Review

Development of rostral prefrontal cortex and cognitive and behavioural disorders Iroise Dumontheil* PhD; Paul W Burgess PhD; Sarah-Jayne Blakemore PhD, Institute of Cognitive Neuroscience and Department of Psychology, University College London, UK. *Correspondence to first author at Institute of Cognitive Neuroscience, University College London, 17 Queen Square, Alexandra House, London WCIN 3AR, UK. E-mail: [email protected] DOI: 10.1111/j.1469-8749.2008.02026.x Published online 10th January 2008 Information on the development and functions of rostral prefrontal cortex (PFC), or Brodmann area 10, has been gathered from different fields, from anatomical development to functional neuroimaging in adults, and put forward in relation to three particular cognitive and behavioural disorders. Rostral PFC is larger and has a lower cell density in humans than in other primates. It also has a large number of dendritic spines per cell and numerous connections to the supramodal cortex. These characteristics suggest that rostral PFC is likely to support processes of integration or coordination of inputs that are particularly developed in humans. The development of rostral PFC is prolonged, with decreases in grey matter and synaptic density continuing into adolescence. Functions attributed to rostral PFC, such as prospective memory, seem similarly to follow a prolonged development until adulthood. Neuroimaging studies have generally found a reduced recruitment of rostral PFC, for example in tasks requiring response inhibition, in adults compared with children or adolescents, which is consistent with maturation of grey matter. The examples of autism, attention-deficit– hyperactivity disorder, and schizophrenia show that rostral PFC could be affected in several disorders as a result of the susceptibility of its prolonged maturation to developmental abnormalities.

See end of paper for list of abbreviations.

168

Developmental Medicine & Child Neurology 2008, 50: 168–181

Rostral prefrontal cortex (PFC), which corresponds approximately to Brodmann area 10 (BA10),1,2 is the largest single architectonic region of the frontal lobes of the human brain.3 In humans, this region continues developing throughout childhood and adolescence. Evidence about the cognitive function of rostral PFC has been scarce until the past decade, partly because its position within the skull prevents the successful use of techniques such as electroencephalography, animal lesions, or single-cell recording. Moreover, the proximity to facial nerves makes electrical stimulations of the scalp, used in techniques such as transcranial magnetic stimulation, painful. Therefore, the recent data that have been obtained on the function of rostral PFC tend to come from studies of human lesions and from functional imaging. Several theories of rostral PFC function have been proposed on the basis of these data, mostly attributing to this region a role in certain executive functions. The purpose of this article is to integrate these findings with what is known about the structural development of rostral PFC and research on cognitive and behavioural disorders. We hope that this review will be a useful tool to inform psychological studies of children and adolescents, show the relevance of this field of research to a range of psychological disorders, and indicate possible future areas of investigation. First, the anatomy and anatomical development of rostral PFC will be presented, before describing evidence obtained in adults on the function of this region, and some theories that have been proposed. Second, experimental psychology and neuroimaging studies that shed light on rostral PFC development will be reviewed. Finally, it is proposed that as a result of the prolonged development of rostral PFC, extending to adolescence, impairments in the functions associated with this region should be observed in a large range of developmental disorders. Autism spectrum disorders (ASDs),

attention-deficit–hyperactivity disorder (ADHD), and schizophrenia, three cognitive and behavioural disorders with different ages at onset, were studied to test this hypothesis. The results do indeed suggest that rostral PFC function might be affected by developmental anatomical abnormalities in a large spectrum of psychological disorders. Anatomy of rostral PFC Recent anatomical investigations have provided detailed data on the localization of BA10, its size, and its connections with other areas, revealing an apparent distinction of subregions of rostral PFC. ROSTRAL PFC ACROSS SPECIES

A century ago, Brodmann2 suggested that homology between the human and monkey frontal cortical regions was very unclear. However, Semendeferi et al.4 recently demonstrated the presence of a homologous area 10 in the frontal pole of humans and other primates (see also Petrides and Pandya5). Area 10 in the right hemisphere of humans was estimated by Semendeferi et al.4 to contain 254.4 million neurons, whereas the estimate for great apes is less than onethird of that number and for the gibbon it is of 8 million neurons only. The reverse relationship held true for the density of neurons: human area 10 was found to have the lowest density and that of the gibbon the highest. Additionally, neuropil, which consists of the tangle of dendrites, axons, and glial processes that remain when cell bodies of neurons and glia are removed from grey matter, was found to represent a higher fraction of grey matter in BA10 in humans than in the other apes. These findings are consistent with the description of the frontal pole of the human brain as being cellsparse and pale in its overall appearance in comparison with the surrounding areas.5 Jacobs et al.6 similarly observed that

a

the number of dendritic spines per cell and the spine density are higher in human rostral PFC than in other comparable areas of the cortex, but that the density of cell bodies is markedly low. In terms of volume, area 10 was found to be larger in the human brain than in the other hominoids: even in relative terms it may be up to twice the size in the human brain than in any of the great apes4 (see Holloway7 for discussion on this result). SUBDIVISIONS OF ROSTRAL PFC

The large size of BA10 suggests the possible differentiation of subregions. Semendeferi et al.4 found no subdivisions of area 10 in the human brain. Öngür et al.,8 however, suggested that area 10 could be divided into different regions: caudally lie the medial and rostral subdivisions of area 10 (areas 10m and 10r respectively), which form an area with a relatively homogeneous granular structure, although axonal tracing experiments have suggested that the caudal and rostral portions have distinct connections;9–11 a large highly differentiated cortical area covers the frontal pole itself and was named polar area 10 (10p) by the authors, to distinguish it from area 10o in monkeys, which occupies the rostral orbital region and the frontal pole.12 Öngür et al.8 found a marked difference between human and monkeys, with a striking expansion in the human brain of the granular cortex at the frontal pole, corresponding to area 10 (Fig. 1). CONNECTIONS BETWEEN ROSTRAL PFC AND OTHER BRAIN REGIONS

Some subdivisions of rostral PFC are also apparent from connectivity data. Öngür et al.8 proposed that a ‘medial’ network involves areas on the medial wall including area 10 and projects to visceral control centres in the hypothalamus and periaqueductal grey. These regions receive auditory or polymodal input from the superior temporal sulcal region.13,14

b

Figure 1: Architectonic subdivisions of human area 10. (a) shows the orbital surface, (b) the medial surface (10m, medial area 10; 10r, rostral area 10; 10p, polar area 10). Adapted from Öngür et al.,8 and reprinted with kind permission from the authors and the publishers of Journal of Comparative Neurology, Wiley-Liss, Inc., a subsidiary of John Wiley & Sons, Inc. © 2003 Wiley-Liss, Inc.

Review 169

This network has been suggested to be a ‘viscero-motor’ or ‘emoto-motor’ system that could modulate visceral activity in response to affective stimuli.15 Rostral PFC does not seem to be interconnected with ‘downstream’ areas in the way in which other prefrontal areas are. It is the only prefrontal region that is predominantly interconnected with supramodal cortex in the PFC,16 anterior temporal cortex,17 and cingulate cortex.18 In addition, its projections are broadly reciprocal19 (reviewed by Ramnani and Owen20). Anatomical studies of rostral PFC thus suggest that this area is more extended and differentiated in humans than in other primates. It has a low cell density, which may indicate that rostral PFC in humans has more space available for extrinsic and intrinsic connections.4 Rostral PFC connections are mostly limited to interconnections with the supramodal cortex. Finally, BA10 also has a particularly high number of dendritic spines per cell, which indicates that the computational properties of rostral PFC are more likely to involve the integration or coordination of inputs than are those of comparable areas.20 Anatomical development of rostral PFC Anatomical development studies have focused on different age groups and employed a wide range of methods, from postmortem to neuroimaging studies. Studies of neuroanatomical changes have shown that different brain structures mature at different rates. In particular, recent evidence from studies in humans suggests that frontal brain regions, including rostral PFC, develop more slowly than other regions, maturing into late adolescence and beyond. DENDRITIC SYSTEMS

Post-mortem studies of the development of the dendritic systems in rostral PFC suggest that they mature later than in primary sensory and motor regions, and continue maturing until late adolescence. Travis et al.21 observed that total dendritic length and dendritic spine number of certain pyramidal neurons of neonatal human cortex were greater in BA4 than in BA10. According to the authors, these results show a roughly inverse regional pattern of dendritic complexity in the newborn infant than in the adult, and indicate that the developmental time courses of these dendritic systems are more protracted for supramodal BA10 than for primary and unimodal regions (BA1, BA2, BA3, BA4, and BA18). This suggestion is supported by findings of a prolonged decrease in synaptic density,22,23 and an increase in dendritic arborization of pyramidal neurons24 in the middle frontal gyrus – the rostral part of which corresponds to BA10 – during late childhood and adolescence. WHITE MATTER

Developmental changes in white matter, which reflect differences in myelination and/or the direction or density of fibre tracts, have been investigated in magnetic resonance imaging (MRI) studies of the human brain, but only a few studies have provided information on PFC. An increased white matter volume in adults (mean age 27y) compared with that in children (mean age 10y) was reported in the frontal lobes.25 Nagy et al.26 observed a positive correlation between white matter density and age in relatively deep white fibre tracks of the left inferior frontal cortex. Finally, Barnea-Goraly et al.27 found that diffusion along fibre tracks was more and more

170

Developmental Medicine & Child Neurology 2008, 50: 168–181

anisotropic (i.e. along one axis) with age (range 6–19y) in a number of prefrontal regions, including right lateral, and medial, BA10 (see also Woo et al.29). GREY MATTER

