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Brain Modules: Mosaic Evolution
Brain Modules: Mosaic Evolution 389
Brain Modules: Mosaic Evolution R A Barton, University of Durham, Durham, UK ã 2009 Elsevier Ltd. All rights reserved.
Brains Evolved The only plausible scientific explanation for the existence of what Charles Darwin termed ‘‘organs of extreme perfection and complication,’’ such as the brain, is that they evolved over millennia by natural selection. The human brain contains millions or even billions of neurons organized into a variety of interacting but distinct neural systems. Neuroscience has predominantly approached the problem of understanding this complex organization without explicit reference to how it evolved. Yet the functional organization of a system must reflect both the adaptive problems to which it is a solution, and the combined evolutionary and developmental processes from which it emerges. The way that brains are organized is thus intrinsically related to the way that they evolved.
Using Extant Species to Study Brain Evolution Because the internal structure of brains does not fossilize, their evolution can be studied only indirectly. One indirect method is to analyze the patterns of surface convolutions whose imprints are visible on the internal surface of fossilized crania (or on endocasts of the crania). While useful for documenting evolutionary changes in gross morphological features and certain fine details of cortical surface structure, these data are of limited use in understanding how neural organization evolved, as they reveal nothing about internal structural changes. The second indirect method is potentially more useful in this respect. It reconstructs evolutionary changes by analyzing comparative anatomy of extant species within a phylogenetic context. A variety of phylogenetic techniques for the analysis of comparative data are now available. The basic rationale for all of these techniques is that the way a trait evolved can be reconstructed by mapping character states in extant species (e.g., the size of a particular nucleus) onto a phylogenetic tree of those species. Reconstruction of the evolution of two or more traits allows evaluation of the patterns of correlated change among the traits (see Figure 1). For example, the hypothesis that two brain nuclei are components of an integrated system implies that they underwent
correlated evolution, in terms of their overall size, number of neurons, synaptic connectivity, or other neural characteristics. Whether any such correlated evolution reflects a specific neurobiological relationship between the nuclei, that is, whether it exists independently of more general evolutionary changes, such as those in overall brain size, can be tested by using statistical methods for controlling the effects of confounding variables. Hence, comparative tests of neural organization are based on analyses of the way in which brain structures evolved in relation to one another. Why is it necessary to use information on the phylogenetic relationships among species in comparative analyses, rather than just correlating the raw species values? The answer is that species are not independent data points in analyses that aim to test hypotheses about how traits evolved. Closely related species are more similar to one another than are distantly related species, because closely related species have undergone less evolutionary divergence. If we are interested in the way in which traits (e.g., the sizes of brain nuclei) evolved in relation to one another, evolutionary changes, rather than species differences, are the most relevant data. Phylogenetic methods explicitly take into account the branching patterns and divergence times between species, and consequently allow valid inferences about trait evolution. These methods are currently little known within neuroscience, despite the fact that neuroscientists often make evolutionary inferences, either explicitly or implicitly, based on comparisons among species. The process of reconstructing trait evolution on phylogenetic trees emphasizes that extant species do not represent ancestor–descendant relationships. Each extant species is the endpoint of both shared and independent evolutionary histories over an identical period of evolutionary time since their common ancestor. The significance of this point is that no extant species can be treated as an embodiment of the ancestral traits of any other species. Although this point has been made repeatedly, it is still possible to find either implicit or explicit reference in the neuroscience literature to evolutionary pathways between supposedly ‘primitive’ and ‘advanced’ extant species. Invariably, such interpretations of species differences are anthropocentrically biased, with phylogenetic affinity to humans being cast as a primitive–advanced continuum (the so-called scala natura). This way of thinking about species differences is wrong, and it distracts attention from the ways in which brains have become specialized in different kinds of ways
390 Brain Modules: Mosaic Evolution
d
13.5,6
7,3
Species:
a
Trait A: Trait B:
c a
i 6 2
17,7
ii
iii
iv
8 4
10 5
18 8
b
v 16 6
Contrasts trait B
4 2 0 −2 −4 b
0 4 8 Contrasts trait A
12
Figure 1 Basic rationale for the phylogenetic analysis of comparative data, using the method of independent contrasts. Independent contrasts are calculated as differences between pairs of species, between species values and an ancestral node, or between higher nodes. (a) Beginning at the tips of the tree, contrast a is calculated as the difference between species i and ii, while contrast b is calculated as the difference between species iv and v. Then, contrasts are calculated using higher nodes (the ‘mixed’ contrast c and internal contrast d; pairs of values at internal nodes are the reconstructed ancestral values of traits A and B). Contrasts are thus calculated up the tree, maximizing the number of possible contrasts while using each branch no more than once. The direction of subtraction for calculating contrasts is arbitrary but it is usual to force the independent variable to be positive. These contrasts are independent of one another and can be analyzed using standard statistical techniques. Strictly, the contrasts should be standardized according to the amount of time since evolutionary divergence. In this simplified example, however, unstandardized contrasts are plotted against one another for illustrative purposes (b). Here, traits A and B show positively correlated evolution.
in different lineages. In contrast, studying the patterns of brain evolution across numerous independently changing lineages has the potential to illuminate general aspects of brain organization.
