Biology subjects

Popp, J. L.

Publications and source records attributed to Popp, J. L..

5 recordsLinked to original sources

bioRxiv2026

The Neural Code of Neuroticism

High neuroticism is a risk factor for mental disorders. Understanding whether individuals share a common foundation in brain function underlying neuroticism is therefore an essential goal of neuroscience. We applied Inter-Subject Representational Similarity Analysis on data from 174 Human Connectome Project participants to investigate if their similarity in neuroticism is reflected in their similarity of fMRI-recorded brain activity during movie watching. To test behavioral personality theories that consider trait expression as dependent on situational context, we examined whether brain-trait representational similarity varied between trait-relevant and trait-irrelevant movie scenes (independently rated, N = 86). Higher neuroticism was associated with greater heterogeneity in brain responses, a pattern that was particularly pronounced during trait-relevant scenes. Our study informs dimensional conceptualizations of psychopathology viewing neuroticism as risk factor and extends behavioral personality theories to the neural level. Broadly, it highlights the value of naturalistic imaging and underscores the importance of stimulus selection when investigating brain-behavior associations.

neuroscience↗

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

Derivation of cardiac reference ranges for Mus musculus using the Collaborative Cross and identification of new cardiac models

Contemporary approaches for developing interventions and assessing pre-clinical cardiovascular risk frequently utilize animal and in vitro models. However, these models currently lack normal species-specific reference ranges similar to what exists for humans. The genetically diverse Collaborative Cross (CC) population that models human genetic heterogenetiety was characterized to develop mouse-specific cardiac reference ranges for Mus muscuslus, the most commonly used pre-clincial model. Heart function was analyzed in males and females from 58 CC strains and C57BL/6J using high-frequency ultrasound under both conscious and anesthetized conditions, as well as conscious electrocardiography to develop two standard deviation-based reference ranges. The sources and magnitude of measurement variability were identified, and inter-laboratory comparisons determined to quantify phenotypic robustness and heritability. Strain was the largest source of variability, while laboratory where data were collected was also significant but sex was not. Additionally, strains were identified that have characteristics of disease-associated phenotypes in cardiac function and electrophysiology similar to human cases including dilated cardiomyopathy, systolic cardiomyopathy, cancer therapy-related cardiac dysfunction, and long QT. These new models allow a more natural, and therefore more translatable progession to a cardiac disease state, supporting development of strain-specific models for cardiac pathologies, ultimately allowing more accurate diagnoses and informative safety assessments in humans. Article SummaryThe Collaborative Cross (CC) mouse reference population, modeling human genetic diversity, was utilized to derive cardiac reference values for Mus musculus. Strain was the primary source of variability, with the laboratory also playing a significant role, while sex had no impact. Despite inter-laboratory reproducibility challenges, the study identified strains resembling disease-associated phenotypes that offer a more natural progression to cardiac disease and provide crucial data for early disease identification and accurate safety assessments in humans.

genetics↗

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

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↗