Chantal Roggeman

Influence of the COMT Genotype on Working Memory and Brain Activity Changes During Development

BACKGROUND The Valine158Methionine (Val158Met) polymorphism of the COMT gene leads to lower enzymatic activity and higher dopamine availability in Met carriers. The Met allele is associated with better performance and reduced prefrontal cortex activation during working memory (WM) tasks in adults. Dopaminergic system changes during adolescence may lead to a reduction of basal dopamine levels, potentially affecting Met allele benefits during development. METHODS We investigated the association of COMT genotype with behavioral (n = 322) and magnetic resonance imaging data (n = 81-84) collected during performance of a visuospatial WM task and potential changes in these effects during development (reflected in age × genotype interactions). Data were collected from a cross-sectional and longitudinal typically developing sample of 6- to 20-year-olds. RESULTS Visuospatial WM capacity exhibited an age × genotype interaction, with a benefit of the Met allele emerging after 10 years of age. There was a parallel age × genotype interaction on WM-related activation in the right inferior frontal gyrus and intraparietal sulcus (IPS), with increases in activation with age in the Val/Val group only. Main effects of COMT genotype were also observed in the IPS, with greater gray matter volumes bilaterally and greater right IPS activation in the Val/Val group compared with the Met carriers. CONCLUSIONS These results suggest that COMT genotype effects on WM brain activity and behavior are not static during development. The full developmental picture should be considered when trying to understand the impact of genetic polymorphisms on the mature cognition of healthy adult or psychiatric populations.

Number Processing Pathways in Human Parietal Cortex

Numerous studies have identified the intraparietal sulcus (IPS) as an area critically involved in numerical processing. IPS neurons in macaques are tuned to a preferred numerosity, hence neurally coding numerosity in a number-selective way. Neuroimaging studies in humans have demonstrated number-selective processing in the anterior parts of the IPS. Nevertheless, the processes that convert visual input into a number-selective neural code remain unknown. Computational studies have suggested that a neural coding stage that is sensitive, but not selective to number, precedes number-selective coding when processing nonsymbolic quantities but not when processing symbolic quantities. In Experiment 1, we used functional magnetic resonance imaging to localize number-sensitive areas in the human brain by searching for areas exhibiting increasing activation with increasing number, carefully controlling for nonnumerical parameters. An area in posterior superior parietal cortex was identified as a substrate for the intermediate number-sensitive steps required for processing nonsymbolic quantities. In Experiment 2, the interpretation of Experiment 1 was confirmed with a connectivity analysis showing that a shared number-selective representation in IPS is reached through different pathways for symbolic versus nonsymbolic quantities. The preferred pathway for processing nonsymbolic quantities included the number-sensitive area in superior parietal cortex, whereas the pathway for processing symbolic quantities did not.

Hippocampal contribution to early and later stages of implicit motor sequence learning

Implicit motor sequence learning refers to an important human ability to acquire new motor skills through the repeated performance of a motor sequence. This learning process is characterized by slow, incremental gains of motor performance. The present fMRI study was developed to better delineate the areas supporting these temporal dynamics of learning. By using the serial color matching paradigm, our study focused on the motor level of sequence learning and tracked the time course of learning-related neural changes. Imaging results showed a significant contribution of the left anterior hippocampus in an early sequence acquisition stage (first scanning session) as well as during a later stage with stabilized learning effects (second scanning session). Hippocampal activation significantly correlated with the behavioral learning process and was affected by a change of the motor sequence. These results suggest a strong involvement of the hippocampus in implicit motor sequence learning. On the other hand, a very extensive and bilateral neural network of parietal, temporal and frontal cortical areas (including SMA, pre-SMA) together with parts of the cerebellum and striatum were found to play a role during random visuo-motor task performance.

Priming reveals differential coding of symbolic and non-symbolic quantities

Number processing is characterized by the distance and the size effect, but symbolic numbers exhibit smaller effects than non-symbolic numerosities. The difference between symbolic and non-symbolic processing can either be explained by a different kind of underlying representation or by parametric differences within the same type of underlying representation. We performed a primed naming study to investigate this issue. Prime and target format were manipulated (digits or collections of dots) as well as the numerical distance between prime and target value. Qualitatively different priming patterns were observed for the two formats, showing that the underlying representations differed in kind: Digits activated mental number representations of the place coding type, while collections of dots activated number representations of the summation coding type.

The Neural Basis of Implicit Perceptual Sequence Learning

The present fMRI study investigated the neural areas involved in implicit perceptual sequence learning. To obtain more insight in the functional contributions of the brain areas, we tracked both the behavioral and neural time course of the learning process, using a perceptual serial color matching task. Next, to investigate whether the neural time course was specific for perceptual information, imaging results were compared to the results of implicit motor sequence learning, previously investigated using an identical serial color matching task (Gheysen et al., 2010). Results indicated that implicit sequences can be acquired by at least two neural systems: the caudate nucleus and the hippocampus, having different operating principles. The caudate nucleus contributed to the implicit sequence learning process for perceptual as well as motor information in a similar and gradual way. The hippocampus, on the other hand, was engaged in a much faster learning process which was more pronounced for the motor compared to the perceptual task. Interestingly, the perceptual and motor learning process occurred on a comparable implicit level, suggesting that consciousness is not the main determinant factor dissociating the hippocampal from the caudate learning system. This study is not only the first to successfully and unambiguously compare brain activation between perceptual and motor levels of implicit sequence learning, it also provides new insights into the specific hippocampal and caudate learning function.