Modularity and Brain Evolution The different components of organisms tend to evolve in a coordinated fashion. Hence, as overall body size increases, so does the size of limbs, muscles, tendons, and internal organs, and this is necessary for the animal to function well. At the molecular level, such coordinated evolutionary change may involve regulatory genes that determine overall patterns of growth, such as the rate and duration of cellular division. However, the very fact that separate components of organisms can be differentiated implies that they have a degree of structural and functional
independence. The concept of modularity refers to the idea of integration within, and relative autonomy among, the different components. Modularity may refer to the way that particular structures function, to the way in which they evolve, or to the way in which they develop during ontogeny. These types of modularity are conceptually distinct yet often related. For example, the idea that a set of components comprises a functional module implies that they are structurally integrated to perform the function. If the integrity of such a functional module is to be preserved when it is modified by natural selection, the constituent components must change together. Such coordinated evolutionary change is facilitated if there is developmental modularity, allowing relatively few genetic changes to cause a cascade of coordinated structural changes. On the other hand, developmental modularity may act as a constraint on ‘evolvability’: the evolutionary changes that are developmentally possible or likely. Modularity in organism design is associated with mosaic evolution: to the extent that modules are functionally and developmentally dissociable, organisms can be expected to evolve in a mosaic fashion, with different modules changing in response to different selection pressures. For example, testes are structurally and histologically distinct tissues with the specific functions of sperm and testosterone production. While the size of the testes correlates closely with the size of other organs and overall body size, comparative studies have shown that testes of promiscuously mating species are large relative to body size, as an adaptation to sperm competition. Hence, testes evolved in part independently of changes in the size of other organs and in overall size. Such mosaic evolutionary change appears to have been ubiquitous, and other well-known examples include increased heart and lung size as an adaptation to the energetic costs of flying in bats and birds, variation in relative spleen size associated with degree of exposure to nematode infection, and relatively elongated loops of Henle in the kidneys of desert mammals. Unlike bodily organs, individual brain components are extensively – even massively – interconnected to support the integrated processing of information and coordinated behavior, potentially limiting the importance of mosaic evolution. The extent to which modularity and mosaic evolution apply to the systems of the brain has, therefore, proved relatively contentious. Nevertheless, much evidence suggests that individual brain components are grouped within structurally, functionally, and developmentally differentiated neural systems specialized for handling particular cognitive operations. Indeed, systems neuroscience is largely predicated on such modularity.
Brain Modules: Mosaic Evolution 391
Although there is little argument that the brain is at least partly modular, the nature, extent, and developmental causes of this modularity are still hotly debated. It has also been suggested that, once the fundamental structure of the central nervous system was established early in the evolution of the chordates, the extent of mosaic change in the brain may have been limited by developmental constraints. The fact that all major brain structures tend to show highly correlated size variation across species has been taken as evidence for this developmental constraint hypothesis. Recent evidence from comparative studies, however, provides evidence for mosaic evolution of brain structure.
Goldfish (Carassius) Tec Tec VL
Tel
Cbl
VL FL FL
a Catfish (Clarias) Tel
Tec
FL FL
EL FL FL
Cbl Cbl
VL
Cbl
EL FL b
Comparative Evidence for Mosaic Evolution of Brain Structure
Glass knifefish (Eigenmannia)
Relative Size Change in Major Brain Structures
The extent to which brain structure appears to have evolved in a mosaic fashion depends on the anatomical level at which the analysis is done. Analyses of variation in the size of major brain subdivisions, such as the telencephalon, tectum, cerebellum, and medulla, generally indicate a pattern of strongly coordinated evolutionary change, with 90% or more of the variance in a brain structure being attributable to overall size. Even at this crude level of analysis, however, there are striking examples of variation that is independent of overall size. Among teleost fishes, specializations for different modes of foraging and sensory processing are associated with dramatic differences in the relative sizes of relevant brain structures (Figure 2). In mammals, the superior colliculus is approximately 10 times larger in ground squirrels than in rats, despite the fact that overall brain size is very similar. And also among mammals, primates have neocortices that are, on average, 5 times larger than in insectivores of similar brain size, with some comparisons again indicating tenfold differences. These examples of relative size differences are impressive, particularly given that the structures involved connect with many other brain regions and process a variety of types of information. The mosaic evolution hypothesis implies that the differences reflect size changes in specific components within these structures. For example, the neocortex is a highly heterogenous organ, processing information from all the senses and being involved in many different aspects of sensory, motor, and cognitive processing. Concordant with the concept of mosaic evolution, differences in neocortex size are related to the evolution of specific sensory regions. Primate visual specialization provides an example. Primates exhibit a range of peripheral and
EL
Tec Tel
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c
EL
1 mm
Figure 2 Mosaic brain evolution in teleost fish. Dorsal views of brains on the left, transverse sections on the right. VL, vagal lobes; FL, facial lobes; Tec, tectum; Tel, telencephalon; Cbl, cerebellum; EL, electrosensory lateral line lobe. Sensory specializations are associated with dramatic differences in the size of associated brain structures. Goldfish have enlarged vagal lobes (involved in taste and processing of food). Catfish have large facial lobes (processing gustatory information from the barbells), and relatively large electrosensory lateral line lobes, though the latter are not as large as in electric fish such as the glass knifefish. Courtesy of Striedter GF (2005) Principles of Brain Evolution. Sunderland, MA: Sinauer Associates.
