Biology subjects

Pletzer, B.

Publications and source records attributed to Pletzer, B..

5 recordsLinked to original sources

bioRxiv2026

Brain dynamics predict oral contraceptive treatment duration

Oral contraceptives are used by millions of women worldwide, yet their cumulative effects on the female brain remain poorly understood. We analyzed resting-state fMRI from 192 women (never, current, and past users) using brain-dynamics metrics that quantify information transfer across spatiotemporal scales. Duration of use was associated with a progressive, age-independent modulation of brain dynamics: prolonged use was related to restricted local synchronization but enhanced information flow across scales. Machine learning predicted individual duration of use from brain dynamics, and node-level classifiers distinguished users from never-users based on a spatially distributed cortical pattern spanning multiple functional networks, most clearly detectable in long-term users. In past users, these associations were inverted in sign, scaling with prior duration rather than returning to the never-user baseline, suggesting active reorganization following cessation. Together, these results indicate that cumulative oral contraceptive exposure acts as a neuromodulator of information processing.

neuroscience↗

bioRxiv2025

Modeling Hierarchical Brain Dynamics Outperforms Hormonal Biomarkers in Predicting Menstrual Cycle Phases

Hormonal fluctuations across the menstrual cycle influence large-scale brain dynamics, yet the underlying neurobiological mechanisms remain poorly understood. In this study, 60 nat-urally cycling women were scanned using resting-state fMRI during the early follicular, pre-ovulatory, and mid-luteal phases. We then applied a thermodynamics-inspired framework to explore the functional hierarchical organization of whole-brain dynamics across these phases. First, we found that brain dynamics are significantly modulated by estradiol, progesterone, and age across multiple resting-state networks. Second, to elucidate underlying mechanisms, we es-timated generative effective connectivity (GEC) matrices using whole-brain models and trained support vector machine classifiers to predict menstrual phases. These model-based biomarkers outperformed traditional functional connectivity and hormone measures in classifying men-strual cycle phases. These findings reveal that menstrual cycle-related changes modulate the hierarchical reorganization of brain dynamics, highlighting the potential of model-based ap-proaches to advance womens brain health research.

neuroscience↗

bioRxiv2024

Whole-brain dynamics and hormonal shifts throughout women's lifespan: From reproductive stages to menopausal transition and beyond

Neuroimaging studies have identified significant age-related disruptions in whole-brain dynamics, yet the influence of womens reproductive stages and associated hormonal shifts remains underexplored. This study leverages resting-state fMRI data from the Human Connectome Project in Aging to examine brain dynamics through five reproductive stages: reproductive, late reproductive, perimenopause, early postmenopause, and late postmenopause. Our results indicate that the late reproductive stage is characterized by the highest dynamical complexity across whole-brain and resting-state networks, while brain dynamics significantly decline at menopause onset. Additionally, we employ machine learning classifiers using two approaches: (1) brain dynamics alone and (2) brain dynamics combined with follicle-stimulating hormone (FSH) and estradiol (brain-hormone model). Both models accurately distinguished reproductive stages, but the brain-hormone model outperformed the brain dynamics model. Key predictors included decreased estradiol, increased FSH, and altered brain dynamics in later life stages. These results offer a framework for assessing brain health across womens reproductive lifespan.

neuroscience↗

bioRxiv2023

Whole-brain dynamics and hormonal fluctuations across the menstrual cycle: The role of progesterone and age in healthy women

Recent neuroimaging research suggests that female sex hormone fluctuations modulate brain activity. Nevertheless, how brain network dynamics change across the female menstrual cycle remains largely unknown. Here, we investigated the dynamical complexity u nderlying three menstrual cycle phases (i.e., early follicular, pre-ovulatory, and mid-luteal) in 60 healthy naturally-cycling women scanned using resting-state fMRI. Our results revealed that the preovulatory phase exhibited the highest variability over time (node-metastability) across the whole-brain functional network compared to the early follicular and mid-luteal phases, while the early follicular showed the lowest. Additionally, we found that large-scale resting-state networks reconfigure along the menstrual cycle phases. Finally, we used multilevel mixed-effects models to examine the impact of hormonal fluctuations and age on whole-brain and resting-state networks. We found significant age-related changes across the whole brain, control, and dorsolateral attention networks. Additionally, we observed progesterone-related changes, specifically within limbic and somatomotor networks. Overall, these findings evidence that both age and progesterone modulate brain network dynamics along the menstrual cycle.

neuroscience↗

bioRxiv2020

Predicting ovulation from brain connectivity: Dynamic causal modelling of the menstrual cycle.

Longitudinal menstrual cycle research allows the assessment of sex hormones effects on brain organization in a natural framework. Here, we used spectral dynamic causal modelling (spDCM) in a triple network model consisting of the default mode, salience and executive central networks (DMN, SN, and ECN), in order to address the changes in effective connectivity across the menstrual cycle. Sixty healthy young women were scanned three times (menses, pre-ovulatory and luteal phase) and spDCM was estimated for a total of 174 scans. Group level analysis using Parametric empirical Bayes showed lateralized and anterior-posterior changes in connectivity patterns depending on the cycle phase and related to the endogenous hormonal milieu. Right before ovulation the left insula recruited the frontoparietal network, while the right middle frontal gyrus decreased its connectivity to the precuneus. In exchange, the precuneus engaged bilateral angular gyrus, decoupling the DMN into anterior/posterior parts. During the luteal phase, bilateral insula engaged to each other decreasing the connectivity to parietal ECN, which in turn engaged the posterior DMN. Remarkably, the specific cycle phase in which a woman was in could be predicted by the connections that showed the strongest changes. These findings further corroborate the plasticity of the female brain in response to acute hormone fluctuations and have important implications for understanding the neuroendocrine interactions underlying cognitive changes along the menstrual cycle.

neuroscience↗