Biology subjects

Fraenz, C.

Publications and source records attributed to Fraenz, C..

11 recordsLinked to original sources

bioRxiv2026

Distinct and overlapping correlates of fear acquisition and extinction across different neuroimaging modalities

Interindividual differences in fear acquisition and extinction have been related to variation in specific brain correlates. However, variability in experimental setups complicates the integration of findings. Here, we present a combined fear acquisition (n = 101) and extinction (n = 88) study from which we obtained distinct microstructural, macrostructural, and connectivity brain properties with magnetic resonance imaging in healthy, young participants. The properties included regional brain volume, cortical surface area and thickness, neurite density and orientation dispersion, and nodal efficiency of structural and functional connectivity. Fear responses were quantified as changes in skin conductance. Data from 360 cortical and 16 subcortical brain regions as well as the efficiency of network connections between them were used as independent variables in bootstrapped and cross-validated regularized regression models. Results show that numerous brain regions, spanning the so-called 'fear and extinction network' and extending beyond it, contribute to fear acquisition, to extinction, or dynamically shift between both phases. For several brain regions, data from multiple imaging modalities showed a high degree of concordance for several brain regions. These findings call for further research to examine the potential interplay between brain correlates shaping fear acquisition and extinction, as opposed to studying the imaging modalities in isolation.

neuroscience↗

bioRxiv2025

Predicting individual differences of fear and cognitive learning and extinction

The abilities to acquire new information and to modify previously learned knowledge are critical in an ever-changing world. However, the efficacy of learning is notably variable among individuals, with extinction learning being the epitome of such variability. Abundant studies have identified a core network of brain regions including amygdala, hippocampus, dorsal anterior cingulate cortex (ACC), ventromedial prefrontal cortex (PFC) and, more recently, the cerebellum, as key players in learning and extinction. Yet, the precise interactions within this network and their relationship to individual learning abilities and extinction have remained largely unexplored. In the present study, we examined how functional (FC), effective (EC), and structural (SC) connectivity patterns in the core learning network allow predicting individual differences in the efficacy of learning, extinction, and renewal. Analysing a large dataset of over 500 participants across a multitude of paradigms, our results revealed that FC predicted better acquisition, with a central role of ACC and hippocampus, whereas SC, involving ACC and amygdala, predicted higher levels of extinction learning. EC results suggested a predominantly inhibitory coupling among core learning network nodes, with paradigm-specific EC connectivity patterns predicting learning. Our predictions not only generalised between fear and cognitive predictive learning paradigms but were also successful in predicting learning from task-related FC and simulated data. Together, these results describe the multimodal neural determinants of learning, extinction, and renewal, and may inform individualised interventions for affective disorders based on neural connectivity patterns.

neuroscience↗

bioRxiv2025

Multi-modal brain properties are associated with interindividual differences in fear acquisition and extinction

Interindividual differences in fear acquisition and extinction have been related to variation in specific brain correlates. However, variability in experimental setups complicates the integration of findings. Here, we present a combined fear acquisition (n = 101) and extinction (n = 88) experiment in which both phenomena were related to brain correlates obtained via functional magnetic resonance imaging in healthy, young participants. Correlates included regional brain volume, cortical surface area and thickness, neurite density and orientation dispersion, structural and functional connectivity. Fear responses were quantified as changes in skin conductance. Data from 376 brain areas and 70,500 network connections were used as independent variables in regularized regression models. Regression models of fear acquisition could be obtained for all modalities but regional brain volume. There were 284 predictors of which 77 appeared in exactly two models and 19 in exactly three. The latter primarily included brain areas from the somatosensory, insular, cingulate, and frontal cortices. Fear extinction yielded regression models based on neurite density, structural connectivity, and functional connectivity with 112 predictors in total. Two predictors, located in the dorsolateral prefrontal cortex, replicated across exactly two regression models (neurite density and structural connectivity). This study is the first to investigate the neural correlates of both fear acquisition and extinction in an explorative, multi-modal fMRI approach. Results show that numerous brain regions contribute to fear conditioning, some of them via more than one correlate. These findings call for further research to examine the potential interplay between brain correlates shaping fear conditioning.