MRI studies have also provided information on the development of grey matter in rostral PFC, which seems to follow a nonlinear time course. Sowell et al.30 scanned 45 children twice (2y apart) between the ages of 5 and 11 years. They showed that the thickness of grey matter in the right frontal cortex, including right rostral PFC, decreased significantly in absolute terms over the 2-year period between the scanning sessions, with 77.8% of points on this surface showing a significant loss. Konrad et al.31 observed a reduction in grey matter during adolescence, with a smaller grey matter volume in adults (20–34y) than in children (8–12y) bilaterally in rostral PFC. Giedd et al.32 found that frontal grey matter volumes peaked at about 11 to 13 years of age and then decreased during adolescence and early adulthood. In line with these findings, Sowell et al.33 observed a reduction in grey matter density between adolescence (12–16y) and adulthood (23–30y) in rostral PFC and other regions. O’Donnell et al.34 studied cortical thickness both in rostral PFC and dorsolateral PFC in children and adolescents. Cortical thickness was found to decrease with age from 8 to 20 years, but there was no difference in the rate of change between the two frontal regions. A study by Gogtay et al.,35 however, suggests that grey matter maturation ends earlier over rostral PFC than over the dorsolateral PFC (Fig. 2). In this longitudinal study, MRIs of children were obtained repeatedly (a minimum of three times) at 2-year intervals (range 4–21y). Decreases in grey matter volume during adolescence have been attributed to synaptic pruning.22 However, it is important to note that developmental changes in grey matter volume could, in addition, be a consequence of the increase in cortical myelination.35–37 BRAIN GROWTH

Sowell et al.30 evaluated another measure of brain development: brain growth, assessed by using a ‘distance from the centre’ method, which measures the radial expansion of the brain. (In this ‘distance from the centre’ method, a measure of radial expansion is calculated in millimetres from the centre of the brain to each point on the lateral surfaces, and from the centre of the hemispheres to each point on the medial surfaces.) This measure does not indicate increases in grey or white matter but simply reflects radial expansion of the exterior cortical surface, presumably caused by changes in the underlying grey and white matter. The results indicated that rostral PFC is one of the regions with the highest rate of brain growth in this age range (5–11y), with rates of growth between 0.5 and 1mm per year in right lateral and medial rostral PFC. Rostral PFC has thus been found to continue developing during childhood and adolescence (and beyond), with a reduction in synaptic density, reduction in grey matter density, and, particularly, fast brain growth. The protracted development of rostral PFC, as well as its anatomical characteristics (see the previous section), are two reasons to suppose that rostral PFC may support cognitive processing, which is especially important to humans.38

Cognitive functions supported by rostral PFC: studies in adults The results of neuropsychological and neuroimaging studies of rostral PFC function will be summarized below and followed by a brief discussion of some of the theories of PFC function (for more details see Burgess et al.39). EVIDENCE FROM HUMAN LESION STUDIES

Despite having profound problems in everyday life, patients with a lesion in rostral PFC often pass a wide range of cognitive tasks in the laboratory. A typical example is patient ‘AP’ from Shallice and Burgess40 (called ‘NM’ by Metzler and Parkin41). AP sustained a head injury that led to almost complete removal of the rostral PFC. However, on standard neuropsychological measures of intellectual functioning, memory, perception, and even traditional tests of executive function, AP performed within the superior range.42 This lack of general cognitive impairment has been replicated in several different patients.40,42–46 Some specific impairments, however, have been described: lesions in rostral PFC seem to lead to deficits when patients need to coordinate the performance of several tasks (multitasking), or in ill-structured situations. Multitasking typically involves maintaining superordinate or subordinate goals while performing another task (see Burgess44 for more details). Accordingly, one of AP’s most noticeable impairments in everyday life was a marked problem with multitasking, which manifested itself as tardiness and disorganization and which, despite his excellent intellect and social skills, ensured he never managed to return to work at the level he had enjoyed premorbidly.40–42 Shallice and Burgess40 designed two new tests of multitasking to assess these problems, a real-life multitasking test based on a shopping exercise, the ‘Multiple Errands Test’, and a multitasking test for use in the laboratory, the ‘Six Element Test’.44 Despite excellent general cognitive skills, AP and the other patients reported by Shallice and Burgess40 all performed these tasks below the 5% level compared with controls matched for age and IQ (see Burgess44 for a review of further patients described in the literature). Additionally, rostral PFC lesions have also been found to impair performance in ‘ill-structured’ situations dispropor-

tionately (e.g. Goel and Grafman46 and Grafman47); i.e. when the optimal way of behaving is not precisely signalled by the situation, so one has to impose one’s own structure. To summarize, patients with rostral PFC lesions may have a generally preserved IQ, episodic memory, and normal performance on standard tests of intelligence and executive functions. However, they are often impaired in multitasking, and in ‘ill-structured’ situations (namely those in which there are many possible ways of behaving, and the most advantageous is not immediately apparent). EVIDENCE FROM NEUROIMAGING

Another approach that has been used to study rostral PFC function is neuroimaging. The studies mentioned here have used either positron emission tomography or functional MRI. The latter measures blood-oxygen-level-dependent (BOLD) signal change, whereas the former usually measures differences in regional cerebral blood flow (rCBF). These techniques have shown that BOLD signal or rCBF changes in rostral PFC occur during a wide range of cognitive tasks,48 from the simplest paradigms (e.g. conditioning paradigms49) to highly complex tests involving memory and judgment50–53 or problem-solving,3 but also during rest (see Gusnard et al.54). Indeed, one can find recruitment of the rostral PFC in just about any kind of task.39 However, there does seem to be some evidence for functional specialization within rostral PFC. A recent meta-analysis of the changes in BOLD signal or rCBF observed in BA1055 showed that some paradigms show differences in BOLD signal or rCBF in lateral or medial BA10 depending on condition manipulations (e.g. working memory or episodic memory), whereas others tend to lead to the increase in BOLD signal or rCBF in a unique subregion of rostral PFC. In particular, two types of task seem to elicit BOLD signal or rCBF changes that occur both in the lateral and medial portions of BA10; further, these changes seem to reveal a dissociation of these two regions depending on the conditions that are contrasted. (1) Gilbert et al.55 defined a ‘Multitask’ category that included studies involving the performance of more than one task within any given block of trials, for example tasks in which one has to ‘bear something in mind’ while doing something else (voluntary task switching after a delay53,56),

Grey matter >0.27 0.22–0.26 0.17–0.21 <0.17

~9

Age

~16

Figure 2: Right lateral view of dynamic sequence of grey matter maturation over the cortical surface in age range 9 to 16 years obtained by Gogtay et al.35 The legend shows a shade representation of four categories of grey matter volume units. Rostral prefrontal cortex (PFC) seems to undergo earlier maturation than nearby dorsolateral PFC. (Adapted from Gogtay et al.,35 with kind permission from the authors and the publishers of Proceedings of the National Academy of Science of the USA, © 2004 National Academy of Sciences, USA.)

Review 171

prospective memory (PM; see Burgess et al.,51 Burgess et al.,57 and Okuda et al.58). There is an increased recruitment of the lateral parts of BA10 in PM or multitasking conditions in comparison with a control task, whereas in medial BA10 the BOLD signal or rCBF is higher in the control task. (2) A similar dissociation of medial/lateral recruitment was observed in the different conditions of attention shifting.59,60 Some tasks seem to lead to changes in the recruitment of specific subregions of BA10, suggesting a functional specialization of the underlying brain regions. Gilbert et al.55 found that lateral changes in rCBF or BOLD signal were more likely in ‘working memory’ tasks (86% of reported BOLD or rCBF signal changes in these tasks were lateral), defined in the manner of Cabeza and Nyberg61 and including studies involving the phonological loop (verbal/numerical maintenance), the visuo–spatial sketchpad (maintenance of object or spatial information), and the central executive (problem solving3,62,63). The recruitment of lateral rostral PFC was also more likely in ‘episodic retrieval’ tasks (86% of reported BOLD or rCBF signal changes in these tasks were lateral) – i.e. those involving the search, access, and monitoring of stored information about personally experienced past events – as well as to the sustained mental set underlying these processes.64,65 A reverse pattern was observed by Gilbert et al.55 in ‘mentalizing’ tasks – i.e., tasks requiring reflection on one’s mental states and those of other agents – which tended to recruit medial rostral PFC rather than lateral PFC (88% of reported BOLD or rCBF signal changes in these tasks were medial66–68; Fig. 3).

Gilbert et al.55 also observed a further subdivision of rostral PFC: conditions requiring mentalizing tended to be associated with increases in BOLD signal or rCBF in a medial region with a y coordinate lower (i.e. more posterior) than the mean of all studies, whereas multitasking tended to involve a rostral PFC region with a y coordinate higher (i.e. more anterior) than the mean of all studies. In other words, mentalizing tasks recruit a medial rostral PFC region located posteriorly within area 10, whereas multitasking tests tend to involve a medial rostral PFC region that is relatively polar (i.e. anterior; Fig. 3). To summarize, neuroimaging studies have found changes in BOLD signal or rCBF in rostral PFC in a large variety of tasks. These activities tend to be independent of the precise characteristics of the response mode or stimuli. PM and attention paradigms reveal a dissociation of BOLD signal or rCBF changes in lateral and medial BA10. In other tasks, only one subregion tends to show an increased BOLD signal or rCBF: episodic memory and working memory paradigms mostly lead to the recruitment of lateral rostral PFC, whereas mentalizing tasks mostly recruit medial rostral PFC. Additionally, changes in BOLD signal or rCBF during mentalizing have been found to be more caudal, and multitasking changes more rostral (see Burgess et al.69 for more details). THEORIES OF ROSTRAL PFC FUNCTION

A number of theories of rostral PFC function have been put forward in recent years on the basis of the neuropsychological and neuroimaging results. These theories will be presented

Predictions of the classification algorithm:

Multitasking Episodic memory

Mentalizing

Episodic memory

Figure 3: Partitioning of rostral prefrontal cortex according to a classification algorithm that predicted task category from absolute x and y coordinates of each peak of blood-oxygen-level-dependent signal or regional cerebral blood flow change. The algorithm predicted Episodic retrieval, Mentalizing, or Multitask on 92% occasions, so only these three categories are presented (leaving out the categories Perception, Attention, Language, Working memory, and Other memory). The algorithm used the absolute x coordinate to make predictions; left- and right-hemisphere shading overlays are therefore mirror images. Results are plotted on an axial slice at z=0. (Adapted from Gilbert et al.,55 with kind permission from the authors and the publishers of Journal of Cognitive Neuroscience, © 2006 Massachusetts Institute of Technology.)

