The brain is not hierarchically organized: Is it a “small world” or actually a “tiny world”?

Engineers think of systems in terms of inputs and outputs. In a steam engine, heat (input) applied to water produces steam, and the force generated pushes a piston back and forth inside a cylinder; the pushing force is transformed into rotational force (output) that can be used for other purposes. Reasoning in terms of input-output relationships became even more commonplace with the invention of computers and the concept of a software program. Thus, it’s only natural to consider the brain in terms of the “inflow” and “outflow” of signals tied to sensory processing and motor acts. During sensory processing, energy of one kind or another is transduced, action potential reach the cortex, and are further processed. During motor acts, activity from the cortex descends to the brainstem and spinal cord, eventually moving muscles. Information flows in for perception and flows out for action.

Let’s describe a substantially different view based on what I call functionally integrated systems. To do so, it helps to discuss six broad principles of brain organization. To anticipate, some of the consequences of the principles are as follows: the brain’s anatomical and functional architectures are highly non-modular; signal distribution and integration are the norm, allowing the confluence of information related to perception, cognition, emotion, motivation, and action; and, the functional architecture is composed of overlapping networks that are highly dynamic and context-sensitive[1].

Principle 1: Massive combinatorial anatomical connectivity

Dissecting anatomical connections is incredibly painstaking work. Chemical substances are injected at a specific location and, as they diffuse along axons, traces of the molecules are detected elsewhere. After diffusion stabilizes (in some cases, it takes weeks), tissue is sectioned in razor-thin slices that are further treated chemically and inspected, one by one. Because the slices are very thin, researchers focus on examining particular target regions. For example, one anatomist may make injections in a few sites in parietal cortex, and examine parts of lateral prefrontal cortex for staining that indicates the presence of an anatomical connection. Injection by injection, study by study, neuroanatomists have compiled enough information to provide a good idea of the pathways crisscrossing the brain.

Figure 1. A graph is a mathematical object that can represent arbitrary collections of elements (person, computer, genes), called nodes (circles), and their relationships, called edges (lines joining pairs of nodes).

Although anatomical knowledge of pathways (and their strengths) is incomplete, the overall picture is one of massive connectivity. This is made clearer when computational analyses are used to combine the findings across a large number of individual studies. A field of mathematics that comes in handy here is called graph theory, which has become popular in the last two decades under the more appealing term of “network science.” Graphs are very general abstract structures that can be used to formalize the interconnectivity of social, technological, or biological systems. They are defined by nodes and the links between them, called edges (Figure 1). A node represents a particular object: a person in a social group, a computer in a technological network, or a gene in a biological system. Edges indicate a relationship between the nodes: people who know each other, computers that are physically connected, or genes with related functions. So, in the case of the brain, areas can be represented by nodes, and edges interlinking them represent a pathway between them. (A so-called directed graph can be used if the direction of the pathways are known; for example, from A to B but not vice versa.)

Graph analysis demonstrates that brain regions are richly interconnected, a property of both cortical and subcortical regions. In the cortex, this property is not confined to the prefrontal cortex (which is often highlighted in this regard), but is observed for all lobes. Indeed, the overall picture is one of enormous connectivity, leading to combinatorial pathways between sectors. In other words, one can go from point A to point B in multiple ways, much like navigating a dense set of roads. Computational neuroanatomy has greatly refined our understanding of connectivity.

High global accessibility. Rumors spread more or less effectively depending on the pattern of communication. It will spread faster and farther among a community of college students than among faculty professors, assuming that the former is more highly interconnected than the latter. This intuition is formalized by a graph measure called efficiency, which captures information spread effectiveness across members of a network, even those who are least connected (in the social setting, the ones who know or communicate the least with other members). How about the brain? Recent studies suggest that its efficiency is very high. Signals have the potential to travel efficaciously across the entire organ, even between parts not near each other, and even between parts that are not directly connected; in this case, the connection is indirect, such as travelling through C, and possibly D, to get from A to B. The logic of the connectivity structure seems to point to a surprising property: physical distance matters little.

