# The costs of hemispheric specialization in a fish

Marco Dadda, Eugenia Zandonà, Christian Agrillo, Angelo Bisazza
Published 30 September 2009.DOI: 10.1098/rspb.2009.1406
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Eugenia Zandonà
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Christian Agrillo
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Angelo Bisazza
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## Abstract

Laboratory and field studies have documented better cognitive performance associated with marked hemispheric specialization in organisms as diverse as chimpanzees, domestic chicks and topminnows. While providing an evolutionary explanation for the emergence of cerebral lateralization, this evidence represents a paradox because a large proportion of non-lateralized (NL) individuals is commonly observed in animal populations. Hemispheric specialization often determines large left–right differences in perceiving and responding to stimuli. Using topminnows selected for a high or low degree of lateralization, we tested the hypothesis that individuals with greater functional asymmetry pay a higher performance cost in situations requiring matching information from the two eyes. When trained to use the middle door in a row of a nine, NL fish correctly chose the central door in most cases, while lateralized fish showed systematic leftward or rightward biases. When choosing between two shoals, each seen with a different eye, NL fish chose the high-quality shoal significantly more often than the lateralized fish, whose performance was affected by eye preference for analysing social stimuli. These findings suggest the existence of a trade-off between computational advantages of hemispheric specialization and the ecological cost of making suboptimal decisions whenever relevant information is located on both sides of the body.

## 1. Introduction

In most vertebrates, the eyes are laterally placed and each eye largely sees a different portion of the visual field. As lateral positioning of the eyes is often accompanied by an almost complete crossing of fibres at the optic chiasm, the contralateral hemisphere primarily processes visual input of each eye. In these species, the presence of left–right functional asymmetries often leads the organism to analyse and respond to a stimulus in a different way depending on its placement on the left or the right side of the observer ( Vallortigara & Andrew 1991 ; Deckel 1995 ; Rogers et al. 2004 ; Wiltschko et al. 2007 ).

The occurrence of functional brain asymmetries is now well documented for both bony fish and land vertebrates (reviewed in Andrew & Rogers 2002 ; Vallortigara & Bisazza 2002 ). Having two specialized hemispheres can be very advantageous. Lateralized chicks that had to learn to discriminate between food and non-food while a model of avian predator was moved overhead learned faster than the non-lateralized (NL) chicks, and were also more responsive to the model predator ( Rogers et al. 2004 ). In the teleost Girardinus falcatus, fish artificially selected for a high degree of lateralization were twice as fast as NL fish at catching live prey when fish had to share attention with a concurrent task, predator vigilance ( Dadda & Bisazza 2006 ). Lateralized fish could attain this result by attending the feeding task primarily with one eye while using the other eye to monitor the predator. These examples suggest that hemispheric specialization might increase the capacity to carry out simultaneous processing, by channelling different types of information into the two separate halves of the brain and by enabling separate and parallel processing to take place in the two hemispheres. Indeed, it has been suggested that this might have been the major selective force driving evolution of lateralization of cognitive functions in early vertebrates (Rogers 2000 , 2002 ). Strongly lateralized individuals have been found to outperform less lateralized individuals in many other contexts that do not explicitly involve the sharing of attentional resources among concurrent tasks, such as termite fishing in chimpanzees ( McGrew & Marchant 1999 ), visual discrimination in pigeons ( Gunturkun et al. 2000 ), and schooling and spatial orientation in fish ( Bisazza & Dadda 2005 ; Sovrano et al. 2005 ), implying that other, unidentified advantages of cerebral lateralization may exist.

Some heritability of lateralization has been demonstrated in fish ( Barth et al. 2005 ; Bisazza et al. 2007 ), rodents ( Collins 1990 ) and primates ( Hopkins et al. 2001 ; Anneken et al. 2004 ), and one would expect natural selection to favour individuals with specialized hemispheres. Yet the literature rarely reports a highly skewed or an antisymmetric distribution of laterality (but see Zucca & Sovrano 2008 ; Giljov et al. 2009 ). Animal populations normally show a great variation in the degree of laterality and, not infrequently, NL (or weakly lateralized) individuals outnumber strongly lateralized ones (Bisazza et al. 1997 , 2000 ; Gunturkun et al. 2000 ; Brown et al. 2007 ; Takeuchi & Hori 2008 ).

As noted by Rogers (2002) , there are potential costs associated with cerebral asymmetries, and in particular with transferring and integrating the information that reaches the two hemispheres. Toads, for example, are more likely to strike at a prey moving in their right lateral field of vision while agonistic responses are delivered preferentially to a conspecific seen on their left side ( Vallortigara et al. 1998 ). Similar differences have been found in birds and reptiles ( Deckel 1995 ; Dharmaretnam & Rogers 2005 ). Even in species with frontally placed eyes, such as humans, hemispheric dominance can sometimes hinder performance when strict cooperation between the two halves of the brain is required. For example, the human right hemisphere is usually dominant for spatial processing, and this determines left–right perceptual and attentional biases, a phenomenon known as ‘pseudoneglect’. Simple tests show that more attention is paid to the left side of a happy–sad chimeric face ( David 1989 ), that a systematic leftward error is made in the manual bisection of a line ( Jewell & McCourt 2000 ) or that objects appear to have significantly different size when seen by the right and the left eye ( McManus & Tomlinson 2004 ).

Normally, biologically relevant stimuli such as a predator, a prey or a rival, are equally likely to appear on the left or the right side, and it is not difficult to see the potential disadvantages arising from having side biases in the promptness or effectiveness of a response to a particular class of objects (see discussion in Rogers 2000 , 2002 ; Vallortigara & Rogers 2005 ). As a consequence, a trade-off is expected between these disadvantages and the cognitive advantages of lateralization such as the possibility of parallel processing ( Rogers 2002 ; Corballis 2006 ). However, to date, the possibility that a left–right difference in the way an animal analyses and responds to environmental stimuli translates into a disadvantage for more lateralized individuals has not been empirically tested.

Here, we tested the hypothesis that individuals with marked cerebral lateralization pay a higher cost in terms of reduced efficiency in tasks relying on hemispheric communication and cooperation. Lateralization in fish is partly under genetic control, and this allows one to obtain fish that differ in the degree or direction of cerebral lateralization ( Barth et al. 2005 ; Bisazza et al. 2007 ). To pursue our goal, we compared fish from lines artificially selected for a high or low degree of lateralization in two conditions requiring the integration of information from left and right visual hemifields. In the first experiment, fish were asked to find the middle of a row of small doors that was presented frontally, an adaptation for fish of the ‘line bisection test’, a standard method of neuropsychology to measure visuo-spatial biases. In the second experiment, we measured how efficiently a subject chose between two stimuli (two groups of social companions) differing in quality that were presented at the opposite sides of the body, thus with the critical information split up between the two lateral hemifields.

## 2. Material and methods

### (a) Subjects

The goldbelly topminnow, G. falcatus (Cyprinodontiformes, Poeciliidae), is a small viviparous fish originally from Cuba.

For this experiment, we used subjects from three stocks of fish that differed in laterality and that were obtained through selective breeding ( Bisazza et al. 2007 ). From 1997 to 2001, topminnows were artificially selected for eye preference to monitor a potential predator using the detour test, which scores the direction taken by a fish when facing a barrier behind which a model predator is visible ( Facchin et al. 1999 ; see details in the electronic supplementary material).

One hundred and ten females were used in this study. Subjects (approx. six to seven months old) were subdivided into three experimental groups: fishes that turned 80 per cent or more to the left (LD), fishes that turned 80 per cent or more to the right (RD) and fishes that turned 50 per cent of times in each direction (NL). Groups were maintained in 80 l glass aquaria with abundant vegetation (Ceratophillum sp.) and a 14D : 10L photoperiod; water temperature was 25 ± 2°C and all fish were fed dry fish food and Artemia salina nauplii twice a day.

### (b) Experiment 1: finding the centre of a figure (bisection test)

In this experiment, 16 lateralized (eight LD and eight RD, collectively called LAT) and 10 NL adult females were trained to use the central door in a row of nine, using the possibility to rejoin the social group as reinforcement. Only females were used in this experiment because they are several times heavier than males and can more easily open the experimental doors.

#### (i) Apparatus

The apparatus ( figure 1 ) consisted of a rectangular glass tank (80 × 40 × 40 cm) divided into three sectors. A small ‘start box’ (9.5 × 5.5 × 9 cm), which contained the focal female at the beginning of the test, was provided with a trapdoor (8 × 12 cm) leading into the choice area. Once in this area, the fish faced a white partition (40 × 40 cm) provided with a succession of nine identical doors (4 × 2 cm each) placed at 2 cm from the bottom and spaced 5 mm one from another, constituting a ‘line’ of 22 cm. All the doors were similar, but only the fifth, central door could be opened by pressing on the flexible plastic material with the snout. Through this door, the subject could gain access to a sector (35 × 40 × 40 cm) in which a group of four stimulus fish were visible and acted as reward. The apparatus was placed in a dark room and lit by two neon lamps (15 W). A video camera was suspended 1 m above the experimental tank and used to record the behaviour of the focal fish during the tests.

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Figure 1.

(a) Apparatus used in the bisectioning task. (b) Subjects were required to use the middle door in a row of nine.

Prior to the experiment, subjects were placed in a pre-training tank for 10 days. This procedure had the twofold aim of accustoming the fish to use of the movable doors and training them to use the middle door in a short row (three doors). A partition divided the tank into two compartments, one provided with vegetation acting as cover and the other containing food. Three doors identical to those of the experimental apparatus were positioned at the centre of the partition and only the central one allowed the fish to move between the compartments.

#### (ii) Procedure

At the beginning of the test, the focal female was dip-netted and released into the sector of the experimental apparatus facing the four stimuli fish for a 15 min period of acclimatization. The female was then gently dip-netted and inserted into the start box for a 2 min period, where the plastic door was raised and the focal female released into the choice area. The female was allowed to try the different doors until the correct door was found. The intertrial interval was 5 min, during which the fish was allowed to remain in the sector with visible social group and reinforced with food (A. salina nauplii). The fish were then gently captured and reinserted into the start box for another trial and the procedure was repeated until six trials were completed. From the video recordings, we scored the frequencies of attempts for each of the nine doors.

### (c) Experiment 2: efficiency in bilateral information processing

In this experiment, 44 LAT (21 LD and 23 RD) and 40 NL adult females were allowed to choose between two social groups that differed in quality (see below) in a situation in which each stimulus was seen by a different eye. Only females were used in this experiment because males of this species demonstrate a reduced tendency to shoal, behave aggressively towards same sex stimuli and mate with opposite sex stimuli. The experiment was carried out in two variants. Twenty-six LAT (13 LD and 13 RD) and 22 NL females were tested with variant A, in which stimulus shoals differed by number of fish (four versus two females). Eighteen LAT (eight LD and 10 RD) and 18 NL females were tested with variant B, in which stimulus shoals differed by the size of the fish (same size as the subject versus smaller size).

#### (i) Apparatus

The experimental apparatus consisted of an aquarium (60 × 60 × 35 cm; figure 2 ) subdivided into four compartments. One, the start box, consisted of a small rectangular area (25 × 8 × 22 cm) made by green opaque plastic walls. Fish could exit only through a small corridor (4 × 3 × 2 cm) that led to the choice compartment. The corridor was built so that the focal female, upon exiting, saw two simultaneous stimulus shoals, each visible in a different visual hemifield. A transparent door (5 × 11.5 cm) was placed at the beginning of the corridor and was connected to a monofilament line on a pulley, which made it possible for an observer to raise it from a remote location. Two transparent glass cubes (20 × 20 × 20 cm) were placed at the opposite sides 25 cm apart and hosted stimulus fish. Two 8 W fluorescent lamps were suspended on both cubes while the start box was kept in dark. The floor was covered with gravel (with the exception of the start box) and the tank was filled with 15 cm of water (temperature 25 ± 2°C). A video camera was suspended 1 m above the experimental tank and used to record the choice of the focal fish.

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Figure 2.

(a) Apparatus used in the bilateral information processing task. (b) Once outside the small corridor, the focal female simultaneously saw two stimulus shoals, each visible in a different visual hemifield.

