When we are asleep, previously established memories are reprocessed, leading to the stabilization and improved retention of the learned information. Theoretical accounts suggest such sleep-related memory consolidation relies on the precisely coordinated reactivation of brain-wide memory networks. However, a firm neural basis for this intriguing notion has been lacking. Sleep spindles - brief rhythmic brain oscillations - hold considerable promise in this regard, considering their intimate relation to memory performance. Yet, whether sleep spindle activity reflects the reinstatement of widespread memory networks is presently an unanswered question. Applying advanced network analysis tools, I propose to characterize human sleep spindle networks in unprecedented detail. Using this approach, I aim to demonstrate that patterns of spindle activity reflect distinct reactivated memory networks. If successful, the proposed studies would offer the strongest support thus far that human sleep-related memory reprocessing is reliant on the patterned reactivation of pre-existing memory networks, and that sleep spindles mediate this process. As such, this project holds the promise of greatly expanding our understanding of how our brain enhances our memories while we sleep.

The project will investigate the following research questions: 1. Can certain negative effects of age on cognitive ability be reduced with appropriate training? Brain training has seen a tremendous rise in the past decade. Yet, the general effects of the type of exercises that are typically offered on cognitive health are probably very small. In this project, the focus will therefore be on two areas of training in which previous research indicates that more general effects can be expected: cognitive control and memory strategies. 2. What is the role of individual differences in the neurocognitive mechanisms underlying the effects of training? and 3. How can we elucidate the neural and cognitive mechanisms using covariance-based functional magnetic resonance imaging, diffusion tensor imaging and model-based adaptive training of cognitive functions? Older adults differ as much from each other as they differ from young adults. These individual differences are typically reflected in error variance, working against interpretable research outcomes. Covariance-based fMRI, by contrast, capitalizes on individual differences, utilizing them to establish the extent to which brain structures covary in their activation with the efficiency of specific cognitive processes. Combined DTI and covariance-based fMRI techniques will allow us to address the hitherto overpowering problem of heterogeneity among older adults.

Autism is a lifelong, heterogeneous, and impairing developmental disorder. To fully understand the neurodevelopmental nature of this disorder it is important to broaden the developmental perspective by including old age. The course of autism during later life has remained exempt of empirical scrutiny, but the possibility for such research is emerging now, as the first cohorts formally diagnosed with autism are currently approaching old age. This presents a unique opportunity for a new line of research. People with autism show deficits in cognitive control in childhood and adulthood. These deficits are associated with structural and functional abnormalities in the underlying fronto-striatal network. In healthy people this brain network and the related cognitive control abilities deteriorate when aging. If and how aging has an impact on the behavioral and brain anomalies in individuals with autism is unknown. Based on what is known about autism and normal aging, I hypothesize that age-related compensatory brain mechanisms cannot be recruited in people with autism, which leads to a faster decrease in cognitive control in people with autism as compared to controls. To test this novel hypothesis, I propose a series of studies across adulthood (20-30, 40-50, & 65-75 years) focusing on central aspects of cognitive control and the integrity of its underlying brain circuitries. Analyses will focus on individual differences between groups, but also within groups to capture the substantial heterogeneity characterizing both autism and healthy aging. The integrative approach of using newly developed tasks and various brain imaging techniques enables us to study the relationship between behavioral and symptomatic age-related changes and alterations in brain circuitries in autism. Results from the proposed studies will advance the understanding of (1) neurodevelopmental aspect of this lifelong disorder; (2) relationship of cognitive control to the underlying brain networks in autism; (3) heterogeneity across people with autism.

Although the adult brain was once seen as a rather static organ, it is now clear that the organization of brain circuitry is constantly changing as a function of experience and learning. Yet, research also shows that learning is often specific to the trained stimuli and task and does not improve performance on novel tasks, even very similar ones. Notably, several training paradigms have recently been identified that induce learning that is not stimulus-specific, but linked to cognition (or processes of thought) and more general than once thought possible. How such cognitive learning occurs, is, however, not clear. The current projects aim to gain a better understanding of the brain mechanisms that underlie cognitive learning. Training protocols that focus on fundamental cognitive abilities and that minimize stimulus-specific learning will be used in combination with neuroimaging techniques. Modern neuroimaging techniques provide a new possibility for understanding cognitive learning by revealing its underlying mechanisms. Cognitive skills will be trained (i) using cognitive tasks that include many different stimuli, and (ii) by teaching an individual to control the activation of a particular brain system, and thereby, its cognitive processes using real-time neuroimaging. The attractiveness of the latter approach is that no stimuli or tasks are used during training. Thus, any learning that occurs should be process-specific. We hypothesize that cognitive learning is best reflected in changes in interactions between brain regions and dependent on pre-training cognitive ability. Primary analyses will therefore examine learning-related changes in brain network connectivity as a function of initial cognitive performance. The proposed research will elucidate how cognitive functioning can be improved through training. This knowledge is not only important for determining the extent of plasticity available to cognitive functions and theories of cognition and learning, but is also critical for designing effective clinical and educational intervention studies.

How does the brain shape our conscious perception? Researchers interested in this fundamental question search for the neural correlate of consciousness, and have often used bistable perception. In bistable perception, one unchanging set of sensory inputs gives rise to multiple conscious interpretations that alternately dominate the mind’s eye. These ongoing fluctuations in conscious perception dissociate the neural processing of the unchanging input from the dynamic neural correlates of conscious perception. When perception switches as in bistable perception, the observer notices these changes, predictions are violated, and attention is suddenly drawn to these visual changes. This realisation begs the question: what is the role of top-down processing such as prediction and attention in the dynamics of conscious perception? Can we describe this role in terms of feedforward and feedback streams of neural processing? Recent results by dr. Zhang show that, on the one hand, attention is necessary for the process of alternation to take place. Thus, attention is fundamental to the existence of vacillating conscious content in bistable perception. On the other hand, research from the Knapen lab shows that switches in bistable perception can take place even without the observer noticing them. This is a conundrum: can the brain pay attention to things we cannot see? Or is our conceptualisation of bistable perception, or even consciousness itself, fundamentally flawed? State-of-the-art psychophysics will describe the time-course and phenomenology in detail. And, using both fMRI and MEG we will record activations from the microscopic to the whole-brain level to investigate how bottom-up and top-down information streams are integrated. Our experiments will describe the interplay of top-down and bottom-up mechanisms that creates our conscious experience.