For many neuroscientists, this conclusion is surprising, if not counterintuitive. Their training favors a processing-is-local type of reasoning. After all, areas implement particular functions. That is to say, they are the proper computational units – or so the thinking goes (see chapter 4). This interpretation is reinforced by the knowledge that anatomical pathways are dominated by short-distance connections. In fact, 70% of all the projections to a given locus on the cortical sheet arise from within 1.5 to 2.5 mm (to give you an idea, parts of occipital cortex toward the back of the head are a good 15 cm away from the prefrontal cortex). Doesn’t this dictate that processing is local, or quasi-local? This is where math, and the understanding of graphs, helps sharpen our thinking.

In a 1998 paper entitled “Collective dynamics of ‘small-world’ networks” (cited tens of thousands of times in the scientific literature), Duncan Watts and Steven Strogatz showed that systems made of locally-clustered nodes (those that are connected to nearby nodes), but that also have a small number of random connections (which link arbitrary pairs of nodes), allow all nodes to be accessible within a small number of connectivity steps[2]. Starting at any arbitrary node, one can reach another, no matter which one, by traversing a few edges. Helping make the paper a veritable sensation, they called this property “small-world”. The strength of their approach was to show that this is a hallmark of graphs with such connectivity pattern, irrespective of the type of data at hand (social, technological, or biological). Watts and Strogatz emphasized that the arrangement in question – what’s called network topology – allows for enhanced signal-propagation speed, computational power, and synchronizability between parts. The paper was a game changer in how one thinks of interconnected systems[3].

In the 2000s, different research groups proposed that the cerebral cortex is organized as a small world. If correct, this view means that signal transduction between parts of the cortex can be obtained via a modest number of paths connecting them. It turns out that the brain is more interconnected than would be necessary for it to be a small world[4]. That is to say, there are more pathways interconnecting regions than the minimum needed to attain efficient communicability. So, while it’s true that local connectivity predominates within the cortex, there are enough medium- and long-range connections – in fact, more than the “minimum” required – for information to spread around remarkably well.

Connectivity core (“rich club”). A central reason the brain is not a small world is because it contains a subgroup of regions that is very highly interconnected. The details still are being worked out, not least because knowledge of anatomical connectivity is incomplete, especially in humans.

In 2010, the computer scientists Dharmendra Modha and Raghavendra Singh gathered data from over four hundred anatomical tracing studies of the macaque brain[5]. Unlike most investigations, which have focused on the cortex, they included data on subcortical pathways, too (Figure 2). Their computational analyses uncovered a “tightly integrated core circuit” with several properties: (i) it is a set of regions that is far more tightly integrated (that is, more densely connected) than the overall brain; (ii) information likely spreads more swiftly within the core than through the overall brain; and (iii) brain communication relies heavily on signals being communicated via the core. The proposed core circuit was distributed throughout the brain; it wasn’t just in the prefrontal cortex, a sector often underscored for its integrative capabilities, or some other anatomically well-defined territory. Instead, the regions were found in all cortical lobes, as well as subcortical areas such as the thalamus, striatum, and amygdala.

Figure 2. Massive interconnectivity between all brain sectors. Computational analysis of anatomical connectivity by collating pathways (lines) from hundreds of studies. To improve clarity, pathways with a common origin or destination are bundled together (otherwise the background would be essentially black given the density of connections). Figure from Modha and Singh (2010).

In another study, a group of neuroanatomists and physicists collaborated to describe formal properties of the monkey cortex[6]. They discovered a set of 17 brain regions across parietal, temporal, and frontal cortex that is heavily interconnected. For these areas, 92% of the connections that could potentially exist between region pairs have indeed been documented in published studies. So, in this core group of areas, nearly every one of them can talk directly to all others, a remarkable property. In a graph, when a subset of its nodes is considerably more well connected than others, it is sometimes referred to as a “rich club,” in allusion to the idea that in many societies a group of wealthy individuals tends to be disproportionately influential.  

