27 With that observation, the idea of a default mode of brain function was born along with its poster child, the brain’s DMN. But the story did not end there. At the time of our work on a default mode of brain function using positron emission tomography (PET) scanning, the world of functional brain imaging had largely migrated to the use of magnetic resonance imaging (MRI) for routine functional brain imaging (i.e., fMRI). Many of the experimental strategies developed with PET were adopted by fMRI and, in many cases, significantly refined. But the core idea of subtracting a control state from a task state remained. This not only produced increasingly sophisticated maps of task-related brain activity but also served to eliminate the background “noise” in the fMRI signal. The idea that this background activity was noise was seriously challenged by the work of Bharat Biswal, working in the labo- ratory of Jim Hyde at the Medical College of Wisconsin.28 He showed that, within the motor cortex of a relaxed but awake, motionless human, fMRI “noise” was spatially correlated in an anatomically specific way. Specifically, when they monitored spontaneous activity in the left motor cortex, they observed that it was correlated with spontaneous activity in the right motor cortex. Despite earlier work that made the findings of Biswal and colleagues plausible (e.g., see 29) there were initial doubts about the importance of their findings. Those doubts were erased, certainly for us, when Michael Greicius and colleagues at Stanford University30 did the same experiment on the DMN, monitoring spontaneous activity in its posterior medial component (Figure 5.1A, yellow arrow) and asking what correlated with that activity (Figure 5.1B, yellow time-activity curve). Remarkably, the entire DMN revealed itself (Figure 5.1C). For us, it was a stunning observation. Studies followed from laboratories worldwide in what became known as resting- state functional connectivity.31 Literally all major functional networks in the human brain were revealed by this technique (Figure 5.1D). As a result, studies of the brain’s intrinsic activity have become a major feature of cognitive neuroscience research. Not surprisingly, the DMN has a prominent role in this research. Creativity and the Brain’s Default Mode Network 113

Functions of the Default Mode Network

Research has revealed much about the functions of the DMN’s individual components. Moving from front to back, the ventral-medial prefrontal cortex component is involved in motivation and emotional processing,32 whereas the dorsal medial prefrontal cortex component is involved in self-referential mental activity (e.g., responding to the query: “Do the following words describe me?— ‘thoughtful,’ ‘kind,’ ‘deceitful’ ”; e.g., see 33) and theory of mind (i.e., the analysis of the beliefs, desires, and intentions of others).34 The posterior medial and lateral components of the DMN are involved in the recollection of prior personal experiences and an imagination of the future,35 which involves a special relationship to the hippocampus.36 Each of these arguably self-referential components of the DMN can be attenuated or augmented in their activity depending on the particular self-referential demands of the moment.37 While it is informative to consider the functions of individual components of the DMN, I would emphasize that the DMN represents an integration of these self-referential functions, placing it high in the hierarchy of large-scale, mammalian brain network organization.38 Moving forward, in an attempt to close in on a discussion of creativity, I would like to consider two important DMN functions. The first is memory, and the second is risk-taking. I begin with memory.

