The developmental cognitive neuroscience approach to the study of developmental disorders

Functional magnetic resonance imaging studies of developmental disorders and normal cognition that include children are becoming increasingly common and represent part of a newly expanding field of developmental cognitive neuroscience. These studies have illustrated the importance of the process of development in understanding brain mechanisms underlying cognition and including children in the study of the etiology of developmental disorders. Our current understanding of how the brain is organized owes a great deal to the study of brain lesions in adult patients. These studies have helped scientists gain insight into the brain mechanisms underlying language (Broca 1861), memory (Scoville & Milner 2000), attention (Posner et al. 1984), vision (Farah et al. 1989), and many other cognitive and perceptual systems. Thomas & Karmiloff-Smith (T&K-S) argue that it may not be appropriate to utilize the same adult patient model for the study of developmental disorders and the development of brain mechanisms for cognition. They make the case that it is imperative to take into account the process of development when studying developmental disorders. This argument has many implications, including the primary one emphasized by T&K-S, that one cannot equate developmental disorders with adult brain damage even if the symptoms are similar. Another implication of this argument is that it may be inappropriate to study the etiology of developmental disorders by studying adults who have the disorder. Most functional brain imaging studies of the etiology of developmental disorders use adult subjects. In the early days of brain imaging, when positron emission tomography (PET) was the only choice for studying functional localization in humans’ brains, this was a necessary experimental choice. PET requires the injection of radioactive isotopes and cannot be utilized in minors except in very specific circumstances. However, with the advent of functional magnetic resonance imaging (fMRI), a noninvasive technique for studying brain function and functional localization became available that can be used in children – as long as they are able to keep their movement restricted. Even with the advent of fMRI, many studies on developmental disorders have continued to use adult subjects. This practice reflects an assumption that the process of development is not important, and implies that the disrupted brain process associated with the disorder is isolated from and does not affect the rest of the brain. fMRI studies of developmental disorders and normal cognition that include children are becoming increasingly common and represent part of a newly expanding field of developmental cognitive neuroscience. These studies have already illustrated the importance of studying children as opposed to adults. Some results have confirmed previous adult findings, but others have shown differences between the adult and child organization. For example, a study of amygdala response to fearful faces in children (Thomas et al. 2001) showed less amygdala response in children as compared to adults; interestingly, this was because of an increased response in the children to the neutral faces. In a developmental study of inhibitory control (Luna et al. 2001), differences were found not only between children and adults but also in adolescents. Some of the brain differences shown in adolescents were different from both the children and the adults, illustrating the need to study the whole developmental trajectory (even in the relatively simple task of controlling eye movements). In addition, a study of cognitive control (Bunge et al. 2002) showed differences between adults and children in the brain regions associated with effective cognitive control, suggesting that the frontal network adults use to suppress interference and inhibit responses is not fully developed in children. In our own studies of developmental dyslexia, my colleagues and I have found both similarities and differences between children with dyslexia and adult studies of dyslexia. For example, we found that children with developmental dyslexia showed decreased activity in left hemisphere posterior language areas where adults with dyslexia had shown decreases in previous studies (Temple et al. 2001; 2003). This decrease in left temporo-parietal cortex has been shown in other studies of children (Shaywitz et al. 2002; Simos et al. 2002), suggesting that this disruption in temporo-parietal response seen in adults with dyslexia may be fundamental to the disorder (Temple 2002). However, in our studies we have also seen differences in the functional brain organization of children with dyslexia. We examined the brain response to rapid auditory stimuli in adults with dyslexia (Temple et al. 2000) and found that whereas normal reading adults showed left prefrontal responsivity to rapid auditory stimuli, the dyslexic reading adults did not show left prefrontal response to the same stimuli. In addition, two of three adult subjects who participated in a training program to improve their reading and rapid auditory processing ability showed increases in left prefrontal cortex after training. When we studied the brain response to rapid auditory processing in normal and dyslexic reading children (Temple 2001), we found some similarities to and differences from the adult study. In normally reading children, left prefrontal cortex was responsive to rapid auditory stimuli, but other brain regions were also involved in a network of response that was more distributed than we had seen in adults. In children with dyslexia, we saw the same lack of prefrontal responsivity to rapid auditory stimuli that we had seen in adults, but we also saw lack of response in the whole larger network of brain areas responsive in normally reading children. After training we saw increased activity in not only the left prefrontal cortex of the children with dyslexia but across the larger network. These results suggest to us that the neural response to rapid auditory stimuli undergoes developmental changes from childhood to adulthood, becoming more focused and involving an increasingly smaller network. In addition, this study suggests that this larger network is disrupted in developmental dyslexia and the results seen in adults with dyslexia represent only one aspect of the disrupted response seen in children with dyslexia. These examples of recent studies of normal and abnormal developmental cognitive neuroscience illustrate the point that the brain mechanisms involved in cognitive processes do undergo developmental changes. In the case of developmental disorders, it may be that entire networks are disrupted. Taking these processes of development