Computational analysis of anatomical pathways has been instrumental in unravelling properties of the brain’s large-scale architecture. We now have a vastly more complete and broader view of how different parts are linked with each other. At the same time, we must acknowledge that the current picture is rather incomplete. For one, computational studies frequently focus on cortical pathways. As such, they are cortico-centric, reflecting a bias of many neuroscientists who tend to neglect the subcortex when investigating connectional properties of the brain. In sum, the theoretical insights by network scientists about “small worlds” demonstrated that signals can influence distal elements of a system even when physical connections are fairly sparse. But cerebral pathways vastly exceed what it takes to be a small world. Instead, what we find is a “tiny world.”


[1] The ideas in this chapter are developed more technically elsewhere (Pessoa, 2014, 2017).

[2] Watts and Strogatz (1998).

[3] Another very influential paper was published soon after by Barabási and Albert (1999). The work was followed by an enormous amount of research in the subsequent years.

[4] Particularly useful here is the work by Kennedy and collaborators. For discussion of mouse and primate data, see Gămănuţ et al. (2018).

[5] Modha and Singh (2010). Modha, D. S., & Singh, R. (2010). Network architecture of the long-distance pathways in the macaque brain. Proceedings of the National Academy of Sciences, 107(30), 13485-13490.

[6] Markov et al. (2013). Markov, N. T., Ercsey-Ravasz, M., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013). Cortical high-density counterstream architectures. Science, 342(6158).

The “reptilian” / triune brain: The origins of a misguided idea

The misguided idea of the “reptilian brain”. Figure from the excellent review by Ann Butler. Butler A B (2009). Triune Brain Concept: A Comparative Evolutionary Perspective. In: Squire LR (ed.) Encyclopedia of Neuroscience, volume 9, pp. 1185-1193. Oxford: Academic Press.

One of the presentations at the 1881 International Medical Congress in London was by Friedrich Goltz, a professor of physiology at the University of Strasburg. Like several of his contemporaries, Goltz was interested in the localization of function in the brain. He not only published several influential papers on the problem, but attracted widespread attention by exhibiting dogs with brain lesions at meetings throughout Europe. His presentations were quite a spectacle. He would take the lectern and bring a dog with him to demonstrate an impaired or spared behavior that he wanted to discuss. Or, he would open his suitcase and produce the skull of a dog with the remnants of its brain. In some cases, a separate panel of internationally acclaimed scientists would even evaluate the lesion and report their assessment to the scientific community.

In some of his studies, Goltz would remove the entire cortical surface of a dog’s brain, and let the animal recover. The now decorticated animal would survive, though it would generally not initiate action and remain still. Goltz showed that animals with an excised cortex still exhibited uncontrolled “rage” reactions, leading to the conclusion that the territory is not necessary for the production of emotional expressions. But if the cortex wasn’t needed, the implication was that subcortical areas were involved. That emotion was a subcortical affair was entirely consistent with nineteenth century thinking.

Victorian England and the beast within

In the conclusion of The Descent of Man, Charles Darwin wrote in 1871 that “the indelible stamp of his lowly origin” could still be discerned in the human mind, with the implied consequence that it was necessary to suppress the “beast within” – at least at times. This notion was hardly original, of course, and in the Western world can be traced back to at least ancient Greece. At Darwin’s time, with emotion being considered primitive and reason the more advanced faculty, “true intelligence” was viewed as residing in cortical areas, most notably in the frontal lobe, while emotion was viewed as residing in the basement, the lowly brainstem.