central specializations for vision, including highly convergent orbits supporting binocular vision, large eyes, trichromatic vision (in Old World monkeys and apes), a concentration of ganglion cells in the central retina; a greatly expanded representation of the central field in visual cortex; a distinctive pattern of projections between eye and brain; a distinctly laminated lateral geniculate nucleus (LGN) within which information from the same hemifield of each retina is brought into visuotopic register in separate layers, before converging on numerous single binocular neurons in the visual cortex; and relatively numerous and extensive visual cortical regions. This suite of visual features was a fundamental component of the adaptive shift in the evolution of the first primates, but there is variability in most or all of these features within the order. Both variation in neocortex size within
Relative neocortex volume (contrasts)
392 Brain Modules: Mosaic Evolution 0.04 0.02 0 −0.02 −0.08 −0.04 0 0.04 Relative LGN volume (contrasts)
a
0.08
log visual cortex size
5
b
4 3 2 1 0 −1
0
1 2 3 4 log somatosensory cortex size
Figure 3 Visual specialization underlies the evolution of large neocortex size in primates. Graph (a) shows the results of a phylogenetic analysis indicating positively correlated evolution between the relative volumes of the whole neocortex and of the LGN, after removing the effects of variation in other brain structures. Graph (b) plots surface areas of visual cortices against those of somatosensory cortices for primates and two other taxonomic groups (red circles are primates, blue diamonds are marsupials, green squares are insectivores). (a) Data from publications by H Stephan and colleagues. (b) Data provided by P Kaskan (Kaskan PM, Franco ECS, Yamada ES, Silveira LCD, Darlington RB, and Finlay BL (2005) Peripheral variability and central constancy in mammalian visual system evolution. Proceedings of the Royal Society of London B 272: 91–100).
the primates and differences in neocortex size between primates and nonprimates are related specifically to size change in visual cortices relative to other cortical regions (Figure 3). The data in Figure 3(b) are plotted in log–log coordinates for analytical convenience, masking the absolute size differences. For example, tamarin monkeys have somatosensory cortices slightly smaller than do hedgehogs (22.5 vs. 28.5 mm2, respectively), but their visual cortices are approximately 9 times larger (158 vs. 17.4 mm2, respectively). Hence, primates are relative visual specialists, while insectivores are relative somatosensory specialists, reflected in the mosaic nature of neocortical evolution. Correlated Evolution between Components of Functional Systems
Because neural systems are extended networks with connections that cut across the major structures of the brain, mosaic evolution should be evident in correlated evolutionary changes among connected
components, independent of changes in other systems. Quantitative phylogenetic comparative analyses strongly support this prediction: anatomical and functional connections closely predict patterns of correlated evolution among nuclei in a range of neural systems, including the hippocampal complex, amygdala, olfactory system, auditory system, and motor system (Figure 4). In some cases, it has been possible to test the prediction of correlated evolution at the level of individual nuclei. In the corticocerebellar system, evolutionary changes in the size of connected nuclei in the hind-, mid-, and forebrain correlate strongly after controlling for variation in other brain structures. Of the four main vestibular nuclei and three cerebellar nuclei, the two with direct connections (the lateral vestibular and middle cerebellar nuclei) are those that show correlated evolution after controlling for the size of the other nuclei. Hence, anatomical connections predict detailed patterns of correlated evolution, substantiating the hypothesis of mosaic change at the level of neural systems. Comparative Studies Inform Systems Neuroscience
The cases cited above are examples of testing predictions of evolutionary hypotheses derived from experimental neurobiological data. The fact that brain nuclei co-evolved according to their functional connections also allows us to carry out the reciprocal program, that of testing hypotheses about brain organization using comparative data. A recent example concerns debate about the structural and functional integrity of the amygdala. On the basis of anatomical, pharmacological, and electrophysiological data, Swanson and Petrovich questioned the existence of the amygdala as a structurally and functionally coherent unit, arguing that the term ‘amygdala’ refers to a collection of disparate nuclei that are really parts of different neural systems. Some other researchers, however, emphasize the dense connections between nuclei within the amygdala. The problem here is in deciding what constitutes sufficient evidence to conclude that the amygdala either does or does not exist as a structurally and functionally integrated entity, hence breaking the cycle of claim and counterclaim based on affinities among nuclei and differences between them respectively. Accepting that, like all complex biological organs, neural systems exist because they evolved by natural selection, the question of whether particular components constitute components of such a unified system is intrinsically an evolutionary question. The crux of the matter is thus how such nuclei evolved: did they evolve together, in a coordinated fashion,
Brain Modules: Mosaic Evolution 393
Rest of brain Olfactory cortex Hippocampus CM amygdala Vestibular nuclei Lateral geniculate
0.18 n.s. 0.37 n.s. −0.36 n.s. −0.38 n.s. 1.04 n.s.
Olfactory Entorhinal CBL Cerebellar bulb cortex amygdala nuclei 0.31 ** −0.06 n.s. 0.12 n.s. 0.13 n.s. −0.52 *
0.09 n.s. 0.61 *** −0.30 n.s. −0.11 n.s. 0.49 n.s.
0.32 * −0.12 −0.77 0.91 *** 0.35 * −0.40 n.s.
0.23 n.s. 0.14 n.s. 0.65 * 0.95 **** −0.70 n.s.
Visual cortex
0.83 *
Figure 4 Correlated evolution among primate brain structures is predicted by functional and anatomical connections. Each cell in the figure contains the results of a phylogenetic analysis of the volumes of the corresponding structures. The figures are partial regression coefficients for pairs of structures, after controlling for evolutionary change in all of the other structures in the table. Asterisks indicate statistical significance (*, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001; n.s., nonsignificant). Predicted correlations are in the diagonal line of blue boxes. Negative correlations between visual and olfactory systems reflect evolutionary differentiation into nocturnal and diurnal niches. The positive correlation between amygdala and olfactory cortices is unsurprising on anatomical and functional grounds (see below). Hence, there are only two correlations that would not be predicted (both between the amygdala and the cerebellar–vestibular system). Overall, these analyses and similar ones for insectivores indicate a clear pattern of strongly and positively correlated evolution among structures within systems, and a lack of such correlated evolution among structures in different systems. Reprinted by permission from Macmillan Publishers Ltd.: Nature (Barton RA and Harvey PH (2000) Mosaic evolution of brain structure in mammals. Nature 405: 1055–1058), copyright (2000).
and in a way that cannot be attributed merely to their integration within a larger, more global system (e.g., the limbic system, or even the brain as a whole)? Phylogenetic analysis of comparative data in primates and insectivores clearly shows that the amygdala coheres as an evolutionary unit. After controlling for variation in a range of other brain structures, including other limbic structures, separate groups of nuclei in the amygdala show significantly correlated evolution, hence refuting the claim that they are parts of entirely different neural systems (Figure 5).
