Biology subjects

Tomecek, D.

Publications and source records attributed to Tomecek, D..

10 recordsLinked to original sources

bioRxiv2026

Domain Adaptation Enables Cross-site Classification of First-episode Schizophrenia from Multimodal Neuroimaging Data

Identifying robust neuroimaging markers associated with schizophrenia is essential for advancing research and informing clinical understanding. However, a major obstacle to clinical translation is the limited ability of neuroimaging-based classification models to generalise across scanning sites. In this study, we first establish best performing within-site models, and then systematically investigate cross-site generalisation in first-episode schizophrenia (FES) classification and evaluate strategies for mitigating site-related distribution shifts. Using data from two acquisition sites (n = 389 in total), we perform train-on-site/test-on-site experiments to analyze performance degradation under domain shift and examine the effectiveness of ComBat, optimal transport, and adversarial adaptation strategies. Across functional, structural, and diffusion-based features, both traditional machine learning (TML) and neural network (NN) models achieve comparable performance in within-site classification, with resting state fMRI functional connectivity providing the most robust unimodal features. When models are transferred across sites, performance degrades substantially across all approaches, highlighting the impact of site-related variability. Distribution-alignment methods partially mitigate this degradation, with ComBat and optimal transport yielding more consistent cross-site improvements than adversarial adaptation. Increasing model complexity alone does not result in systematic performance gains, and simple models combined with effective alignment strategies often perform comparably to more complex neural architectures, while multimodal feature fusion does not consistently outperform functional connectivity alone. Overall, our findings indicate that controlling for site effects is more critical than model complexity for achieving generalisable classification in FES, underscoring the importance of rigorous evaluation designs and explicit distribution-alignment strategies for neuroimaging-based predictive models with potential clinical utility.

neuroscience↗

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↗

bioRxiv2025

Spontaneous thought orientation tracked by fMRI networks and EEG alpha power dynamics

Understanding how spontaneous, rather than experimentally induced, thoughts relate to brain activity remains a major challenge. We combined simultaneous fMRI and EEG recordings with Descriptive Experience Sampling (DES) to link momentary, naturally occurring experiences to their neural signatures during rest. Using machine-learning classification of 240 time-locked samples from eight participants--each completing nine 25-minute resting-state sessions--we reliably distinguished internally from externally oriented experiences (fMRI accuracy = 65.4%, EEG = 62.5%). Externally oriented states showed greater fMRI activity in salience, auditory, and visuospatial networks and lower occipital alpha power in EEG, whereas internally oriented states exhibited the opposite pattern, extending prior DMN-focused accounts of internally directed states. Across modalities, integrated resting-state alpha power correlated negatively with BOLD fluctuations in parietal and occipital regions. These multimodal findings reveal distinct neural signatures of spontaneous experience and demonstrate that coordinated large-scale network dynamics and alpha-band oscillations track the natural alternation between inward and outward focus in the human mind.

neuroscience↗

bioRxiv2025

Altered Sense of Agency in First-Episode Schizophrenia Patients: A Comparative Study Across Two Sites

BackgroundDisturbances in the sense of agency are a key marker of schizophrenia and are closely linked to core symptoms such as hallucinations and delusions of influence. Neuroimaging studies have implicated cortical midline structures and the defaul t mode network (DMN) associated with self-related processing, but replication across sites and larger samples is needed. MethodsWe examined neural correlates of self versus other-agency judgments in two independent cohorts of first-episode schizophrenia patients (n = 177) and controls (n = 123) recruited at separate MRI centers. During fMRI, participants performed an agency-related task. Data was analyzed using independent component analysis and statistical parametric mapping, focusing on networks associated with self-referential processing. ResultsAcross both sites, self-agency consistently engaged the anterior and posterior cingulate cortex and the precuneus, key regions of the DMN. Schizophrenia patients exhibited significantly reduced DMN activation duri ng self-referential processing compared to controls. While site-related differences were noted in the magnitude of activation, the core pattern of DMN disruption was robust across analytic approaches and datasets. ConclusionsThis study replicates and extends prior findings of altered self-agency processing in schizophrenia, demonstrating consistent DMN hypo-activation in first-episode patients across two independent cohorts. These results validate the agency paradigm as a reliable probe of self-related neural mechanisms and support models of schizophrenia as a disorder of altered self-perception.

neuroscience↗

bioRxiv2025

Impact of Connectivity Granularity: A Comparison of ROI and Network-Level Approaches for Early Schizophrenia Classification

While schizophrenia diagnosis relies on clinical interviews, there is growing interest in neuroimaging-based computational tools to aid classification. In particular, resting-state fMRI-derived functional connectivity has been explored as a potential biomarker, with applications not only in supporting clinical assessment but also in research contexts such as patient stratification and probing disease mechanisms. Here, we compare two common approaches to computing functional connectivity - region of interest (ROI)-level and brain network-level - and evaluate their predictive power for classifying first-episode schizophrenia patients, in contrast to most prior work focusing on chronic patients. We show that ROI-level features consistently outperform network-level features. Despite the simplicity of our classification models, we achieved accuracies up to 83.15% using the AAL90 atlas. We also found that non-lagged functional connectivity generally outperforms lagged variants, suggesting that added temporal complexity may introduce noise rather than improve predictive power. Overall, our findings highlight region-based connectivity from a medium-resolution atlas as a promising representation for early-stage schizophrenia classification, while emphasising the need for validation on independent datasets to confirm generalisability.