Memory and Creativity

Anatomical studies of the connectional anatomy of the DMN in the human brain39 reveal a strong, bilateral connection between the posterior-medial component of the DMN and medial temporal lobe memory structures (i.e., entorhinal cortex and hippocampus; see the medial view of the cerebral hemi- sphere in Figure 5.1C). This would predict a consistent functional relationship between the DMN and these memory structures. Surprisingly, studies of the functional connectivity of this relationship have been variable. This mystery was nicely clarified when measurements of functional connectivity were made with fMRI before and after a full night of sleep.40 Functional connectivity, not anatomical, was weak to nonexistent on arising in the morning and very robust in the evening before retiring. Furthermore, the regions of the DMN involved in this relationship, including the hippocampus, were regions activated during suc- cessful memory retrieval.41 Adding to this observation, more recent work has shown that, during slow- wave sleep,42 the “dialogue” between medial temporal memory structures and the remainder of the DMN exhibits a remarkable transformation. During 114 Creativity and the Brain wake, this relationship in humans is characterized by infra-slow activity moving from the hippocampus to the cortex and higher frequency delta activity moving in the opposite direction (i.e., cortex to hippocampus). During slow-wave sleep, these relationships were reversed. This was the first demon- stration in humans of a bidirectional relationship thought to exist based on neurophysiological observations in experimental animals.43 A simple meta- phor might be helpful here. Think of this as a telephone conversation between a listener and a speaker. In wake, the receiver is the hippocampus and the sender is the cortex. In slow-wave sleep, they reverse roles. This illustrates a coupled relationship between different frequencies of brain activity, which is a well-known phenomenon in the neurophysiological literature.44 The receiver acts by changing the excitability of the sender, who then responds at a higher frequency. All of this serves to build an increasingly strong case for the role of the DMN in memory, the bulwark of well-organized intrinsic activity. This has the hallmarks of how an optimal internal model of the environment is built and maintained,45 which is critical for prediction, imagination, and creativity.46 Interestingly, this is not a new idea. In Lewis Carroll’s classic Through the Looking Glass and What Alice Found There,47 the White Queen was heard to say to Alice “It’s a poor sort of memory that only works backwards!” The late David Ingvar took it a step further, calling it “memory of the future.”48 In addition to these arguments for a prominent role of the DMN in a “memory of the future,” it may surprise some that additional evidence comes from metabolism and genetics. A summary of this work on the metabolism of intrinsic activity seems appropriate. An underlying premise is that brain metabolism is not only devoted to the production of energy but also to the building and continual remodeling of the brain in preparation for future events. Glucose, under normal circumstances, is the exclusive substrate for brain energy metabolism. Importantly, a fraction of glucose used by the brain is not used for energy production but is genetically programmed for biosynthesis and synaptic remodeling.49 This fraction accounts for almost 30% of the glucose used by the developing human brain and drops to about 15% in young adults. For some, that may not seem like much, but what is important is where this special fraction of glucose metabolism resides in the brain. It is largely in the DMN!50 Thus, the DMN, with its metabolic properties, appears genetically programmed for biosynthesis and synaptic remodeling in concert with its role in memory consolidation. An extension of this work is that, across the life span of normal people, brain metabolism declines significantly. The consensus has been that this is due to a decline in energy metabolism in the aging brain. Recent work clearly demonstrates that this is wrong. The decline is almost exclusively due to a loss of the fraction Creativity and the Brain’s Default Mode Network 115 devoted to biosynthesis and synaptic remodeling and is occurring in those areas of the brain that exhibited the highest levels, namely the DMN.51 These data provoke an appraisal of evidence showing that creativity declines across the life span. It was Max Planck who famously said “a new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.”52 Over the intervening years, significant scholarly work has been done on the subject of declining creativity over the life span that began with the pioneering work of Harvey Lehman.53 In the intervening years, scholarly literature has emerged on the subject,54 reinforcing the idea that aging has an effect on creativity. Most recently it has been suggested that “super-aging” is characterized by preserved neuroanatomy in the DMN and the salience networks.55 It would be of some interest to know whether preserved glycolysis in the DMN accompanies super-aging. Finally, it should be noted that the role of the DMN in creativity may be a risky one in that the DMN is a target of Alzheimer’s disease. Imaging maps of the dis- tribution of amyloid plaques, a hallmark of the pathology of Alzheimer’s disease, reveal a remarkable concentration within the DMN.56 Obviously, such an observation raises more questions than it answers, but in doing so may stimulate new ways of thinking about neurodegenerative disease.