The decades following the publication of Darwin’s Origin of Species (in 1859) were a time of much theorizing not only in biology but in the social sciences, too. Herbert Spencer and others applied key concepts of biological evolutionary theory to social issues, including culture and ethics. Hierarchy was at the core of this way of thinking. For the survival of evolved societies, it was necessary to legitimize a hierarchical governing structure, as well as a sense of self-control at the level of the individual – it was argued[1]. These ideas, in turn, had a deep impact on neurology, the medical specialization characterizes the consequences of brain damage on survival and behavior. John Hughlings Jackson, to this day the most influential English neurologist, embraced a hierarchical view of brain organization rooted in a logic of evolution as a process of the gradual accrual of more complex structures atop more primitive ones. What’s more, “higher” centers in the cortex bear down on “lower” centers underneath, and any release from this control could make even the most civilized human act more like his primitive ancestors[2]. This stratified scheme was also enshrined in Sigmund Freud’s framework of the id (the lower level) and the super-ego (the higher level). (Freud also speculated that the ego played an in-between role between the other two.) Interestingly, Freud was initially trained as a clinical neurologist and was a great admirer of Jackson’s work.

Against this backdrop, it’s not surprising that brain scientists would search for the neural basis of emotion in subcortical territories, while viewing “rational thinking” as the province of the cerebral cortex, especially the frontal lobe.

The “reptilian” brain

In 1896 the German anatomist Ludwig Edinger published The Anatomy of the Central Nervous System of Man and other Vertebrates. The book, which established Edinger’s reputation as the founder of comparative neuroanatomy, described the evolution of the forebrain as a sequence of additions, each of which establishing new brain parts that introduced new functions.

Edinger viewed the forebrain as containing an “old encephalon” found in all vertebrates. On top of the old encephalon, there was the “new encephalon,” a sector only more prominent in mammals. In one of the most memorable passages of his treatise, Edinger illustrates his concept by asking the reader to imagine precisely inserting a reptilian brain into that of a marsupial (a “simple” mammal). When he superimposed them, the difference between the two was his new encephalon. He then ventures that, in the brain of the cat, the old encephalon “persists unchanged underneath the very important” new encephalon[3]. Put differently, the part that was present before is left unaltered. Based on his coarse analysis of morphological features, his suggestion was reasonable. But to a substantial degree, his ideas were very much in line with the notion of brain evolution as progress toward the human brain – à la old Aristotle and the scala naturae. Given the comprehensive scope of Endinger’s analysis across vertebrates, his views had a lasting impact and shaped the course of research for the subsequent decades.

More than a century later, knowledge about the brains of vertebrates has expanded by leaps and bounds. Yet, old thinking dies hard. Antiquated views of brain evolution continue to influence, if only implicitly, neuroscience. As an example, bear in mind that most frameworks of brain organization are heavily centered on the cortex. These descriptions view “newer” cortex as controlling subcortical regions, which are assumed to be (relatively) unchanged throughout eons of evolution. Modern research on brain anatomy from a comparative viewpoint indicates, in contrast, that brain evolution is better understood in terms of the reorganization of large-scale connectional systems (Figure 2). These ideas are developed extensively in [4].

Figure 2. The basic architecture of the brain is shared across all vertebrates. Extensive anatomical connectivity between sectors of the brain is observed in birds, reptiles, and mammals. The “pallium” corresponds to “cortex” in mammals. From: Pessoa, L., Medina, L., Hof, P. R., & Desfilis, E. (2019). Neural architecture of the vertebrate brain: Implications for the interaction between emotion and cognition. Neuroscience & Biobehavioral Reviews, 107, 296-312.

[1] Edinger (1910, p. 446).

[2] See Parvizi (2009) for an accessible discussion.

[3] For discussion, see Finger (1994, p. 271).

[4] Pessoa, L., Medina, L., Hof, P. R., & Desfilis, E. (2019). Neural architecture of the vertebrate brain: Implications for the interaction between emotion and cognition. Neuroscience & Biobehavioral Reviews, 107, 296-312.

What do brain areas do? They are inherently multifunctional

We’ll start with the simplest formulation, namely by assuming a one-to-one mapping between an area and its function. We’re assuming for the moment that we can come up with, and agree on, a set of criteria that defines what an area is. Maybe it’s what Brodmann defined early in the twentieth century. (A great source for many of the ideas discussed here is Passingham, R. E., Stephan, K. E., & Kötter, R. (2002). The anatomical basis of functional localization in the cortex. Nature Reviews Neuroscience, 3(8), 606-616.)

