Biology subjects

Fallgatter, A. J.

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

7 recordsLinked to original sources

bioRxiv2026

Menstrual cycle phase and premenstrual symptoms in relation with the stress response: an fMRI study

Objective: Women with premenstrual syndrome (PMS) experience affective lability, irritability, depressed mood, and anxiety during the late luteal phase of the menstrual cycle. Stress reactivity may contribute to the development of these symptoms, yet neural correlates remain unexplored. We examined neural, behavioral and endocrine stress responses across menstrual cycle phases in women with and without PMS. Methods: 26 women with PMS and 24 women without PMS participated in two fMRI sessions, during the mid-follicular (days 5 to 11) and late-luteal phases (days -7 to -1). Participants completed the Montreal Imaging Stress Task (MIST). Subjective stress ratings and salivary cortisol were collected at six time points before and after stress induction. Stress-related brain activation was assessed using whole-brain and region-of-interest analyses. Results: Subjective stress ratings increased as expected from before to after the MIST, with no group or phase differences. Salivary cortisol was significantly higher during the mid-follicular phase, with no group effect or significant pre-post MIST difference. While the stress task elicited activation patterns in stress-related regions, whole-brain analysis revealed no significant effects of group (PMS vs. no PMS) or menstrual cycle phase (follicular vs. luteal) on the neural stress response. Considering premenstrual symptom severity as a predictor revealed significant group-by-symptom severity interactions in the right inferior frontal gyrus and the right superior temporal gyrus. Conclusion: Using a within-subject repeated-measures design, we found neural responses to psychosocial stress to be stable across menstrual cycle phases. Individual differences in symptom severity may nevertheless be associated with differences in stress-related brain activation.

neuroscience↗

bioRxiv2026

A normative reference for large-scale human brain dynamics across the lifespan

Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed the study of static brain structure and connectivity, an equivalent normative framework for intrinsic brain dynamics has been lacking. This gap has limited our ability to characterize individual variability, development, ageing, and mental health conditions at scale. Here, we establish a population-level normative reference for large-scale human brain dynamics using resting-state fMRI data from more than 10,000 individuals spanning the lifespan and 91 scanning sites. We derive a compact set of recurring brain-state configurations that are reproducible across scanners and acquisition paradigms and that generalize to previously unseen cohorts. Anchoring these dynamic states to normative lifespan models enables the quantification of individual deviations relative to population reference distributions. We show that intrinsic brain dynamics undergo systematic reorganization across development and ageing, with pronounced changes before early adulthood and more gradual modulation thereafter. Applying this framework across multiple mental health conditions reveals disorder-specific and highly heterogeneous deviations in brain dynamics that are not captured by static neuroimaging measures. Robust transfer to independent cohorts and longitudinal analyses demonstrate that normative brain dynamics can be reliably assessed out of distribution. These results delineate a population-scale dynamic architecture of the human brain and extend normative brain mapping from static phenotypes to the temporal domain, providing a reference framework for studying brain function across the lifespan in health and disease.

neuroscience↗

bioRxiv2025

Disease Severity Across Psychiatric Disorders Is Linked to Pro-Inflammatory Cytokines

ImportanceNumerous studies indicate that the traditional categorical classification of severe mental disorders (SMD), such as schizophrenia, bipolar disorders, and major depressive disorders, does not align with the underlying biology of those disorders as they frequently overlap in terms of symptoms and risk factors. ObjectiveThis study aimed to identify transdiagnostic patient clusters based on disease severity and explore the underlying biological mechanisms independently of the traditional categorical classification. DesignWe utilized data from 443 participants diagnosed with SMD of the PsyCourse Study, a longitudinal study with deep phenotyping across up to four visits. We performed longitudinal clustering to group patients based on symptom trajectories and cognitive performance. The resulting clusters were compared on cross-sectional variables, including independent measures of severity as well as polygenic risk scores, serum protein quantification, miRNA expression, and DNA methylation. ResultsWe identified two distinct clusters of patients that exhibited marked differences in illness severity but did not differ significantly in age, sex, or diagnostic proportions. We found 19 serum proteins significantly dysregulated between the two clusters. Functional enrichment pointed to a convergence of immune system dysregulation and neurodevelopmental processes. ConclusionThe observed differences in serum protein expression suggest that disease severity is associated with the convergence of immune system dysregulation and neurodevelopmental alterations, particularly involving pathways related to inflammation and brain plasticity. The identification of pro-inflammatory proteins among the differentially expressed markers underscores the potential role of systemic inflammation in the pathophysiology of SMD. These results highlight the importance of considering illness severity as a core dimension in psychiatric research and clinical practice and suggest that targeting immune-related mechanisms may offer promising new therapeutic avenues for patients with SMD. Key points QuestionCan analyzing symptom trajectories and cognitive profiles across diagnostic categories reveal clinically relevant subgroups in severe mental disorders? FindingsIn this longitudinal study of 443 individuals with severe mental disorders, two distinct clusters emerged, differing significantly in illness severity, with the more severe group displaying elevated pro-inflammatory serum proteins, suggesting an association between disease severity and inflammation. MeaningThese findings suggest that transdiagnostic clustering clarifies shared mechanisms, underscores the importance of inflammation in severe mental disorders, and highlights a promising avenue for novel therapeutic approaches.

immunology↗

bioRxiv2024

Disturbance of the Sense of Agency in Obsessive-Compulsive Disorder and its Modulation by Social Context