Risk-Taking and Creativity

The Canadian psychologist Donald Hebb, deservedly famous for his neuronal theory of learning, commented in an essay entitled “Alice in Wonderland or Psychology Among the Biological Sciences” that “the scientist who does not speculate is no scientist at all.”57 That clearly applies to endeavors outside of science. Risk is at the core of creativity, but how is a balance between creative risk-taking and irresponsible behavior maintained? Is there any hint of how that might be expressed in the brain? To explore these questions, I would like to focus briefly on the relationship between the DMN and a group of other networks we have collectively referred to as the task positive networks or TPNs.58 I begin by introducing the TPNs. The TPNs are a bilateral group of areas in the cerebral cortex that collectively increase their activity when participants engage in demanding non–self- referential tasks.59 Such a task-associated grouping of systems has been has been observed by others as well.60 This increase in the TPNs is opposite the direction of change exhibited by the DMN under the same task conditions. Within the grouping we have dubbed the TPNs are the dorsal attention network (DAN), executive control networks, and a salience network,61 all of which are depicted 116 Creativity and the Brain in Figure 5.1D. Remarkably, this anticorrelated relationship between the DMN and the TPNs is also present in the non-task or resting state, as exhibited in their spontaneous activity (Figure 5.1E). This interesting relationship between the DMN and the TPNs has led to an egocentric view of the DMN as most concerned with our internal mental state, whereas the TPNs offer an alternative or allocentric perspective. Following this logic, it is easy to accept that activity in the DMN would naturally decrease during a demanding, non–self- referential task while activity in the TPNs increases (Figure 5.1E). But accumulating evidence suggests a more nuanced relationship. Illustrative is a study involving self-control in juveniles. As part of a larger study of juveniles incarcerated for criminal activity, resting state fMRI data were obtained on 107 individuals.62 Employing a data analysis strategy designed to look at network relationships in relation to behavior, the analysis focused on the degree of impulsivity measured in each individual. The surprising finding was that the functional connectivity associated with a single area in premotor cortex bilaterally was most predictive of the measured level of impulsivity, so much so that the level of impulsivity in individual participants could be predicted from the resting-state functional connectivity of this area. This area, known as thefrontal eye fields or FEF (Figure 5.1E), is a critical component of the dorsal attention network or DAN,63 a component of the TPNs. The critical feature of this relationship is the balance between these two networks: a stronger connectivity of the DMN predicted greater impulsivity, whereas stronger connectivity to the DAN predicted lesser impulsivity. A com- panion group of normal young individuals ranging in age from 7 to 31 exhibited the same relationship as a function of age.64 Impulsivity was inversely correlated with age, something that most parents would agree with. The general lesson learned from the preceding study and subsequent work65 is that internetwork communication, as revealed to us through studies of intrinsic activity, involves a delicate balance between and among networks with potentially significant behavioral implications. In the extreme, an imbalance between the DMN and the DAN might well be a significant contributing factor in criminal behavior or, at the other extreme, a disabling inability to take any risks at all. Individual differences along this gradient might well include those whose intellectual pursuits flourish because of the comfort and confidence associated with a measured degree of risk-taking. Given our present ability to measure human brain function and its relationship to behavior, this is a testable hypothesis.

* * *

Distilling the essence of the brain’s DMN has become a formidable task given the tremendous interest it has generated in the scientific community.66 Integrating Creativity and the Brain’s Default Mode Network 117 the DMN into a discussion of creativity, which has also commanded significant attention over centuries, has seemed at times an almost impossible task. However, several themes seemed important as I thought about the way forward. First, there was an appreciation of the brain’s intrinsic activity, which commands most of the brain’s resources, within which lies the basic structure of the brain’s functional organization, and which operates nonconsciously. Second, there was the recognition of the preeminent role of the brain’s DMN in the organization of this intrinsic activity. Understanding its role gives meaning to the organization of intrinsic activity. Third, at the heart of the DMN’s functionality is memory, which is fundamentally designed not for those personally enjoyable recol- lective moments, but for its ability to imagine the future based on past experi- ence. I would assert that without this ability creativity in any form would not be possible. Finally, appreciating the delicate balance among the systems of the brain in crafting our behaviors is critical to our understanding of brain function. I be- lieve the importance of that balance is beautifully illustrated by the relationship between the DMN and the TPNs (Figure 5.1E). This balance adjudicates our ability to assume risk in the pursuit our goals. Within a normal range, this balance permits both comfort and enjoyment in experiencing something truly new and different, something that is clearly at the heart of science as I know it. Some subjects were omitted; several deserve mention in this summation. First, it is now clear that the DMN is not an exclusive feature of the human brain. Sightings of the DMN have now been made in chimpanzees, monkeys, rats and mice, and ferrets.67 This work suggests that the DMN is a highly conserved feature of the mammalian brain. But the instantiation of the components of the DMN in these different species varies. For example, in the rat, the lateral pa- rietal components are situated in primary sensory cortices. Variations like this are seen in other species. Understanding the evolutionary significance of these differences will clearly add richness to our understanding of the DMN. Furthermore, evidence of creativity is clearly present in species other than humans. Second, I have said nothing in this review about mind-wandering . In a detailed analysis we did a few years ago of the literature associated with the DMN,68 mind-wandering was a significant component of papers on the DMN. It is clearly not only a fascinating but a critical component of human cognition. It role in creativity almost goes without saying (e.g., I have lost track of the times I have “written” elements of this chapter in my head before committing the words to paper). How language69 has been added to the evolution of the DMN is well be- yond the scope of this chapter, but it presents a very attractive subject for further research and discussion. 118 Creativity and the Brain