Primates Striatum
Olfactory bulbs
CM
Neocortex
***
** AOB
Olfactory cortex
NLOT
a Insectivores Neocortex
Striatum
Correlated Evolution between Brain Structure and Ecology If, as suggested, neural system evolution is a result of natural selection on modular information-processing capacities, brain structure should correlate with relevant aspects of animals’ ecology and behavioral specialization. Comparative studies have begun to reveal such correlates of mosaic brain evolution. In birds, the relative sizes of the HVC nucleus and of the hippocampus correlate with song repertoire size and spatial memory demands (food caching), respectively. In primates, visual and olfactory system evolution are both associated with ecology, though in different ways: diurnal habits correlate with increased relative volume and cell number in parvocellular, but not magnocellular layers of the LGN, and with decreased
BL
Olfactory bulbs
AOB
CM
**
BL
Olfactory cortex
b Figure 5 Evolutionary coherence of the amygdala in two mammalian groups. Phylogenetic comparative analysis indicates strongly correlated evolution between separate amygdala components, even after removing the effects of a variety of other limbic and nonlimbic structures. Bars between structures represent significant partial regression coefficients, with bar width reflecting strength of the correlation (**, p < 0.01; ***, p < 0.001). AOB, accessory olfactory bulbs; BL, basolateral nuclei; CM, centromedial nuclei; NLOT, nucleus of lateral olfactory tract. Summarized from analyses in Barton RA, Aggleton J, and Grenyer R (2003) Evolutionary coherence of the mammalian amygdala. Proceedings of the Royal Society of London B 270, 539–544.
394 Brain Modules: Mosaic Evolution
olfactory system size. The evolutionary dissociation between parvo- and magnocellular pathways accords with experimental data on functional segregation within the visual system and on behavioral attributes of different species. Parvocellular projections to visual cortex mediate fine-grained, acute, and color vision, abilities that are characteristic of diurnal primates. Relative parvocellular LGN size and visual cortex size also correlate with foraging behavior and with social group size, independently of the correlation with activity period, suggesting that visual system evolution may have been affected by several aspects of lifestyles. The size of the accessory olfactory bulbs, which mediate pheromonal processing, are correlated with social variables, such as social group size (in New World monkeys) and spatial dispersion (in nocturnal lemurs and lorises), whereas the main olfactory bulbs correlate more strongly with nonsocial variables such as diet. In carnivores, relative olfactory bulb size also correlates with ecological variables. In bats, wing area and habitat complexity correlate with the relative size of the auditory inferior colliculi (among echolocating species) and with relative hippocampus size.
Neural System Evolution and Brain Size The adaptive and cognitive significance of brain size has been much debated. If brains are mosaics of independently evolving neural systems, it should be possible to document at least broad types of information processing that have been enhanced during brain expansion (or compromised during brain size reduction). Primates’ visual specialization again provides an example. Evolutionary changes in the volumes of both the LGN and primary visual cortices correlate positively with brain size relative to body size after removing the effects of evolutionary changes in other structures. One specific visual factor implicated in brain size evolution is stereopsis, because the degree of convergence of the orbits also correlates with brain size. Thus, brain size evolution in primates is specifically associated with modifications of the visual system. Similar evidence is emerging for mammals (in which the size of the optical input to the brain correlates with brain size) and birds (in which eye size correlates with brain size). These recent results provide the first explanations of brain size variation in terms of specific neural systems. Broader understanding of mammalian brain size evolution will, however, require
investigation of mosaic size change across a variety of neural systems and phylogenetic lineages.
Conclusions The components of any adaptive complex, such as the brain, by definition undergo coordinated evolution. At the same time, functional differentiation of neural systems within the mammalian brain potentially creates scope for a mosaic pattern of evolutionary change overlying the allometric covariation of individual components. Comparative analyses unequivocally show that specific systems and structures have evolved in part independently of change in overall brain size, and that such system-specific change correlates with behavioral ecology. The brains of different species evolved under different selection pressures and mediate different behavioral adaptations. These differences caution against making sweeping inferences about ‘the brain’ based on study of any one model species, and provide strong justification for comparative studies of brain structure informed by evolutionary principles. By studying how natural selection modified brains, we can derive insights into their functional organization. See also: Brain Connectivity and Brain Size; Brain Development: The Generation of Large Brains; Brain Evolution: Developmental Constraints and Relative Developmental Growth; Brain Scaling Laws.
Further Reading Allman JM (1999) Evolving Brains. New York: Scientific American Publications. Barton RA (2004) Binocularity and brain evolution in primates. Proceedings of the National Academy of Sciences of the United States of America 101: 10113–10115. Barton RA and Harvey PH (2000) Mosaic evolution of brain structure in mammals. Nature 405: 1055–1058. Breuker CJ, Debat V, and Klingenberg CP (2006) Functional evodevo. Trends in Ecology and Evolution 21: 488–492. Finlay BL and Darlington RB (1995) Linked regularities in the development and evolution of mammalian brains. Science 268: 1578–1584. Harvey PH and Pagel MD (1991) The Comparative Method in Evolutionary Biology. Oxford: Oxford University Press. Jerison HJ (1973) Evolution of the Brain and Intelligence. New York: Academic Press. Nunn C and Barton RA (2001) Comparative methods for studying primate adaptation and allometry. Evolutionary Anthropology 10: 81–98. Striedter GF (2005) Principles of Brain Evolution. Sunderland, MA: Sinauer Associates. Swanson LW and Petrovich GD (1998) What is the amygdala? Trends in Neurosciences 21: 323–331.