172

Developmental Medicine & Child Neurology 2008, 50: 168–181

succinctly below. The aim of this section is to provide an overview of the possible functions of rostral PFC that will be used to approach the developmental and clinical data. As presented in the previous section, rostral PFC changes in rCBF or BOLD signal have been observed in a wide range of tasks. The recruitment of rostral PFC observed during episodic memory studies have led to the suggestion that rostral PFC is involved in aspects of memory retrieval,70 including retrieval mode,71–75 success monitoring (or retrieval verification), and source memory, i.e. remembering details of events that were not central to the event at the time, such as the temporal order of them, or thoughts and feelings that were provoked by them65 (reviewed by Ramnani and Owen20 and Burgess et al.39), or contextual recollection.50,65,76,77 Independently, direct empirical studies of mentalizing also suggest that this region may be engaged when we attend to our own or others’ mental states52 (reviewed by Ochsner et al.78 and Amodio and Frith79). Other theories have focused on rostral PFC’s recruitment in multitasking situations, which are consistent with the impairments in multitasking observed in patients with lesions in this area.44 These theories concentrate on the different demands made by multitasking. (1) Burgess et al.57 have proposed that rostral PFC is crucial for PM, which allows an intended act to be executed after a delay.51,58,80–82 (2) Pollmann and colleagues proposed that rostral PFC is involved in controlling the reallocation of attention, which can be necessary when the most salient stimulus, or the most salient feature of a stimulus, is not the most relevant for a task; i.e. where intentional selection of perceptual information is required.83–85 (3) Another set of hypotheses of rostral PFC function focuses on the ability to manage more than one behavioural goal. Koechlin et al.53,86 proposed that the observation of selective bilateral activity in rostral PFC when volunteers were required to keep in mind a main goal (a working memory task) while performing concurrent subgoals (dual-task performance) suggests that this region mediates ‘cognitive branching’, or the ability to ‘hold in mind goals while exploring and processing secondary goals’. Braver and Bongiolatti56 obtained similar data, but proposed that rostral PFC is involved in the integration of the results of the subgoal processing with the main goal, rather than in the storage of information (see also Ramnani and Owen20). (4) Finally, in a review of reasoning and episodic memory functional neuroimaging studies, Christoff and Gabrieli87 proposed that rostral PFC is specifically involved in ‘self-referential evaluation’, a process that would be critical when non-routine cognitive strategies have to be generated and selected in the context of novel tasks or activities. This process would be particularly involved in multitasking, where strategies are used to perform goals and subgoals, and could be related to impairments in illstructured situations observed in patients with rostral PFC lesions. Burgess and colleagues recently proposed a further theory of rostral PFC function39,69,88,89 that attempts to integrate the findings from human lesion studies with those from functional neuroimaging. The authors suggest that the impairments of patients with rostral PFC lesions in multitasking and ill-structured situations, and the recruitment of rostral PFC during PM indicate that rostral PFC is involved in coordinating the allocation of attention either towards perceptually derived information (the current stimuli) or towards stimulus-independent information that has been generated internally (e.g. the charac-

teristics of the PM condition, or the overall goal). To perform PM tasks or to multitask, participants need to switch regularly between these two types of representation. This theory could account for the recruitment of rostral PFC in a wide range of studies, because it is proposed that any type of task that requires either regular reallocation of attention between perceptually derived information and self-generated information (e.g. between cues and episodic memories) or forceful attending to a particular type of thoughts (e.g. attending to the less salient feature of a stimulus) would lead to the recruitment of rostral PFC. To summarize this section, whereas lesion studies show only limited impairments associated with rostral PFC lesions, principally in multitasking and dealing with novel situations, neuroimaging data demonstrate the recruitment of this region in a wide range of tasks. On the basis of these data, several theories of rostral PFC function have been proposed, attributing to this region a role in episodic memory, mentalizing, PM, reallocation of attention, cognitive branching, self-referential evaluation, or allocation of attention towards perceptually derived or self-generated information. In the next two sections, psychological and neuroimaging data relating to the development of some of these functions will be presented. Development of cognitive abilities supported by rostral PFC This section will first present behavioural studies of development in children and adolescents, and then neuroimaging studies that provide some information about the recruitment of rostral PFC during development in different cognitive tasks. BEHAVIOURAL STUDIES

The study of the development of executive functions, which is relatively recent,14,90,91 has provided evidence that the development of attentional and executive functions is a multistage process: different components have been found to develop at different times, beginning in infancy and continuing at least until adolescence (see Brocki and Bohlin92). Because of the breadth of evidence obtained in this field, this section will focus on two particular cognitive functions which have been associated with rostral PFC: PM and mentalizing. PM is an ability recruited during multitasking, or branching, and has been suggested to be supported by rostral PFC on the basis of neuroimaging and lesion studies (see above). Mentalizing has been associated in imaging studies with changes in BOLD signal or rCBF in medial rostral PFC52 (reviewed by Gilbert et al.55). Prospective memory Infants 11 to 12 months old are able to carry out an intention on the basis of stored information.93,94 Performance on PM tasks continues to improve during childhood95 and adolescence, with significant differences between late childhood and adolescence96–98 and between adolescence and adulthood.99 Children become increasingly skilled at using external cues95,100 and time-checking strategies.97 Ward et al.101 studied the performance of children (7–10y), adolescents (13–16y), and young adults (18–21y) on PM tasks. Children were worse than adolescents and adults at PM tasks. However, despite their similar performances, adolescents and adults differed in the strategies they reported using. Adolescents reported keeping the intention in mind and looking out for the PM cues more often than adults, while most adults reported remembering an

Review 173

intention only when they saw the PM cues (Table I). This suggests that the use of cognitive resources vary between adolescence and adulthood in PM tasks. Theory of Mind and mentalizing Frith and Frith52 reviewed behavioural studies of mentalizing development, and proposed the existence of two developmental spurts in mentalizing. First, children develop an understanding of desires, goals, and intentions at around 18 months, which suggest that the understanding of many mental states such as wanting, knowing, pretending, or believing is available in implicit form to 2-year-olds. The second developmental spurt is proposed to take place between the ages of 4 and 6 years. Typical tests of mentalizing at these ages involve false belief paradigms such as those used by Wimmer and Perner,102 in which children’s understanding of two sketches was tested: ‘in each sketch participants observed how a protagonist put an object into a location x and then witnessed that in the absence of the protagonist the object was transferred from x to location y. Since this transfer came as a surprise they had to assume that the protagonist still believed that the object was in x. Subjects had to indicate where the protagonist will look for the object at his return’ (p. 103). Children about 4 years old start to understand this scenario.102 At the age of 6 years all typically developing children understand the task (see, for example, BaronCohen et al.103), and also understand tasks involving more complex scenarios.104,105 Some studies have investigated the development of PM and mentalizing, which have both been associated with rostral PFC. PM, although present in a rudimentary form very early on, continues to develop throughout childhood and adolescence, in particular in relation to the use of strategies.101 There have been very few studies investigating mentalizing ability after the age of 6 years. However, it is logical to predict that, as a result of the development of the neural substrates of mentalizing during late childhood and adolescence, subtle changes in mentalizing ability might occur during this period of life. FUNCTIONAL NEUROIMAGING STUDIES

In this section we review neuroimaging studies that provide information on developmental changes in the recruitment of rostral PFC in a range of tasks. Response inhibition Studies of inhibition-related BOLD or rCBF changes during

development do not always observe variations in rostral PFC recruitment (see, for example, Tamm et al.106 and Durston et al.107); however, when they do, they consistently report decreases in BOLD or rCBF signal in adulthood compared with younger ages. Indeed, in go–no-go paradigms, Casey et al.108 observed that the middle frontal gyrus (BA9/10/46) was recruited less strongly and over a smaller region in adults (21–24y) than in children (7–12y), and Booth et al.109 observed reduced medial BA10 involvement in adults (20–30y) in comparison with children (9–12y). In a paradigm of emotional self-regulation, Levesque et al.110,111 found that voluntary suppression of emotion when shown sad film excerpts was associated with the recruitment of more prefrontal regions, including bilateral and medial BA10, in children (8–10y) than in adults (20–30y). Interestingly, Casey et al.112 focused on a young age group and observed that over this age range (9–11y) BOLD signal was found to increase with age in the middle and inferior frontal gyri. These results suggest a change in the recruitment of rostral PFC in situations of response inhibition during late childhood and adolescence. An initial increase in BOLD signal in this region112 followed by a decrease in BOLD signal108–111 seems consistent with the anatomical findings suggesting that grey matter volumes in the frontal cortex peak during early adolescence.32 Response competition Similarly to what has been observed for response inhibition, a study has shown evidence of a decrease in the recruitment of rostral PFC during response competition during development. Konrad et al.31 observed reduced BOLD signal in left lateral BA10 in adults (20–34y) in comparison with children (8–12y) when comparing incongruent targets with congruent targets in an Eriksen flanker task’s type of paradigm,113 where distractors can be congruent or incongruent with the target placed between them. Inferior frontal gyrus showed the reverse pattern. Two other contrasts: ‘reorienting’, comparing invalidly cued trials to validly cued trials, and ‘alerting’, comparing double-cue trials (no direction) to no-cue trials, did not show differences in BA10 BOLD signal between adults and children. However other studies did not find an effect of age on rostral PFC involvement when testing response competition with the Stroop task114,115 or in another study that employed the flanker task.116

Table I: Group frequencies of remembering strategies in a prospective memory task Strategy used Children (7–10y) Don’t know/can’t remember Remembered only when saw the cues Thought about all the time/looked out for the cues Thought about at the start, but with all the other things to think about, switched to remembering only with the cues Remembered only when saw the cues at the start but, with the reminders, began to think about all the time and looked out for the cues

Group frequencies (%) Adolescents Adults (13–16y) (18–21y)