Figure 1. Structure-function mapping in the brain. The mapping from structure to function is many-to-many Abbreviations: A1, … , A4: areas 1 to 4; amyg: amygdala; F1, … , F4: functions 1 to 4. Figure from: Pessoa, L. (2014). Understanding brain networks and brain organization. Physics of Life Reviews, 11(3), 400-435.

For example, we could say that the function of primary visual cortex is visual perception, or perhaps a more basic visual mechanism, such as detecting “edges” (sharp light-to-dark transitions) in images. The same type of description can be applied to other sensory (auditory, olfactory, and so on) and motor areas of the brain. This exercise becomes considerably less straightforward for areas that are not sensory or motor, as their workings become much more difficult to determine and describe. Nevertheless, in theory, we can imagine extending the idea to all parts of the brain. The result of this endeavor would be a list of area-function pairs: L = {(A1,F1), (A2,F2),…, (An,Fn)}, where areas A implement functions F.

To date, no such list has been systematically generated. However, current knowledge indicates that this strategy would not yield a simple area-function list. What may start as a simple (A1,F1) pair, as research progresses, gradually is revised and grows to include a list of functions, such that area A1 participates in a series of functions F1, F2,…, Fk. From initially proposing that the area implements a specific function, as additional studies accumulate, we come to see that it participates in multiple ones. In other words, from a basic one-to-one A1F1 mapping, the pictures evolves to a one-to-many mapping: A1 → {F1, F2,…, Fk} (Figure 1).

Consider this example. Starting in the 1930s, lesion studies in monkeys suggested that the prefrontal cortex implements “working memory,” such as the ability to keep in mind a phone number for several seconds before dialing it. As research focusing on this part of the brain ramped up, the list of functions grew to include many cognitive operations, and the prefrontal cortex became central to our understanding of what is called executive function. In fact, today, the list is not limited to cognitive processes, but includes contributions to emotion and motivation. The prefrontal cortex is thus multifaceted. One may object that this sector is “too large” and that it naturally would be expected to participate in multiple processes. While this is a valid interjection, the argument holds for “small areas,” too. For example, take the amygdala, a region often associated with handling negative or aversive information. However, the amygdala also participates in the processing of appetitive items (and this multi-functionality applies even to amygdala subnuclei).

Let’s consider the structure-function (AF) mapping further from the perspective of the mental functions: where in the brain is a given function F carried out? In experiments with functional MRI, tasks that impose cognitive challenges engage multiple areas of frontal and parietal cortex; for example, tasks requiring participants to selectively pay attention to certain stimuli among many and answer questions about the ones that are relevant (in a screen containing blue and red objects, are there more rectangles or circles that are blue?). These regions are important for paying attention and selecting information that may be further interrogated. Such attentional control regions are observed in circumscribed sectors of frontal and parietal cortex. Thus, multiple individual regions are capable of carrying out a mental function, an instance of a many-to-one mapping: {A1 or A2,…, or Aj}→ F1. The explicit use of “or” here indicates that, say, A1 is capable of implementing F1, but so are A2, and so on[1]. Now, together, if brain regions participate in many functions and functions can be carried out by many regions, the ensuing structure-function mapping will be many-to-many. Needless to say, the study of systems with this property will be considerably more challenging than systems with a one-to-one organization (Figure 1). (For a related case, consider a situation where a gene contributes to many traits or physiological processes; conversely, traits or physiological processes depend on large sets of genes.)

Structure-function relationships can be defined at multiple levels, from the precise (for instance, primary visual cortex is concerned with detecting object borders) to the abstract (for instance, primary visual cortex is concerned with visual perception). Accordingly, structure-function relationships will depend on the granularity in question. Some researchers have suggested that, at some level of description, a brain region does not have more than one function; at the “proper” one, it will have a single function[2]. In contrast, the central idea here is that the one-to-one framework, even if implicitly accepted or adopted by neuroscientists, is an oversimplification that hampers progress in understanding the mind and the brain.