Notably, I have found that the “spectacular” has been overemphasized when it comes to creativity. What seems to surface reflexively are discussions of the paradigm-shifting accomplishments across the spectrum of human en- deavor. But in the daily lives of most of us there are those “ordinary” creative moments where adjustments are made to accommodate new challenges, often accompanied by a sense of personal satisfaction. It seems reasonable to posit that these also reflect brain activities involving the DMN. Recognizing creativity as a continuum present across a wide range of behaviors could serve to broaden the potential research opportunities as we seek to more fully under- stand creativity.

Notes

1. Hans Berger, “Uber das Elektrenkephalogramm des Menschen,” Arch Psychiat Nervenkr 87, no. 1 (1929): 527–570. 2. Hans Berger, Hans Berger on the Electroencephalogram of Man. The Fourteen Original Reports on the Human Electroencephalogram. Electroencephalography and Clinical Neurophysiology, Supplement 28, trans. Pierre Gloor (Amsterdam: Elsevier, 1969), 72. 3. Donald D. Clarke and Louis Sokoloff, “Circulation and Energy Metabolism of the Brain,” in Basic Neurochemistry. Molecular, Cellular and Medical Aspects, ed. Bernard W. Agranoff and George J. Siegel (Philadelphia: Lippincott-Raven, 1999), 637–670; Jonathan W. Mink, Robert J. Blumenschine, and David B. Adams, “Ratio of Central Nervous System to Body Metabolism in Vertebrates: Its Constancy and Functional Basis,” American Journal of Physiology 241 (1981): R203–212; 4. Manu S. Goyal et al., “Aerobic Glycolysis in the Human Brain is Associated with Development and Neotenous Gene Expression,” Cell Metabolism 19 (2014): 49– 57, doi:10.1016/j.cmet.2013.11.020; Manu S. Goyal et al., “Feeding the Brain and Nurturing the Mind: Linking Nutrition and the Gut Microbiota to Brain Development,” PNAS 112 (2015): 14105–14112, doi:10.1073/pnas.1511465112. 5. Marcus E. Raichle and Mark A. Mintun. “A Brain Work and Brain Imaging,” Annual Review of Neuroscience 29, no. 1 (2006): 449–476. 6. Louis Sokoloff et al., “The Effect of Mental Arithmetic on Cerebral Circulation and Metabolism,” Journal of Clinical Investigation 34, no. 7 (1955): 1101–1108. 7. William James, Principles of Psychology, vol. 2 (New York: Henry Holt & Company, 1890), 103. 8. Vernon B. Mountcastle, “The View from Within: Pathways to the Study of Perception,” Johns Hopkins Medical Journal 136, no. 3 (1975): 109–131. 9. Charles H. Anderson, David C. Van Essen, and Bruno A. Olshausen, “ Directed Visual Attention and the Dynamic Control of Information Flow” in Neurobiology of Attention, ed. Laurent Itti, Geraint Rees, and John Tsotsos (San Diego: Elsevier, 2005), 11–17. Creativity and the Brain’s Default Mode Network 119