#### (ii) Procedure

As in the previous experiment, during the week preceding the experiment, subjects were placed in a pre-training tank in order to familiarize them with the corridor that allows movement between the compartments.

Ten minutes before the test, the two stimulus shoals were inserted in the two cubes. A single experimental female was inserted in the start box and allowed to acclimatize for 1 min. The door was then raised and the female was allowed to enter the experimental area. A preliminary test had shown that in this circumstance fish joined one shoal immediately without stopping or turning around. We recorded the first choice made by the subject as marked by reaching one body length (4 cm) from the front glass of the cube containing the shoal. Each subject was tested twice daily at about a 120 min interval and the left–right position of the two stimuli was inverted between the two trials.

#### (iii) Determination of social preference

Shoal preferences have been shown in a number of teleosts, and virtually all studied species show consensus in preferring shoals containing more individuals and shoals containing fish of the same size as the subject ( Hager & Helfman 1991 ; Krause & Godin 1994 ).

A pilot experiment was performed to determine the preference of female topminnows for these two features. Thirty-two females from an unselected laboratory stock (therefore containing females with a variable degree of lateralization) were tested in an apparatus similar to that used for the experiment except for the corridor, which measured 16 × 20 cm and was provided with a glass door allowing the subjects to see the two stimulus shoals with both eyes for 2 min before being released in the choice compartment. Sixteen females were allowed to choose between a large (four females) and a small (two females) shoal, and 16 females were allowed choose between three similar-sized females and three smaller females (70% of the length of the subject). When tested for their preference between a shoal of two and a shoal of four females, 15 out of 16 fish tested preferred the latter (χ2 = 12.3, p < 0.001). When tested for their preference between a shoal containing smaller females and one containing same-size females, 14 out of 16 fish tested preferred the latter (χ2 = 9.0, p = 0.004).

Both these social preferences were used in the main experiment. In one variant, subjects were presented with four females (the preferred stimulus in pilot experiment) and two females. In the other variant, the subject could choose between three similar-sized females (preferred stimulus) and three smaller females (70% of the length of the subject). Half of the subjects did the first trial with the preferred shoal on the left and the second trial with the preferred shoal on the right; the other half of the subjects did the reverse. The performance of each subject was scored on three levels: both choices of the preferred stimulus; one choice of the preferred stimulus and one for the non-preferred one; and both choices of the non-preferred stimulus.

## 3. Results

### (a) Finding the centre of a figure (bisection test)

Topminnows rapidly generalized line bisection from the three-door row pre-training phase to the nine-door row in the testing phase. As shown in figure 3 , from the second trial onwards, the proportion of choices of the normally preferred shoal was higher in NL than in LAT fish and both progressively reduced the number of incorrect choices in successive trials. On the whole, we found a significant difference between NL and LAT in the number of choices for preferred stimuli and a significant reduction in the proportion of choice of non-preferred stimuli in successive trials (repeated measure ANOVA, degree of lateralization F1,24 = 9.655, p = 0.005; difference among trials F5,120 = 2.111, p = 0.069; linear trend F1,24 = 10.65, p < 0.003; interaction F5,120 = 0.975, p = 0.436). We found no difference in accuracy between RD and LD fish (F1,14 = 0.461, p = 0.508; difference among trials F5,70 = 1.796, p = 0.125; interaction F5,70 = 0.446, p = 0.815).

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Figure 3.

Proportion of correct choices (mean ± s.e.) of central door during the six trials of the bisectioning task in lateralized and NL topminnows. Filled circles, lateralized; open circles, non-lateralized.

To obtain detailed information about the type of errors made by the three groups of subjects, we scored the results assigning a value from 1 for the leftmost door to 9 for the rightmost. Correct choice of the central door corresponded to a value of 5. The average score of NL fish is close to this value (mean ± s.d., 4.73 ± 0.50) with no significant left–right bias in bisection (t(9) = 1.698, p = 0.124). LD and RD fish showed significant leftward (4.21 ± 0.66; t(7) = 3.315, p = 0.013) and rightward biases (5.56 ± 0.65; t(7) = 2.435, p = 0.045), respectively. The difference between RD and LD is highly significant (t(14) = 4.071, p < 0.001).

### (b) Efficiency in bilateral information processing

The frequency of choices of the preferred stimulus was not significantly different between variant A and B trials (χ2 = 1.468, p = 0.428, figure 4 ), and the results of the two variants were considered together for subsequent analyses. The majority of NL fish chose the preferred stimulus in both trials while most LAT fish chose the preferred stimulus in one trial and the non-preferred stimulus in the other. The difference between these two groups in the proportion of subjects choosing the preferred shoal in both trials, in one trial or in none is significantly different (χ2 = 7.355, p = 0.025). The difference between LD and RD is not significant (Fisher’s exact test, p = 0.752); however, owing to reduced sample size, the chance to detect a difference between the two subgroups of LAT fish is lower.

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Figure 4.

Bilateral information processing task. Lateralized and NL topminnows were given the choice between two different social groups each seen by a different eye. Bars represent the number of individuals choosing the normally preferred shoal in both trials, in one trial or in none. In variant A, the choice was between four (preferred stimulus) and two females. In variant B, the choice was between similar-sized (preferred stimulus) and smaller females. Black bars, variant A; white bars, variant B.

RD and LD differ significantly in left–right preference (χ2 = 4.482, p = 0.034), with both groups choosing more often the stimulus presented on the eye dominant for analysing social stimuli (the right eye in RD, the left eye in LD; Dadda et al. 2007 ). In NL fish, no difference was found in the proportion of choices of the preferred stimulus between the right and left presentation (χ2 = 0.313 p = 0.584).

## 4. Discussion

The results of our experiments support the hypothesis that a marked cerebral lateralization may hinder efficiency when tasks require hemispheric communication and cooperation. To our knowledge, this is the first study documenting an advantage of less lateralized individuals and indicating possible ecological costs of brain asymmetries that may be responsible for the maintenance of NL phenotypes in animal populations.

Previous studies comparing the cognitive performance of poorly and strongly lateralized individuals have found a greater efficiency of the latter in all organisms examined (fish: Bisazza & Dadda 2005 ; birds: Dharmaretnam & Rogers 2005 ; primates: McGrew & Marchant 1999 ; insects: Pascual et al. 2004 ). Many of the observed differences involved functions that significantly affect survival, such as finding food or escaping predators. Yet at the behavioural level lateralization often results in side biases in perception, information processing and motor output that could potentially give rise to disadvantages ( Rogers 2002 ; Vallortigara & Rogers 2005 ). In particular, vertebrates with laterally positioned eyes encounter difficulties integrating information from left and right visual fields ( Ingle 1968 ; Prior & Wilzeck 2008 ; Xiao & Gunturkun 2009 ), and one can envisage circumstances in which individuals with reduced left–right functional differences might outperform the more strongly lateralized ones.

We have compared poorly and strongly lateralized topminnows in two situations that might be expected to hinder individuals with pronounced left–right differences in analysing the sensory input. In both experiments, subjects were required to integrate information from the left and the right visual hemifield in order to take the appropriate decision. Experiment 1 consisted of an adaptation for the fish of the line bisection test, a major diagnostic tool for the identification of visuo-spatial deficits in patients with brain lesions and population-level visuo-spatial biases in normal human subjects (reviewed in Jewell & McCourt 2000 ). Fish familiar with using the middle door of three were trained to find the middle of a nine-door row. NL topminnows rapidly learned the new task, making only a few errors that were equally distributed on either side. By contrast, the performances of the two groups of LAT fish were impaired by systematic errors on the left or right of the centre. In particular, the efforts of RD subjects were centred nearly one door to the right of the correct door, while LD subjects made similar systematic errors on the left of the correct door.

Comparable data are available only for humans. Studies on neurologically normal subjects have found substantial individual variation in both the magnitude and the direction of the bisection bias. Individual performance can vary from approximately 10 per cent to the left to 10 per cent to the right, but is usually much less ( Schenkenberg et al. 1980 ; Manning et al. 1990 ; McCourt & Olafson 1997 ), a range that appears similar to that observed in our study. Unlike most other vertebrates, humans show strong population biases in many lateralized functions. For example, more than 90 per cent of the population is right-handed, and a similar percentage shows left hemisphere dominance for language. The strong leftward population bias in line bisection that is normally observed in human studies is traditionally ascribed to a bias in the allocation of attention resources towards the left visual field deriving from right hemisphere dominance in spatial tasks in the majority of the population ( Heilman & Van Den Abell 1980 ; Jewell & McCourt 2000 ).

It is not easy to estimate the extent to which such deficits can affect an individual’s fitness in a fish. It is possible, for example, that a topminnow in need of rapidly gaining a refuge and using visible landmarks fails to follow the most favourable trajectory as a consequence of a strong lateralization of spatial attention.

The second situation considered in this paper is one in which an animal must select between two options, each seen by a different eye. Shoaling is one of the major antipredatory strategies of fish. However, not all shoals are equally safe. Fish shoaling with individuals different from themselves are more easily spotted by predators and those shoaling in large shoals benefit from better vigilance and greater dilution of risk (reviewed in Krause & Ruxton 2002 ). Not surprisingly, fish consistently avoid associating with individuals of a different size and prefer large shoals to small ones ( Hager & Helfman 1991 ; Krause & Godin 1994 ). Our pilot tests showed that goldenbelly topminnows have a strong preference for both these qualities, with around 90 per cent of fish choosing the larger shoal and that containing similar-sized stimuli. In our experiment, the subject entered an unfamiliar area where it could choose between two shoals differing in quality but in a condition in which each hemisphere had direct access only to one-half of the information necessary to accomplish the task. In this condition, NL fish chose the normally preferred shoal significantly more often than the LAT fish, which in most cases chose the option seen with the eye that in their stock was dominant for analysing social stimuli (the right eye in RD, the left eye in LD; Dadda et al. 2007 ), irrespective of its relative quality. The most plausible interpretation of these data is that in LAT fish information relative to two different properties of the stimulus, shoal size and fish size, is confined, at least for the short lapse of time necessary to take a decision, to the hemisphere that initially receives the visual input, and therefore the hemisphere dominant for analysing social stimuli in this phase can only (or predominantly) access the information it receives from the contralateral eye.

The lack of integration of information reaching the two eyes that was observed especially in LAT fish might surprise a reader not familiar with lateralization literature. Our findings are consistent, however, with current knowledge of the way the teleost visual system integrates the two lateral inputs. The left and right eye systems can operate quite independently, as suggested by the observation that opposite discriminations can be simultaneously established in the two eye systems ( Ingle 1968 ). Experiments involving subjects trained monocularly to discriminate patterns have shown that interocular information transfer is slow and incomplete ( McCleary 1960 ; Mark 1966 ; Ingle 1968 ). Ingle found some interocular transfer of simple discrimination, but loss of information for more difficult discrimination ( Ingle 1965 ). As yet, nothing is known about the neural bases of visual lateralization in fish and what differentiates lateralized from NL fish. This topic has been extensively investigated in birds. Neuroanatomical asymmetries have been described in detail in the two major ascending visual projections, the tectofugal and the thalamofugal pathway, and several studies have shown that stronger behavioural lateralization is associated with greater degree of asymmetry in these pathways ( Deng & Rogers 2002b ; Gunturkun 2002 ). In pigeons and domestic chicks, it was found that these neuroanatomical asymmetries are accompanied by significant left–right asymmetries of interocular transfer ( Sandi et al. 1993 ; Skiba et al. 2000 ), a feature that could also be present in fish and explain the results of our experiments.

The relevance of the differences found in the second experiment for a fish’s everyday life is perhaps easier to envisage. Most fish have a visual field covering almost 360° and the frontal overlap of the opposite visual fields is usually around 10° ( Collin & Shand 2003 ). Therefore, the probability that two stimuli fall into two different visual hemifields is relatively high. Because many other aspects of behaviour—such as mating, prey capture and intraspecific aggression—are lateralized in topminnows (Bisazza et al. 2001 , 2005 ; Dadda & Bisazza 2006 ), they may make suboptimal decisions in other contexts whenever a quick assessment is needed and the alternatives are placed at the opposite sides of the body.

