Biology subjects

Thiele, J. A.

Publications and source records attributed to Thiele, J. A..

7 recordsLinked to original sources

bioRxiv2025

Trait-Relevant Tasks Improve Personality Prediction from Structural-Functional Brain Network Coupling

Personality traits capture stable patterns of behavior and thought, and neurobiological correlates were identified in structural and functional brain networks. Here, we investigate whether the coupling between structural and functional brain networks (SC-FC coupling), during resting state and seven tasks of varying trait-relevance, is associated with individual differences in the Big Five personality traits. We used diffusion-weighted and functional magnetic resonance imaging from 764 participants of the Human Connectome Project and modelled individual differences in SC-FC coupling with similarity and communication measures. These measures approximate functional interactions unfolding on top of the structural connectome and were set in relation to individual variations in personality traits. Significant associations were only observed during trait-relevant tasks: for agreeableness during social cognition, and conscientiousness could be predicted from task-general coupling patterns. We conclude that optimizing trait-relevance of tasks during neuroscientific measurements presents a promising means to increase effect sizes in studies on brain-behavior associations.

neuroscience↗

bioRxiv2025

Decoding the Human Brain during Intelligence Testing

Understanding the brain mechanisms underlying complex human cognition is a major objective in neuroscience. Previous studies have identified neural correlates of intelligence at different temporal and spatial scales using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) separately. This study treats intelligence as a multilayer phenomenon across temporal and spatial scales and examines how the connectedness of brain regions as well as the complexity of multilayer brain dynamics relates to performance in an established intelligence test. Graph-theoretical analyses of fMRI-derived functional connectivity (N=67) revealed that the connectedness of frontal and parietal regions was associated with individual performance. Further, multiscale entropy analyses of EEG signals (N=131) disclosed that higher test scores were linked to more complex long-range processes and, at a trend level, to less complex short-range processes. These findings support the Multilayer Processing Theory, proposing the interplay between flexible long-range and modular short-range processes as the neural bases of intelligence.

neuroscience↗

bioRxiv2024

Structural-Functional Brain Network Coupling During Task Performance Reveals Intelligence-Relevant Communication Strategies

Intelligence is a broad mental capability influencing human performance across tasks. Individual differences in intelligence have been linked to characteristics of structural and functional brain networks. Here, we consider their alignment, the structural-functional brain network coupling (SC-FC coupling) during resting state and during active cognition, to predict general intelligence. Using DWI and fMRI data from 764 participants of the Human Connectome Project (Replication: N1=126, N2=180), we modelled SC-FC coupling with similarity and communication measures that capture functional interactions unfolding on top of structural brain networks. By accounting for variations in brain region-specific neural signaling strategies, we show that individual differences in SC-FC coupling patterns predict individual intelligence scores. Most accurate predictions resulted from cognitively demanding tasks and task combinations. Our study suggests the existence of an intrinsic SC-FC coupling organization enabling fine-drawn intelligence-relevant adaptations that support efficient information processing by facilitating brain region-specific adjustment to external task demands.

neuroscience↗

bioRxiv2023

Can machine learning-based predictive modelling improve our understanding of human cognition?

A growing body of research predicts individual cognitive ability levels from brain characteristics including functional brain connectivity. The majority of this research achieves good prediction performance but provides limited insight into neurobiological processes underlying the predicted concepts. The insufficient identification of predictive characteristics may present an important factor critically contributing to this constraint. Here, we encourage to design predictive modelling studies with an emphasis on interpretability to enhance our conceptual understanding of human cognition. As an example, we investigated in a preregistered study which functional brain connections successfully predict general, crystallized, and fluid intelligence in a sample of 806 healthy adults (replication: N = 322). The choice of the predicted intelligence component as well as the task during which connectivity was measured proved crucial for better understanding intelligence at the neural level. Further, intelligence could be predicted not solely from one specific set of brain connections, but from various combinations of connections with system-wide locations. Such partially redundant, system-wide functional characteristics complement intelligence-relevant connectivity of brain regions proposed by established intelligence theories. In sum, our study showcases how future predictive studies on human cognition can enhance explanatory value by prioritizing a systematic evaluation of predictive characteristics over maximizing prediction performance. Significance StatementIntelligence represents a hallmark of human behavior, and a surge number of studies predicted individual scores from functional brain connectivity. However, actual understanding about its neural basis remains limited. We demonstrate how predictive modelling can be applied strategically to improve tracing predictive functional brain connections to enhance our understanding of intelligence. Our study unveils crucial findings about intelligence: differences in the neural code of distinct intelligence facets not detectable on a behavioral level and a brain-wide distribution of functional brain characteristics relevant to intelligence that extends those proposed by major intelligence theories. In a broader context, it offers a framework for future prediction studies that prioritize meaningful insights into the neural basis of complex human traits over predictive performance.

neuroscience↗

bioRxiv2023

Structural-Functional Brain Network Coupling Predicts Human Cognitive Ability

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = .25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = .19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

neuroscience↗

bioRxiv2022

Multimodal Brain Signal Complexity Predicts Human Intelligence

Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Ravens Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ~ .20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the defaultmode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits. Significance StatementSpontaneous brain activity builds the foundation for intelligent processing - the ability of humans to adapt to various cognitive demands. Using resting-state EEG, we extracted multiple aspects of temporally highly resolved intrinsic brain dynamics to investigate their relationship with individual differences in intelligence. Single associations were of small effect sizes and varied critically across spatial and temporal scales. However, combining multiple measures in a multimodal cross-validated prediction model, allows to significantly predict individual intelligence scores in unseen participants. Our study adds to a growing body of research suggesting that observable associations between complex human traits and neural parameters might be rather small and proposes multimodal prediction approaches as promising tool to derive robust brain-behavior relations despite limited sample sizes.

neuroscience↗

bioRxiv2021

Multi-Task Brain Network Reconfiguration is Inversely Associated with Human Intelligence

Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated. We used fMRI data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different task states as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system, and replicates in two independent samples (N = 138, N = 184). Our findings suggest that the intrinsic network architecture of individuals with higher intelligence scores is closer to the network architecture as required by various cognitive demands. Multi-task brain network reconfiguration may, therefore, represent a neural reflection of the behavioral positive manifold - the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multi-task brain network.

neuroscience↗