Brain Modules: Mosaic Evolution R A Barton, University of Durham, Durham, UK ã 2009 Elsevier Ltd. All rights reserved.
Brains Evolved The only plausible scientific explanation for the existence of what Charles Darwin termed ‘‘organs of extreme perfection and complication,’’ such as the brain, is that they evolved over millennia by natural selection. The human brain contains millions or even billions of neurons organized into a variety of interacting but distinct neural systems. Neuroscience has predominantly approached the problem of understanding this complex organization without explicit reference to how it evolved. Yet the functional organization of a system must reflect both the adaptive problems to which it is a solution, and the combined evolutionary and developmental processes from which it emerges. The way that brains are organized is thus intrinsically related to the way that they evolved.
Using Extant Species to Study Brain Evolution Because the internal structure of brains does not fossilize, their evolution can be studied only indirectly. One indirect method is to analyze the patterns of surface convolutions whose imprints are visible on the internal surface of fossilized crania (or on endocasts of the crania). While useful for documenting evolutionary changes in gross morphological features and certain fine details of cortical surface structure, these data are of limited use in understanding how neural organization evolved, as they reveal nothing about internal structural changes. The second indirect method is potentially more useful in this respect. It reconstructs evolutionary changes by analyzing comparative anatomy of extant species within a phylogenetic context. A variety of phylogenetic techniques for the analysis of comparative data are now available. The basic rationale for all of these techniques is that the way a trait evolved can be reconstructed by mapping character states in extant species (e.g., the size of a particular nucleus) onto a phylogenetic tree of those species. Reconstruction of the evolution of two or more traits allows evaluation of the patterns of correlated change among the traits (see Figure 1). For example, the hypothesis that two brain nuclei are components of an integrated system implies that they underwent
correlated evolution, in terms of their overall size, number of neurons, synaptic connectivity, or other neural characteristics. Whether any such correlated evolution reflects a specific neurobiological relationship between the nuclei, that is, whether it exists independently of more general evolutionary changes, such as those in overall brain size, can be tested by using statistical methods for controlling the effects of confounding variables. Hence, comparative tests of neural organization are based on analyses of the way in which brain structures evolved in relation to one another. Why is it necessary to use information on the phylogenetic relationships among species in comparative analyses, rather than just correlating the raw species values? The answer is that species are not independent data points in analyses that aim to test hypotheses about how traits evolved. Closely related species are more similar to one another than are distantly related species, because closely related species have undergone less evolutionary divergence. If we are interested in the way in which traits (e.g., the sizes of brain nuclei) evolved in relation to one another, evolutionary changes, rather than species differences, are the most relevant data. Phylogenetic methods explicitly take into account the branching patterns and divergence times between species, and consequently allow valid inferences about trait evolution. These methods are currently little known within neuroscience, despite the fact that neuroscientists often make evolutionary inferences, either explicitly or implicitly, based on comparisons among species. The process of reconstructing trait evolution on phylogenetic trees emphasizes that extant species do not represent ancestor–descendant relationships. Each extant species is the endpoint of both shared and independent evolutionary histories over an identical period of evolutionary time since their common ancestor. The significance of this point is that no extant species can be treated as an embodiment of the ancestral traits of any other species. Although this point has been made repeatedly, it is still possible to find either implicit or explicit reference in the neuroscience literature to evolutionary pathways between supposedly ‘primitive’ and ‘advanced’ extant species. Invariably, such interpretations of species differences are anthropocentrically biased, with phylogenetic affinity to humans being cast as a primitive–advanced continuum (the so-called scala natura). This way of thinking about species differences is wrong, and it distracts attention from the ways in which brains have become specialized in different kinds of ways
390 Brain Modules: Mosaic Evolution
d
13.5,6
7,3
Species:
a
Trait A: Trait B:
c a
i 6 2
17,7
ii
iii
iv
8 4
10 5
18 8
b
v 16 6
Contrasts trait B
4 2 0 −2 −4 b
0 4 8 Contrasts trait A
12
Figure 1 Basic rationale for the phylogenetic analysis of comparative data, using the method of independent contrasts. Independent contrasts are calculated as differences between pairs of species, between species values and an ancestral node, or between higher nodes. (a) Beginning at the tips of the tree, contrast a is calculated as the difference between species i and ii, while contrast b is calculated as the difference between species iv and v. Then, contrasts are calculated using higher nodes (the ‘mixed’ contrast c and internal contrast d; pairs of values at internal nodes are the reconstructed ancestral values of traits A and B). Contrasts are thus calculated up the tree, maximizing the number of possible contrasts while using each branch no more than once. The direction of subtraction for calculating contrasts is arbitrary but it is usual to force the independent variable to be positive. These contrasts are independent of one another and can be analyzed using standard statistical techniques. Strictly, the contrasts should be standardized according to the amount of time since evolutionary divergence. In this simplified example, however, unstandardized contrasts are plotted against one another for illustrative purposes (b). Here, traits A and B show positively correlated evolution.
in different lineages. In contrast, studying the patterns of brain evolution across numerous independently changing lineages has the potential to illuminate general aspects of brain organization.