into account may be crucial to our understanding of the etiology of developmental disorders. I would argue that, when feasible, studies of the neurobiology underlying developmental disorders as well as normal cognitive functioning should be conducted with children and taking the process of development into account. Models of atypical development must also be models of normal development Gert Westermann and Denis Mareschal Centre for Brain and Cognitive Development, School of Psychology, Birkbeck College, London WC1E 7HX, United Kingdom. {g.westermann; d.mareschal}@bbk.ac.uk http://www.cbcd.bbk.ac.uk/people/gert; denis/ Abstract: Connectionist models aiming to reveal the mechanisms of atypConnectionist models aiming to reveal the mechanisms of atypical development must in their undamaged form constitute plausible models of normal development and follow a developmental trajectory that matches empirical data. Constructivist models that adapt their structure to the learning task satisfy this demand. They are therefore more informative in the study of atypical development than the static models employed by Thomas & Karmiloff-Smith (T&K-S). Commentary/Thomas & Karmiloff-Smith: Are developmental disorders like cases of adult brain damage? BEHAVIORAL AND BRAIN SCIENCES (2002) 25:6 771 As demonstrated here by Thomas & Karmiloff-Smith (T&K-S), neural network models are a useful tool for assessing how damage at different stages of development affects the outcome of learning. However, if the specific results gained with models are to inform studies of atypical development in children, it is important that the models used to simulate atypical development should otherwise also constitute realistic models of normal development. Only if a model can give a good account of how development proceeds under normal circumstances, will damaging this model yield insights into the mechanisms of atypical development. Both in reading and in past-tense acquisition, a plethora of data exists about the normal course of acquisition and the resulting adult processing system. Pervasive here are dissociations between regular and irregular forms in past-tense learning (Marcus et al. 1992), and between normal words and exception words in reading (Backman et al. 1984). It is important to establish that these developmental dissociations also exist in a model that is later used to explain atypical development by varying its parameters. This is not because the observed developmental profile is significant per se, but because it reveals important aspects of the underlying learning mechanisms, such as the internal reorganization of representations or the extraction of higher-order features from the learned data. Even if a model of learning that does not follow a realistic developmental course reaches a 100% success rate, it is unclear if it does so in ways that are comparable with children’s learning, and hence, if damage to the model is comparable to damage to the child’s learning system, that is, the brain. The analogy between atypical development and learning in a damaged model can thus only hold if the undamaged model can be shown to progress through a realistic course of acquisition, and preferably if the trained model displays dissociations comparable to those in adult subjects, for example, in psycholinguistic experiments or in acquired brain damage. A class of models of cognitive development that have been shown to possess these properties is constructivist neural networks (Mareschal & Shultz 1996; Shultz et al. 1995; Westermann 2000). These are models that adapt their architecture by adding (and sometimes deleting) units and connections during the learning process in an experience-dependent way. As a result, based on interactions between initial constraints, experience with the environment, and the constructivist learning algorithm, these models end up with an architecture that is specifically adapted to the learning task. The motivation for this class of models comes from developmental theories that argue for a progressive increase in representational power (Mareschal & Shultz 1996), from learning theoretic considerations that show a structurally developing system to be fundamentally different from a static one (Quartz 1993), and from neurobiological evidence about experience-dependent brain development in infants and children (Johnson 1997; Quartz & Sejnowski 1997; Westermann 2000). An important aspect of development that constructivist models can capture is the reorganization of representations and processing during development. As such they have been shown to model developmental stages in Piagetian tasks (for an overview, see Shultz et al. 1995) and functional specialization in the acquisition of verb inflections (Westermann 1998). A reason why constructivist models are especially suited to modeling atypical development is that apart from the constraint alterations employed in the models discussed by T&K-S, here the developmental mechanism itself can be varied. For example, often in these models newly added structure takes on a specialized function, either to encode higher-level features or to process exceptional or difficult items. A variation of the rate at which new structure can be added would then be expected to lead to significant variations in the developmental outcome. Further, this type of variations is especially close to the effects of genetically based developmental disorders. For example, Williams syndrome is characterized by distinct deviations from normal brain structure, such as decreased cerebral volume, especially in the right occipital lobe (Reiss et al. 2000). A specific constructivist model that has been employed in a task used by T&K-S is the CNN (Constructivist Neural Network) model (Westermann 1999; 2000). This model learned to inflect the German participle and, in doing so, it developed regions of specialization for regular and irregular participles, respectively. These regions could be selectively lesioned after training, leading to double dissociations that closely resembled those observed in subjects with acquired agrammatic aphasia (Penke et al. 1999). A comparison between a version of the model that developed its architecture based on experience with the learning task, and a static model that started out with the full architecture, showed that the developing but not the static model passed through a learning process that exhibited the same characteristic errors as children, and the developing model showed a clearer functional specialization in its subregions than the static model (Westermann 2000). Taken together, these points suggest that constructivist models could give additional and more informative insights into the mechanisms of atypical development than the static models employed by T&K-S.