Stages of Nonsymbolic Number Processing in Occipitoparietal Cortex Disentangled by fMRI Adaptation

The neurobiological mechanisms of nonsymbolic number processing in humans are still unclear. Computational modeling proposed three successive stages: first, the spatial location of objects is stored in an object location map; second, this information is transformed into a numerical summation code; third, this summation code is transformed to a number-selective code. Here, we used fMRI-adaptation to identify these three stages and their relative anatomical location. By presenting the same number of dots on the same locations in the visual field, we adapted neurons of human volunteers. Occasionally, deviants with the same number of dots at different locations or different numbers of dots at the same location were shown. By orthogonal number and location factors in the deviants, we were able to calculate three independent contrasts, each sensitive to one of the stages. We found an occipitoparietal gradient for nonsymbolic number processing: the activation of the object location map was found in the inferior occipital gyrus. The summation coding map exhibited a nonlinear pattern of activation, with first increasing and then decreasing activation, and most activity in the middle occipital gyrus. Finally, the number-selective code became more pronounced in the superior parietal lobe. In summary, we disentangled the three stages of nonsymbolic number processing predicted by computational modeling and demonstrated that they constitute a pathway along the occipitoparietal processing stream.

Working memory brain activity and capacity link MAOA polymorphism to aggressive behavior during development

A developmental increase in working memory capacity is an important part of cognitive development, and low working memory (WM) capacity is a risk factor for developing psychopathology. Brain activity represents a promising endophenotype for linking genes to behavior and for improving our understanding of the neurobiology of WM development. We investigated gene–brain–behavior relationships by focusing on 18 single-nucleotide polymorphisms (SNPs) located in six dopaminergic candidate genes (COMT, SLC6A3/DAT1, DBH, DRD4, DRD5, MAOA). Visuospatial WM (VSWM) brain activity, measured with functional magnetic resonance imaging, and VSWM capacity were assessed in a longitudinal study of typically developing children and adolescents. Behavioral problems were evaluated using the Child Behavior Checklist (CBCL). One SNP (rs6609257), located ∼6.6 kb downstream of the monoamine oxidase A gene (MAOA) on human chromosome X, significantly affected brain activity in a network of frontal, parietal and occipital regions. Increased activity in this network, but not in caudate nucleus or anterior prefrontal regions, was correlated with VSWM capacity, which in turn predicted externalizing (aggressive/oppositional) symptoms, with higher WM capacity associated with fewer externalizing symptoms. There were no direct significant correlations between rs6609257 and behavioral symptoms. These results suggest a mediating role of WM brain activity and capacity in linking the MAOA gene to aggressive behavior during development.

Salience maps in parietal cortex: Imaging and computational modeling

Models of spatial attention are often based on the concept of a salience map. In computational cognitive neuroscience, such maps are implemented as a collection of nodes with self-excitation and lateral inhibition between all nodes (competitive interaction map). Here, we test some critical predictions of this idea. We argued that task demands, more precisely the level of attention required, can top-down modulate the level of lateral inhibition in a salience map, and thus induce different activation functions. We first show that a model with a high lateral inhibition parameter generates a monotonous activation curve as a function of set size similar to that typically observed in the literature (e.g. Todd and Marois, 2004). Next, we show that a competitive interaction map with medium lateral inhibition leads to a Lambda-shaped activation curve when set sizes increase. This prediction is confirmed in an fMRI experiment with medium attention demands where a similar Lambda-shaped activation curve is found in a posterior superior parietal area that was proposed to house a salience map (Todd and Marois, 2004). Finally, we show that a qualitatively different V-shaped activation curve is predicted with a very low inhibition parameter. An fMRI experiment with low attentional demands revealed this V-shaped activation curve in the same region. These findings provide critical support for the existence of a salience map based on competitive interactions in posterior superior parietal cortex, and suggest that its parameters (in particular, lateral inhibition) can be modulated in a top down manner dependent on task demands.

Trade-off between capacity and precision in visuospatial working memory

Limitations in the performance of working memory (WM) tasks have been characterized in terms of the number of items retained (capacity) and in terms of the precision with which the information is retained. The neural mechanisms behind these limitations are still unclear. Here we used a biological constrained computational model to study the capacity and precision of visuospatial WM. The model consists of two connected networks of spiking neurons. One network is responsible for storage of information. The other provides a nonselective excitatory input to the storage network. Simulations showed that this excitation boost could temporarily increase storage capacity but also predicted that this would be associated with a decrease in precision of the memory. This prediction was subsequently tested in a behavioral (38 participants) and fMRI (22 participants) experiment. The behavioral results confirmed the trade-off effect, and the fMRI results suggest that a frontal region might be engaged in the trial-by-trial control of WM performance. The average effects were small, but individuals differed in the amount of trade-off, and these differences correlated with the frontal activation. These results support a two-module model of WM where performance is determined both by storage capacity and by top–down influence, which can vary on a trial-by-trial basis, affecting both the capacity and precision of WM.