These disadvantages are expected to decrease with increasing time allowed for decision-making as this provides a greater opportunity for integration of information from the two eyes and for looking at the different options with the same eye. In food-storing birds, for example, each hemisphere processes qualitatively different information about the location of food caches, but the different types of information are integrated when food items are retrieved some time later ( Clayton & Krebs 1994 ). In our pilot experiment, almost all subjects chose same-size companions or the larger shoal after they were allowed 2 min of free observation of stimuli.

Problems arising from the integration of bilateral input are probably reduced also when stimuli fall into the binocular portion of the visual field as they are seen by the two eyes simultaneously. In addition, the left and right frontal binocular fields may work in a more coordinated fashion compared with the two lateral monocular fields. In pigeons, reliable interocular transfer of visual discrimination was observed when the stimuli were presented in the frontal visual field but not when they were presented in the lateral visual field ( Mallin & Delius 1983 ). In chicks, comparison of binocular–monocular testing has shown that the left and the right frontal field are equally efficient in complex discrimination tasks, although the birds’ performance was superior when both eyes were involved ( Prior & Wilzeck 2008 ). Anatomical evidence for the integration of the two frontal binocular fields has been provided also for some teleosts ( Northmore & Gallagher 2003 ).

A tight integration of information from left and right eyes has become the prevalent condition in the primate visual system. In human and non-human primates, there is in fact a large overlap of the two eye fields. Information from one portion of the visual field reaches both eyes and, owing to the partial decussation of the optic nerves, input from the two eyes is sent to the same hemisphere (contralateral to stimulus position). In addition, the corpus callosum enables fast and efficient information transfer between the hemispheres. However, the price to pay is that humans are no longer able to use one eye to monitor a potential danger while simultaneously and independently using the other eye to coordinate another activity, such as food gathering, in the way fish and birds are able to do ( Bisazza & Dadda 2005 ; Dharmaretnam & Rogers 2005 ). Interestingly, the condition observed in topminnows more closely resembles that observed in split-brain patients. To some extent, these patients have the ability to run independent tasks with the two disconnected hemispheres. Unlike normal observers, they are capable of directing their attention to left and right field locations simultaneously and have been found to outperform normal controls in dual-task experiments ( Gazzaniga & Sperry 1966 ; Luck et al. 1989 ). Yet, as expected, performance is frequently impaired in split-brain patients relative to controls in tasks relying more upon the collaboration of the hemispheres (reviewed in Gazzaniga 2000 ).

In all, the picture emerging from this study indicates that advantages of hemispheric specialization, such as the possibility of processing multiple information flows in parallel ( Rogers et al. 2004 ; Dadda & Bisazza 2006 ), may be counterbalanced by some ecological disadvantages associated with left–right differences in the response to stimuli. The relative weights of these costs and benefits are likely to vary with ecological conditions (structure of habitat, predation risk, social density, food abundance, etc.) and the degree of lateralization is therefore expected to vary among species and populations in relation to the importance of the different factors. So far, two studies provide some support to this hypothesis. A study of 16 species of fish found that all shoaling species showed population-level lateralization of predator evasion behaviour, whereas non-shoaling species tended to have individual but not population lateralization ( Bisazza et al. 2000 ). A field study reported in the poeciliid fish Brachyraphis episcopi that individuals from high-predation populations were more lateralized than their low-predation counterparts ( Brown et al. 2004 ). The authors suggested that in a population with a high predation pressure, selection has favoured lateralized fish because they are better able to cope with two simultaneous tasks, such as foraging and predator vigilance.

Heritability of direction and strength of cerebral asymmetries have been reported in several vertebrates and may provide a basis for population and species differentiation. However, hereditary influences seem to account for only a fraction of the interindividual variation in laterality ( Hopkins et al. 2001 ; Barth et al. 2005 ; Bisazza et al. 2007 ).

There is now considerable evidence that the development and expression of cerebral asymmetries can be modulated by environmental factors such as stress ( Fride & Weinstock 1988 ), androgen exposure ( Zappia & Rogers 1987 ) and asymmetry in physical ( Collins 1975 ) or social ( Vallortigara et al. 1999 ) environment. Some of these effects may represent adaptive mechanisms, allowing parents to adjust the developmental trajectories of their offspring to the environmental conditions in which they will subsequently live ( Deng & Rogers 2002a ; Andrew 2009 ). For example, maternal glucocorticoids deposited in the egg or crossing the placenta profoundly affect the development of lateralization ( Diaz et al. 1995 ; Rogers & Deng 2005 ), an effect that may enable the mother experiencing stress situations (such as predator attack) at the time of embryo formation to adaptively influence the laterality pattern of their offspring ( Deng & Rogers 2002a ; Halpern et al. 2005 ).

Development of lateralization is also influenced by the amount of light reaching the embryo. In zebrafish, differential exposure to light produces wide differences in lateralization that have effects on multiple aspects of behaviour (Andrew et al. 2009a , b ). In domestic chicks, the amount of light that enters through the eggshell in the days prior to hatching greatly affects the development of lateralized visual behaviour (reviewed by Deng & Rogers 2002a ) and has a dramatic effect on the capacity of chicks to perform two simultaneous tasks such as feeding and predator vigilance ( Rogers et al. 2004 ). It has been suggested that ecological factors, such as social density or abundance of predators, by influencing the choice of laying site or the time spent on the nest, affect the lateralization of the offspring and ultimately generate phenotypes with appropriate coping strategies ( Deng & Rogers 2002b ; Vallortigara & Rogers 2005 ; Andrew et al. 2009b ).

Here, we demonstrated the potential disadvantages of a marked subdivision of the function between hemispheres in two contexts. The literature contains many other examples of remarkable left–right differences in the behavioural response. For example, toads, chicks and dunnarts differ in their promptness to react to a predator depending on the visual hemifield in which it appears (Lippolis et al. 2002 , 2005 ; Dharmaretnam & Rogers 2005 ), and mosquitofish make closer cooperative predator inspection when predator and shoalmates are seen with the correspondingly preferred eye ( De Santi et al. 2001 ). Gelada baboons and Anolis lizards are more likely to attack a conspecific on one side than the other ( Deckel 1995 ; Casperd & Dunbar 1996 ), and side biases are shown by toads, chicks and pigeons in food detection ( Vallortigara et al. 1998 ; Diekamp et al. 2005 ). Investigating whether individuals with greater left–right differences pay larger costs even in these cases will help us to assess the generality of our findings and expand our understanding of the selective mechanisms maintaining individual differences in lateralization.

## Acknowledgements

The authors would like to thank Debi Roberson, George Georgiou and Annette Sieg for their useful comments.

## Footnotes

• Accepted September 3, 2009.
• © 2009 The Royal Society

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22 December 2009
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The costs of hemispheric specialization in a fish

Marco Dadda, Eugenia Zandonà, Christian Agrillo, Angelo Bisazza
Proc. R. Soc. B 2009 276 4399-4407; DOI: 10.1098/rspb.2009.1406. Published 10 November 2009

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Neuropsychologia
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# Mechanisms of hemispheric specialization: Insights from analyses of connectivity

Klaas Enno Stephan ,a, Gereon R. Fink ,b,c and John C. Marshall d

## Abstract

Traditionally, anatomical and physiological descriptions of hemispheric specialization have focused on hemispheric asymmetries of local brain structure or local functional properties, respectively. This article reviews the current state of an alternative approach that aims at unraveling the causes and functional principles of hemispheric specialization in terms of asymmetries in connectivity. Starting with an overview of the historical origins of the concept of lateralization, we briefly review recent evidence from anatomical and developmental studies that asymmetries in structural connectivity may be a critical factor shaping hemispheric specialization. These differences in anatomical connectivity, which are found both at the intra- and inter-regional level, are likely to form the structural substrate of different functional principles of information processing in the two hemispheres. The main goal of this article is to describe how these functional principles can be characterized using functional neuroimaging in combination with models of functional and effective connectivity. We discuss the methodology of established models of connectivity which are applicable to data from positron emission tomography and functional magnetic resonance imaging and review published studies that have applied these approaches to characterize asymmetries of connectivity during lateralized tasks. Adopting a model-based approach enables functional imaging to proceed from mere descriptions of asymmetric activation patterns to mechanistic accounts of how these asymmetries are caused.

Keywords: Lateralization, Effective connectivity, Dynamic causal modeling, Corpus callosum, Inter-hemispheric integration

## 1. Introduction

Traditional approaches to characterizing hemispheric specialization have relied on four main approaches: (i) neuropsychological investigation of patients with brain lesions ( Damasio & Damasio, 1989 ) or iatrogenic splits of the corpus callosum ( Gazzaniga, 2000 ), (ii) psychological assessment of hemispheric performance differences using tachistoscopic visual or dichotic auditory stimulus presentation techniques ( Hugdahl, 1988 ; Miran & Miran, 1984 ; Nagae & Moscovitch, 2002 ), (iii) post-mortem investigations of human brains that focus on differences in microstructural properties (e.g. cytoarchitecture, myeloarchitecture) between homotopic regions of the two hemispheres ( Amunts et al., 1999 ; Amunts, Jäncke, Mohlberg, Steinmetz, & Zilles, 2000 ; Jenner, Rosen, & Galaburda, 1999 ) and (iv) in vivo studies of both structural and functional asymmetries using a variety of techniques, e.g. magnetic resonance (MRI) morphometry, positron emission tomography (PET), functional MRI (fMRI), electroencephalography (EEG) and magnetoencephalography (MEG). All these approaches have been complementary and enormously helpful in delineating brain asymmetries. Tachistoscopic/dichotic investigations of healthy volunteers provide a behavioral characterization of lateralized cognitive processes, treating the brain as a black box, whereas neuropsychological, anatomical and physiological approaches describe hemispheric asymmetries in terms of local properties of the neurobiological “machinery”, i.e. regional asymmetries in functional involvement, cortical structure or neuronal activity, respectively. This article reviews the current state of an additional approach that is gaining increasing importance. This approach, the study of asymmetries of brain connectivity, goes beyond a mere description of hemispheric asymmetry and aims at clarifying its functional principles and computational mechanisms.

In this article, after discussing the historical origins of the concept of lateralization, we briefly review recent evidence from anatomical and developmental studies that hemispheric asymmetries in structural connectivity are a fundamental constraint of brain architecture and may be the cause for functional hemispheric specialization. These hemispheric differences in structural connectivity, which have been found both at the level of intra-areal microcircuits and inter-regional connections, are likely to form the structural substrate of different functional principles of information processing in the two hemispheres. We review how functional imaging data can be analyzed by models of functional and effective connectivity in order to characterize these functional principles. To familiarize the reader with the strengths, assumptions and limitations of available models of functional and effective connectivity, we briefly discuss several established methods, in particular structural equation models and dynamic causal models, which can be applied to data obtained from different functional imaging techniques. Finally, we review the results of several published studies that have successfully used models of functional and effective connectivity to address questions of hemispheric specialization and highlight how some of these models start to merge methodologically with other approaches from computational neuroscience. The focus is strictly on direct and quantitative measures of intra- and inter-hemispheric functional coupling derived from fMRI and PET data. In contrast, it is beyond the scope of this article to discuss the rich literature of analysis of functional coupling in terms of coherence or phase synchrony as measured by EEG or MEG (see Varela, Lachaux, Rodriguez, & Martinerie, 2001 , for review). Also, we do not cover those approaches that are only indirectly related to analyses of connectivity, e.g. transcranial magnetic stimulation (TMS) or electroencephalographic latency differences.

Overall, we hope to convince the reader that formal system models, fitted to neuroimaging data of lateralized cognitive processes, are a useful, and indeed necessary, approach for lateralization research to proceed from mere descriptions of asymmetric activation patterns to mechanistic accounts of how these asymmetries are caused.