3.3 73.3 23.3

0.0 37.9 48.9

0.0 73.3 20.0

0.0

10.3

3.3

0.0

3.4

3.3

Table taken from Ward et al.,101 with kind permission from the authors and the publishers of Child Neuropsychology, © 2005 Taylor & Francis Ltd, http://www.informaworld.com

174

Developmental Medicine & Child Neurology 2008, 50: 168–181

Working memory Studies of working memory tasks do not seem to follow the same pattern of decreased recruitment in adults compared with children and adolescents, although only a few studies have investigated the involvement of PFC during development in these tasks. Kwon et al.117 tested participants from 7 to 22 years old in a two-back spatial working memory task and found that the BOLD signal in bilateral rostral PFC increased with age during the working memory task in comparison with a control task. More specifically, both the size of these clusters and the t value of the contrast increased with age, whereas accuracy and reaction times were not significantly different. Other studies did not find age effects on rostral PFC recruitment in tasks with a working memory component. Thomas et al.118 found that when comparing an n-back task with a motor condition, a similar right lateral BA10/46 region showed increased BOLD signal both in adults (19–26y of age) and children (8–10y; see also Klingberg et al.119). Crone et al.120 observed a greater recruitment of right dorsolateral PFC during the delay period, when the working memory items had to be manipulated rather than only maintained, in adolescents (13–17y) and adults (18–25y) but not in children (8–12y); however, no effect was found in rostral PFC. Intelligence quotient Shaw et al.121 obtained MRIs of a large number of children in a longitudinal study and correlated cortical thickness and IQ, on the basis of Wechsler intelligence scales.122 The results showed that the relationship between cortical thickness and IQ changes during development and also differs across brain regions (see also Passingham123 and Reiss et al.124). Of particular interest here was a comparison between the ‘superior intelligence’ and ‘average intelligence’ groups that showed that from about 10 years old there is a rapid increase in cortical thickness in the superior intelligence group in comparison with the average intelligence group, which peaks at the age of 13 years and wanes in late adolescence. This effect is observed particularly strongly and over an extended period, over the rostral PFC. Mentalizing A recent functional MRI study investigated the development of communicative intent by using an irony comprehension task and found that children (aged between 9 and 14y) engaged frontal regions (medial PFC and left inferior frontal gyrus) more than adults did in this task.125 A similar result was revealed in a study in which we investigated the development during adolescence of the neural network underlying thinking about intentions.126 In this study, 19 adolescent participants (12–18y) and 11 adults (22–37y) were scanned with functional MRI. In both adults and adolescents, answering questions about intentional causality versus physical causality activated the mentalizing network, including medial PFC, superior temporal sulcus, and temporal poles. In addition, there was a significant interaction between group and task in the medial PFC. During intentional relative causality to physical causality, adolescents activated part of the medial PFC more than adults did, and adults activated part of the right superior temporal sulcus more than adolescents did. These results suggest that the neural strategy for thinking about intentions changes between adolescence and adulthood. Although the same neural network is active, the relative roles

of the different areas change, with activity moving from anterior (medial prefrontal) regions to posterior (temporal) regions with age. To summarize, because of the novelty of the field of neuroimaging of brain development, there is only limited evidence about changes in rostral PFC recruitment. However, when changes have been observed, they have quite consistently shown reduced recruitment of this region between late childhood–adolescence and adulthood. These results should be considered in relation to the anatomical findings of decreased grey matter volume in rostral PFC during adolescence presented above.31,33,34 Indeed, the progressive growth of the nervous system during development results in an overabundance of connections. Several mechanisms have been found to have a role in removing or modifying the exuberant connections to ensure that a functional organization of circuitry is established. These include cell death,127 elimination of synapses,128 and the fine tuning of axon terminals and elimination of long axon collaterals, or ‘axon pruning’.129,130 Maturation of the neural networks through such mechanisms during late childhood–adolescence could lead to an increase in the efficiency of the cerebral regions involved in particular tasks, and could thus lead to a reduction in the observed increases in BOLD signal or rCBF during the performance of these tasks. Role of rostral PFC in adult and childhood psychological disorders As we have seen, rostral PFC is a region that undergoes protracted development that continues through adolescence in humans. Thus, developmental abnormalities occurring at any point from conception through to adolescence could affect rostral PFC function. It has been suggested that rostral PFC can be subdivided into regions on the basis of both anatomical and functional differences (see also Burgess et al.69). It is possible that these regions mature at different rates. Thus, abnormal development of early-developing and late-developing subregions might result in different disorders. In this section we briefly present three developmental disorders that have different onsets and have been associated with abnormal functioning of BA10. AUTISM SPECTRUM DISORDERS

ASDs are characterized by impairments in reciprocal social interaction and communication, together with the presence of stereotyped or repetitive behaviours (Diagnostic and Statistical Manual of Mental Disorders, 4th edn; DSMIV131). ASDs are usually diagnosed in early childhood, but rarely before the age of 2 years, and are expressed throughout life.132 Neuroanatomical differences between children with ASD and controls have been observed as early as 2 to 3 years of age.133 One recent finding is that people with autism have a greater total brain volume (Bolton et al.134), which appears during the first few years of life and disappears from adolescence onwards.133,135 One speculative theory is that brain enlargement might be due to a lack of early synaptic pruning.136 Some neuroanatomical and functional neuroimaging studies have pointed to abnormalities in rostral PFC, although the directions of the effects are inconsistent.137–140 Several cognitive abilities that have been associated with rostral PFC are impaired in ASD. These include multitasking,141,142 episodic memory,143–148 performance in novel

Review 175

situations,149–151 and mentalizing.103,152–157 Hill and Bird142 found that impairment of individuals with Asperger’s syndrome (a disorder at the high functioning end of the autism spectrum) correlated, on a multitasking paradigm, with the severity of their symptoms. PM does not seem to have been studied, but Farrant et al.157 found that children with autism were not particularly impaired in reflecting on memory strategies, including PM ones. ATTENTION - DEFICIT – HYPERACTIVITY DISORDER

ADHD is a neuropsychiatric disorder with onset at preschool age, characterized by hyperactivity, inattentiveness, and impulsivity, which can persist into adulthood (DSM-IV131).158 Structural studies show that children with ADHD have a smaller total brain volume than control children do.159 Reduced volumes of grey and/or white matter in the PFC have been observed, although no study has found a specific abnormality in rostral PFC (reviewed by Durston,158 Krain and Castellanos,160 and Schneider et al.161). In relation to possible rostral PFC dysfunction, individuals with ADHD seem to be impaired in multitasking and PM.162–166 Further, one of these studies found a significant correlation between PM performance and a clinical measure of ADHD.166 Mentalizing abilities have not been shown to be impaired in one study,167 whereas evidence is mixed regarding impairments in episodic memory.148,168–170 SCHIZOPHRENIA

Schizophrenia is a disorder in which individuals experience positive symptoms such as auditory hallucinations and delusions as well as negative symptoms including loss of motivation, poverty of thought, and emotional blunting (DSMIV131). People with schizophrenia often have executive and memory deficits.171 Schizophrenia seems to have a significant developmental component, with abnormalities possibly taking place at different stages of development,172 including before birth.173,174 Although the onset of psychosis tends to be during early adulthood, many children who go on to develop schizophrenia tend to display early neurological and cognitive problems.175–178 It has recently been suggested that schizophrenia may be associated with synaptic abnormalities that occur during cortex maturation in adolescence.179,180 Structural imaging studies suggest some abnormalities in rostral medial PFC/orbito-frontal cortex, including a reduction in grey matter181–183 and increased mean diffusivity.184 Harrison185 has suggested that the decreased cortical volume observed in schizophrenia is due to decreased neuropil and neuronal size, in particular in the prefrontal cortex (see also Selemon and Goldman-Rakic186). Reduced neuropil fraction has been observed in BA10,187 as well as reduced grey-level index, possibly associated with a reduced neuropil fraction.188 Several cognitive abilities associated with rostral PFC function have been observed to be impaired in schizophrenia, and one might speculate that there would be a relation with particular symptoms. For instance, mentalizing impairments (reviewed by Harrington et al.189 and Brune190) may relate to delusions of persecution.191–196 Episodic memory impairment also seems to be associated with current symptoms (see Turetsky et al.;197 reviewed by Keri and Janka198), and some have argued that source memory impairment could be associated with hallucination symptoms.199–202 However, impairment in PM203–207

176

Developmental Medicine & Child Neurology 2008, 50: 168–181

is present mainly in patients with other executive functions deficits.208 Finally, multitasking impairments have been observed in schizophrenic patients,209,210 particularly in patients with a high level of negative symptoms211 (see also Chan et al.212 and Katz et al.213). The overview above suggests that ASDs, many of which are diagnosed in early development, are associated with widespread impairment of cognitive abilities associated with rostral PFC function. Thus, it is possible that ASDs are associated with abnormal early development of rostral PFC. ADHD, which has a later onset, seems to be associated with less widespread impairment: e.g. mentalizing seems to be preserved and episodic memory has less consistently been found to be impaired. This would support the idea that mentalizing abilities are well in place, and episodic memory possibly partly so, by the time that ADHD-related developmental abnormalities occur. Schizophrenia seems to be more complicated, as developmental abnormalities at different stages overlap. Similarly to ASDs, a wide range of cognitive abilities are impaired in schizophrenia, although generally these impairments are associated with specific symptomatology. The different symptoms observed might be the consequences of different developmental abnormalities during childhood and adolescence. Conclusion We have presented the current knowledge of the development of rostral PFC in relation to theories of the cognitive functions supported by this region. Anatomical studies show that rostral PFC is larger and more differentiated in humans than in other primates. This region has a low cell density and numerous connections to the supramodal cortex. Additionally, there is evidence for a subdivision of rostral PFC along a posterior–anterior axis. Anatomical and MRI studies reveal prolonged development of BA10, with rapid brain expansion during late childhood, decreases in grey matter and synaptic density, and increases in dendritic arborization during childhood and adolescence. In adults, lesion studies suggest a role of rostral PFC in multitasking and novel situations. Neuroimaging studies, in contrast, show widespread involvement of this region in multiple tasks. A meta-analysis of these studies55 suggests a subdivision of this region along the posterior–anterior axis, between the areas recruited preferentially in mentalizing and multitasking paradigms respectively. An additional subdivision along a lateral–medial axis is suggested, which has not been observed anatomically, and which would differentiate between episodic and working memory tasks and mentalizing tasks. On the basis of the neuropsychological and neuroimaging data, several theories have been proposed, attributing to rostral PFC a role in episodic and prospective memory, mentalizing, allocation of attention, cognitive branching, and self-referential evaluation. In this review we have attempted to link experimental psychology and neuroimaging findings of developmental studies to these proposed functions of rostral PFC. Psychological studies indicate that certain executive function capacities undergo extended development, with the presence early on of a certain level of ability and prolonged improvements in performance in the more complex tasks until late adolescence. This pattern thus follows the prolonged development, in particular the decreases in grey matter volumes and densities, observed in rostral PFC during childhood and