Modularity and Brain Evolution The different components of organisms tend to evolve in a coordinated fashion. Hence, as overall body size increases, so does the size of limbs, muscles, tendons, and internal organs, and this is necessary for the animal to function well. At the molecular level, such coordinated evolutionary change may involve regulatory genes that determine overall patterns of growth, such as the rate and duration of cellular division. However, the very fact that separate components of organisms can be differentiated implies that they have a degree of structural and functional
independence. The concept of modularity refers to the idea of integration within, and relative autonomy among, the different components. Modularity may refer to the way that particular structures function, to the way in which they evolve, or to the way in which they develop during ontogeny. These types of modularity are conceptually distinct yet often related. For example, the idea that a set of components comprises a functional module implies that they are structurally integrated to perform the function. If the integrity of such a functional module is to be preserved when it is modified by natural selection, the constituent components must change together. Such coordinated evolutionary change is facilitated if there is developmental modularity, allowing relatively few genetic changes to cause a cascade of coordinated structural changes. On the other hand, developmental modularity may act as a constraint on ‘evolvability’: the evolutionary changes that are developmentally possible or likely. Modularity in organism design is associated with mosaic evolution: to the extent that modules are functionally and developmentally dissociable, organisms can be expected to evolve in a mosaic fashion, with different modules changing in response to different selection pressures. For example, testes are structurally and histologically distinct tissues with the specific functions of sperm and testosterone production. While the size of the testes correlates closely with the size of other organs and overall body size, comparative studies have shown that testes of promiscuously mating species are large relative to body size, as an adaptation to sperm competition. Hence, testes evolved in part independently of changes in the size of other organs and in overall size. Such mosaic evolutionary change appears to have been ubiquitous, and other well-known examples include increased heart and lung size as an adaptation to the energetic costs of flying in bats and birds, variation in relative spleen size associated with degree of exposure to nematode infection, and relatively elongated loops of Henle in the kidneys of desert mammals. Unlike bodily organs, individual brain components are extensively – even massively – interconnected to support the integrated processing of information and coordinated behavior, potentially limiting the importance of mosaic evolution. The extent to which modularity and mosaic evolution apply to the systems of the brain has, therefore, proved relatively contentious. Nevertheless, much evidence suggests that individual brain components are grouped within structurally, functionally, and developmentally differentiated neural systems specialized for handling particular cognitive operations. Indeed, systems neuroscience is largely predicated on such modularity.
Brain Modules: Mosaic Evolution 391
Although there is little argument that the brain is at least partly modular, the nature, extent, and developmental causes of this modularity are still hotly debated. It has also been suggested that, once the fundamental structure of the central nervous system was established early in the evolution of the chordates, the extent of mosaic change in the brain may have been limited by developmental constraints. The fact that all major brain structures tend to show highly correlated size variation across species has been taken as evidence for this developmental constraint hypothesis. Recent evidence from comparative studies, however, provides evidence for mosaic evolution of brain structure.
Goldfish (Carassius) Tec Tec VL
Tel
Cbl
VL FL FL
a Catfish (Clarias) Tel
Tec
FL FL
EL FL FL
Cbl Cbl
VL
Cbl
EL FL b
Comparative Evidence for Mosaic Evolution of Brain Structure
Glass knifefish (Eigenmannia)
Relative Size Change in Major Brain Structures
The extent to which brain structure appears to have evolved in a mosaic fashion depends on the anatomical level at which the analysis is done. Analyses of variation in the size of major brain subdivisions, such as the telencephalon, tectum, cerebellum, and medulla, generally indicate a pattern of strongly coordinated evolutionary change, with 90% or more of the variance in a brain structure being attributable to overall size. Even at this crude level of analysis, however, there are striking examples of variation that is independent of overall size. Among teleost fishes, specializations for different modes of foraging and sensory processing are associated with dramatic differences in the relative sizes of relevant brain structures (Figure 2). In mammals, the superior colliculus is approximately 10 times larger in ground squirrels than in rats, despite the fact that overall brain size is very similar. And also among mammals, primates have neocortices that are, on average, 5 times larger than in insectivores of similar brain size, with some comparisons again indicating tenfold differences. These examples of relative size differences are impressive, particularly given that the structures involved connect with many other brain regions and process a variety of types of information. The mosaic evolution hypothesis implies that the differences reflect size changes in specific components within these structures. For example, the neocortex is a highly heterogenous organ, processing information from all the senses and being involved in many different aspects of sensory, motor, and cognitive processing. Concordant with the concept of mosaic evolution, differences in neocortex size are related to the evolution of specific sensory regions. Primate visual specialization provides an example. Primates exhibit a range of peripheral and
EL
Tec Tel
Cbl Cbl
c
EL
1 mm
Figure 2 Mosaic brain evolution in teleost fish. Dorsal views of brains on the left, transverse sections on the right. VL, vagal lobes; FL, facial lobes; Tec, tectum; Tel, telencephalon; Cbl, cerebellum; EL, electrosensory lateral line lobe. Sensory specializations are associated with dramatic differences in the size of associated brain structures. Goldfish have enlarged vagal lobes (involved in taste and processing of food). Catfish have large facial lobes (processing gustatory information from the barbells), and relatively large electrosensory lateral line lobes, though the latter are not as large as in electric fish such as the glass knifefish. Courtesy of Striedter GF (2005) Principles of Brain Evolution. Sunderland, MA: Sinauer Associates.