## 2. Historical origins of the concept of hemispheric specialization

The Hippocratic physicians should have discovered cerebral lateralization of language in the brain: they observed that injury to one side of the head was associated with contralesional hemiparesis. And that right hemiparesis was often associated with disturbance of speech. But they never linked the two phenomena, presumably because it seemed theoretically parsimonious that two very similar anatomical structures (the cerebral hemispheres) would have equally similar cognitive functions. The ventricular theory, expounded by Herophilus of Alexandria (circa 300 BCE), in which distinct psychological faculties such as imagination, conceptual thought and memory were represented in midline structures of the brain (the ventricles), also militated against any conception of left–right functional asymmetry ( Marshall, 1977 ). Two millennia later, Thomas Willis (1621–1675) could see no virtue in the ventricular theory but continued to assign the seat of imagination to a midline structure: the corpus callosum. A century later, the French anatomist Felix Vicq d’Azyr argued that “the commissures are intended to establish sympathetic communications between different parts of the brain” (see Joynt, 1974 ). The corpus callosum connecting the right hemisphere with the left must, he argued, “play an important role in the unknown mechanism” of cerebral functions ( Joynt, 1974 ). The crucial word here is clearly “unknown”.

One might have expected that Franz Joseph Gall (1757–1828) would conjecture, in the early nineteenth century, that different cognitive organs (modules) were punctately localised in different hemispheres. After all, Gall did describe a very perspicuous case of circumscribed amnestic aphasia after unilateral left frontal lesion. But he too was overly impressed by the apparent symmetry of the cerebral hemispheres. Thus, rather than give up his postulate that all mental organs were bilaterally represented, Gall argued that a sudden insult to one hemisphere would “upset the balance between the hemispheres, thus affecting the faculties on both sides” ( Finger, 2000 ). This early concept of diaschisis is, of course, interesting and important in its own right. But the consequences of its deployment in this context meant that it would be Broca (1863) who convinced the neurological world that unilateral lesions of the left inferior frontal convolution (and not the right) gave rise to loss of “the memory of the procedure that is employed to articulate language” ( Marshall & Fink, 2003 ).

Broca’s paper opened the floodgates to the discovery of other lateralized impairments of higher mental functions (and, by extrapolation, lateralized cognitive modules representing those functions). There followed, for example, reports of spatial impairment after right posterior damage ( Jackson, 1876 ), impairment of language comprehension after left temporal damage ( Wernicke, 1874 ) and impairment of skilled praxis after left parietal damage ( Liepmann, 1905 ). Studies of disconnection syndromes in which two relatively intact modules (that should interact) became isolated from each other due to commissural lesion concentrated for the most part on intra-hemispheric connectivity. The best known nineteenth century example was, of course, conduction aphasia consequent upon lesion of the arcuate fasciculus which disconnected Wernicke’s area from Broca’s area ( Wernicke, 1874 ). There were, however, also some convincing examples of disorders that implicated inter-hemispheric commissures. Dejerine (1892) showed that alexia without agraphia could arise from the combination of lesions to the left occipital cortex and the splenium of the corpus callosum. The intact visual word form centre in left temporal–parietal cortex was thereby cut off from input from both the left and right visual fields. Likewise, Liepmann and Maas (1907) reported failure of the left hand to execute commands given verbally after callosal lesion. Psycho-physical evidence for the time course of normal callosal transmission was then obtained by Poffenberger (1912) .

Despite the general agreement that the adult human brain is strongly characterized by hemispheric specialization, there has been comparatively little discussion of how or why relatively punctuate unilateral localisation of function should be found. Freud (1891) speculated that it would be sensible biological engineering to have Broca’s area in close anatomical proximity to the motor strip representation of the vocal tract, and likewise sensible to have Wernicke’s area adjacent to primary auditory cortex. Later, Lashley (1937) conjectured that “separate localisation of functions is determined by the existence of diverse kinds of integrative mechanisms which cannot function in the same field without interference”. Consistent with Lashley’s argument, it is frequently claimed that fine motor control of the midline structure such as the vocal tract will be more effective if the command and control centre is unilaterally placed. Otherwise, conflict or noise could arise if two Broca’s areas in opposite hemispheres were attempting to control speech production.

Although lateral specialization of function seems to be a fact, the execution of many even moderately complex tasks will draw upon some modules that are left-lateralized and some that are right-lateralized. As nineteenth and early twentieth century behavioral neurologists began to realize, this will require the transmission of structured information between the hemispheres ( Liepmann, 1912; Poffenberger, 1912 ). In addition to this information transfer theory, two other concepts of hemispheric interactions have become important themes in laterality research: inter-hemispheric inhibition and hemispheric recruitment. These different concepts, all of which emphasize the relevance of connectivity for lateralization of brain function, will be discussed in more detail in the section on connectivity studies investigating inter-hemispheric integration below.

It is obvious from the examples of lateralized disconnection syndromes like conduction aphasia and from the importance of inter-hemispheric interactions that brain connectivity must play a fundamental role in hemispheric specialization. In particular, hemispheric specialization may be more appropriately characterized in terms of structural and functional connectional asymmetries between hemispheres rather than in terms of asymmetries in the local structure or intrinsic function of homotopic regions ( Crow, 2005; McIntosh et al., 1994; Stephan et al., 2003 ; Stephan, Penny, Marshall, Fink, & Friston, 2005 ). With this notion gaining increasing importance in laterality research, the following sections of this article review the current state of efforts to (i) characterize hemispheric asymmetries in structural connectivity, within and between regions and (ii) to infer mechanistic principles of lateralization from functional neuroimaging and neurophysiological data using analyses of effective connectivity.

## 3. Asymmetries in structural brain connectivity

Structural asymmetries of the human brain have been described in various forms and at different scales. The comparison of homotopic regions in the two hemispheres has disclosed differences that range from dendritic tree features ( Seldon, 1981 ), neuronal cell size ( Hutsler & Gazzaniga, 1996 ) and cytoarchitecture ( Amunts et al., 1999, 2000; Jenner et al., 1999 ) to differences in location, shape or volume of areas, sulci, gyri or whole lobes (see Toga & Thompson, 2003 , for a comprehensive review). Several studies have found structural brain asymmetries, in terms of gyrification, regional volumes or white matter microstructure, to be expressed early during human ontogenesis (e.g. Chi, Dooling, & Gilles, 1977 ; de Lacoste, Horvath, & Woodward, 1991 ; Galaburda, LeMay, Kemper, & Geschwind, 1978 ; Gupta et al., 2005 ; Witelson & Pallie, 1973 ). The question is what developmental mechanisms underlie these asymmetries. This section discusses the currently available evidence that initial asymmetries in connectivity may, at least in part, cause other asymmetries of brain structure, both with regard to cytoarchitecture and macroscopic properties.

Our present understanding of brain development implies that these mechanisms are likely to consist of a mixture of intrinsic and extrinsic processes ( Sur & Rubenstein, 2005 ). Intrinsic processes are those that induce regional parcellations within the cortical progenitor zone (the epithelium of the neural tube) whose neurons eventually migrate to form the cortex in an inside-out layered fashion. The existence of several molecules involved in the formation of such regional parcellations is well-established ( Donoghue & Rakic, 1999 ; Fukuchi-Shimogori & Grove, 2001 ; Rubenstein et al., 1999 ). Processes that affect this parcellation of the progenitor zone differentially between hemispheres could lead to hemispheric differences in the microstructure or size of cortical regions. Indeed, a developmental study in rats provides some evidence that this type of process contributes to establishing microstructural asymmetries ( Rosen, Sherman, & Galaburda, 1991 ). In contrast, extrinsic processes comprise changes due to the inputs conveyed by thalamo-cortical (and other) connections. Elegant experiments have shown that different cortices can radically change their microstructure and functional properties when they are surgically connected to different sensory inputs ( Schlaggar & O’Leary, 1991 ). For example, primary auditory cortex develops the cytoarchitectonic and functional features of primary visual cortex, including functional orientation columns, when receiving retinal inputs after surgical rerouting in early development ( Newton, Ellsworth, Miyakawa, Tonegawa, & Sur, 2004 ; Sur & Leamey, 2001 ). Similarly, many normal processes in cortical development depend on activity-dependent synaptic plasticity that induces strong microstructural changes, e.g. concerning the size and shape of dendritic trees ( Cohen-Cory, 2002 ; Hua & Smith, 2004 ).

Overall, independently or additionally to regional parcellations within the cortical progenitor zone, structural hemispheric asymmetry between homotopic areas can result if the areas differ significantly in one or several of the three following factors: (i) their afferent connectivity, (ii) the sensory inputs conveyed by those connections or (iii) mechanisms of synaptic plasticity that translate these differences in inputs into microstructural changes. The role of the first factor, i.e. afferent connectivity per se, is highlighted by the studies cited above ( Sur & Leamey, 2001 ). The importance of the third factor for microstructural features of cortex is emphasized by multiple studies that show changes in neuronal morphology, e.g. changes in dendritic tree size, after experimental manipulations of synaptic plasticity, e.g. blockage of NMDA receptors ( Monfils & Teskey, 2004 ; Monfils, VandenBerg, Kleim, & Teskey, 2004 ). An intriguing demonstration of the second factor, i.e. the role of sensory inputs which are conveyed by connections and induce experience-dependent forms of synaptic plasticity, is provided by animal experiments in different species. For example, chicken and pigeon embryos are usually positioned such that only the right eye is exposed to light. This stimulates the growth of different visual projections systems in the left, as compared to the right, hemisphere and leads to pronounced functional differences in the visual performance of the hemispheres ( Koshiba, Nakamura, Deng, & Rogers, 2003 ; Manns & Güntürkün, 1999 ; Rogers, 1990 ; Rogers & Deng, 1999 ). These brain asymmetries can be completely reversed, both structurally and functionally, if the normal lateralization of sensory inputs during development is altered (see Halpern, Güntürkün, Hopkins, & Rogers, 2005 , for review). Another interesting phenomenon that is likely to result from experience-dependent plasticity is that musicians with absolute pitch have increased left–right asymmetries of planum temporale volume compared to musicians without absolute pitch or non-musicians ( Schlaug, Jäncke, Huang, & Steinmetz, 1995 ; Keenan, Thangaraj, Halpern, & Schlaug, 2001 ).

Both mechanisms, regional differences in the progenitor zone and connectivity-dependent restructuring and plasticity, are now widely accepted as co-existing processes responsible for structural and functional patterning of the cortex during brain development ( Redies, Treubert-Zimmermann, & Luo, 2003 ; Sur & Rubenstein, 2005 ). In addition to the studies cited above, the relevance of these approaches for the expression of brain asymmetry has been demonstrated by recent molecular developmental studies. For example, a comprehensive study of prenatal gene expression by Sun et al. (2005) have found a large number of genes that are asymmetrically expressed in corresponding parts of left and right human cortex at 12, 14 and 19 weeks after gestation, respectively. They focused on one particular gene, LMO4, which was differentially expressed in left and right perisylvian cortex at 12 and 14 weeks after gestation. The authors concluded that “the left–right differences in LMO4 expression in humans could potentially reflect either a differing topographic mapping in the two hemispheres or a difference in the tempo of cortical development …”. Given the importance of connectivity for both types of processes and the well-established role of LMO4 in neuritogenesis ( Manetopoulos, Hansson, Karlsson, Jonsson, & Axelson, 2003 ; Vu et al., 2003 ), one may speculate that these hemispheric asymmetries in LMO4 expression contribute to differences in the development of connectivity in the two hemispheres. Of further interest is the additional finding of Sun et al. (2005) that N-cadherin and interacting molecules like CREB are also differentially expressed in left and right perisylvian cortex during development (see supplementary information to Sun et al., 2005 ). This is interesting because N-cadherin is crucially involved in both brain connectivity development and synaptic plasticity ( Huntley, Gil, & Bozdagi, 2002 ; Salinas & Price, 2005 ; see also Garcia-Castro, Vielmetter, & Bronner-Fraser, 2000 who found that N-cadherin also regulates the asymmetry of visceral organs like the heart during development). Overall, even though there are currently only very few studies on the role of individual molecules in the development of brain asymmetry, the available data seem consistent with a critical role of connectivity for the development of hemispheric asymmetries.