Executing precise actions and perceiving them as ones own is a fundamental ability underlying the sense of agency (SoA). The SoA thereby heavily relies on the accuracy and reliability of forward models, capturing sensory movement consequences. Impairments thereof thus represent a promising candidate mechanism contributing to cases of SoA pathogenesis. In obsessive-compulsive disorder (OCD), for example, the feeling of control over ones actions is perturbed: Compulsive actions are often experienced as uncontrollable and performed without conscious awareness. At the same time, compulsions can be coupled with an inflated sense of illusory control for uncontrollable events. Here we studied self-action perception in virtual reality with and without veridical or rotated visual feedback about subjects pointing movements to test whether patients internal forward models are indeed less reliable compared to controls. Interestingly, OCD patients did not exhibit deficits in their accuracy and reliability of motor performance and self-action perception in the absence of visual feedback, suggesting intact forward models. Nonetheless, OCD patients weighted rotated visual action-feedback significantly stronger perceptually. Furthermore, they adapted their movement to this false feedback on a trial-by-trial basis. Finally, increasing the social relevance of action consequences led to stronger feedback weighting in all participants while this effect increased with the strength of OCD symptomatology under conditions with strongest social relevance. We suggest that internal forward models are equally reliable in OCD but their weight is pathologically decreased leading to patients overreliance on explicit visual action-feedback and, more generally, to their over-attribution of unrelated events to themselves.

neuroscience↗

bioRxiv2022

Blood transcriptome analysis suggests an indirect molecular association of early life adversities and adult social anxiety disorder by immune-related signal transduction

Social anxiety disorder (SAD) is a psychiatric disorder characterized by severe fear in social situations and avoidance of these. Multiple genetic as well as environmental factors contribute to the etiopathology of SAD. One of the main risk factors for SAD is stress, especially during early periods of life (early life adversity; ELA). ELA leads to structural and regulatory alterations contributing to disease vulnerability. This includes the dysregulation of the immune response. However, the molecular link between ELA and the risk for SAD in adulthood remain largely unclear. Evidence is emerging that long-lasting changes of gene expression patterns play an important role in the biological mechanisms linking ELA and SAD. Therefore, we performed a transcriptome study of SAD and ELA using RNA sequencing. Analyzing differential gene expression, 13 significantly differentially expressed genes (DEGs) were identified with respect to SAD whilst no significant differences in expression were identified with respect to ELA. The most significantly expressed gene was MAPK3 being upregulated in the SAD group compared to control individuals. In contrary, weighted gene co-expression network analyses (WGCNA) identified only modules significantly associated with ELA, not with SAD. Furthermore, analyzing interaction networks of the genes from the ELA-associated modules and the SAD-related MAPK3) revealed complex interactions of those genes. Gene functional enrichment analyses indicate a role of signal transduction pathways as well as inflammatory responses supporting an involvement of the immune system in the association of ELA and SAD. In conclusion, we did not identify a direct molecular link between ELA and adult SAD by transcriptional changes. However, our data indicate an indirect association of ELA and SAD mediated by the interaction of genes involved in immune-related signal transduction.

genomics↗

bioRxiv2021

Brain signaling dynamics after vagus nerve stimulation

The vagus nerve projects to a well-defined neural circuit via the nucleus tractus solitarii (NTS) and its stimulation elicits a wide range of metabolic, neuromodulatory, and behavioral effects. Transcutaneous vagus nerve stimulation (tVNS) has been established as a promising technique to non-invasively alter brain function. However, the precise dynamics elicited by tVNS in humans are still largely unknown. Here, we performed fMRI with concurrent right-sided tVNS (vs. sham) following a randomized cross-over design (N=40). First, to unravel the temporal profile of tVNS-induced changes in the NTS, we compared fMRI time series to canonical profiles for stimulation ON and OFF cycles. Model comparisons indicated that NTS time series were best fit by block-wise shifts in signal amplitude with stimulation ON and OFF estimates being highly correlated. Therefore, we compared stimulation (ON + OFF) versus baseline phases and found that tVNS increased fMRI BOLD activation in the NTS, but this effect was dependent on sufficient temporal signal-to-noise ratio (tSNR) in the mask. Second, to identify the spatiotemporal evolution of tVNS-induced changes in the brain, we examined lagged co-activation patterns and phase coherence. In contrast to our hypothesis, tVNS did not alter dynamic functional connectivity after correction for multiple comparisons. Third, to establish a positive control for future research, we measured changes in gastric myoelectrical frequency via an electrogastrogram. Again, in contrast to our hypothesis, tVNS induced no changes in gastric frequency. Collectively, our study provides evidence that tVNS can perturb brain signaling in the NTS, but these effects are dependent on tSNR and require precise localization. In light of an absence of acute tVNS-induced effects on dynamic functional connectivity and gastric motility, we discuss which steps are necessary to advance future research on afferent and efferent effects of tVNS.

neuroscience↗

bioRxiv2018

Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important

Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources of information. Their simple combination usually does not provide an improvement if compared with using the best source alone. In this paper, we proposed a framework based on a recent multiple kernel learning algorithm called EasyMKL and we investigated the benefits of this approach for diagnosing two different mental health diseases. The well known Alzheimers Disease Neuroimaging Initiative (ADNI) dataset tackling the Alzheimer Disease (AD) patients versus healthy controls classification task, and a second dataset tackling the task of classifying an heterogeneous group of depressed patients versus healthy controls. We used EasyMKL to combine a huge amount of basic kernels alongside a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our results show that the proposed approach, called EasyMKLFS, outperforms baselines (e.g. SVM and SimpleMKL), state-of-the-art random forests (RF) and feature selection (FS) methods.

neuroscience↗