adolescence. Neuroimaging data of response inhibition, response competition, and working memory, although limited, suggest that when a change during development is observed in rostral PFC, it tends to be in the direction of a reduction of the recruitment of this region between late childhood–adolescence and adulthood. Again, these results are consistent with the anatomical findings of decreased grey matter volume in rostral PFC. Moreover, the study by Shaw et al.121 suggests that processes of cortical extension and pruning might have an important role in the variations of intellectual abilities between individuals. Studying the development of functions attributed to rostral PFC, and their impairments, could help tease apart different components of this brain region. This would be particularly relevant for rostral PFC, as neuroimaging and anatomical studies already suggest subdivisions of this region. Accepted for publication 30th August 2007. References 1. Brodmann K. Beitraege zur histologischen Lokalisation der Grosshirnrinde. VI. Mitteilung: die Cortex-gliederung des Menschen. J Psychol Neurol (Lpz) 1908; 10: 231–46. 2. Brodmann K. Vergleichende Localisationslehre der Grosshirnrinde in ihren prinzipien Dargestellt auf Grund des Zellenbaues. Leipzig: Barth, 1909. 3. Christoff K, Prabhakaran V, Dorfman J, et al. Rostrolateral prefrontal cortex involvement in relational integration during reasoning. Neuroimage 2001; 14: 5136–49. 4. Semendeferi K, Armstrong E, Schleicher A, Zilles K, Van Hoesen GW. Prefrontal cortex in humans and apes: a comparative study of area 10. Am J Phys Anthropol 2001; 114: 324–41. 5. Petrides M, Pandya DN. Comparative architectonic analysis of the human and the macaque frontal cortex. In: Boller F, Grafman J, editors. Handbook of Neuropsychology vol. 9. Amsterdam: Elsevier Science, 1994: 17–58. 6. Jacobs B, Schall M, Prather M, et al. Regional dendritic and spine variation in human cerebral cortex: a quantitative golgi study. Cereb Cortex 2001; 11: 658–71. 7. Holloway RL. Brief communication: how much larger is the relative volume of area 10 of the prefrontal cortex in humans? Am J Phys Anthropol 2002; 118: 499–501. 8. Öngür D, Ferry AT, Price JL. Architectonic subdivision of the human orbital and medial prefrontal cortex. J Comp Neurol 2003; 460: 325–49. 9. Carmichael ST, Price JL. Connectional networks within the orbital and medial prefrontal cortex of macaque monkeys. J Comp Neurol 1996; 371: 279–307. 10. An X, Bandler R, Öngür D, Price JL. Prefrontal cortical projections to longitudinal columns in the midbrain periaqueductal gray in macaque monkeys. J Comp Neurol 1998; 401: 455–79. 11. Öngür D, An X, Price JL. Prefrontal cortical projections to the hypothalamus in macaque monkeys. J Comp Neurol 1998; 401: 480–505. 12. Carmichael ST, Price JL. Architectonic subdivision of the orbital and amedial prefrontal cortex in the macaque monkey. J Comp Neurol 1994; 346: 366–402. 13. Carmichael ST, Price JL. Sensory and premotor connections of the orbital and medial prefrontal cortex of macaque monkeys. J Comp Neurol 1995; 363: 442–64. 14. Romanski LM, Bates JF, Goldman-Rakic PS. Auditory belt and parabelt projections to the prefrontal cortex in the rhesus monkey. J Comp Neurol 1999; 403: 241–57. 15. Öngür D, Price JL. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex 2000; 10: 306–19. 16. Petrides M, Pandya DN. Dorsolateral prefrontal cortex: comparative cytoarchitectonic analysis in the human and the macaque brain and corticocortical connection patterns. Eur J Neurosci 1999; 11: 3011–36.

17. Moran MA, Mufson EJ, Mesulam MM. Neural inputs into the temporopolar cortex of the rhesus monkey. J Comp Neurol 1987; 256: 88–103. 18. Arikuni T, Sako H, Murata A. Ipsilateral connections of the anterior cingulate cortex with the frontal and medial temporal cortices in the macaque monkey. Neurosci Res 1994; 21: 19–39. 19. Passingham RE, Stephan KE, Kotter R. The anatomical basis of functional localization in the cortex. Nat Rev Neurosci 2002; 3: 806–16. 20. Ramnani N, Owen AM. Anterior prefrontal cortex: insights into function from anatomy and neuroimaging. Nat Rev Neurosci 2004; 5: 384–94. 21. Travis K, Ford K, Jacobs B. Regional dendritic variation in neonatal human cortex: a quantitative Golgi study. Dev Neurosci 2005; 27: 577–87. 22. Huttenlocher PR. Synaptic density in human frontal cortex – developmental changes and effects of aging. Brain Res 1979; 163: 195–205. 23. Huttenlocher PR, Dabholkar AS. Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol 1997; 387: 267–78. 24. Schade JP, van Groenigen GW. Structural organization of the human cerebral cortex. 1. Maturation of the middle frontal gyrus. Acta Anat (Basel) 1961; 47: 74–111. 25. Klingberg T, Vaidya CJ, Gabrieli JD, Moseley ME, Hedehus M. Myelination and organization of the frontal white matter in children: a diffusion tensor MRI study. Neuroreport 1999; 10: 2817–21. 26. Nagy Z, Westerberg H, Klingberg T. Maturation of white matter is associated with the development of cognitive functions during childhood. J Cogn Neurosci 2004; 16: 1227–33. 27. Barnea-Goraly N, Menon V, Eckert M, et al. White matter development during childhood and adolescence: a crosssectional diffusion tensor imaging study. Cereb Cortex 2005; 15: 1848–54. 28. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B 1996; 111: 209–19. 29. Woo J, Asato M, Terwilliger R, et al. White matter development from childhood to adulthood corresponding to cognitive development. CNS Conference: New York, 2007. 30. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW. Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci 2004; 24: 8223–31. 31. Konrad K, Neufang S, Thiel CM, et al. Development of attentional networks: an fMRI study with children and adults. Neuroimage 2005; 28: 429–39. 32. Giedd JN, Blumenthal J, Jeffries NO, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci 1999; 2: 861–63. 33. Sowell ER, Thompson PM, Holmes CJ, Jernigan TL, Toga AW. In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nat Neurosci 1999; 2: 859–61. 34. O’Donnell S, Noseworthy MD, Levine B, Dennis M. Cortical thickness of the frontopolar area in typically developing children and adolescents. Neuroimage 2005; 24: 948–54. 35. Gogtay N, Giedd JN, Lusk L, et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci USA 2004; 101: 8174–79. 36. Paus T. Mapping brain maturation and cognitive development during adolescence. Trends Cogn Sci 2005; 9: 60–68. 37. Sowell ER, Thompson PM, Tessner KD, Toga AW. Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: Inverse relationships during postadolescent brain maturation. J Neurosci 2001; 21: 8819–29. 38. Amati D, Shallice T. On the emergence of modern humans. Cognition 2007; 103: 358–85. 39. Burgess PW, Simons JS, Dumontheil I. The gateway hypothesis of rostral prefrontal cortex (area 10) function. In: Duncan J, Philips L, McLeod PD, editors. Measuring the mind: speed, control, and age. Oxford: Oxford University Press, 2005: 217–48. 40. Shallice T, Burgess PW. Deficits in strategy application following frontal lobe damage in man. Brain 1991; 114: 727–41. 41. Metzler C, Parkin AJ. Reversed negative priming following frontal lobe lesions. Neuropsychologia 2000; 38: 363–79.