Whatever the exact developmental mechanisms, the existence of hemispheric differences in structural connectivity, particularly but not exclusively with regard to language-relevant areas, have been clearly demonstrated, both in the fetal and the adult human brain. This finding has been made possible by two recent methodological advances that allow one to investigate structural connectivity in the human brain, albeit at very different levels of resolution: post-mortem delineation of microcircuits by means of lipophilic dyes ( Galuske, Schlote, Bratzke, & Singer, 2000 ) and in vivo fiber tracking based on non-invasive diffusion weighted imaging (DWI; Behrens et al., 2003 ; Mori & Barker, 1999 ; Parker et al., 2002 ).

Galuske et al. (2000) used refined post-mortem tracing techniques to characterize the cortical microcircuitry in the language-relevant Wernicke region, the posterior part of the superior temporal gyrus and the posterior temporal plane, corresponding to the posterior part of Brodmann’s area 22 ( Brodmann, 1909 ). This area had previously been shown to possess microstructural ( Hutsler & Gazzaniga, 1996 ) and macroscopic ( Galaburda et al., 1978 ) hemispheric asymmetries, and its critical role for the auditory analysis of speech has been repeatedly demonstrated ( Binder et al., 1997; Narain et al., 2003 ). Galuske et al. (2000) found a regularly spaced pattern of columnar neuronal clusters, corresponding to cortical macrocolumns, around their injection sites. While the cluster sizes did not differ between hemispheres, the average distance between these clusters was significantly larger in the left hemisphere. Due to their simultaneous use of multiple dyes, Galuske et al. (2000) could further demonstrate that the clusters were not all part of a single microcircuit but formed multiple independent (i.e. not directly connected) subsystems. They were thus able to conclude that the larger inter-column spacing in the left hemisphere would provide a structural basis for implementing a larger number of independent subsystems per volume unit. In analogy to visual cortex, where increases in inter-column spacing have been found in higher visual areas of the visual cortex ( Amir, Harel, & Malach, 1993 ), it has been speculated that this higher number of subsystems, each of them specialized for processing particular features of the auditory input, could allow for representation of more complex auditory feature constellations ( Galuske et al., 2000 ; Hutsler & Galuske, 2003 ). This increase in the range of computational complexity could explain the superiority of left Wernicke’s area in the analysis of speech, compared to its right counterpart.

The above findings at the level of cortical microcircuits have been complemented by several DWI studies. In addition to the study by Gupta et al. (2005) , who demonstrated the presence of differences in frontal white matter microstructure in fetal brains, several DWI studies of the adult brain have shown hemispheric asymmetries in different properties of fiber tracts connecting posterior temporal and inferior frontal cortex, particularly the arcuate fasciculus ( Fig. 1 ). Büchel et al. (2004) applied voxel-based morphometry (VBM) to whole-brain maps of fractional anisotropy (FA), a diffusion-based measure of white matter microstructure ( Pierpaoli & Basser, 1996 ). Testing for hemispheric differences in white matter microstructure (and correcting for multiple comparisons) across the whole brain, they found a selective increase in FA in the left arcuate fasciculus compared to the right (see Fig. 1 ). Parker et al. (2005) used algorithmic tractography to trace connections between Wernicke’s and Broca’s regions in both hemispheres. They found two separate pathways, a dorsal one corresponding to the arcuate fasciculus and a ventral one that connects the two regions via the external capsule, uncinate fasciculus and the medial superior temporal gyrus. The ventral pathway was only found in the left hemisphere, and the connection strengths were overall higher in the left than in the right hemisphere. Nucifora, Verma, Melhem, Gur, and Gur (2005) performed a hypothesis-driven investigation of the arcuate fasciculus by means of diffusion tensor tractography. They demonstrated a higher fiber density in the left as compared to the right arcuate fasciculus.

Open in a separate window
Fig. 1

Results from a study of healthy adults by Büchel et al. (2004) who applied voxel-based morphometry to fractional anisotropy (FA), a diffusion-based measure of white matter microstructure ( Pierpaoli & Basser, 1996 ). Testing for hemispheric differences in FA across the whole brain, they found a selective increase in the left arcuate fasciculus compared to the right (p < 0.05, whole-brain corrected for multiple comparisons). After initially demonstrating this asymmetry in a group of 15 volunteers (A), they subsequently replicated this finding in an independent group of 28 volunteers (B). Figure reproduced with permission from Oxford University Press.

Several DWI and post-mortem studies have also found asymmetries of fiber tracts between areas unrelated to language. For example, a post-mortem study of the human uncinate fasciculus, based on stereological methods, found a significantly larger volume and a significantly higher fiber density of the right as compared to the left uncinate fasciculus ( Highley, Walker, Esiri, Crow, & Harrison, 2002 ). Another post-mortem study delineated the optic radiation histologically in 10 human brains and found that the volume of the left optic radiation was significantly higher than the right ( Bürgel, Schormann, Schleicher, & Zilles, 1999 ). Gong et al. (2005) used DWI to investigate the symmetry of the cingulum, a prominent fiber tract near the midline of the brain, and found significantly higher FA for most parts of the left cingulum. A structure-function correlation approach was chosen by Tuch et al. (2005) who focused on the relation between microstructural properties of white matter tracts, as characterized by FA, and behavioral performance, measured in terms of reaction times (RTs), on a speeded visuospatial attention task. They found a significant correlation between individual RTs and FA values in fiber tracts involved in visuospatial attention, including the right optic radiation and white matter tracts located near right posterior thalamus and right medial precuneus WM. Although the lateralization of RT–FA correlations to right visual and parietal WM pathways is compatible with the specialization of right visual and parietal cortices for visuospatial attention, the unexpected aspect of their results was that the correlation was positive, i.e. higher FA was associated with longer RTs. While this appears to rule out a simple interpretation of FA as a microstructural measure primarily determined by the degree of myelinization, other potential explanations have been offered, e.g. a higher proportion of large caliber axons in the right visuospatial pathways which could allow for more diffusion orthogonal to the main direction of the axons (see Tuch et al., 2005 , for details).

For completeness, it should finally be mentioned that despite the large literature on brain connectivity in non-human primates as assessed by invasive tract tracing studies, rather little attention has been devoted to asymmetries in primate brain connectivity. While the large majority of studies do not even indicate whether injections or labeled neurons were located in the left or right hemisphere, the few studies so far that have explicitly investigated connectional asymmetries have failed to find any (e.g. Cavada & Goldman-Rakic, 1989 ; McGuire, Bates, & Goldman-Rakic, 1991 ).

## 4. Characterizing the functional consequences of asymmetric structural connectivity

### 4.1. The need for formal system models

Altogether, the anatomical studies described in the previous section demonstrated hemispheric differences in the adult human brain, both in intra- and inter-areal connectivity and particularly with regard to areas involved in language. Given the dependency of information processing by neuronal units on their connectivity ( Passingham, Stephan, & Kötter, 2002 ; Young, Hilgetag, & Scannell, 2000 ), these asymmetries in connectivity suggest differences in the computational principles used by the left and right hemisphere, particularly with regard to the processing of language-associated stimuli. What exactly these principles are, however, cannot be inferred from knowing anatomical connectivity alone, even if this knowledge was perfect. We also need to know the functional properties of the individual connections, e.g. whether they convey linear or non-linear effects, how strong these effects are and whether they happen almost instantaneously or with a delay. Systems with identical structural connectivity can show entirely different behavior if the functional properties of the connections are changed ( Strogatz, 2001 ).

Therefore, if we want to understand the functional consequences of hemispheric asymmetries in structural connectivity we need to characterize the functional properties of connections in the system, for example, in terms of the synaptic strength of individual connections and how these change depending on the computational context (task requirements, learning, etc.). Connection strengths and other parameters (e.g. delay terms) can only be estimated from empirical observations of the neural system of interest. Therefore, testing specific hypotheses about the consequences of hemispheric differences in connectivity requires one to measure the system in action, e.g. using electrophysiological or functional imaging techniques, and explain mathematically how the observed system behavior is generated as a function of the structure of the system and the inputs it receives. Ideally, we therefore need formal system models in order to explain hemispheric differences in terms of functional principles ( Stephan, 2004 ).

But what exactly is a “system” and why is the systems concept so useful for framing scientific questions? One could informally define a system as being a set of elements which interact with each other in some spatially and temporally specific fashion. More formally, a system can be defined as a set of elements with n time-variant properties that interact with each other. Each time-variant property xi (1 ≤ i ≤ n) is called a state variable, and the n-vector x(t) of all state variables in the system is called the state vector (or simply state) of the system at time t:

$x(t)=x1(t)⋮xn(t)$
(1)

Taking an ensemble of interacting neurons as an example, the system elements would correspond to the individual neurons, each of which is represented by one or several state variables. These state variables could refer to various neurophysiological properties of the neurons, e.g. postsynaptic potentials, status of ion channels, etc. The crucial point is that the state variables interact with each other, i.e. the evolution of each state variable usually depends on other state variables. These functional dependencies between the state variables of the system have to be specified mathematically which requires a set of parameters θ. In neural systems, these parameters comprise at least the synaptic strengths of the connections between the system elements. Furthermore, we must not forget that biological systems are not autonomous but interact with their environment and that external perturbations have a considerable impact on the dynamics of the system. We, therefore, need to consider the input into the system, e.g. sensory information entering the brain. For a given system model, the set of all m known inputs can be represented by the m-vector function u(t). Assuming a deterministic behavior of the system, one can thus formulate a very general state equation for non-autonomous dynamic systems (see Stephan, 2004 for details and assumptions underlying the mathematical form chosen here):

$dxdt=F(x,u,θ)$
(2)

Any model following this general schema provides a causal description of how system dynamics result from system structure, because it describes (i) when and where external inputs enter the system and (ii) how the state changes induced by these inputs evolve in time depending on the system’s structure. As explained below in more detail, Eq. (2) therefore provides a general form for so-called models of effective connectivity in neural systems, i.e. the causal influences that neural units exert over another ( Friston, 1994 ).

### 4.2. System concepts in functional neuroimaging

Modern cognitive neuroscience has adopted an explicit system perspective. A commonly accepted view is that the brain regions that constitute a given system are computationally specialized, but that the exact nature of their individual computations depends on context, e.g. the inputs from other regions. The aggregate behavior of the system depends on this neural context, the context-dependent interactions between the system components ( McIntosh, 2000 ). An equivalent formulation of this perspective is provided by the twin concepts of functional specialization and functional integration ( Friston, 2002b ). Functional specialization assumes a local specialization for certain aspects of information processing but allows for the possibility that this specialization is anatomically segregated across different cortical areas. The majority of current functional neuroimaging experiments have adopted this view and interpret the areas that are jointly correlated to a certain task component as the elements of a distributed system which represents the neural basis of that task. However, this explanation is incomplete as long as no insight is provided into how the locally specialized computations are bound together by context-dependent interactions between these areas, i.e. the functional integration within the system.

The concepts of functional specialization and functional integration are highly relevant for questions on functional brain asymmetries. Conventional functional neuroimaging studies on hemispheric specialization have usually compared, explicitly or implicitly, the degree of functional specialization exhibited by homotopic regions, e.g. activation of left but not right Broca’s area during semantic processing ( Bookheimer, 2002 ; Hagoort, Hald, Bastiaansen, & Petersson, 2004 ) or predominant activation of the right as compared to left parietal areas during visuospatial attention tasks ( Corbetta & Shulman, 2002 ; Fink et al., 2000 ). As already implied by the term “hemispheric specialization”, the notion of functional specialization can also be applied to a whole hemisphere, e.g. by classifying the response profiles of left and right hemisphere as “analytic” and “holistic”, respectively ( Bradshaw & Nettleton, 1981 ).

Characterizing hemispheric asymmetries in terms of functional specialization alone, however, is insufficient. Functional integration is also fundamentally important for lateralized processes, both within a hemisphere (e.g. the functional cooperation of different language areas in the left hemisphere) and across hemispheres (e.g. the binding of processes lateralized to opposite hemispheres), and it is this aspect of hemispheric lateralization that we wish to highlight here. Generally, functional integration within distributed neural systems can be characterized in two ways, functional connectivity and effective connectivity ( Friston, 1994 ). In the remainder of this article, we will first look in detail at some established approaches for characterizing functional and effective connectivity and then review studies which have applied these models to questions of functional integration during lateralized cognitive processes.