neuroscience↗

bioRxiv2025

From ancient fears to airborne threats: fMRI insights into neural fear responses

Threat perception is a fundamental aspect of human cognition, shaped by evolutionary pressures and modern environmental demands. While ancestral threats (e.g., snakes) have been shown to elicit stronger neural responses than modern threats (e.g., guns), less is known about how the brain processes airborne threats, such as depictions of individuals wearing face masks. This fMRI study investigates neural responses to ancestral, modern, and airborne threats to identify shared and distinct activation patterns. Sixty participants viewed visual stimuli from the three categories while undergoing fMRI scanning. Results showed heightened activation in the fear-processing network for all affective stimuli. In addition, activation of the ventral attention network was found for the ancestral threats. Modern threats elicited less intense responses, primarily engaging cortical regions associated with context-specific analysis. Notably, airborne threats elicited neural responses of similar intensity to ancestral threats but activated cortical regions overlapping with those for modern threats. This dual pattern highlights the brains capacity to integrate evolutionary biases with socially constructed threat awareness. These findings underscore the importance of recognizing airborne threats as a unique category of threat processing, with implications for public health and mental well-being.

neuroscience↗

bioRxiv2024

Resting-state hyper- and hypo-connectivity in early schizophrenia: which tip of the iceberg should we focus on?

In this study, we explore the intricate landscape of brain connectivity in the early stages of schizophrenia, focusing on the patterns of hyper- and hypoconnectivity. Despite existing literatures support for altered functional connectivity (FC) in schizophrenia, inconsistencies and controversies persist regarding specific dysconnections. Leveraging a large sample of 100 first-episode schizophrenia patients (42 females/58 males) and 90 healthy controls (50 females/40 males), we compare the functional connectivity across 90 brain regions of the Automated Anatomical Labeling atlas. We inspected the effects of medication and examined the association between FC changes and duration of untreated psychosis, duration of antipsychotic treatment, as well as symptom severity of the disorder. Our approach also includes a comparative analysis of three denoising strategies for functional magnetic resonance imaging data. In patients, 15 region pairs exhibited increased FC, whereas 150 pairs showed reduced FC relative to controls. Despite this numerical asymmetry, the overall distribution of FC changes was relatively balanced: the median FC was not systematically shifted, indicating no global tendency toward either hyper- or hypoconnectivity. Notably, seveFC alterations were significantly associated with variability in symptom severity and antipsychotic medication across patients. Taken together, these results suggest a pattern of localized dysconnections embedded within an otherwise globally balanced change in connectivity profile in early schizophrenia. Importantly, this balance was substantially disrupted towards dominant observation of hypoconnectivity when less stringent denoising strategies were applied, with results increasingly dominated by hypoconnectivity, pointing to data preprocessing as a critical source of variability across studies.

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↗

bioRxiv2023

Estimating multimodal brain variability in schizophreniaspectrum disorders: A worldwide ENIGMA study

ObjectiveSchizophrenia is a multifaceted disorder associated with structural brain heterogeneity. Despite its relevance for identifying illness subtypes and informative biomarkers, structural brain heterogeneity in schizophrenia remains incompletely understood. Therefore, the objective of this study was to provide a comprehensive insight into the structural brain heterogeneity associated with schizophrenia. MethodsThis meta- and mega-analysis investigated the variability of multimodal structural brain measures of white and gray matter in individuals with schizophrenia versus healthy controls. Using the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6139 individuals for a given brain measure, we examined variability in cortical thickness, surface area, folding index, subcortical volume and fractional anisotropy. ResultsWe found that individuals with schizophrenia are distinguished by higher heterogeneity in the frontotemporal network with regard to multimodal structural measures. Moreover, individuals with schizophrenia showed higher homogeneity of the folding index, especially in the left parahippocampal region. ConclusionsHigher multimodal heterogeneity in frontotemporal regions potentially implies different subtypes of schizophrenia that converge on impaired frontotemporal interaction as a core feature of the disorder. Conversely, more homogeneous folding patterns in the left parahippocampal region might signify a consistent characteristic of schizophrenia shared across subtypes. These findings underscore the importance of structural brain variability in advancing our neurobiological understanding of schizophrenia, and aid in identifying illness subtypes as well as informative biomarkers.

neuroscience↗

bioRxiv2020

Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines

Functional connectivity analysis of resting state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix, calculated by correlating signals from regions of interest, is a useful approximate representation of the brains connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Treatment of such artifacts poses a standing challenge because of their high variability. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, the Typicality of Functional Connectivity, to capture deviations from normal brain functional connectivity pattern. Based on results of resting state fMRI for 245 healthy subjects we show that this measure is significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity and preprocessing options, as well as other datasets including 1081 subjects from the Human Connectome Project. The Typicality of Functional Connectivity provides individual proxy measure of motion effect on functional connectivity and is more sensitive to inter-individual variation of motion than individual functional connections. In principle it should be sensitive also to other types of artifacts, processing errors and possibly also brain pathology, allowing wide use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

neuroscience↗