Review 177

42. Wood RL, Rutterford NA. Relationships between measured cognitive ability and reported psychosocial activity after bilateral frontal lobe injury: an 18-year follow-up. Neuropsychol Rehabil 2004; 14: 329–50. 43. Bird CM, Castelli F, Malik O, Frith U, Husain M. The impact of extensive medial frontal lobe damage on ‘Theory of Mind’ and cognition. Brain 2004; 127: 914–28. 44. Burgess PW. Strategy application disorder: the role of the frontal lobes in human multitasking. Psychol Res 2000; 63: 279–88. 45. Goldstein LH, Bernard S, Fenwick PB, Burgess PW, McNeil J. Unilateral frontal lobectomy can produce strategy application disorder. J Neurol Neurosurg Psychiatry 1993; 56: 274–76. 46. Goel V, Grafman J. Role of the right prefrontal cortex in illstructured planning. Cogn Neuropsychol 2000; 17: 415–36. 47. Grafman J. The structured event complex and the human prefrontal cortex. In: Stuss DT, Knight RT, ed. Principles of frontal lobe function. New York: Oxford University Press, 2002: 292–10. 48. Grady CL. Neuroimaging and activation of the frontal lobes. In: Miller BL, Cummings JL, ed. The human frontal lobes: function and disorders. New York: Guilford Press, 1999: 196–230. 49. Blaxton TA, Zeffiro TA, Gabrieli JD, et al. Functional mapping of human learning: a positron emission tomography activation study of eyeblink conditioning. J Neurosci 1996; 16: 4032–40. 50. Burgess N, Maguire EA, Spiers HJ, O’Keefe J. A temporoparietal and prefrontal network for retrieving the spatial context of lifelike events. Neuroimage 2001; 14: 439–53. 51. Burgess PW, Scott SK, Frith CD. The role of the rostral frontal cortex (area 10) in prospective memory: a lateral versus medial dissociation. Neuropsychologia 2003; 41: 906–18. 52. Frith U, Frith CD. Development and neurophysiology of mentalizing. Phil Trans R Soc B 2003; 358: 459–73. 53. Koechlin E, Basso G, Pietrini P, Panzer S, Grafman J. The role of the anterior prefrontal cortex in human cognition. Nature 1999; 399: 148–51. 54. Gusnard DA, Akbudak E, Shulman GL, Raichle ME. Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function. Proc Natl Acad Sci USA 2001; 98: 4259–64. 55. Gilbert SJ, Spengler S, Simons JS, et al. Functional specialization within rostral prefrontal cortex (area 10): a meta-analysis. J Cogn Neurosci 2006; 18: 932–48. 56. Braver TS, Bongiolatti SR. The role of frontopolar cortex in subgoal processing during working memory. Neuroimage 2002; 15: 523–36. 57. Burgess PW, Quayle A, Frith CD. Brain regions involved in prospective memory as determined by positron emission tomography. Neuropsychologia 2001; 39: 545–55. 58. Okuda J, Fujii T, Yamadori A, et al. Participation of the prefrontal cortices in prospective memory: evidence from a PET study in humans. Neurosci Lett 1998; 253: 127–30. 59. Pollmann S, Weidner R, Muller HJ, von Cramon DY. A frontoposterior network involved in visual dimension changes. J Cogn Neurosci 2000; 12: 480–94. 60. Weidner R, Pollmann S, Muller HJ, von Cramon DY. Top-down controlled visual dimension weighting: an event-related fMRI study. Cereb Cortex 2002; 12: 318–28. 61. Cabeza R, Nyberg L. Imaging cognition II: an empirical review of 275 PET and fMRI studies. J Cogn Neurosci 2000; 12: 1–47. 62. Braver TS, Barch DM, Kelley WM, et al. Direct comparison of prefrontal cortex regions engaged by working and long-term memory tasks. Neuroimage 2001; 14: 48–59. 63. Christoff K, Ream JM, Geddes LP, Gabrieli JD. Evaluating selfgenerated information: anterior prefrontal contributions to human cognition. Behav Neurosci 2003; 117: 1161–68. 64. Simons JS, Gilbert SJ, Owen AM, Fletcher PC, Burgess PW. Distinct roles for lateral and medial anterior prefrontal cortex in contextual recollection. J Neurophysiol 2005; 94: 813–20. 65. Rugg MD, Fletcher PC, Chua PM, Dolan RJ. The role of the prefrontal cortex in recognition memory and memory for source: an fMRI study. Neuroimage 1999; 10: 520–29. 66. Castelli F, Happe F, Frith U, Frith C. Movement and mind: a functional imaging study of perception and interpretation of complex intentional movement patterns. Neuroimage 2000; 12: 314–25.

178

Developmental Medicine & Child Neurology 2008, 50: 168–181

67. Zysset S, Huber O, Samson A, Ferstl EC, von Cramon DY. Functional specialization within the anterior medial prefrontal cortex: a functional magnetic resonance imaging study with human subjects. Neurosci Lett 2003; 335: 183–86. 68. Zysset S, Muller K, Lohmann G, von Cramon DY. Color-word matching stroop task: separating interference and response conflict. Neuroimage 2001; 13: 29–36. 69. Burgess PW, Gilbert SJ, Dumontheil I. Function and localization within rostral prefrontal cortex (area 10). Phil Trans R Soc B 2007; 362: 887–99. 70. Duncan J, Owen AM. Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends Neurosci 2000; 23: 475–83. 71. Tulving E. Elements of Episodic Memory. Oxford: Clarendon Press, 1983. 72. Duzel E, Cabeza R, Picton TW, et al. Task-related and item-related brain processes of memory retrieval. Proc Natl Acad Sci USA 1999; 96: 1794–99. 73. Kapur S, Craik FI, Jones C, Brown GM, Houle S, Tulving E. Functional role of the prefrontal cortex in retrieval of memories: a PET study. Neuroreport 1995; 6: 1880–84. 74. Nyberg L, McIntosh AR, Cabeza R, Habib R, Houle S, Tulving E. General and specific brain regions involved in encoding and retrieval of events: what, where, and when. Proc Natl Acad Sci USA 1996; 93: 11280–85. 75. Velanova K, Jacoby LL, Wheeler ME, McAvoy MP, Petersen SE, Buckner RL. Functional-anatomic correlates of sustained and transient processing components engaged during controlled retrieval. J Neurosci 2003; 23: 8460–70. 76. Dobbins IG, Foley H, Schacter DL, Wagner AD. Executive control during episodic retrieval: multiple prefrontal processes subserve source memory. Neuron 2002; 35: 989–96. 77. Ranganath C, Johnson MK, D’Esposito M. Left anterior prefrontal activation increases with demands to recall specific perceptual information. J Neurosci 2000; 20: 1–5. 78. Ochsner KN, Knierim K, Ludlow DH, et al. Reflecting upon feelings: an fMRI study of neural systems supporting the attribution of emotion to self and other. J Cogn Neurosci 2004; 16: 1746–72. 79. Amodio DM, Frith CD. Meeting of minds: the medial frontal cortex and social cognition. Nat Rev Neurosci 2006; 7: 268–77. 80. den Ouden HE, Frith U, Frith C, Blakemore SJ. Thinking about intentions. Neuroimage 2005; 28: 787–96. 81. Simons JS, Schölvinck ML, Gilbert SJ, Frith CD, Burgess PW. Differential components of prospective memory? Evidence from fMRI. Neuropsychologia 2006; 44: 1388–97. 82. Coull JT, Frith CD, Frackowiak RS, Grasby PM. A fronto-parietal network for rapid visual information processing: a PET study of sustained attention and working memory. Neuropsychologia 1996; 34: 1085–95. 83. Pollmann S. Switching between dimensions, locations, and responses: the role of the left frontopolar cortex. Neuroimage 2001; 14: S118–S24. 84. Pollmann S. Anterior prefrontal cortex contributions to attention control. Exp Psychol 2004; 51: 270–78. 85. Lepsien J, Pollmann S. Covert reorienting and inhibition of return: an event-related fMRI study. J Cogn Neurosci 2002; 14: 127–44. 86. Koechlin E, Ody C, Kouneiher F. The architecture of cognitive control in the human prefrontal cortex. Science 2003; 302: 1181–85. 87. Christoff K, Gabrieli JDE. The frontopolar cortex and human cognition: Evidence for a rostrocaudal hierarchical organization within the human prefrontal cortex. Psychobiology 2000; 28: 168–86. 88. Burgess PW, Dumontheil I, Gilbert SJ. The gateway hypothesis of rostral prefrontal cortex (area 10) function. Trends Cogn Sci 2007; 11: 290–98. 89. Burgess PW, Gilbert SJ, Okuda J, Simons JS. Rostral prefrontal brain regions (area 10): a gateway between inner thought and the external world? In: Sebanz N, Prinz W, eds. Disorders of volition. Cambridge, MA: MIT Press, 2006: 373–96. 90. Hughes C. Executive functions and development: emerging themes. Infant Child Dev 2002; 11: 201–09. 91. Hughes C. Executive functions and development: why the interest? Infant Child Dev 2002; 11: 69–71.

92. Brocki KC, Bohlin G. Executive functions in children aged 6 to 13: a dimensional and developmental study. Dev Neuropsychol 2004; 26: 571–93. 93. Diamond A. Development of the ability to use recall to guide action, as indicated by infants’ performance on AB. Child Dev 1985; 56: 868–83. 94. Goldman-Rakic PS. Development of cortical circuitry and cognitive function. Child Dev 1987; 58: 601–22. 95. Guajardo NR, Best DL. Do preschoolers remember what to do? Incentive and external cues in prospective memory. Cogn Dev 2000; 15: 75–97. 96. Ceci SJ, Bronfenbrenner U. ‘Don’t forget to take the cupcakes out of the oven’: prospective memory, strategic time-monitoring, and context. Child Dev 1985; 56: 152–64. 97. Kerns KA. The CyberCruiser: an investigation of development of prospective memory in children. J Int Neuropsychol Soc 2000; 6: 62–70. 98. Kvavilashvili L, Messer DJ, Ebdon P. Prospective memory in children: the effects of age and task interruption. Dev Psychol 2001; 37: 418–30. 99. Wang L, Kliegel M, Yang Z, Liu W. Prospective memory performance across adolescence. J Genet Psychol 2006; 167: 179–88. 100. Meacham JA, Colom JA. External retrieval cues facilitate prospective remembering in children. J Educ Res 1980; 73: 299–301. 101. Ward H, Shum D, McKinlay L, Baker-Tweney S, Wallace G. Development of prospective memory: tasks based on the prefrontal-lobe model. Child Neuropsychol 2005; 11: 527–49. 102. Wimmer H, Perner J. Beliefs about beliefs: representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition 1983; 13: 103–28. 103. Baron-Cohen S, Leslie AM, Frith U. Does the autistic child have a ‘theory of mind’? Cognition 1985; 21: 37–46. 104. Perner J, Wimmer H. ‘John thinks that Mary thinks that...’: attribution of second-order beliefs by 5- to 10-year-old children. J Exp Child Psychol 1985; 39: 437–71. 105. Sullivan K, Zaitchik D, Tager-Flusberg H. Preschoolers can attribute second-order beliefs. Dev Psychol 1994; 30: 395–402. 106. Tamm L, Menon V, Reiss AL. Maturation of brain function associated with response inhibition. J Am Acad Child Adolesc Psychiatry 2002; 41: 1231–38. 107. Durston S, Thomas KM, Yang YH, Ulug AM, Zimmerman RD, Casey BJ. A neural basis for the development of inhibitory control. Dev Sci 2002; 5: F9–F16. 108. Casey BJ, Trainor RJ, Orendi JL, et al. A developmental functional MRI study of prefrontal activation during performance of a Go/No-go task. J Cogn Neurosci 1997; 9: 835–47. 109. Booth JR, Burman DD, Meyer JR, et al. Neural development of selective attention and response inhibition. Neuroimage 2003; 20: 737–51. 110. Levesque J, Eugene F, Joanette Y, et al. Neural circuitry underlying voluntary suppression of sadness. Biol Psychiatry 2003; 53: 502–10. 111. Levesque J, Joanette Y, Mensour B, et al. Neural basis of emotional self-regulation in childhood. Neuroscience 2004; 129: 361–69. 112. Casey BJ, Cohen JD, Jezzard P, et al. Activation of prefrontal cortex in children during a nonspatial working memory task with functional MRI. Neuroimage 1995; 2: 221–29. 113. Eriksen BA, Eriksen CW. Effects of noise letters on the identification of a target letter in a nonsearch task. Percept Psychophys 1974; 16: 143–49. 114. Adleman NE, Menon V, Blasey CM, et al. A developmental fMRI study of the Stroop color–word task. Neuroimage 2002; 16: 61–75. 115. Marsh R, Zhu H, Schultz RT, et al. A developmental fMRI study of self-regulatory control. Hum Brain Mapp 2006; 27: 848–63. 116. Bunge SA, Dudukovic NM, Thomason ME, Vaidya CJ, Gabrieli JD. Immature frontal lobe contributions to cognitive control in children: evidence from fMRI. Neuron 2002; 33: 301–11. 117. Kwon H, Reiss AL, Menon V. Neural basis of protracted developmental changes in visuo-spatial working memory. Proc Natl Acad Sci USA 2002; 99: 13336–41. 118. Thomas KM, King SW, Franzen PL, et al. A developmental