### 4.3. Analyses of functional connectivity

Functional connectivity is operationally defined as the temporal correlation between spatially segregated neurophysiological processes ( Friston, 1994 ). For example, considering two voxels X and Y with time series xt and yt, the functional connectivity between the two voxels simply corresponds to the Pearson correlation coefficient r of the two time series

$rxy=covxysx⋅sy$
(3)

where sx and sy are the standard deviations and covxy is the covariance of the two time series. Note that functional connectivity suffers from the general problem of interpreting correlations: are the two time series correlated because (i) X influences Y, (ii) Y influences X, (iii) both influence each other or (iv) both are functionally unrelated but similarly influenced by a third variable? Disambiguating these options requires a model of the causal influences, i.e. effective connectivity (see below).

One approach to applying the concept of functional connectivity to PET and fMRI data is to choose a particular reference voxel and compute, for the whole brain, the correlation of all other voxel time series with this seed voxel time series ( Bokde, Tagamets, Friedman, & Horwitz, 2001 ; Horwitz, Rumsey, & Donohue, 1998 ). An alternative is to characterize orthogonal components of the temporal covariance matrix by decomposing it into its eigenvectors, e.g. using singular value decomposition ( Friston, 1994 ). A related approach is partial least squares ( McIntosh & Lobaugh, 2004 ), a technique which has found multiple applications in the analysis of neuroimaging data.

### 4.4. Models of effective connectivity

In contrast to functional connectivity, the notion of effective connectivity is based on a model of the causal influences between the elements of a system ( Friston, 1994 ). Therefore, there is no single mathematical definition for effective connectivity; instead, a variety of different models of effective connectivity have been proposed (for overviews, see Friston, 2002b; Stephan, 2004 ). Models of effective connectivity describe the mechanisms that determine the dynamics of neural systems, i.e. how activity induced by external inputs is propagated within the system according to its connectivity. It is therefore useful to consider each particular implementation of effective connectivity as a special case of Eq. (2) (see Stephan, 2004 , for a more detailed exposition), and this is the perspective we will take here. In this section, we briefly summarize three commonly used models of effective connectivity which were used by the studies discussed in the following sections of this paper: psycho-physiological interactions (PPI), structural equation modeling (SEM) and dynamic causal modeling (DCM).

#### 4.4.1. Psycho-physiological interactions

For the regression-like model used by PPI the static form of Eq. (2) is appropriate, i.e. x(t) = F(x, u, θ, t) which assumes that the system is at equilibrium at each point of observation (see appendix to Friston, Harrison, & Penny, 2003 ). Introduced by Friston et al. (1997) , PPI are one of the simplest models available to assess functional interactions in neuroimaging data. Given a chosen reference time series y0 (obtained from a reference voxel or region), PPI computes whole-brain connectivity maps of this reference voxel with all other voxels yi in the brain according to the regression-like equation

yiay0b(y0 × u) + cuXβε
(4)

Here, a is the strength of the intrinsic (context-independent) connectivity between y0 and yi. The bilinear term y0 × u represents the interaction between physiological activity y0 and a psychological variable u which can be construed as a contextual input into the system, modulating the connectivity between y0 and yi (× represents the Hadamard product, i.e. element-by-element multiplication). The third term describes the strength c by which the input u evokes activity in yi directly, independent of y0. Finally, β are parameters for effects of no interest X (e.g. confounds) and ɛ is an error term. Although this is a very simple and non-dynamic model, PPI do contain the basic components of system descriptions as outlined above (see Eq. (2) ). There is also a general similarity between the form of Eq. (4) and that of the state equation of DCM (Eq. (7) , see below). However, since only pair-wise interactions between the reference voxel and all other brain voxels are considered, this model is rather restricted in its ability to represent real neural systems. Although PPIs are therefore not a full system model, they have a very useful role in exploring the context-dependent functional interactions of a chosen region across the whole brain. This exploratory nature bears some similarity to analyses of functional connectivity. Unlike analyses of functional connectivity, however, PPIs represent the contextual modulation of connectivity, and this modulation has a directional character, i.e. testing for a PPI from y0 to yi is not identical to testing for a PPI from yi to y0. This is because regressing y0 × u on yi is not equivalent to regressing yi × u on y0.

#### 4.4.2. Structural equation modeling

After having been used in the social sciences for several decades, SEM was introduced to neuroimaging in the early 1990s by McIntosh and Gonzalez-Lima (1991) . It is a multivariate, hypothesis-driven technique that is based on a structural model which represents the hypothesis about the causal relations between several variables (see Büchel & Friston, 1997 ; Bullmore et al., 2000 ; McIntosh & Gonzalez-Lima, 1994 ; Penny, Stephan, Mechelli, & Friston, 2004a , for methodological details). In the context of neuroimaging, these variables are the measured time series y1, …, yn of n brain regions and the hypothetical causal relations are based on anatomically plausible connections between the regions. The strength of each connection yi → yj is specified by a so-called “path coefficient” which, similarly to a partial regression coefficient, indicates how the variance of yj depends on the variance of yi if all other influences on yj are held constant.

One way to summarize the statistical model of SEM implementations for neuroimaging data is given by the equation

yAyu
(5)

where y is the n × s matrix of n area-specific time series with s scans each, A the n × n matrix of path coefficients (with zeros for non-existent connections) and u is the n × s matrix of “innovations”, i.e. zero mean Gaussian error terms, which are driving the modeled system ( Penny et al., 2004a ; see also McIntosh & Gonzalez-Lima, 1994 , for an equivalent formulation). Parameter estimation rests on minimizing the difference between the observed and the predicted covariance matrix Σ of the areas ( Bollen, 1989 ). Σ can be computed by transforming Eq. (5) :

y = (IA)−1uΣyyT = (IA)−1uuT(IA)−1T
(6)

where I is the identity matrix and ‘T’ denotes the transpose operator. The first line of Eq. (6) can be understood as a generative model of how system function results from the system’s connectional structure: the measured time series y results by applying a function of the inter-regional connectivity matrix, i.e. (I − A)−1, to the Gaussian innovations u.

It is beyond the scope of this paper to discuss SEM in full methodological detail and the reader is referred to the large body of existing literature (e.g. Bollen, 1989 ; McIntosh & Gonzalez-Lima, 1994 ; Penny et al., 2004a ). One particular detail, however, that is important for studies of hemispheric specialization is the limitation of SEM to models of relatively low complexity. The problem is that models with reciprocal connections and loops easily become non-identifiable (see Bollen, 1989 , for details). Given that callosal connections seem to be generally reciprocal and one usually needs to study bidirectional interactions between the hemispheres, this constraint is particularly problematic for models of inter-hemispheric integration. Heuristics for dealing with complex models have been established that use multiple fitting steps in which different parameters are held constant while changing others (see McIntosh et al., 1994 , for an example), yet this constraint has been a limiting factor for the application of SEM to questions on inter-hemispheric integration.

#### 4.4.3. Dynamic causal modeling

An important limitation of the models discussed so far is that they operate at the level of the measured signals. Taking the example of fMRI, the model parameters are fitted to BOLD series which result from a haemodynamic convolution of the underlying neural activity. Any inference about inter-regional connectivity obtained by PPI or SEM is only an indirect one because these models do not include the forward model linking neuronal activity to the measured haemodynamic data. The causal architecture of the system that we would like to identify, however, is expressed at the level of neuronal dynamics. Therefore, to enable inferences about connectivity between neural units we need models that combine two things: (i) a parsimonious but neurobiologically plausible model of neural population dynamics and (ii) a biophysically plausible forward model that describes the transformation from neural activity to the measured signal. Such models make it possible to fit jointly the parameters of the neural and of the forward model such that the predicted time series are optimally similar to the observed time series. In principle, any of the models described above could be combined with a modality-specific forward model. So far, however, dynamic causal modeling is the only approach where the marriage between models of neural dynamics and biophysical forward models is a mandatory component. DCM has been implemented both for fMRI ( Friston et al., 2003 ) and EEG/MEG data ( David et al., 2006 ). For simplicity, we here only briefly summarize the implementation of DCM for fMRI.

DCM for fMRI offers a simple model for the neural dynamics in a system of n interacting brain regions. It models the change of a neural state vector x in time, with each region in the system being represented by a single state variable, using the following bilinear differential equation:

$dxdt=F(x,u,θn)=A+∑j=1mujB(j)x+Cu$
(7)

Note that this neural state equation follows exactly the general form for deterministic system models introduced by Eq. (2) . Here, the neural state variables represent a summary index of neural population dynamics in the respective regions. The neural dynamics are driven by experimentally controlled external inputs that can enter the model in two different ways: they can elicit responses through direct influences on specific regions (e.g. evoked responses in early sensory cortices; the C matrix) or they can modulate the coupling among regions (e.g. during learning or attention; the B matrices). The neural parameters θn = A, B, C can be expressed as partial derivatives of F (n in θn is not an exponent but a superscript that denotes “neural”):

$A=∂F∂xu=0; B(j)=∂2F∂x∂uj; C=∂F∂ux=0$
(8)

The matrix A represents the effective connectivity among the regions in the absence of input, the matrices B(j) encode the change in effective connectivity induced by the jth input uj and C embodies the strength of direct influences of inputs on neuronal activity.

DCM for fMRI combines this model of neural dynamics with an experimentally validated haemodynamic model that describes the transformation of neuronal activity into a BOLD response. This “Balloon model” was initially formulated by Buxton, Wong, and Frank (1998) and later extended by Friston, Mechelli, Turner, and Price (2000) . Briefly, it consists of a set of differential equations that describe the relations between four haemodynamic state variables, using five parameters (θh). Changes in neural activity elicit a vasodilatory signal that leads to increases in blood flow and subsequently to changes in blood volume and deoxyhemoglobin content. The predicted BOLD signal is a non-linear function of blood volume and deoxyhemoglobin content. Details of the haemodynamic model can be found in other publications ( Friston et al., 2000 ; Stephan, Harrison, Penny, & Friston, 2004 ).

The neural and haemodynamic parameters θ = θn, θh are jointly estimated from the measured BOLD data, using a fully Bayesian approach with empirical priors for the haemodynamic parameters and conservative shrinkage priors for the coupling parameters. Details of the parameter estimation scheme, which rests on a gradient ascent procedure embedded into an expectation maximization (EM) algorithm and uses a Laplace (i.e. Gaussian) approximation to the true posterior, can be found in Friston (2002a) . Eventually, the posterior distributions of the parameter estimates can be used to test hypotheses about connection strengths. Usually, these hypotheses concern context-dependent changes in coupling. If there is uncertainty about the connectional structure of the modeled system, or if one would like to compare competing hypotheses (represented by different DCMs), a Bayesian model selection procedure can be used to find the DCM that exhibits an optimal balance between model fit and model complexity ( Penny, Stephan, Mechelli, & Friston, 2004b ).

### 4.5. Neurobiological interpretability of models of effective connectivity

One may wonder what degree of neurobiological interpretability the models of effective connectivity discussed above possess. DCM is particularly relevant in this discussion because it is currently the only model of effective connectivity for fMRI data that explicitly models the neural level. DCM for fMRI is obviously not specified at a level of neurobiological finesse that allows one to distinguish between different processes at synaptic, cellular, columnar or laminar levels. Instead, the mechanisms represented by DCM, e.g. context-dependent changes of particular connection strengths, refer to the level of large neural populations contained by one or several voxels (note that even a single standard size voxel contains hundreds of thousands of neurons). However, this relatively high degree of abstraction present does not mean that the causal mechanisms modeled by DCM are neurobiologically meaningless. Many of the processes that one typically models with DCM, e.g. changes in synaptic strength during learning or context-specific modulation of connections due to attention or other cognitive factors, have been investigated at the level of single neurons or microcircuits by invasive recording experiments (e.g. Luck, Chelazzi, Hillyard, & Desimone, 1997 ), and DCMs provide a simple mechanistic description of these processes at the level of neuronal populations. In particular, the distinction between direct and modulatory effects in DCMs represents a direct analogy at the population level to the concept of driving and modulatory afferents in studies of single neurons ( Sherman & Guillery, 1998 ). A more detailed discussion of these issues can be found elsewhere ( Penny et al., 2004b; Stephan, 2004 ). Finally, one should mention that much more fine-grained DCMs have been developed than for fMRI, for example, for EEG/MEG data. Here, each region is characterized by eight state variables that represent quite detailed components of the neurobiological machinery, including firing rates and membrane potentials of different neuronal units, e.g. pyramidal cell populations and inhibitory interneuron populations ( David et al., 2006 ).