Brain areas are multifaceted

If brain areas don’t implement single processes, how should we characterize them? Instead of focusing on a single “summary function,” it is better to describe an area’s functional repertoire: across a possibly large range of functions, to what extent does an area participate in each of them? No consensus has emerged about how to do this, but below we’ll discuss some early results. But the basic idea is simple. Coffee growers around the world think of flavor the same way: via a profile or palette. For example, Brazilian coffee is popular because it is very chocolaty, nutty, and with light acidity, to mention three attributes.

Research with animals utilizes electrophysiological recordings to measure neuronal responses to varied stimuli. The work is meticulous and painstaking because, until recently, the vast majority of studies recorded from just a single (or very few) electrode(s), in a single brain area. Setting up a project, a researcher thus decides what processes to investigate at what precise location of the cortex or subcortex; for example, probing classical conditioning in the amygdala. Having elected to do so, the electrode is inserted in multiple neighboring sites as the investigator determines the response characteristics of the cells in the area (newer techniques exist where grids of finely spaced electrodes can record from adjacent cells simultaneously)[3]. For some regions, researchers have catalogued cell response properties for decades; considering the broader published literature thus allows them to have a fairly comprehensive view. In particular, the work of mapping cell responses has been the mainstay of perception and action research, given that the stimulus variables of interest can be manipulated systematically; it is easy to precisely change the physical properties of a visual stimulus, for example. In this manner, the visual properties of cells across more than a dozen areas in occipital and temporal cortex have been studied. And several areas in parietal and frontal cortex have been explored to determine neuronal responses during the preparation and elicitation of movements. 

It is thus possible to summarize the proportions of functional cell types in a brain region[4]. Consider, for example, two brain regions in visual cortex called V4 (visual area number 4) and MT (found in the middle temporal lobe). Approximately 85% of the cells in area MT show preference for the direction that a stimulus is moving (they respond more vigorously to rightward versus leftward motion, say), whereas only 5% of the cells in area V4 do so. In contrast, 50% of the cells in area V4 show a strong preference to the wavelength of the visual stimulus (related to a stimulus’s color), whereas no cells in area MT appear to do so. Finally, 75% of the cells in area MT are tuned to the orientation of a visual stimulus (the visual angle between the major elongation of a stimulus and a horizontal line), and 50% of the cells in area V4 do so, too. If we call these three properties ds, ws, and os (for stimulus direction, wavelength, and orientation, respectively), we can summarize an area’s responses by the triplet (ds, ws, os), such that area MT can be described by (.85, 0, .75) and area V4 by (.05, .50, .50).

This type of summary description can be potentially very rich, and immediately shifts the focus from thinking “this region computes X” to “this region participates in multiple processes.” At the same time, the approach prompts us to consider several thorny questions. In the example only three dimensions were used, each of which related to an attribute thought to be relevant – related to computing an object’s movement, color, and shape, respectively. But why stop at three features? Sure, we can add properties, but there is no guarantee that we will cover all of the “important” ones. In fact, at any given point in time, the attributes more likely reflect what researchers know and likely find interesting. This is one reason the framework becomes increasingly difficult for areas that aren’t chiefly sensory or motor; whereas sensorimotor attributes may be more intuitive, cognitive, emotional, and motivational dimensions are much less so – in fact, they are constantly debated by researchers! So, what set of properties should we consider for the regions of the prefrontal cortex that are involved in an array of mental processes? 

