Biology subjects

Fajnerova, I.

Publications and source records attributed to Fajnerova, I..

4 recordsLinked to original sources

bioRxiv2026

Replication Challenges in Linking Personality to Resting-State Functional Connectomics

An increasing number of studies are currently focusing on personality neuroscience, a term denoting the research aimed at neuroimaging correlates of inter-individual temperament and character variability. Among other methods, a graph theoretical analysis of the functional connectivity in resting-state functional magnetic resonance imaging data was applied in a study by Gao et al. (2013), reporting novel functional connectivity correlates of personality traits. The current paper presents a conceptual replication of the results of this study and discusses the related challenges, including an extension of the original statistical methods in order to illustrate the effect of the multiple comparison problem. Five personality dimensions were obtained using the revised Big Five Personality Inventory, including scores of Extraversion and Neuroticism covered in the original paper. Using a larger sample (84 subjects) with adequate statistical power (ranging from 0.75 to 0.95 across analyses), we failed to replicate any of the nine specific neuroimaging correlates of personality presented by Gao et al. While acknowledging differences in the experimental procedures, we discuss that the lack of replication might be caused by the relatively liberal control of false positives in the original study. Indeed, the original testing scheme leads to an expected count of about 10 false positive observations among all tests; applying this scheme to our data we observed a similar number of positive tests, albeit for different relations. No significant correlations were found in our data when standard family-wise error control was applied. These results illustrate the importance of combining exploration with independent validation, use of large datasets, as well as appropriate control of multiple comparison problem in order to prevent false alarms in research into neural substrates of personality differences. Importantly, our findings do not disprove the existence of a link between personality and the brains intrinsic functional architecture; but rather suggest that such a link might be even more subtle and elusive than previously reported.

neuroscience↗

bioRxiv2024

Deviation from typical brain activity during naturalistic stimulation predicts personality traits

The relationship between personality and brain activity has been an increasingly popular topic of neuroscientific research. Naturalistic viewing has been shown to enhance individual differences and might, therefore, be particularly useful for exploring this relationship. Here, we thus examine neural signatures of personality using naturalistic fMRI of 82 healthy subjects. We implemented a simple dimensionality reduction method to characterize brain activity by its typicality, assessed a range of personality traits using widely-used personality inventories, and tested the relationship between the two. We found that there is, indeed, a relationship between personality and the typicality of brain activity, which appears to be most consistently manifested by lower typicality in subjects with higher Neuroticism/Harm Avoidance. Our results highlight the usefulness of naturalistic viewing data for exploring the relationship between individual differences in personality and brain activity.

neuroscience↗

bioRxiv2024

No cardiac phase bias for threat perception under naturalistic conditions in immersive virtual reality

Previous studies have found that threatening stimuli are more readily perceived and more intensely experienced when presented during cardiac systole compared to diastole. Also, threatening stimuli are judged as physically closer than neutral ones. In a pre-registered study, we tested these effects and their interaction using a naturalistic (interactive, 3D) experimental design in immersive virtual reality: We briefly displayed threatening and non-threatening animals (four each) at varying distances (1.5-5.5 meters) to a group of young, healthy participants (n = 41), while recording their ECGs (electrocardiograms). Participants then pointed to the location where they had seen the animal (ca. 29k trials in total). Our pre-registered analyses indicated that perceived distances to both threatening and non-threatening animals did not differ significantly between cardiac phases - with Bayesian analysis supporting the null hypothesis. There was also no evidence for an association between subjective fear and perceived proximity to threatening animals. These results contrast with previous findings that used verbal or declarative distance measures in less naturalistic experimental conditions. Furthermore, our findings suggest that the cardiac phase-related variation in threat processing may not generalize across different paradigms and may be less relevant in naturalistic scenarios than under more abstract experimental conditions. Impact statementTo our knowledge, this is the first study to assess the influence of interoceptive cardiac signals on visual perception using naturalistic stimuli in immersive virtual reality. We based the design of our experiment on previous reports about threat processing biases and the role of the cardiac cycle but did not observe the expected effects. This poses the question of their generalizability from abstract settings to more naturalistic setups, featuring greater behavioral engagement and a richer sensory environment.

physiology↗

bioRxiv2019

Estimating Sparse Neuronal Signal from Hemodynamic Response: the Mixture Components Inference Approach

The approximate knowledge of the hemodynamic response to neuronal activity is widely used in statistical testing of effects of external stimulation, but has also been applied to estimate the neuronal activity directly from functional magnetic resonance data without knowing the stimulus timing. To this end, sparse linear regression methods have been previously used, including the well-known LASSO and the Dantzig selector. These methods generate a parametric family of solutions with different sparsity, among which a choice is finally based using some information criteria. As an alternative we propose a novel approach that instead utilizes the whole family of sparse regression solutions. Their ensemble provides a first approximation of probability of activation at each timepoint, and together with the conditional neuronal activity distributions estimated with the theory of mixtures with varying concentrations, they serve as the inputs to a Bayes classifier ultimately deciding between the true and false activations. As we show in extensive numerical simulations, the new method performs favourably in comparison with standard approaches in a range of realistic scenarios. This is mainly due to the avoidance of overfitting and underfitting that commonly plague the solutions based on sparse regression combined with model selection methods, including the corrected Akaike Information Criterion. This advantage is finally documented on fMRI task dataset.

neuroscience↗