## 5. Functional imaging studies of brain connectivity in lateralized cognitive functions

### 5.1. Asymmetries of intra-hemispheric connectivity

Traditionally, as explained above, hemispheric specialization has been characterized in terms of asymmetries in the local structure or function of homotopic regions. An alternative approach that has gained momentum over the last years is the notion that lateralization may be more appropriately characterized in terms of connectivity asymmetries between hemispheres. In this section, we review some of the most influential neuroimaging studies of this kind which have used analyses of functional or effective connectivity. We restrict this review to those studies that explicitly assess asymmetries of intra-hemispheric connectivity. This excludes conventional activation studies in which co-activation of areas is interpreted as putative evidence for connectivity between them; this kind of analysis does not allow for unambiguous inference about connectivity (see Stephan, 2004 , for a discussion of this point). Also, there are multiple elegant studies of effective connectivity during lateralized tasks that have deliberately restricted their connectivity analysis to the dominant hemisphere (e.g. Bitan et al., 2005 ; Coull, Büchel, Friston, & Frith, 1999 ; Mechelli, Penny, Price, Gitelman, & Friston, 2002 ; Smith, Stephan, Rugg, & Dolan, 2006 ); these studies are not discussed in detail either.

A pioneering study of hemispheric differences in connectivity was conducted by McIntosh et al. (1994) who applied PET to two matching tasks for faces and locations where the volunteers had to choose which of two stimuli corresponded to a reference stimulus. Both face and location matching tasks are known to have a right-hemispheric dominance and should show a relative preference for engaging the ventral and dorsal stream of the visual system, respectively. Surprisingly, the activation pattern was found to be fairly bilateral for both tasks. A connectivity analysis using SEM revealed, however, that the selective functional dependencies between ventral stream areas during the face matching task and between dorsal stream areas during the location matching task, respectively, were much stronger in the right than in the left hemisphere ( Fig. 2 A). In fact, in the left hemisphere the two tasks did not differ with regard to the effective connectivity between visual areas. Furthermore, McIntosh et al. (1994) demonstrated top–down effects during the location matching task that were restricted to the right hemisphere, i.e. an influence of the right middle frontal gyrus (area 46) onto right extrastriate areas ( Fig. 2 A). This fronto-occipital top–down influence may represent the mechanism by which the right hemisphere alters early visual processing in accord with task demands.

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Fig. 2

Results from structural equation modeling, applied to PET data from a study by McIntosh et al. (1994) who used two right-lateralized matching tasks for faces and locations. (A) The analysis of intra-hemispheric connectivity showed that the selective functional dependencies between ventral stream areas during the face matching task and between dorsal stream areas during the location matching task, respectively, were much stronger in the right than in the left hemisphere. (B) Strong asymmetries of the inter-hemispheric connection strengths, with right-to-left callosal connections between homotopic regions being positive and much stronger during both tasks than left-to-right connections. See main text for more details. Figure reproduced with permission from Springer Verlag.

Complementary findings exist for tasks with left-hemispheric dominance, e.g. language paradigms. Horwitz and colleagues have demonstrated that even simple approaches to characterizing connectivity, i.e. seed voxel functional connectivity analyses, can contribute to a better understanding of lateralization during language processing. For example, Horwitz et al. (1998) used PET to compare dyslexic to healthy subjects during different reading tasks. In healthy subjects, they found the expected robust functional connectivity between the left angular gyrus and other reading-related areas in inferior frontal and temporal cortices. In dyslexic subjects, the left angular gyrus appeared to be disconnected from these areas. This functional disconnection in dyslexic patients is paralleled by a fractional anisotropy decrease in the same region, corresponding to a diminished microstructural integrity of white matter, which was found in a DWI study of adults with poor reading skills ( Klingberg et al., 2000 ). Another study by Bokde et al. (2001) applied the same approach to fMRI data in a one-back orthographic matching task on different word stimuli (i.e. words, pseudowords, letter strings and false fonts; Tagamets, Novick, Chalmers, & Friedman, 2000 ). Bokde et al. (2001) investigated the hypothesis that the left anterior inferior frontal gyrus (aIFG) is involved in the semantic analysis of words whereas the left posterior inferior frontal gyrus (pIFG) plays a role in the phonological analysis of words. They found that left pIFG exhibited a pronounced functional connectivity with left temporal language areas during the presentation of all stimuli that could be processed phonologically (i.e. words, pseudowords, letter strings, but not false fonts). In contrast, left aIFG showed significant functional connectivity with these areas only during the presentation of real words, but not during processing of pseudowords, letter strings and false fonts, none of which have a semantic content (see Fig. 3 ). The critical point was that in both cases this pattern of functional connectivity was entirely restricted to the left hemisphere: analyses of the functional connectivity of the homotopic voxels in right aIFG and pIFG did not show any significant coupling with language-relevant temporal areas. Altogether, studies of the kind described above demonstrate how hemispheric specialization can be conceptualized in terms of hemispheric differences in the functional integration of cooperating areas.

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Fig. 3

Functional connectivity of the left anterior inferior frontal gyrus (aIFG) during an orthographic task ( Bokde et al., 2001 ). All voxels are highlighted in color whose BOLD signal correlated strongly positively (r > 0.4) with the BOLD signal in a reference voxel in left aIFG (white arrow). The color legend indicates for which type of stimuli (words, pseudowords and letter strings) these correlations were found. The image is shown according to radiological convention. Figure is reproduced from Bokde et al. (2001) with permission by Elsevier Ltd.

Beyond language, attention plays a particular role for the discussion of connectivity and lateralization. Selective attention provides one of the best studied examples of context-dependent changes in connection strength (e.g. Büchel & Friston, 1997 ; Friston et al., 2003 ), and some recent studies have begun to characterize how selective attention in visual and auditory space is associated with a corresponding connection strengths increase in the associated hemisphere. For example, a PET study of dichotic listening employed a PPI analysis to show that selective orientation towards one ear as (compared to a control condition with identical words presented to both ears, evoking a centrally located fused percept) led to increases in the effective connectivity of the superior temporal gyrus and the intra-parietal sulcus with other regions, but only within the hemisphere contralateral to the attended ear ( Lipschutz, Kolinsky, Damhaut, Wikler, & Goldman, 2002 ). An elegant fMRI study by Haynes, Tregellas, and Rees (2005) used four rotating spirals, one in each of the four quadrants of the visual fields. During central fixation, the volunteers were instructed to attend covertly to two of the four spirals at a time and decide whether their directions of rotation were identical or opposite. Using DCM, they found that the change in spatial attention was paralleled by a change in the connection strength between the retinotopically corresponding parts of areas V1 and V2. For example, when subjects covertly compared the two spirals in the left visual field, the functional coupling increased between the corresponding retinotopically mapped parts of V1 and V2 in the right hemisphere (and vice versa for attention to the spirals in the right visual field). Corresponding effects were found when subjects attended to two spirals in opposite hemifields; then, the inter-hemispheric coupling between the retinotopic representations increased.

Although spatial attention can induce changes in connectivity in both hemispheres as described above, there is good evidence that several right-hemispheric areas, particularly frontal eye field (FEF), intra-parietal sulcus and temporo-parietal junction (TPJ), play a dominant role in the actual implementation of spatial attention, regardless where it is directed ( Corbetta & Shulman, 2002 ; Fink et al., 2000 ; Fink, Marshall, Weiss, & Zilles, 2001 ; Gitelman et al., 1999 ; Halligan, Fink, Marshall, & Vallar, 2003 ; Marshall & Fink, 2001 ). These right-hemispheric areas are thus likely candidate sources of the modulatory effects exerted by spatial attention on connection strengths throughout the brain. Yet, other than the study by McIntosh et al. (1994) described above, there is surprisingly limited work so far that provides direct evidence for this notion. An fMRI study by Gitelman, Parrish, Friston, and Mesulam (2002) that examined changes in connectivity of the superior colliculus between overt visuospatial search and a saccade control condition by means of PPI gave mixed results, showing similar degrees of collicular coupling with left- and right-hemispheric areas, with the exception that only the right, but not left, FEF increased its coupling with the superior colliculus during overt visuospatial search relative to controlled saccades. Another study by Ruff and Driver (2006) investigated whether anticipation of a distractor stimulus, located in the opposite hemifield to the target stimulus, would alter the spatiotopic activations elicited by anticipation of the target. Using fMRI, they demonstrated that both anticipation of targets and distractors induced activations in contralateral occipital cortex but there was no additional modulation of target anticipation by knowledge about the presence of a distractor. However, an analysis of functional coupling using PPI showed that the right, but not left, TPJ showed stronger functional coupling with occipital regions contralateral to the target, in both the left and the right hemisphere, during preparation for trials with an isolated target than for trials with an anticipated distractor. This pattern of connectivity is compatible with the putative role of right TPJ in bottom–up (stimulus-driven) rather than top–down attentional selection ( Corbetta & Shulman, 2002 ), because in the paradigm by Ruff and Driver (2006) stimulus-driven direction of attention is likely to operate successfully only in the trials where no distractor is present.

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Fig. 4

Schematic summary of a study by Stephan et al. (2003) that applied letter and spatial decision tasks to identical word stimuli. (A) Brain areas that were significantly activated during both letter and spatial decisions (contrast between the letter decision task and the baseline condition, masked by the contrast between the spatial decision task and the baseline condition; p < 0.05 whole-brain cluster-level corrected). Crosshairs highlight the anterior cingulate cortex (ACC) which was bilaterally activated in both conditions. (B) Results from a PPI analysis of the effective connectivity of the left ACC. Left ACC specifically increased its coupling with left inferior frontal gyrus during letter decisions (p < 0.05, small-volume corrected). Dashed arrows denote non-significant modulation of couplings. (C) Results from a PPI analysis of the effective connectivity of the right ACC. Right ACC specifically increased its coupling with anterior and posterior parts of right intra-parietal sulcus during spatial decisions (p < 0.05, small-volume corrected). Figure is adapted from Stephan et al. (2003) , with permission by Science (The American Association for the Advancement of Science).

### 5.2. Asymmetries of inter-hemispheric connectivity

One of the fundamental constraints of human brain function is the requirement to integrate processes from both hemispheres. Efficient inter-hemispheric integration would still be required even if the hemispheres were perfectly functionally symmetrical. This is illustrated by the simple example where one has to respond with a right arm movement (executed by the left motor cortex) to a visual stimulus presented in the periphery of the left visual (and thus received by the right visual cortex). Regardless of how symmetric the brain is, this situation requires stimulus information to be transferred from the right to the left hemisphere. It is likely that this need for inter-hemispheric integration is considerably amplified in an asymmetrically organized brain because it will often be the case that a cognitive operation will depend on subprocesses that are lateralized to opposite hemispheres. The above example brings about many fundamental questions, for example: when, how and where in the brain is information transferred between hemispheres? What determines whether the brain processes information in the hemisphere specialized for that information alone or whether it draws on additional computational resources in the less specialized hemisphere? How are simultaneous workings of the two hemispheres synchronized, e.g. to prevent interference of processes (see Lashley, 1937 )?

These issues have been the subject of much theoretical work, and as a result three major complementary theories have been formulated that have guided investigations of inter-hemispheric integration. As mentioned in the historical section, the oldest concept is probably that of information transfer between the hemispheres (e.g. Poffenberger, 1912 ). In the context of lateralized tasks with hemisphere-specific inputs (e.g. peripheral visual presentation), this theory predicts that transfer of sensory information should be asymmetrically enhanced from the non-dominant to the dominant hemisphere to ensure maximally efficient processing in the specialized hemisphere (e.g. Endrass, Mohr, & Rockstroh, 2002 ; Nowicka, Grabowska, & Fersten, 1996 ). In terms of effective connectivity, it predicts a task-dependent increase in connectivity from the non-dominant to the dominant hemisphere but only when stimulus information is initially restricted to the non-dominant hemisphere.