More fundamentally, we would have to know, or have a good way of guessing, the appropriate space of functions. Is there a small set of functions that describes all of mentation? Are mental functions like phonemes in a language? English has approximately 42 phonemes, the basic sounds that make up spoken words. Are there 42 functions that define the entire “space” of mental processes? How about 420? Although we don’t have answers to these fundamental questions[5], some form of multi-function, multi-dimensional description of an area’s capabilities is needed. A single-function description is like a strait jacket that needs to be shed. (For readers with a mathematical background, an analogy to basic elements like phonemes is a “basis set” that spans a subpace, like in linear algebra; or “basis functions” that can be used to reconstruct arbitrary signals, like in Fourier or Wavelet analysis.)

The multi-function approach can be illustrated by considering human neuroimaging research, including functional MRI. Despite the obvious limitations imposed by studying participants lying on their backs (many feel sleepy and may even momentarily doze off; not to mention that we can’t ask them to walk around and “produce behaviors”), the ability to probe the brain non-invasively and harmlessly means that we can scrutinize a staggering range of mental processes, from perception and action to problem solving and morality. With the growth of this literature, which accelerated in earnest after the publication in 1992 of the first functional MRI studies, several data repositories have been created that combine the results of thousands of studies in a single place.

Figure 2. Multifunctionality. (A): Functional profile of a sample region. The radial plot includes 20 attributes, or “task domains.” The green line represents the degree of engagement of the area for each attribute. (B): Distribution of a measure of functional diversity across cortex. Warmer colors indicate higher diversity; cooler colors, less diversity.
Figure from: Anderson, M. L., Kinnison, J., & Pessoa, L. (2013). Describing functional diversity of brain regions and brain networks. Neuroimage, 73, 50-58.

In one study, we capitalized on this treasure trove of results to characterize the “functional profile” of regions across the brain. We chose twenty “task domains” suggested to encompass a broad range of mental processes, including those linked to perception, action, emotion, and cognition. By considering the entire database of available published studies, at each brain location, we generated a twenty-dimensional functional description indicating the relative degree of engagement of each of the twenty domain attributes (Figure 2). Essentially, we counted the number of times an activation was reported in that brain location, noting the task domain in question. For example, a study reporting stronger responses during a language task relative to a control task, would count toward the “language” domain, at the reported location. We found that brain regions are rather functionally diverse, and are engaged by tasks across many domains. But this didn’t mean that they respond uniformly; they have preferences, which are at times more pronounced. To understand how multi-functionality varied across the brain, we computed a measure that summarized functional diversity. A brain region engaged by tasks across multiple domains would have high diversity, whereas those engaged by tasks in only a few domains would have low diversity. Functional diversity varied across the brain (Figure 2), with some brain regions being recruited by a very diverse range of experimental conditions.

The findings summarized in Figure 2 paint a picture of brain regions as functionally diverse, each with a certain style of computation. The goal here was to illustrate the multi-dimensional approach rather than to present a more definitive picture. For one, conclusions were entirely based on a single technique, which has relatively low spatial resolution. (In functional MRI, signal at each location pools together processing related to a very large number of neurons; a typical location, called a “voxel,” can easily contain millions of neurons.) The approach also doesn’t account for the confirmation bias present in the literature. For example, researchers often associate amygdala activation with emotion and are thus more likely to publish results reflecting this association, a tendency that will increase the association between the amygdala and the domain “emotion” (not to mention that investigators might mean different things when they say “emotion”). Finally, the study makes the assumption that the twenty-dimensional space of mental tasks is a reasonable decomposition. Many other breakdowns are possible, of course, and it might be even more informative to consider a collection of them at the same time (this would be like describing a coffee in terms of a given set of attributes but then using separate groups of attributes).


[1] When regions A1, A2 etc. jointly implement a function F, the situation is conceptually quite different from the scenario being described. We can think of the set of regions {A1, A2 , … } as a network of regions that, in combination, generates the function F.

[2] See discussion by Price and Friston (2005).

[3] Newer techniques, like two-photon imaging, allow the study of hundreds or even thousands of neurons simultaneously.

[4] Example in this paragraph discussed by Passingham et al. (2002).

[5] The book by Varela et al. (1990) offers among the best, and most accessible, treatment of these issues.