central specializations for vision, including highly convergent orbits supporting binocular vision, large eyes, trichromatic vision (in Old World monkeys and apes), a concentration of ganglion cells in the central retina; a greatly expanded representation of the central field in visual cortex; a distinctive pattern of projections between eye and brain; a distinctly laminated lateral geniculate nucleus (LGN) within which information from the same hemifield of each retina is brought into visuotopic register in separate layers, before converging on numerous single binocular neurons in the visual cortex; and relatively numerous and extensive visual cortical regions. This suite of visual features was a fundamental component of the adaptive shift in the evolution of the first primates, but there is variability in most or all of these features within the order. Both variation in neocortex size within
Relative neocortex volume (contrasts)
392 Brain Modules: Mosaic Evolution 0.04 0.02 0 −0.02 −0.08 −0.04 0 0.04 Relative LGN volume (contrasts)
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Figure 3 Visual specialization underlies the evolution of large neocortex size in primates. Graph (a) shows the results of a phylogenetic analysis indicating positively correlated evolution between the relative volumes of the whole neocortex and of the LGN, after removing the effects of variation in other brain structures. Graph (b) plots surface areas of visual cortices against those of somatosensory cortices for primates and two other taxonomic groups (red circles are primates, blue diamonds are marsupials, green squares are insectivores). (a) Data from publications by H Stephan and colleagues. (b) Data provided by P Kaskan (Kaskan PM, Franco ECS, Yamada ES, Silveira LCD, Darlington RB, and Finlay BL (2005) Peripheral variability and central constancy in mammalian visual system evolution. Proceedings of the Royal Society of London B 272: 91–100).
the primates and differences in neocortex size between primates and nonprimates are related specifically to size change in visual cortices relative to other cortical regions (Figure 3). The data in Figure 3(b) are plotted in log–log coordinates for analytical convenience, masking the absolute size differences. For example, tamarin monkeys have somatosensory cortices slightly smaller than do hedgehogs (22.5 vs. 28.5 mm2, respectively), but their visual cortices are approximately 9 times larger (158 vs. 17.4 mm2, respectively). Hence, primates are relative visual specialists, while insectivores are relative somatosensory specialists, reflected in the mosaic nature of neocortical evolution. Correlated Evolution between Components of Functional Systems
Because neural systems are extended networks with connections that cut across the major structures of the brain, mosaic evolution should be evident in correlated evolutionary changes among connected
components, independent of changes in other systems. Quantitative phylogenetic comparative analyses strongly support this prediction: anatomical and functional connections closely predict patterns of correlated evolution among nuclei in a range of neural systems, including the hippocampal complex, amygdala, olfactory system, auditory system, and motor system (Figure 4). In some cases, it has been possible to test the prediction of correlated evolution at the level of individual nuclei. In the corticocerebellar system, evolutionary changes in the size of connected nuclei in the hind-, mid-, and forebrain correlate strongly after controlling for variation in other brain structures. Of the four main vestibular nuclei and three cerebellar nuclei, the two with direct connections (the lateral vestibular and middle cerebellar nuclei) are those that show correlated evolution after controlling for the size of the other nuclei. Hence, anatomical connections predict detailed patterns of correlated evolution, substantiating the hypothesis of mosaic change at the level of neural systems. Comparative Studies Inform Systems Neuroscience
The cases cited above are examples of testing predictions of evolutionary hypotheses derived from experimental neurobiological data. The fact that brain nuclei co-evolved according to their functional connections also allows us to carry out the reciprocal program, that of testing hypotheses about brain organization using comparative data. A recent example concerns debate about the structural and functional integrity of the amygdala. On the basis of anatomical, pharmacological, and electrophysiological data, Swanson and Petrovich questioned the existence of the amygdala as a structurally and functionally coherent unit, arguing that the term ‘amygdala’ refers to a collection of disparate nuclei that are really parts of different neural systems. Some other researchers, however, emphasize the dense connections between nuclei within the amygdala. The problem here is in deciding what constitutes sufficient evidence to conclude that the amygdala either does or does not exist as a structurally and functionally integrated entity, hence breaking the cycle of claim and counterclaim based on affinities among nuclei and differences between them respectively. Accepting that, like all complex biological organs, neural systems exist because they evolved by natural selection, the question of whether particular components constitute components of such a unified system is intrinsically an evolutionary question. The crux of the matter is thus how such nuclei evolved: did they evolve together, in a coordinated fashion,
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Rest of brain Olfactory cortex Hippocampus CM amygdala Vestibular nuclei Lateral geniculate
0.18 n.s. 0.37 n.s. −0.36 n.s. −0.38 n.s. 1.04 n.s.
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0.09 n.s. 0.61 *** −0.30 n.s. −0.11 n.s. 0.49 n.s.
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0.23 n.s. 0.14 n.s. 0.65 * 0.95 **** −0.70 n.s.
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Figure 4 Correlated evolution among primate brain structures is predicted by functional and anatomical connections. Each cell in the figure contains the results of a phylogenetic analysis of the volumes of the corresponding structures. The figures are partial regression coefficients for pairs of structures, after controlling for evolutionary change in all of the other structures in the table. Asterisks indicate statistical significance (*, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001; n.s., nonsignificant). Predicted correlations are in the diagonal line of blue boxes. Negative correlations between visual and olfactory systems reflect evolutionary differentiation into nocturnal and diurnal niches. The positive correlation between amygdala and olfactory cortices is unsurprising on anatomical and functional grounds (see below). Hence, there are only two correlations that would not be predicted (both between the amygdala and the cerebellar–vestibular system). Overall, these analyses and similar ones for insectivores indicate a clear pattern of strongly and positively correlated evolution among structures within systems, and a lack of such correlated evolution among structures in different systems. Reprinted by permission from Macmillan Publishers Ltd.: Nature (Barton RA and Harvey PH (2000) Mosaic evolution of brain structure in mammals. Nature 405: 1055–1058), copyright (2000).
and in a way that cannot be attributed merely to their integration within a larger, more global system (e.g., the limbic system, or even the brain as a whole)? Phylogenetic analysis of comparative data in primates and insectivores clearly shows that the amygdala coheres as an evolutionary unit. After controlling for variation in a range of other brain structures, including other limbic structures, separate groups of nuclei in the amygdala show significantly correlated evolution, hence refuting the claim that they are parts of entirely different neural systems (Figure 5).
