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.

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.

The neural code of neuroticism: Insights from inter-subject representational similarity analysis

Neuroticism, the tendency to experience negative emotions such as anxiety, irritability or emotional instability, is a key risk factor for mental illness. Identifying its neurobiological basis, particularly whether there is a shared neural foundation among individuals, is therefore essential. 

This project aims to determine whether participants’ similarity in neuroticism is also reflected in the similarity of their brain activity during movie watching, as such naturalistic stimuli may best reflect real-world experiences. Building in our recent work and established personality theories suggesting that neural characteristics of traits may be more pronounced in trait-relevant contexts, we additionally investigate whether this brain-trait representational similarity is stronger during movie scenes rated as particularly relevant to neuroticism. 

To identify such trait-relevant scenes, we conducted an independent online study. We then apply Inter-Subject Representational Similarity Analysis (IS-RSA) to a subset of participants (N = 184) from the Human Connectome Project (HCP), with available fMRI data during movie viewing and neuroticism scores from the NEO-FFI. We examine the relationship between neuroticism and brain activity at whole-brain, network, and region-specific levels, while accounting for the influence of trait-relevant contexts. In addition, different models of trait similarity are compared to gain deeper conceptual insights into how neuroticism is reflected in brain function. 

By linking similarity in neuroticism to shared neural representations during naturalistic stimuli, this research contributes to our knowledge of the respective neurobiological foundations. A deeper understanding of these mechanisms could ultimately inform interventions for mental health conditions associated with heightened neuroticism. 

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.