10. Bruno A. Olshausen and David J. Field, “How Close Are We to Understanding V1?” Neural Computation 17, no. 8 (2005): 1665–1699. 11. Charles H. Anderson, David C. Van Essen, and Bruno A. Olshausen, “Directed Visual Attention and the Dynamic Control of Information Flow,” in Neurobiology of Attention, ed. Laurent Itti, Geraint Rees, and John Tsotsos (San Diego: Elsevier, 2005), 11–17. 12. Arthur Schopenhauer, The World as Will and Presentation, vol. 2, trans. David Carus, Richard Aquila, and David Kolak (Routledge: 2011), 154. 13. For an interesting discussion of this perspective, see Stanislas Dehaene, Consciousness and the Brain: Deciphering How Our Brain Codes Our Thoughts (Penguin Books, 2014). 14. Amiram Arieli et al., “Coherent Spatiotemporal Patterns of Ongoing Activity Revealed by Real-Time Optical Imaging Coupled with Single-Unit Recording in the Cat Visual Cortex,” Journal of Neurophysiology 73, no. 5 (1995): 2072–2093; Amiram Ariel et al., “Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses,” Science 273 (1996): 1868–1871; Tal Kenet et al., “Spontaneously Emerging Cortical Representations of Visual Attributes,” Nature 425, no. 6961 (2003): 954–956; Amiram Grinvald et al., “Neuronal Assemblies: Single Cortical Neurons Are Obedient Members of a Huge Orchestra,” Biopolymers 68, no. 3 (2003): 422–436, doi:10.1002/bip.10273. 15. George Bishop, G. “Cyclic Changes in Excitability of the Optic Pathway of the Rabbit,” American Journal of Physiology 103 (1933): 213–224; Karl S. Lashley, Kaol Chow, and Josephine Semmes, “An Examination of the Electrical Field Theory of Cerebral Integration,” Psychological Review 58, no. 2 (1951): 123–136. 16. Dario L. Ringach, “Neuroscience: States of Mind,” Nature 425, no. 6961 (2003): 912–913. 17. Yuji Ikegaya et al. “Synfire Chains and Cortical Songs: Temporal Modules of Cortical Activity,” Science 304, no. 5670 (2004): 559–564, doi:10.1126/science.1093173; Jason N. MacLean et al., “Internal Dynamics Determine the Cortical Response to Thalamic Stimulation,” Neuron 48, no. 5 (2005): 811–823; Jae-Eun Miller et al., “Visual Stimuli Recruit Intrinsically Generated Cortical Ensembles,” PNAS 111, no. 38 (2014) E4053–4061, doi:10.1073/pnas.1406077111; Carl C. Petersen et al., “Interaction of Sensory Responses with Spontaneous Depolarization in Layer 2/3 Barrel Cortex,” PNAS 100, no. 23 (2003): 13638–13643. 18. Charles E. Schroeder and Peter Lakatos, “Low-Frequency Neuronal Oscillations as Instruments of Sensory Selection,” Trends in Neurosciences 32, no. 1 (2008)9– 18; Charles E. Schroeder and Peter Lakatos, “The Signs of Silence,”Neuron 74 (2012): 770–772, doi:10.1016/j.neuron.2012.05.012. 19. See Michael D. Fox and Marcus Raichle, “Spontaneous Fluctuations in Brain Activity Observed with Functional Magnetic Resonance Imaging,” Nature Reviews Neuroscience 8, no. 9 (2007): 700–711. 20. See David Ress, Benjamin T. Backus, and David J. Heeger, “Activity in Primary Visual Cortex Predicts Performance in a Visual Detection Task,” Nature Neuroscience 3 (2000): 940–945; Yevgeniy B. Sirotin and Aniruddha Das, “Anticipatory 120 Creativity and the Brain