Primates Striatum
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a Insectivores Neocortex
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Correlated Evolution between Brain Structure and Ecology If, as suggested, neural system evolution is a result of natural selection on modular information-processing capacities, brain structure should correlate with relevant aspects of animals’ ecology and behavioral specialization. Comparative studies have begun to reveal such correlates of mosaic brain evolution. In birds, the relative sizes of the HVC nucleus and of the hippocampus correlate with song repertoire size and spatial memory demands (food caching), respectively. In primates, visual and olfactory system evolution are both associated with ecology, though in different ways: diurnal habits correlate with increased relative volume and cell number in parvocellular, but not magnocellular layers of the LGN, and with decreased
BL
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b Figure 5 Evolutionary coherence of the amygdala in two mammalian groups. Phylogenetic comparative analysis indicates strongly correlated evolution between separate amygdala components, even after removing the effects of a variety of other limbic and nonlimbic structures. Bars between structures represent significant partial regression coefficients, with bar width reflecting strength of the correlation (**, p < 0.01; ***, p < 0.001). AOB, accessory olfactory bulbs; BL, basolateral nuclei; CM, centromedial nuclei; NLOT, nucleus of lateral olfactory tract. Summarized from analyses in Barton RA, Aggleton J, and Grenyer R (2003) Evolutionary coherence of the mammalian amygdala. Proceedings of the Royal Society of London B 270, 539–544.
394 Brain Modules: Mosaic Evolution
olfactory system size. The evolutionary dissociation between parvo- and magnocellular pathways accords with experimental data on functional segregation within the visual system and on behavioral attributes of different species. Parvocellular projections to visual cortex mediate fine-grained, acute, and color vision, abilities that are characteristic of diurnal primates. Relative parvocellular LGN size and visual cortex size also correlate with foraging behavior and with social group size, independently of the correlation with activity period, suggesting that visual system evolution may have been affected by several aspects of lifestyles. The size of the accessory olfactory bulbs, which mediate pheromonal processing, are correlated with social variables, such as social group size (in New World monkeys) and spatial dispersion (in nocturnal lemurs and lorises), whereas the main olfactory bulbs correlate more strongly with nonsocial variables such as diet. In carnivores, relative olfactory bulb size also correlates with ecological variables. In bats, wing area and habitat complexity correlate with the relative size of the auditory inferior colliculi (among echolocating species) and with relative hippocampus size.
Neural System Evolution and Brain Size The adaptive and cognitive significance of brain size has been much debated. If brains are mosaics of independently evolving neural systems, it should be possible to document at least broad types of information processing that have been enhanced during brain expansion (or compromised during brain size reduction). Primates’ visual specialization again provides an example. Evolutionary changes in the volumes of both the LGN and primary visual cortices correlate positively with brain size relative to body size after removing the effects of evolutionary changes in other structures. One specific visual factor implicated in brain size evolution is stereopsis, because the degree of convergence of the orbits also correlates with brain size. Thus, brain size evolution in primates is specifically associated with modifications of the visual system. Similar evidence is emerging for mammals (in which the size of the optical input to the brain correlates with brain size) and birds (in which eye size correlates with brain size). These recent results provide the first explanations of brain size variation in terms of specific neural systems. Broader understanding of mammalian brain size evolution will, however, require
investigation of mosaic size change across a variety of neural systems and phylogenetic lineages.
Conclusions The components of any adaptive complex, such as the brain, by definition undergo coordinated evolution. At the same time, functional differentiation of neural systems within the mammalian brain potentially creates scope for a mosaic pattern of evolutionary change overlying the allometric covariation of individual components. Comparative analyses unequivocally show that specific systems and structures have evolved in part independently of change in overall brain size, and that such system-specific change correlates with behavioral ecology. The brains of different species evolved under different selection pressures and mediate different behavioral adaptations. These differences caution against making sweeping inferences about ‘the brain’ based on study of any one model species, and provide strong justification for comparative studies of brain structure informed by evolutionary principles. By studying how natural selection modified brains, we can derive insights into their functional organization. See also: Brain Connectivity and Brain Size; Brain Development: The Generation of Large Brains; Brain Evolution: Developmental Constraints and Relative Developmental Growth; Brain Scaling Laws.
Further Reading Allman JM (1999) Evolving Brains. New York: Scientific American Publications. Barton RA (2004) Binocularity and brain evolution in primates. Proceedings of the National Academy of Sciences of the United States of America 101: 10113–10115. Barton RA and Harvey PH (2000) Mosaic evolution of brain structure in mammals. Nature 405: 1055–1058. Breuker CJ, Debat V, and Klingenberg CP (2006) Functional evodevo. Trends in Ecology and Evolution 21: 488–492. Finlay BL and Darlington RB (1995) Linked regularities in the development and evolution of mammalian brains. Science 268: 1578–1584. Harvey PH and Pagel MD (1991) The Comparative Method in Evolutionary Biology. Oxford: Oxford University Press. Jerison HJ (1973) Evolution of the Brain and Intelligence. New York: Academic Press. Nunn C and Barton RA (2001) Comparative methods for studying primate adaptation and allometry. Evolutionary Anthropology 10: 81–98. Striedter GF (2005) Principles of Brain Evolution. Sunderland, MA: Sinauer Associates. Swanson LW and Petrovich GD (1998) What is the amygdala? Trends in Neurosciences 21: 323–331.