functional MRI study of spatial working memory. Neuroimage1999; 10: 327–38. 119. Klingberg T, Forssberg H, Westerberg H. Increased brain activity in frontal and parietal cortex underlies the development of visuospatial working memory capacity during childhood. J Cogn Neurosci 2002; 14: 1–10. 120. Crone EA, Bunge SA, van der Molen MW, Ridderinkhof KR. Switching between tasks and responses: a developmental study. Dev Sci 2006; 9: 278–87. 121. Shaw P, Greenstein D, Lerch J, et al. Intellectual ability and cortical development in children and adolescents. Nature 2006; 440: 676–79. 122. Wechsler D. Manual for the Wechsler Adult Intelligence Scale. New York: The Psychological Corporation, 1955. 123. Passingham R. Cognitive science: brain development and IQ. Nature 2006; 440: 619–20. 124. Reiss AL, Abrams MT, Singer HS, Ross JL, Denckla MB. Brain development, gender and IQ in children. A volumetric imaging study. Brain 1996; 119: 1763–74. 125. Wang AT, Lee SS, Sigman M, Dapretto M. Developmental changes in the neural basis of interpreting communicative intent. Soc Cogn Affect Neurosci 2006; 1: 107–21. 126. Blakemore S-J, den Ouden H, Choudhury S, Frith C. Adolescent development of the neural circuitry for thinking about intentions. Soc Cogn Affect Neurosci 2007; 2: 130–39. 127. Yuan J, Lipinski M. Diversity in the mechanisms of neuronal cell death. Neuron 2003; 40: 401–13. 128. Goda Y, Davis GW. Mechanisms of synapse assembly and disassembly. Neuron 2003; 40: 243–64. 129. Low LK, Cheng HJ. Axon pruning: an essential step underlying the developmental plasticity of neuronal connections. Phil Trans R Soc B 2006; 361: 1531–44. 130. Kantor DB, Kolodkin AL. Curbing the excesses of youth: molecular insights into axonal pruning. Neuron 2003; 38: 849–52. 131. American Psychiatric Association. Diagnostic and statistical manual of mental disorders, 4th edn. Washington, DC: American Psychiatric Association, 1994. 132. Waiter GD, Williams JH, Murray AD, Gilchrist A, Perrett DI, Whiten A. A voxel-based investigation of brain structure in male adolescents with autistic spectrum disorder. Neuroimage 2004; 22: 619–25. 133. Courchesne E, Karns CM, Davis HR, et al. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 2001; 57: 245–54. 134. Bolton PF, Roobol M, Allsopp L, Pickles A. Association between idiopathic infantile macrocephaly and autism spectrum disorders. Lancet 2001; 358: 726–27. 135. Aylward EH, Minshew NJ, Field K, Sparks BF, Singh N. Effects of age on brain volume and head circumference in autism. Neurology 2002; 59: 175–83. 136. Frith C. What do imaging studies tell us about the neural basis of autism? Novartis Found Symp 2003; 251: 149–66; discussion 166–76, 281–97. 137. Abell F, Krams M, Ashburner J, et al. The neuroanatomy of autism: a voxel-based whole brain analysis of structural scans. Neuroreport 1999; 10: 1647–51. 138. Ohnishi T, Matsuda H, Hashimoto T, et al. Abnormal regional cerebral blood flow in childhood autism. Brain 2000; 123: 1838–44. 139. Waiter GD, Williams JH, Murray AD, Gilchrist A, Perrett DI, Whiten A. Structural white matter deficits in high-functioning individuals with autistic spectrum disorder: a voxel-based investigation. Neuroimage 2005; 24: 455–61. 140. Schmitz N, Rubia K, Daly E, Smith A, Williams S, Murphy DG. Neural correlates of executive function in autistic spectrum disorders. Biol Psychiatry 2006; 59: 7–16. 141. Mackinlay R, Charman T, Karmiloff-Smith A. High functioning children with autism spectrum disorder: a novel test of multitasking. Brain Cogn 2006; 61: 14–24. 142. Hill EL, Bird CM. Executive processes in Asperger syndrome: patterns of performance in a multiple case series. Neuropsychologia 2006; 44: 2822–35. 143. Bowler DM, Gardiner JM, Grice SJ. Episodic memory and remembering in adults with Asperger syndrome. J Autism Dev Disord 2000; 30: 295–304.

Review 179

144. Salmond CH, Ashburner J, Connelly A, Friston KJ, Gadian DG, Vargha-Khadem F. The role of the medial temporal lobe in autistic spectrum disorders. Eur J Neurosci 2005; 22: 764–72. 145. Millward C, Powell S, Messer D, Jordan R. Recall for self and other in autism: children’s memory for events experienced by themselves and their peers. J Autism Dev Disord 2000; 30: 15–28. 146. Gardiner JM, Bowler DM, Grice SJ. Further evidence of preserved priming and impaired recall in adults with Asperger’s syndrome. J Autism Dev Disord 2003; 33: 259–69. 147. Toichi M, Kamio Y. Long-term memory in high-functioning autism: controversy on episodic memory in autism reconsidered. J Autism Dev Disord 2003; 33: 151–61. 148. Oie M, Sunde K, Rund BR. Contrasts in memory functions between adolescents with schizophrenia or ADHD. Neuropsychologia 1999; 37: 1351–58. 149. Turner MA. Generating novel ideas: fluency performance in high-functioning and learning disabled individuals with autism. J Child Psychol Psychiatry 1999; 40: 189–201. 150. Channon S, Charman T, Heap J, Crawford S, Rios P. Real-life-type problem-solving in Asperger’s syndrome. J Autism Dev Disord 2001; 31: 461–69. 151. Lopez BR, Lincoln AJ, Ozonoff S, Lai Z. Examining the relationship between executive functions and restricted, repetitive symptoms of autistic disorder. J Autism Dev Disord 2005; 35: 445–60. 152. Ozonoff S, Pennington BF, Rogers SJ. Executive function deficits in high-functioning autistic individuals: relationship to theory of mind. J Child Psychol Psychiatry 1991; 32: 1081–1105. 153. Kleinman J, Marciano PL, Ault RL. Advanced theory of mind in high-functioning adults with autism. J Autism Dev Disord 2001; 31: 29–36. 154. Colle L, Baron-Cohen S, Hill J. Do children with autism have a theory of mind? A non-verbal test of autism vs. specific language impairment. J Autism Dev Disord 2007; 37: 716–23. 155. Baron-Cohen S, Ring H, Moriarty J, Schmitz B, Costa D, Ell P. Recognition of mental state terms. Clinical findings in children with autism and a functional neuroimaging study of normal adults. Br J Psychiatry 1994; 165: 640–49. 156. Brent E, Rios P, Happe F, Charman T. Performance of children with autism spectrum disorder on advanced theory of mind tasks. Autism 2004; 8: 283–99. 157. Farrant A, Boucher J, Blades M. Metamemory in children with autism. Child Dev 1999; 70: 107–31. 158. Durston S. A review of the biological bases of ADHD: what have we learned from imaging studies? Ment Retard Dev Disabil Res Rev 2003; 9: 184–95. 159. Eliez S, Reiss AL. MRI neuroimaging of childhood psychiatric disorders: a selective review. J Child Psychol Psychiatry 2000; 41: 679–94. 160. Krain AL, Castellanos FX. Brain development and ADHD. Clin Psychol Rev 2006; 26: 433–44. 161. Schneider M, Retz W, Coogan A, Thome J, Rosler M. Anatomical and functional brain imaging in adult attention-deficit/ hyperactivity disorder (ADHD) – a neurological view. Eur Arch Psychiatry Clin Neurosci 2006; 256 (Suppl. 1): i32–i41. 162. Clark C, Prior M, Kinsella GJ. Do executive function deficits differentiate between adolescents with ADHD and oppositional defiant/conduct disorder? A neuropsychological study using the Six Elements Test and Hayling Sentence Completion Test. J Abnorm Child Psychol 2000; 28: 403–14. 163. Chan RC, Guo M, Zou X, Li D, Hu Z, Yang B. Multitasking performance of Chinese children with ADHD. J Int Neuropsychol Soc 2006; 12: 575–79. 164. Siklos S, Kerns KA. Assessing multitasking in children with ADHD using a modified Six Elements Test. Arch Clin Neuropsychol 2004; 19: 347–61. 165. Kliegel M, Ropeter A, Mackinlay R. Complex prospective memory in children with ADHD. Child Neuropsychol 2006; 12: 407–19. 166. Kerns KA, Price KJ. An investigation of prospective memory in children with ADHD. Child Neuropsychol 2001; 7: 162–71. 167. Perner J, Kain W, Barchfeld P. Executive control and higherorder Theory of Mind in children at risk of ADHD. Infant Child Dev 2002; 11: 141–58.