Haemodynamic Signals in Sensory Cortex Not Predicted by Local Neuronal Activity,” Nature 457, no. 7228 (2009): 475–479. 21. Thomas H. Huxley, An Introduction to the Study of Zoology Illustrated by the Crayfish, vol. 28 (New York: D. Appleton and Company, 1884), 107; Henry Maudsley, The Physiology of Mind (New York: D. Appleton and Company, 1878), 154; W. James, Principles of Psychology, vol 1, 113. 22. Michael Posner and Marcus E. Raichle, Images of Mind (New York: W. H. Freeman and Company, 1994). 23. See Steve E. Petersen et al., “Positron Emission Tomographic Studies of the Cortical Anatomy of Single-Word Processing,” Nature 331, no. 6157 (1988): 585–589. 24. Gordon L. Shulman et al., “Common Blood Flow Changes across Visual Tasks: II. Decreases in Cerebral Cortex,” Journal of Cognitive Neuroscience 9, no. 5 (1997): 648–663. 25. Nancy C. Andreasen et al. “Remembering the Past: Two Facets of Episodic Memory Explored with Positron Emission Tomography,” American Journal of Psychiatry 152, no. 11 (1995): 1576–1585; Bernard Mazoyer et al., “Cortical Networks for Working Memory and Executive Functions Sustain the Conscious Resting State in Man,” Brain Research Bulletin 54, no. 3 (2001): 287–298. 26. Peter T. Fox and Marcus E. Raichle, “Focal Physiological Uncoupling of Cerebral Blood Flow and Oxidative Metabolism During Somatosensory Stimulation in Human Subjects,” PNAS 83, no. 4 (1986): 1140–1144. 27. Marcus E. Raichle et al., “A Default Mode of Brain Function,” PNAS 98, no. 2 (2001): 676–682. 28. Bharat Biswal et al., “Functional Connectivity in the Motor Cortex of Resting Human Brain Using Echo-Planar MRI,” Magnetic Resonance Medicine 34, no. 4 (1995): 537–541. 29. Boris A. Vern et al., “Low-Frequency Oscillations of Cortical Oxidative Metabolism in Waking and Sleep,” Journal of Cerebral Blood Flow and Metabolism 8, no. 2 (1988): 215–226; Eugene V. Golanov, Shinya Yamamoto, and Donald J. Reis, “Spontaneous Waves of Cerebral Blood Flow Associated with a Pattern of Electrocortical Activity,” American Journal of Physiology 266 (1994): R204–214. 30. Michael D. Greicius et al., “Functional Connectivity in the Resting Brain: A Network Analysis of the Default Mode Hypothesis,” PNAS 100, no. 1 (2003): 253–258. 31. Fox and Raichle, “Spontaneous Fluctuations in Brain Activity Observed with Functional Magnetic Resonance Imaging,” 700–711. 32. Dost Ongür and Joseph L. Price, “The Organization of Networks within the Orbital and Medial Prefrontal Cortex of Rats, Monkeys and Humans,” Cerebral Cortex 10, no. 3 (2000): 206–219; Debra A. Gusnard et al., “Persistence and Brain Circuitry,” PNAS 100, no. 6 (2003): 3479–3484. 33. Debra A. Gusnard et al., “Medial Prefrontal Cortex and Self-Referential Mental Activity: Relation to a Default Mode of Brain Function,” PNAS 98, no. 7 (2001): 4259–4264. Creativity and the Brain’s Default Mode Network 121

