Biology subjects

Hidalgo-Lopez, E.

Publications and source records attributed to Hidalgo-Lopez, E..

4 recordsLinked to original sources

bioRxiv2026

Functional Connectivity Differences Underly Variable Centralized Symptom Relief After Hysterectomy

Approximately 1 in 4 women with chronic pelvic pain (CPP) report persistent pelvic pain after hysterectomy. Centralized symptoms-fatigue, multisite pain, and sleep disturbance, quantified using the Fibromyalgia Survey Score (FSS)-often co-occur with this risk, but the trajectory of centralized symptom burden after surgery, independent of pelvic pain outcome, is not well understood. Whether presurgical differences in central nervous system pain processing distinguish women whose centralized symptoms improve (improvers) from those whose do not (non-improvers) has not been tested. In this observational study, we assessed whether individuals who do and do not report improvement in FSS scores six months post-operatively differ in their baseline/pre-operative phenotypes and brain function. Sensory sensitivity scores were significantly higher in non-improvers than in improvers at baseline. We identified no significant, presurgical differences in age, average pelvic pain intensity, widespread pain, or pain interference between improvers and non-improvers. Functional connectivity was examined at rest and during the application of a distal, sustained pressure pain. We identified no significant group differences at rest. During sustained pressure pain, non-improvers showed greater connectivity than improvers between primary somatosensory cortex and posterior thalamic seeds with frontal, motor, cerebellar and limbic regions, but lower connectivity between the periaqueductal gray and subgenual cingulate cortex. Non-improvers also showed a greater pain-evoked increase in connectivity between the pelvic and leg representations of the primary sensory cortex and other sensorimotor regions in the pain-versus-rest contrast. Overall, non-improvers showed increased functional connectivity between regions associated with the multisensory integration of ascending pain, and decreased connectivity between regions associated with descending pain modulation. In exploratory analyses, some of these connections were additionally associated with symptom burden: sensory sensitivity, number of painful regions, and pain interference. This work may inform mechanisms of persistent symptom burden and guide surgical decision-making in patients with high central sensitization.

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↗

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↗