180

Developmental Medicine & Child Neurology 2008, 50: 168–181

168. Krauel K, Duezel E, Hinrichs H, Santel S, Rellum T, Baving L. Impact of emotional salience on episodic memory in attentiondeficit/hyperactivity disorder: a functional magnetic resonance imaging study. Biol Psychiatry 2007; 61: 1370–79. 169. Felton RH, Wood FB, Brown IS, Campbell SK, Harter MR. Separate verbal memory and naming deficits in attention deficit disorder and reading disability. Brain Lang 1987; 31: 171–84. 170. Oie M, Rund BR. Neuropsychological deficits in adolescentonset schizophrenia compared with attention deficit hyperactivity disorder. Am J Psychiatry 1999; 156: 1216–22. 171. Morrison PD, Murray RM. Schizophrenia. Curr Biol 2005; 15: R980. 172. Murray RM, McDonald C, Bramon E. Neurodevelopmental impairment, dopamine sensitisation, and social adversity in schizophrenia. World Psychiatry 2002; 1: 137–45. 173. Geddes JR, Verdoux H, Takei N, et al. Schizophrenia and complications of pregnancy and labor: an individual patient data meta-analysis. Schizophr Bull 1999; 25: 413–23. 174. Cannon M, Jones PB, Murray RM. Obstetric complications and schizophrenia: historical and meta-analytic review. Am J Psychiatry 2002; 159: 1080–92. 175. Jones P, Rodgers B, Murray R, Marmot M. Child development risk factors for adult schizophrenia in the British 1946 birth cohort. Lancet 1994; 344: 1398–1402. 176. Erlenmeyer-Kimling L, Rock D, Roberts SA, et al. Attention, memory, and motor skills as childhood predictors of schizophrenia-related psychoses: the New York High-Risk Project. Am J Psychiatry 2000; 157: 1416–22. 177. Murray GK, Jones PB, Moilanen K, et al. Infant motor development and adult cognitive functions in schizophrenia. Schizophr Res 2006; 81: 65–74. 178. Cannon M, Jones P, Huttunen MO, et al. School performance in Finnish children and later development of schizophrenia: a population-based longitudinal study. Arch Gen Psychiatry 1999; 56: 457–63. 179. Feinberg I. Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence? J Psychiatr Res 1982; 17: 319–34. 180. Keshavan MS, Anderson S, Pettegrew JW. Is schizophrenia due to excessive synaptic pruning in the prefrontal cortex? The Feinberg hypothesis revisited. J Psychiatr Res 1994; 28: 239–65. 181. Yamada M, Hirao K, Namiki C, et al. Social cognition and frontal lobe pathology in schizophrenia: a voxel-based morphometric study. Neuroimage 2007; 35: 292–98. 182. Riffkin J, Yucel M, Maruff P, et al. A manual and automated MRI study of anterior cingulate and orbito-frontal cortices, and caudate nucleus in obsessive-compulsive disorder: comparison with healthy controls and patients with schizophrenia. Psychiatry Res 2005; 138: 99–113. 183. Narr KL, Toga AW, Szeszko P, et al. Cortical thinning in cingulate and occipital cortices in first episode schizophrenia. Biol Psychiatry 2005; 58: 32–40. 184. Rose SE, Chalk JB, Janke AL, et al. Evidence of altered prefrontal-thalamic circuitry in schizophrenia: an optimized diffusion MRI study. Neuroimage 2006; 32: 16–22. 185. Harrison PJ. The neuropathology of schizophrenia. A critical review of the data and their interpretation. Brain 1999; 122: 593–624. 186. Selemon LD, Goldman-Rakic PS. The reduced neuropil hypothesis: a circuit based model of schizophrenia. Biol Psychiatry 1999; 45: 17–25. 187. Black JE, Kodish IM, Grossman AW, et al. Pathology of layer V pyramidal neurons in the prefrontal cortex of patients with schizophrenia. Am J Psychiatry 2004; 161: 742–44. 188. Vogeley K, Tepest R, Schneider-Axmann T, et al. Automated image analysis of disturbed cytoarchitecture in Brodmann area 10 in schizophrenia. Schizophr Res 2003; 62: 133–40. 189. Harrington L, Siegert RJ, McClure J. Theory of mind in schizophrenia: a critical review. Cogn Neuropsychiatry 2005; 10: 249–86. 190. Brune M. ‘Theory of mind’ in schizophrenia: a review of the literature. Schizophr Bull 2005; 31: 21–42. 191. Frith CD, Corcoran R. Exploring ‘theory of mind’ in people with schizophrenia. Psychol Med 1996; 26: 521–30.

192. Sarfati Y, Hardy-Bayle MC, Besche C, Widlocher D. Attribution of intentions to others in people with schizophrenia: a nonverbal exploration with comic strips. Schizophr Res 1997; 25: 199–209. 193. Corcoran R, Cahill C, Frith CD. The appreciation of visual jokes in people with schizophrenia: a study of ‘mentalizing’ ability. Schizophr Res 1997; 24: 319–27. 194. Harrington L, Langdon R, Siegert RJ, McClure J. Schizophrenia, theory of mind, and persecutory delusions. Cogn Neuropsychiatry 2005; 10: 87–104. 195. Greig TC, Bryson GJ, Bell MD. Theory of mind performance in schizophrenia: diagnostic, symptom, and neuropsychological correlates. J Nerv Ment Dis 2004; 192: 12–18. 196. Pickup GJ, Frith CD. Theory of mind impairments in schizophrenia: symptomatology, severity and specificity. Psychol Med 2001; 31: 207–20. 197. Turetsky BI, Moberg PJ, Mozley LH, et al. Memory-delineated subtypes of schizophrenia: relationship to clinical, neuroanatomical, and neurophysiological measures. Neuropsychology 2002; 16: 481–90. 198. Keri S, Janka Z. Critical evaluation of cognitive dysfunctions as endophenotypes of schizophrenia. Acta Psychiatr Scand 2004; 110: 83–91. 199. Franck N, Rouby P, Daprati E, Dalery J, Michel M-C, Georgieff N. Confusion between silent and overt reading in schizophrenia. Schizophr Res 2000; 41: 357–64. 200. Garrett M, Silva R. Auditory hallucinations, source monitoring, and the belief that ‘voices’ are real. Schizophr Bull 2003; 29: 445–57. 201. Keefe RS, Arnold MC, Bayen UJ, McEvoy JP, Wilson WH. Sourcemonitoring deficits for self-generated stimuli in schizophrenia: multinomial modeling of data from three sources. Schizophr Res 2002; 57: 51–67. 202. Moritz S, Woodward TS, Ruff CC. Source monitoring and memory confidence in schizophrenia. Psychol Med 2003; 33: 131–39. 203. Woods SP, Twamley EW, Dawson MS, Narvaez JM, Jeste DV. Deficits in cue detection and intention retrieval underlie prospective memory impairment in schizophrenia. Schizophr Res 2007; 90: 344–50. 204. Kumar D, Nizamie HS, Jahan M. Event-based prospective memory in schizophrenia. J Clin Exp Neuropsychol 2005; 27: 867–72.

205. Shum D, Ungvari GS, Tang WK, Leung JP. Performance of schizophrenia patients on time-, event-, and activity-based prospective memory tasks. Schizophr Bull 2004; 30: 693–701. 206. Elvevåg B, Maylor EA, Gilbert AL. Habitual prospective memory in schizophrenia. BMC Psychiatry 2003; 3: 9. 207. Meissner F, Hacker W, Heilemann H. Memory performance and instruction effects in schizophrenia. A comparative study of chronic schizophrenic patients and healthy probands. Psychiatr Prax 2001; 28: 180–88. (In German) 208. Kondel TK. Prospective memory and executive function in schizophrenia. Brain Cogn 2002; 48: 405–10. 209. Evans JJ, Chua SE, McKenna PJ, Wilson BA. Assessment of the dysexecutive syndrome in schizophrenia. Psychol Med 1997; 27: 635–46. 210. Chan RC, Chen EY, Cheung EF, Chen RY, Cheung HK. The components of executive functioning in a cohort of patients with chronic schizophrenia: a multiple single-case study design. Schizophr Res 2006; 81: 173–89. 211. Thoma P, Zoppelt D, Wiebel B, Daum I. Context processing and negative symptoms in schizophrenia. J Clin Exp Neuropsychol 2007; 29: 428–35. 212. Chan RC, Chen EY, Cheung EF, Cheung HK. Executive dysfunctions in schizophrenia. Relationships to clinical manifestation. Eur Arch Psychiatry Clin Neurosci 2004; 254: 256–62. 213. Katz N, Tadmor I, Felzen B, Hartman-Maeir A. The Behavioural Assessment of the Dysexecutive Syndrome (BADS) in schizophrenia and its relation to functional outcomes. Neuropsychol Rehabil 2007; 17: 192–205.

List of abbreviations ADHD ASD BA BOLD PFC PM rCBF

Attention-deficit–hyperactivity disorder Autism spectrum disorder Brodmann area Blood-oxygen-level-dependent Prefrontal cortex Prospective memory Regional cerebral blood flow

7th International Congress on Cerebral Palsy 24th – 26th April 2008 Bled, Slovenia Main topics: • Normal anatomy, physiology, development

• Risk factors, protective factors • Diagnostic procedures and techniques, timing • Identification and registration of cerebral palsy • Early intervention For more information, contact Prof. dr Milvoj Velickovic Perat [email protected] or please visit our website: www.cpbled2008.eu

Review 181