34. Demis Hassabis et al., “Imagine All the People: How the Brain Creates and Uses Personality Models to Predict Behavior,” Cerebral Cortex 24, no. 8 (2014): 1979– 1987, doi:10.1093/cercor/bht042. 35. Randy L. Buckner “The Role of the Hippocampus in Prediction and Imagination,” Annual Review of Psychology 61, no. 1 (2010): 27–48, C21–28, doi:10.1146/annurev. psych.60.110707.163508. 36. For a detailed discussion, see Anish Mitra et al., “Human Cortical-Hippocampal Dialogue in Wake and Slow-Wave Sleep,” PNAS 113, no. 44 (2016): E6868– E6876, doi:10.1073/pnas.1607289113; Benjamin J. Shannon et al., “Morning- Evening Variation in Human Brain Metabolism and Memory Circuits,” Journal of Neurophysiology 109, no. 5 (2013): 1444–1456, doi:10.1152/jn.00651.2012. 37. Gusnard et al., “Medial Prefrontal Cortex and Self-Referential Mental Activity: Relation to a Default Mode of Brain Function,” 4259–4264; Denise Teves et al., “Activation of Human Medial Prefrontal Cortex During Autonomic Responses to Hypoglycemia,” PNAS 101, no. 16 (2004): 6217–6221; Debra A. Gusnard and Marcus E. Raichle, “Searching for a Baseline: Functional Imaging and the Resting Human Brain,” Nature Reviews Neuroscience 2, no. 10 (2001): 685–694. 38. Martijn P. van den Heuvel and Olaf Sporns, “An Anatomical Substrate for Integration among Functional Networks in Human Cortex,” Journal of Neuroscience 33, no. 36 (2013): 14489–14500, doi:10.1523/JNEUROSCI.2128-13.2013; Daniel S. Margulies et al., “Situating the Default-Mode Network Along a Principal Gradient of Macroscale Cortical Organization,” PNAS 113, no. 44 (2016): 12574–12579, doi:10.1073/pnas.1608282113. 39. Michael D. Greicius et al., “Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network,” Cerebral Cortex 19, no. 1 (2009): 72–78, doi:bhn059 [pii] 10.1093/cercor/bhn059. 40. Shannon et al., “Morning-Evening Variation in Human Brain Metabolism and Memory Circuits,” 1444–1456. 41. Ibid, 1452. See Figure 5. 42. Anish Mitra et al., “Propagated Infra-Slow Intrinsic Brain Activity Reorganizes Across Wake and Slow Wave Sleep, Elife 4 (2015), doi:10.7554/eLife.10781. 43. For a detailed discussion of the literature, see Mitra et al., “Human Cortical- Hippocampal Dialogue in Wake and Slow-Wave Sleep,” E6868–E6876. 44. See Simo Monto et al., “Very Slow EEG Fluctuations Predict the Dynamics of Stimulus Detection and Oscillation Amplitudes in Humans, Journal of Neuroscience 28, no. 33 (2008): 8268–8272. 45. Pietro Berkes et al., “Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment,” Science 331, no. 6013 (2011): 83–87, doi:331/ 6013/83 [pii] 10.1126/science.1195870. 46. Buckner, “The Role of the Hippocampus in Prediction and Imagination,” 27–48, C21–28; Daniel L. Schacter et al. “The Future of Memory: Remembering, Imagining, and the Brain,” Neuron 76, no. 4 (2012): 677–694, doi:10.1016/j.neuron.2012.11.001; 122 Creativity and the Brain

Rebecca Gotlieb et al., “Cultivating the Social-Emotional Imagination in Gifted Education: Insights from Educational Neuroscience,” Annals of the New York Academy of Science 1377, no. 1 (2016): 22–31, doi:10.1111/nyas.13165. 47. Lewis Carrol, Through The Looking Glass, And What Alice Saw There (London: Macmillan and Company, 1871). 48. David H. Ingvar, “Memory of the Future”: An Essay on the Temporal Organization of Conscious Awareness,” Human Neurobiology 4 (1985): 127–136. 49. Manu S. Goyal et al. “Aerobic Glycolysis in the Human Brain Is Associated with Development and Neotenous Gene Expression,” Cell Metabolism 19, no. 1 (2014): 49– 57, doi:10.1016/j.cmet.2013.11.020. 50. Sanjeev N. Vaishnavi et al. “Regional Aerobic Glycolysis in the Human Brain,” PNAS 107, no. 41(2010): 17757–17762. 51. Manu S. Goyal et al., “Loss of Brain Aerobic Glycolysis in Normal Human Aging,” Cell Metabolism 26, no. 2 (2017): 353–360 e353, doi:10.1016/j.cmet.2017.07.010. 52. Max Planck, Scientific Autobiography and Other Papers (New York: Philosophical Library, 1949), 33–34. 53. Harvey C. Lehman, Age and Achievement (Princeton: Princeton University Press, 1953); Harvey C. Lehman, “The Relationship Between Chronological Age and High Level Research Output in Physics and Chemistry,” Journal of Gerontology 19, no. 2 (1964): 157–164. 54. See Benjamin F. Jones and Bruce A. Weinberg, “Age Dynamics in Scientific Creativity,” PNAS 108, no. 47 (2011): 18910–18914, doi:10.1073/pnas.1102895108; Mikko Packalen and Jay Bhattacharya, “Age and the Trying Out of New Ideas,” Working Paper 20920, National Bureau of Economic Research (2015): 1–29; http:/ www.nber.org/papers/w20920. 55. Felicia W. Sun et al., “Youthful Brains in Older Adults: Preserved Neuroanatomy in the Default Mode and Salience Networks Contributes to Youthful Memory in Superaging,” Journal of Neuroscience 36, no. 37 (2016): 9659–9668, doi:10.1523/ JNEUROSCI.1492-16.2016. 56. Andrei G. Vlassenko et al., “Spatial Correlation Between Brain Aerobic Glycolysis and Amyloid-Beta (Abeta) Deposition,” PNAS 107, no. 41 (2010): 17763–17767, doi:1010461107 [pii] 10.1073/pnas.1010461107. 57. D. O. Hebb, “Alice in Wonderland or Psychology Among the Biological Sciences,” in Biological and Biochemical Bases of Behavior, ed. H. F. Harlow and C. N. Woolsey (University of Wisconsin Press, 1965), 451. 58. Michael D. Fox et al., “The Human Brain Is Intrinsically Organized into Dynamic, Anticorrelated Functional Networks,” PNAS 102, no. 27 (2005): 9673–9678. 59. Maurizio Corbetta and Gordon l. Shulman, “Control of Goal-Directed and Stimulus- Driven Attention in the Brain,” Nature Reviews Neuroscience 3 (2002): 201–215. 60. Kenneth Hugdahl et al., “On the Existence of a Generalized Non-Specific Task- Dependent Network,” Frontiers in Human Neuroscience 9, no. 430 (2015), doi:10.3389/fnhum.2015.00430. 61. Maurizio Corbetta et al., “A Common Network of Functional Areas for Attention and Eye Movements,” Neuron 21, no. 4 (1998): 761–773; Justin L. Vincent et al., Creativity and the Brain’s Default Mode Network 123