neuroscience↗

bioRxiv2025

Electrophysiological resting-state signatures link polygenic scores to general intelligence

Intelligence is associated with important life outcomes. Behavioral, genetic, structural, and functional brain correlates of intelligence have been studied for decades, but questions remain as to how genetics are related to trait expression and what intermediary role brain properties play. This study investigated these mediations in a representative sample of 434 individuals, comprising young and older adults. Polygenic scores (PGS) for intelligence were calculated. Resting-state EEG recordings were analyzed using graph theory quantifying functional connectivity across different frequencies. We tested whether global and local graph metrics like efficiency and clustering mediated the association between PGS and intelligence. PGS significantly predicted variance in intelligence and were related to frequency-specific graph metrics in areas predominantly located in parieto-frontal regions, which in turn were associated with intelligence. These findings, which are based on the first study linking PGS to intelligence using EEG-derived graph metrics, advance our understanding of the neurogenetics of intelligence.

neuroscience↗

bioRxiv2025

Neurite density but not myelination of specific fiber tracts links polygenic scores to general intelligence

White matter is fundamental for efficient and accurate information transfer throughout the human brain and thus crucial for intelligence. Previous studies often demonstrated associations between fractional anisotropy (FA) as a metric of white matter "microstructural integrity" and intelligence, but it is still unclear, whether this relation is due to greater axon density, parallel, homogenous fiber orientation distributions, or greater myelination since all of these measures influence FA. Using neurite orientation dispersion and density imaging (NODDI) and myelin water fraction (MWF) imaging data, we analyzed the microstructural architecture of intelligence in more detail in a sample of 500 healthy young adults. Furthermore, we were interested whether specific white matter microstructural indices play intermediary roles in the pathway that links genetic disposition for intelligence to phenotype. Thus, we conducted for the first time mediation analyses investigating whether neurite density (NDI), orientation dispersion (ODI), and MWF of 64 white matter fiber tracts mediate the effects of polygenic scores for intelligence (PGSGI) on general intelligence. By doing so, we showed that NDI, but not ODI or MWF of white matter fiber tracts was significantly associated with general intelligence and that the NDI of six fiber tracts mediated the relation between genetic variability and g. These findings are a crucial step forward in decoding the neurogenetic underpinnings of general intelligence, as they identify that neurite density of specific fiber tracts relates polygenic variation to g, whereas orientation dispersion and myelination did not.

neuroscience↗

bioRxiv2023

Investigating robust associations between functional connectivity based on graph theory and general intelligence

Previous research investigating relations between general intelligence and graph-theoretical properties of the brains intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency in any sample, but significant positive associations with global clustering coefficient and small-world propensity in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for nodal efficiency and only led to significant predictions between two data sets for local clustering. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.

neuroscience↗

bioRxiv2022

Robust associations between white matter microstructure and general intelligence

Early research on the neural correlates of human intelligence was almost exclusively focused on gray matter properties. The advent of diffusion-weighted imaging led to an exponential growth of white matter brain imaging studies. However, this line of research has yielded mixed observations, especially about the relations between general intelligence and white matter microstructure. We used a multi-center approach to identify white matter regions that show replicable structure-function associations, employing data from four independent samples comprising over 2000 healthy participants. We used tract-based spatial statistics to examine associations between g factor scores and white matter microstructure and identified 188 voxels which exhibited positive associations between g factor scores and fractional anisotropy in all four data sets. Replicable voxels formed three clusters: one located around the forceps minor, crossing with extensions of the anterior thalamic radiation, the cingulum-cingulate gyrus, and the inferior fronto-occipital fasciculus in the left hemisphere, one located around the left-hemispheric superior longitudinal fasciculus, and one located around the left-hemispheric cingulum-cingulate gyrus, crossing with extensions of the anterior thalamic radiation and the inferior fronto-occipital fasciculus. Our results indicate that individual differences in general intelligence are robustly associated with white matter organization in specific fiber bundles.

