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.

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