Biology subjects

Gonzalez Ballester, M. A.

Publications and source records attributed to Gonzalez Ballester, M. A..

4 recordsLinked to original sources

bioRxiv2026

Characterization of Fetal Cortical Development Using Spectral Analysis of Gyrification (SPANGY)

The prenatal period of human brain development is critical for mental health and cognition across the entire lifespan. During this period, the cortex undergoes a dramatic transformation from a smooth lissencephalic surface into an elaborately folded structure, a process whose precise characterization is essential for understanding neurodevelopmental trajectories. This study represents the first application of Spectral Analysis of Gyrification (SPANGY) to a large multi-centric fetal brain MRI dataset (635 subjects, 20-38 weeks gestational age). SPANGY characterizes geometric variations on a surface based on the wavelength of folds, hence, providing a quantitative local description of gyrification at the individual level. Using rigorous normative modeling (GAMLSS) and statistical harmonization (ComBat-GAM), we established age-specific reference trajectories for multi-scale gyrification features (spectral frequency bands). We provide the first ever quantification of the temporally-ordered emergence of cortical folding in successive waves: the earliest-emerging low frequency, deep fissures are progressively superseded by the accelerating expansion of higher frequency folds. The normative curves provide the first step in taking prenatal neurodevelopmental assessment from qualitative inspection into a rigorous statistical inference, creating an objective reference against which deviations from healthy brain growth can be caught earlier, and with greater precision.

neuroscience↗

bioRxiv2026

Data quality biases normative models derived from fetal brain MRI

Normative modeling is increasingly used to characterize typical growth trajectories and identify atypical neurodevelopment, including early brain development using magnetic resonance imaging (MRI) acquired before birth. Recent work has emphasized the importance of large sample sizes for accurate and robust centile estimation. In this study, we investigate how image quality influences fetal brain normative models, a critical factor in this context where MRI is acquired on a moving fetus in utero. Using a multi-centric cohort of 635 fetal MRI scans, we applied a standardized visual quality control (QC) protocol with continuous quality ratings. We fit normative models for multiple brain structures under progressively relaxed QC stringency, and quantified the deviations in centile estimates relative to a high-quality reference subgroup. Our results showed that including lower-quality data systematically biased normative centiles, with the strongest effects observed in the outer centiles, particularly the lower tail (1st-10th). Bias increased progressively as QC stringency was relaxed and could not be attributed solely to the number of subjects used to fit the models. Quality-induced bias was structure-dependent, and often not visually apparent at the segmentation level. These findings highlight that image quality is an important source of bias in normative fetal brain modeling, and that increasing sample size at the expense of quality may systematically affect centile estimates, potentially jeopardizing the utility of the model.

neuroscience↗

bioRxiv2022

The PNt-Methodology: a top-down network modelling approach to estimate dose-and time-dependent cell responses to complex multifactorial environments

BackgroundNetwork models are convenient to represent in a mechanistic way the complexity of cell biological activity. Dynamic simulations of such networks might require approximations of equation parameters through reverse engineering, numerous and costly experimental research, and/or have limited capacity to explore cell responses to chronic, dose-dependent stimulus exposure. Here we present a mechanistic methodology allowing the simulation of interrelated cell responses of multicellular systems to multifactorial stimuli with dose-and time dependent network links. Methodsa mathematical framework to approach systems biology research questions is presented, the Parallel Networks (PN)-Methodology. It consists of a novel concept, where multicellular systems are described as many relatively small feed-forward networks acting in parallel. Each parallel network is calculated through a specifically designed ordinary differential equation (ODE). Through a unique approach to feed the ODE with interrelated parameters, a system of decoupled, analytically resolvable ODE was obtained. ResultsApplied to intervertebral disc multicellular systems, virtual environments of multifactorial stimuli and multiple cell responses simulating daily moving activities, and to microgravity could successfully be created. ConclusionsThe PN-Methodology stands for a one-of-a-kind mathematical methodology to approximate dynamics of complex multicellular systems over long periods of time at low computational costs.

systems biology↗

bioRxiv2019

Simulating intervertebral disc cell behavior within 3D multifactorial environments

MotivationLow back pain is responsible for more global disability than any other condition. Its incidence is closely related to intervertebral disc (IVD) failure, which is likely caused by an accumulation of microtrauma within the IVD. Crucial factors in microtrauma development are not entirely known yet, probably because their exploration in vivo or in vitro remains tremendously challenging. In-silico modelling is, therefore, definitively appealing, and shall include approaches to integrate influences of multiple cell stimuli at the microscale. Accordingly, this study introduces a hybrid Agent-based (AB) model in IVD research and exploits network modelling solutions in systems biology to mimic the cellular behavior of Nucleus Pulposus cells exposed to a 3D multifactorial biochemical environment, based on mathematical integrations of existing experimental knowledge. Cellular activity reflected by mRNA expression of Aggrecan, Collagen type I, Collagen type II, MMP-3 and ADAMTS were calculated for inflamed and non-inflamed cells. mRNA expression over long periods of time is additionally determined including cell viability estimations. Model predictions were eventually validated with independent experimental data. ResultsAs it combines experimental data to simulate cell behavior exposed to a multifactorial environment, the present methodology was able to reproduce cell death within 3 days under glucose deprivation and a 50% decrease in cell viability after 7 days in an acidic environment. Cellular mRNA expression under non-inflamed conditions simulated a quantifiable catabolic shift under an adverse cell environment, and model predictions of mRNA expression of inflamed cells provide new explanation possibilities for unexpected results achieved in experimental research. Contactlaura.baumgartner@upf.edu

systems biology↗