neuroscience↗

bioRxiv2022

Structural architecture and brain network efficiency links polygenic scores to intelligence

Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

genetics↗

bioRxiv2020

Conditioned fear reactions are associated with gray matter density but not cortical microstructure

Research has shown that fear acquisition, in reaction to potentially harmful stimuli or situations, is characterized by pronounced interindividual differences. It is likely that such differences are evoked by variability in the macro- and microstructural properties of brain regions involved in the processing of threat or safety signals from the environment. Indeed, previous studies have shown that the strength of conditioned fear reactions is associated with the cortical thickness or volume of various brain regions. However, respective studies were exclusively targeted at single brain regions instead of whole brain networks. Here, we tested 60 young and healthy individuals in a differential fear conditioning paradigm while they underwent fMRI scanning. In addition, we acquired T1-weighted and multi-shell diffusion-weighted images prior to testing. We used task-based fMRI data to define global brain networks which exhibited increased BOLD responses towards CS+ or CS- presentations, respectively. From these networks, we obtained mean values of gray matter density, neurite density, and neurite orientation dispersion. We found that mean gray matter density averaged across the CS+ network was significantly correlated with the strength of conditioned fear reactions quantified via skin conductance response. Measures of neurite architecture were not associated with conditioned fear reaction in any of the two networks. Our results extend previous findings on the relationship between brain morphometry and fear learning. Most importantly, our study is the first to introduce neurite imaging to fear learning research and discusses how its implementation can be improved in future research.

neuroscience↗

bioRxiv2020

Polygenic Scores for Cognitive Abilities and their Association with Different Aspects of General Intelligence - a Deep Phenotyping Approach

Intelligence is a highly polygenic trait and genome-wide association studies (GWAS) have identified thousands of DNA variants contributing with small effects. Polygenic scores (PGS) can aggregate those effects for trait prediction in independent samples. As large-scale light-phenotyping GWAS operationalized intelligence as performance in rather superficial tests, the question arises which intelligence facets are actually captured. We used deep-phenotyping to investigate the molecular determinants of individual differences in cognitive ability. We therefore studied the association between PGS of intelligence (IQ-PGS), cognitive performance (CP-PGS) and educational attainment (EA-PGS) with a wide range of intelligence facets in a sample of 557 healthy adults. IQ-PGS, CP-PGS and EA-PGS had the highest incremental R2s for general (2.71%; 4.27%; 2.06%), verbal (3.30%; 4.64%; 1.61%) and numerical intelligence (3.06%; 3.24%; 1.26%) and the weakest for non-verbal intelligence (0.89%; 1.47%; 0.70%) and memory (0.80%; 1.06%; 0.67%). These results indicate that PGS derived from light-phenotyping GWAS do not reflect different facets of intelligence equally well, and thus should not be interpreted as genetic indicators of intelligence per se. The findings refine our understanding of how PGS are related to other traits or life outcomes.

genetics↗

bioRxiv2020

Fear learning sculpts functional brain connectivity at rest beyond the traditional fear network in humans

Neuroscientific research has identified specific brain networks involved in the acquisition of fear memories. Using fMRI to assess changes in resting-state functional connectivity (RSFC) induced by fear acquisition, single brain regions from these networks have also been linked to fear memory consolidation. However, previous studies only examined RSFC changes within restricted sets of brain regions or without a proper control group, leaving our knowledge about fear consolidation outside of traditional fear networks incomplete. Here, we tested a group of 84 healthy participants in a differential fear conditioning paradigm and quantified RSFC changes between 358 cortical and 16 subcortical brain areas. Subsequent to fear learning, 21 functional connections exhibited significant RSFC changes. Importantly, these connections were not restricted to the traditional fear networks but also comprised various frontal and visual areas. Our findings indicate that fear memory consolidation is a complex process that integrates relevant information across the entire brain.

neuroscience↗