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Martin Lövdén
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Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2019) 31 (2): 314–325.
Published: 01 February 2019
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The dopamine (DA) system plays an important role in cognition. Accordingly, normal variation in DA genes has been found to predict individual differences in cognitive performance. However, little is known of the impact of genetic differences on the link between empirical indicators of the DA system and cognition in humans. The present work used PET with 11 C-raclopride to assess DA D2-receptor binding potential (BP) and links to episodic memory, working memory, and perceptual speed in 179 healthy adults aged 64–68 years. Previously, the T-allele of a DA D2-receptor single-nucleotide polymorphism, C957T , was associated with increased apparent affinity of 11 C-raclopride, giving rise to higher BP values despite similar receptor density values between allelic groups. Consequently, we hypothesized that 11 C-raclopride BP measures inflated by affinity rather than D2-receptor density in T-allele carriers would not be predictive of DA integrity and therefore prevent finding an association between 11 C-raclopride BP and cognitive performance. In accordance with previous findings, we show that 11 C-raclopride BP was increased in T-homozygotes. Importantly, 11 C-raclopride BP was only associated with cognitive performance in groups with low or average ligand affinity (C-allele carriers of C957T , n = 124), but not in the high-affinity group (T-homozygotes, n = 55). The strongest 11 C-raclopride BP–cognition associations and the highest level of performance were found in C-homozygotes. These findings show that genetic differences modulate the link between BP and cognition and thus have important implications for the interpretation of DA assessments with PET and 11 C-raclopride in multiple disciplines ranging from cognitive neuroscience to psychiatry and neurology.
Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2018) 30 (7): 1033–1046.
Published: 01 July 2018
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Researchers have proposed that solving complex reasoning problems, a key indicator of fluid intelligence, involves the same cognitive processes as solving working memory tasks. This proposal is supported by an overlap of the functional brain activations associated with the two types of tasks and by high correlations between interindividual differences in performance. We replicated these findings in 53 older participants but also showed that solving reasoning and working memory problems benefits from different configurations of the functional connectome and that this dissimilarity increases with a higher difficulty load. Specifically, superior performance in a typical working memory paradigm ( n -back) was associated with upregulation of modularity (increased between-network segregation), whereas performance in the reasoning task was associated with effective downregulation of modularity. We also showed that working memory training promotes task-invariant increases in modularity. Because superior reasoning performance is associated with downregulation of modular dynamics, training may thus have fostered an inefficient way of solving the reasoning tasks. This could help explain why working memory training does little to promote complex reasoning performance. The study concludes that complex reasoning abilities cannot be reduced to working memory and suggests the need to reconsider the feasibility of using working memory training interventions to attempt to achieve effects that transfer to broader cognition.
Journal Articles
Publisher: Journals Gateway
Journal of Cognitive Neuroscience (2011) 23 (9): 2147–2158.
Published: 01 September 2011
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Perceptual decision-making performance depends on several cognitive and neural processes. Here, we fit Ratcliff's diffusion model to accuracy data and reaction-time distributions from one numerical and one verbal two-choice perceptual-decision task to deconstruct these performance measures into the rate of evidence accumulation (i.e., drift rate), response criterion setting (i.e., boundary separation), and peripheral aspects of performance (i.e., nondecision time). These theoretical processes are then related to individual differences in brain activation by means of multiple regression. The sample consisted of 24 younger and 15 older adults performing the task in fMRI before and after 100 daily 1-hr behavioral training sessions in a multitude of cognitive tasks. Results showed that individual differences in boundary separation were related to striatal activity, whereas differences in drift rate were related to activity in the inferior parietal lobe. These associations were not significantly modified by adult age or perceptual expertise. We conclude that the striatum is involved in regulating response thresholds, whereas the inferior parietal lobe might represent decision-making evidence related to letters and numbers.