A more recent and very influential concept has been the notion of inter-hemispheric inhibition ( Kinsbourne, 1970 ). It has been agued that the regulatory mechanisms that “coordinate, select and integrate the processes subserved by each hemisphere” will also require a range of inter-hemispheric inhibitory mechanisms “to achieve unified performance from a bilateral system capable of producing simultaneous and potentially conflicting outputs” ( Chiarello & Maxfield, 1996 ). This paper by Chiarello and Maxfield is an excellent review of the evidence for inter-hemispheric suppression, inter-hemispheric isolation and inter-hemispheric interference, with interesting suggestions about the functional significance of these mechanisms. With regard to connectivity, inter-hemispheric inhibition predicts a task-dependent symmetric pattern of negative connection strengths between hemispheres (strictly speaking, this requires the assumption that neural inhibition leads to a decrease in the measured signal).

In the past, questions on inter-hemispheric interactions have been mainly addressed by means of elegant behavioral studies (for reviews, see Banich, 1998; Hellige, 1990; Liederman, 1998 ), studies of patients with callosal lesions ( Corballis, Corballis, & Fabri, 2003 ; Funnell, Corballis, & Gazzaniga, 2000 ; Gazzaniga, 2000 ), EEG/MEG studies of inter-hemispheric coherence and synchrony (e.g. Schack, Weiss, & Rappelsberger, 2003 ) and invasive recording studies in animals ( Cardoso de Oliveira, Gribova, Donchin, Bergman, & Vaadia, 2001 ; Engel, König, Kreiter, & Singer, 1991 ). All these methods have limitations. Behavioral studies cannot elucidate which neural processes generate the observed responses and where in the brain these processes happen. Callosal lesions are usually quite extended (particularly iatrogenic ones) and the considerable plasticity of the human brain complicates the interpretation of behavioral deficits in these patients. EEG/MEG studies suffer from the inverse problem, i.e. without strong a priori constraints, it is difficult to localize the sources that generate the measured responses. Although advanced methods for solving this problem are now available (e.g. Mattout et al., 2006 ), the large majority of available EEG/MEG studies on inter-hemispheric integration have analyzed functional coupling at the level of the sensor data only and hence do not allow for localization of the neural units that exhibit this coupling. Finally, invasive recordings are not possible in humans (with the exception of presurgical evaluation of epilepsy patients) and can only probe very few locations at a time.

While functional imaging techniques, particularly fMRI, overcome many of these issues and provide both whole-brain investigation and excellent spatial resolution, they are not free of problems when used to investigate inter-hemispheric integration by means of analyses of connectivity. A particular problem is that, due to the reciprocal nature of callosal connections and the multiple pathways by which two hemispheres can interact, models of inter-hemispheric integration are usually quite complex. PPI is too simple a model to allow for a satisfactory investigation of such systems. Also, SEM is only of limited help for complex models because of problems of identifiability, the simplest example being when there are more free parameters than empirically measured covariances (for discussions of this issue, see McIntosh & Gonzalez-Lima, 1994 and Penny et al., 2004a ). Suggestions how to apply SEM to models of inter-hemispheric integration models have been made, e.g. to use an iterative fitting procedure in which intra-hemispheric parameters are estimated in a first pass and then kept fixed when extending the model to include inter-hemispheric connections ( McIntosh et al., 1994 ) or to constrain the callosal connections to have the same path coefficient in both directions ( Rowe et al., 2002; Schlösser et al., 2003 ). The latter approach, however, shares the problem with analyses of functional connectivity that asymmetries in inter-hemispheric influences cannot be investigated.

Focusing on studies that have specifically investigated such asymmetries in inter-hemispheric interactions, there are, to the best of our knowledge, at present only two studies in the literature that fulfill this criterion ( McIntosh et al., 1994; Stephan et al., 2005 ). However, it can be expected that with the advent of DCM, which can deal with complex models, this number will substantially increase in the near future. Given the huge gap of our understanding about the functional principles of inter-hemispheric integration and how they relate to asymmetries of brain function, we hope that the discussion in this paper will contribute to stimulating future studies.

As already described in the section on intra-hemispheric connectivity, McIntosh et al. (1994) performed a PET study of face and location matching tasks. Although it is well-established that both tasks have a right-hemispheric dominance, the activation pattern was surprisingly bilateral for both tasks. A SEM connectivity analysis, however, not only showed higher functional coupling within the right as compared to the left hemisphere (see above), but also helped to understand the bilateral activation pattern from the initial conventional analysis. In their model, McIntosh et al. found strong asymmetries of the inter-hemispheric connection strengths, with right-to-left callosal connections between homotopic regions being positive and much stronger during both tasks than left-to-right connections ( Fig. 2 B). They concluded that the observed bilateral activation during the two right-lateralized tasks was due to a transcallosal recruitment of the non-dominant left hemisphere by the dominant right hemisphere. They could distinguish this interpretation from the alternative of simple information transfer because in their paradigm the stimuli were presented centrally and thus information was available to both hemispheres.

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Fig. 5

Schematic summary of the results from a study of hemispheric integration ( Stephan et al., 2005 ) which applied DCM to data of a single subject from the group studied by Stephan et al. (2003 ; see Fig. 4 ). In this particular subject, inter-hemispheric connections between lingual gyri (LG) and fusiform gyri (FG), respectively, were modulated by letter decisions (LD), but conditional on stimuli being presented in the left visual field (LVF). This modulation was asymmetric, i.e. significantly stronger for right-to-left connections than vice versa (see Stephan et al., 2005 , for details). This result, a task-dependent increase in connectivity from the non-dominant to the dominant hemisphere but only when stimulus information is initially provided to the non-dominant hemisphere, is in accordance with the theory that the corpus callosum subserves information transfer to the specialized hemisphere (see main text).

## 6. Investigating developmental changes in structural connectivity and their relation to functional lateralization

Collectively, the studies described in the sections above demonstrate that asymmetries of intra- and inter-hemispheric effective connectivity are a highly informative index of hemispheric specialization and go beyond the traditional approach of defining lateralization through asymmetries in the local structure or function of homotopic regions. As described in the section on asymmetries of structural brain connectivity and their developmental determinants above, it seems likely that asymmetries in effective connectivity can be causally related to asymmetries in structural connectivity forming during neurodevelopment. While animal studies strongly imply such a relation, definite proof for such a relation, for example, from longitudinal within-subject studies demonstrating a tight relation between developing asymmetries in structural and effective connectivity, has yet to be obtained for the human brain. This is due to methodological and ethical problems associated with longitudinal studies of human brain development. For obvious ethical reasons, any experimental procedure that is invasive (e.g. histological investigations to assess microstructural changes in connectivity) or that may indirectly affect development (e.g. pharmacological manipulations of molecular processes putatively involved in asymmetric formation of connections) are prohibited in humans. Unfortunately, the available non-invasive imaging procedures do not yet provide sufficient resolution that we could detect subtle changes in structural brain connectivity during the early stages of human brain development when decisive processes underlying lateralization presumably take place (see Gupta et al., 2005 , for preliminary attempts to track prenatal connectivity changes using DWI). This is because high-resolution DWI data require high magnetic field strengths and/or long acquisition times, both of which are not permissible or practically feasible for perinatal imaging. Until better non-invasive methods are available for assessing structural connectivity with high resolution, studies of the relation between the development of structural brain asymmetries and the resulting changes of effective connectivity will have to focus on childhood and adolescence. In this period significant changes are still likely to occur, albeit probably at a slower rate than perinatally. Here, we describe two examples of possible research strategies.

First, it would be important to combine analyses of effective connectivity with DWI and morphometric studies that probe changes in hemispheric differences in gray and/or white matter properties over time. So far, to our knowledge, there is a complete lack of such studies. Morphometric measures such as cortical thickness may be particularly useful because there are widespread hemispheric differences ( Luders et al., 2006 ), and a previous study indicated that inter-regional correlations in cortical thickness may be a function of inter-regional structural connectivity ( Lerch et al., 2006 ). The latter could be tested directly in longitudinal studies that jointly investigate changes in cortical thickness, white matter organization and effective connectivity during childhood and adolescence. It would be informative to evaluate the results from such studies in reference to probabilistic cytoarchitectonic atlases, but so far these have only been developed for the adult human brain (cf. Eickhoff et al., 2005 ).

A second strategy rests on developmental studies of animals with different genetic status, e.g. knock-out models with regard to candidate genes (like LMO4 or N-cadherin, see above) for development of asymmetric brain connectivity. By testing for concomitant changes in structural connectivity (derived from quantitative tract tracing studies) and effective connectivity (estimated from neurophysiological measures) that follow experimental manipulations of experience-dependent plasticity, such studies could establish a direct role of current candidate genes for both the formation of asymmetric structural connectivity and for the subsequent functional expression of this asymmetry in terms of effective connectivity. Any positive findings could then be taken back to human studies which investigate, using functional imaging and genotyping, whether there is a statistical relationship between measures of effective connectivity during lateralized tasks and particular genetic haplotypes implicated by the animal models.

## 7. Summary and outlook

This review focused on the role of connectivity for understanding hemispheric specialization. We have reviewed evidence from recent anatomical and developmental studies that asymmetries in structural connectivity may be a key component in the development of hemispheric specialization. Such differences in anatomical connectivity, which have been described both within and between cortical areas, may represent the structural substrate of different styles of information processing in the two hemispheres. After a brief methodological overview of some commonly used models of connectivity, we reviewed published PET and fMRI studies that have applied these approaches to characterize asymmetries of intra- or inter-hemispheric connectivity during lateralized tasks. We hope that these examples have demonstrated three main things: first, that hemispheric specialization can be usefully defined by asymmetries of intra-hemispheric functional and effective connectivity and moreover that connectivity can be a more sensitive marker of hemispheric specialization than asymmetries of activation patterns (cf. McIntosh et al., 1994 ). Second, that analyses of connectivity can provide a mechanistic understanding of how lateralization can (sometimes) be entirely task-driven (cf. Stephan et al., 2003 ). And third, how models of effective connectivity can be used to infer functional principles of inter-hemispheric integration from neuroimaging data.

At the present time, many questions on hemispheric specialization are still open. For example, what are the exact computational advantages (and disadvantages) of an asymmetric brain? Is hemispheric specialization a developmental process that, once a certain stage has been reached, remains in a fixed state or is it a dynamic process? What role do synaptic plasticity and modulatory transmitter systems play? It will be important to address these questions, not only for our general understanding of human brain function, but also with regard to the many clinical disorders that implicate hemispheric asymmetries, either due to asymmetric lesions (like aphasia, apraxia or neglect) or due to an as yet unknown reason, as in dyslexia ( Heim and Keil, 2006 ), autism ( Herbert et al., 2005 ) and schizophrenia ( Mitchell & Crow, 2005 ; Petty, 1999 ). For example, mechanistic models of hemispheric specialization may provide endophenotypes for better diagnosis and classification of diseases with diffuse diagnostic criteria like schizophrenia or autism (cf. Stephan, 2004 ). Moreover, good models may enable us to reverse-engineer asymmetric neural systems and teach us how to induce compensatory changes in case of disorders. With such models, it may be possible to derive better diagnostic tools for presurgical evaluation ( Klöppel & Büchel, 2005 ), novel forms of rehabilitation training for brain-lesioned patients and predict advantageous consequences of physical (e.g. transcranial magnetic stimulation) or pharmacological manipulations. Whatever the exact research strategy chosen to pursue such goals, it seems likely that a computational systems perspective and a model-based approach will be necessary to enable neuroscience to proceed from mere descriptions of brain asymmetries to mechanistic accounts of how these asymmetries are caused and how they can be influenced.

## Acknowledgments

This work was supported by the Wellcome Trust (KES), the Deutsche Forschungsgemeinschaft (GRF) and the Medical Research Council (JCM). We would like to thank Christian Büchel for providing Fig. 1 , Randy McIntosh for providing Fig. 2 , Barry Horwitz for providing Fig. 3 and Stefan Klöppel for comments on an earlier version of this manuscript.

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