“Evidence for a Frontoparietal Control System Revealed by Intrinsic Functional Connectivity,” Journal of Neurophysiology 100, no. 6 (2008): 3328–3342, doi:90355.2008 [pii]10.1152/jn.90355.2008; Nico U. Dosenbach et al., “A Dual- Networks Architecture of Top-Down Control,” Trends in Cognitive Science 12, no. 3 (2008): 99–105, doi:S1364-6613(08)00027-2 [pii]10.1016/j.tics.2008.01.001. 62. Benjamin J. Shannon et al., “Premotor Functional Connectivity Predicts Impulsivity in Juvenile Offenders,” PNAS 108, no. 27 (2011): 11241–11245, doi:1108241108 [pii]10.1073/pnas.1108241108. 63. Corbetta et al., “A Common Network of Functional Areas for Attention and Eye Movements,” 761–773. 64. See again Shannon et al., “Premotor Functional Connectivity Predicts Impulsivity in Juvenile Offenders.” 65. Mitra and Raichle, “Principles of Cross-Network Communication in Human Resting State fMRI,” 83–90; Shuntaro Sasai et al., “Functional Split Brain in a Driving/Listening Paradigm,” PNAS 113, no. 50 (2016): 14444–14449, doi:10.1073/ pnas.1613200113; Shuntaro Sasai et al., “Functional Split Brain in a Driving/Listening Paradigm,” PNAS 113, no. 50 (2016): 14444–14449, doi:10.1073/pnas.1613200113. 66. Web of Science has more than 5,600 citations and Google Scholar has more than 8,700 citations. See Raichle et al., “A Default Mode of Brain Function,” 676–682. 67. Sarah K. Barks, Lisa A. Parr, and James K. Rilling, “The Default Mode Network in Chimpanzees (Pan troglodytes) Is Similar to That of Humans,”Cerebral Cortex 25, no. 2 (2013): 538–544, doi:10.1093/cercor/ bht253; Jean-Louis Vincent et al., “Intrinsic Functional Architecture in the Anaesthetized Monkey Brain,” Nature 447, no. 7140 (2007): 83–86; Hanbing Lu et al., “Rat Brains Also Have a Default Mode Network,” PNAS 109, no. 10 (2012): 3979–3984; James M. Stafford et al., “Large-Scale Topology and the Default Mode Network in the Mouse Connectome,” PNAS 111, no. 52 (2014): 18745–18750, doi:10.1073/pnas.1404346111; Zhe Charles Zhou et al., “Resting State Network Topology of the Ferret Brain,” Neuroimage 143 (2016): 70– 81, doi:10.1016/j.neuroimage.2016.09.003. 68. Marcus E. Raichle, "The Brain's Default Mode Network," Annu Rev Neurosci 38, (2015): 413-427. 69. Dialogic thinking is discussed in Charles Fernyhough, The Voices Within (New York: Basic Books, 2016).