Projects

A new perspective on intelligence and the neural signature of g: Using dynamic graph-theoretical network analyses to clarify the relationship between attention and general intelligence

Given the enormous relevance of intelligence in education, occupation, and for positive life outcomes like health and longevity, it is an important scientific aim to understand the mechanisms behind individual differences in general intelligence. Although psychological research started to address this question long ago, there are still many unresolved issues, like e.g., the relationship between attention and intelligence. The advent of modern neuroimaging opens new perspectives and allows new insights into the biological bases of intelligence. This research project focuses on individual differences in functional interactions between different brain regions and investigates how this neural marker may contribute to clarify open questions of established intelligence conceptions.

Building on my prior research that identified intrinsic (task-independent) brain network efficiency, modularity, and brain network dynamics of attention-related brain regions as possible biological correlates of general intelligence, we apply graph-theoretical network analyses on fMRI data acquired during cognitive tasks (N>1000). On the one hand, we aim to test whether the observed associations between intelligence and intrinsic brain network characteristics may persist in the presence of active cognition. This will add to a more mechanistic understanding about the link between intelligence-related brain network organization, neurocognitive processes underlying cognition, and finally to differences in overt behavior that are, ultimately, what is measured in an intelligence test.

Further, we focus on a fundamental and general brain mechanism, i.e., brain network reconfiguration. This neural marker allows to differentiate between intelligence-related network characteristics specific to a certain task and those that are common to different tasks, and it was proposed as mirroring Spearman’s g on a neural level. This will be tested empirically in the current project. Finally, we link brain network reconfiguration to various cognitive performance measures and investigate whether this approach may contribute to clarify relations between different cognitive constructs, most specifically between attention and general intelligence.

Beyond attention – Towards a new neurocognitive model of intelligence through machine learning-based predictive modelling approaches and methods from artificial intelligence research

Intelligence has predictive relevance for education, occupation, and even for health and longevity. Therefore, it is an important scientific aim to understand the mechanisms behind individual differences in general intelligence. Although the scientific investigation of intelligence has more than 100 years tradition, fundamental models and theoretical concepts have changed only little throughout this time. The introduction of machine learning-based predictive modelling approaches and methods from artificial intelligence research to neuroscience together with advances in network analyses and the release of large neuroimaging data sets opens new opportunities to address the question about the biological bases of intelligence from a new perspective.  

Building on the first results of HI 2185 and further recent empirical findings, the here proposed follow-up project will broaden the focus to a) the consideration of multiple different cognitive abilities, b) neural features beyond our initial operationalization of brain network reconfiguration and even beyond functional brain connectivity, and c) to a new independent sample. To overcome the limitations of explanatory analyses approaches, I will first develop a predictive modelling framework and apply it to data from two large samples (HCP, AOMIC; N > 1000) to test whether general intelligence can be predicted from brain network reconfiguration. Secondly, the prediction framework will be applied to different behavioral tasks and different fMRI assessments to gain insights into unresolved questions about the relevance of different cognitive abilities (e.g., attention, working memory, processing speed) for general intelligence and into the existence of a positive manifold as proposed by the majority of intelligence models (e.g., Spearman’s g-factor theory). Third, the prediction framework will be further developed to the simultaneous inclusion of multiple neural measures to ultimately unravel how exactly the neural equivalent of general intelligence (neuro-g) is implemented within the human brain. Finally, all results will be integrated with existent empirical findings into a new neurocognitive model of intelligence.

NeuroGenConnect: Understanding Neuroticism by Integrating Genetics with Structural Brain Network Connectivity

Neuroticism is a key personality trait with significant public health implications, as elevated levels of neuroticism are linked to a higher risk for mental disorders, physical diseases, and variations in mortality and longevity. While neurobiological research has identified various brain characteristics associated with neuroticism and considerable progress has been made in understanding its molecular genetic foundation, a comprehensive framework that connects genetics, brain structure, and neuroticism is still lacking. The here proposed research project aims to address this gap by adopting an integrative approach that combines machine learning, network neuroscience and molecular genetics to further our understanding of individual differences in neuroticism.

We will leverage all of our complementary experience to achieve three primary research goals: (1) Identifying a robust biomarker of neuroticism based on structural brain connectivity, (2) uncovering novel genetic markers and biological pathways associated with structural brain connectivity, and (3) examining whether the identified structural brain connectivity characteristics mediate the relationship between genetic factors and neuroticism.

To move beyond previous studies, which were often non-reproducible, we will enhance replicability by utilizing data from two large open study samples (Human Connectome Project, UK-Biobank), by implementing multiple forms of cross-validation and by replicating all analyses in independent datasets. To address the challenge of deriving causal insights from correlative research on individual differences, we will employ Mendelian randomization, an approach that uses genetic variants as natural experiments to infer potential causal links between brain structure and neuroticism, thereby offering stronger evidence than traditional correlation-based methods.

Strictly following Open Science practices, we will ensure the preregistration of each study and the free distribution of all developed analysis code. Ultimately, the results will be integrated with existing empirical findings into a new holistic model of neuroticism, providing the basis for the development of new treatment and intervention strategies for mental disorders.

New Insights from Structural-Functional Brain Network Coupling into Human Intelligence and Personality Conceptions 

Understanding individual differences in cognition and behavior by examining their neurobiological foundations is a central goal of neuroscience and psychology. A promising approach involves investigating the relationship between structural and functional brain networks - specifically, how their alignment, known as structural-functional brain network coupling (SC-FC coupling), relates to key psychological traits such as intelligence and personality. Intelligence, defined as the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, and learn from experience, is one of the most widely studied constructs in psychological science due to its strong association with important life outcomes such as academic achievement and occupational success. Similarly, personality traits—particularly those described by the Big Five framework—play a crucial role in shaping behavioral patterns and social functioning across the lifespan. Despite the significance of these traits, it remains insufficiently understood how they are manifested in the brain.

This project addresses this gap by investigating the relationship between SC-FC coupling and both general intelligence and personality in two large-scale, open-access neuroimaging datasets (HCP; AOMIC; N > 1000). SC-FC coupling is modeled under both resting-state and task-based fMRI conditions, using advanced measures that capture functional interactions supported by underlying structural pathways. Furthermore, state-of-the-art machine learning and predictive modeling techniques are employed to assess how well region-specific SC-FC coupling patterns can predict individual differences in intelligence and personality traits.

By combining multimodal neuroimaging data with innovative computational methods, this project seeks to contribute to a deeper understanding of how cognitive and personality traits are rooted in the brain. It also aims to inform psychological theories about the shared and distinct neural mechanisms underlying intelligence and personality, and how these traits might manifest more clearly under trait-relevant conditions—both on a behavioral and neural level. Ultimately, the project will provide new tools and conceptual frameworks for the study of human individual differences.