High-level cognitive functions

Creativity

Creative thinking is essential to solve new or unusual problems, providing solutions that are both original (new) and appropriate (adaptive). The cognitive factors involved in creative thinking and their cerebral correlates are poorly understood. We hypothesized that important cognitive mechanisms required for creative thinking consist in thinking away from pre-established semantic or behavioral associations (inhibition), evoking unusual associations between items (activation), and combining/integrating previously unrelated items (combination).

Aims of our research program:

· To identify distinct cognitive mechanisms (executive and associative processes) allowing original ideas to emerge, establish behavioral measures of these mechanisms, identify individual creative profiles based on these measures

· To explore the cerebral networks supporting these distinct mechanisms using multimodal neuroimaging approaches, integrating brain morphometry, anatomical connectivity, functional imaging and functional connectivity

· To examine the impact of neurological and psychiatric pathologies on creative potential, allowing to better understand the role of brain nodes in creative mechanisms, and to develop new tools to assess higher-level cognitive functions in patients

Apathy is the most common behavioural syndrome affecting the central nervous system. It is present in many neurological or psychiatric diseases. However, apathy remains poorly understood in terms of concept (its definition is subject to debate), pathophysiology (the mechanisms and neural bases are poorly elucidated) and assessment (it is based on questionnaires that are not very specific and subjective). The definition of apathy has long been based on the idea that apathy is a loss of motivation. In clinical practice, however, apathy is considered when a reduction in behaviour is observed. Apathy can be considered as a quantitative reduction of voluntary behaviour directed towards a goal. This definition opens a different window to the pathophysiology of apathy, but also on its assessment and management of patients. Apathy presents itself in different forms, relating to dysfunction at different levels of the generation of goal-directed behaviour: elaboration, initiation, execution and control of behaviour. 

Decision-making is a cognitive process that allows us to select an option or action from a set of alternatives. The choices, which can be translated into the behavior of the participant, result from the decision-making processes. These processes can apply to very concrete situations (choosing a dish from a restaurant menu, choosing a direction when walking), but also to more abstract situations (such as choosing among possible beliefs). In cognitive neuroscience, the study of decision making is mainly focused on two areas: perceptual decisions, and decisions based on rewards (or preferences). These later decisions are often referred to as “economic decisions” because economists have done much to model these decisions as a process of comparing the subjective values ​​of options. In the team, we mainly focus on economics decisions, as they rely principally on the prefrontal cortex.

Network science is a multidisciplinary field that studies complex systems composed of interacting components, often represented as nodes and edges in a graph. These systems can be found in a wide range of fields, from biology and physics to sociology and computer science.

In the context of semantic associations, network science can be used to represent the relationships between words or concepts as a network. Each word or concept is represented as a node in the network, and the relationships between them are represented as edges. For example, two words might be connected if they frequently co-occur in a text corpus, or if they are both associated with a particular topic or theme.

Once the network is constructed, a range of network analysis techniques can be used to identify patterns and structures within the network. For example, one might use centrality measures to identify the most important nodes in the network, or community detection algorithms to identify groups of highly interconnected nodes.

These network analysis techniques can help to uncover hidden relationships and associations between words or concepts that might not be apparent from a simple word list or semantic space. They can also provide insights into the structure and organization of semantic knowledge, and help to identify areas of the network that are particularly important or influential.

Overall, network science provides a powerful framework for understanding complex systems, and can be a valuable tool for studying the structure and organization of semantic knowledge.