Biology subjects

Marti-Juan, G.

Publications and source records attributed to Marti-Juan, G..

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emergence of cortical folding in successive waves: the earliest-emerging low frequency, deep fissures are progressively superseded by the accelerating expansion of higher frequency folds.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↗

bioRxiv2024

Assessing data quality on fetal brain MRI reconstruction: a multi-site and multi-rater study

Quality assessment (QA) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where unpredictable fetal motion can lead to substantial artifacts in the acquired images. Multiple images are then combined into a single volume through super-resolution reconstruction (SRR) pipelines, a step that can also introduce additional artifacts. While multiple studies designed automated quality control pipelines, no work evaluated the reproducibility of the manual quality ratings used to train these pipelines. In this work, our objective is twofold. First, we assess the inter- and intra-rater variability of the quality scoring performed by three experts on over 100 SRR images reconstructed using three different SRR pipelines. The raters were asked to assess the quality of images following 8 specific criteria like blurring or tissue contrast, providing a multi-dimensional view on image quality. We show that, using a protocol and training sessions, artifacts like bias field and blur level still have a low agreement (ICC below 0.5), while global quality scores show very high agreement (ICC = 0.9) across raters. We also observe that the SRR methods are influenced differently by factors like gestational age, input data quality and number of stacks used by reconstruction. Finally, our quality scores allow us to unveil systematic weaknesses of the different pipelines, indicating how further development could lead to more robust, well rounded SRR methods. Our rating protocol is made available at https://doi.org/10.5281/zenodo.15696638.

neuroscience↗

bioRxiv2018

Revealing heterogeneity of brain imaging phenotypes in Alzheimer’s disease based on unsupervised clustering of blood marker profiles

Alzheimers disease (AD) affects millions of people and is a major rising problem in health care worldwide. Recent research suggests that AD could have different subtypes, presenting differences in how the disease develops. Characterizing those subtypes could be key to deepen the understanding of this complex disease. In this paper, we used a multivariate, non-supervised clustering method over blood-based markers to find subgroups of patients defined by distinctive blood marker profiles. Our analysis on ADNI database identified 4 possible subgroups, each with a different blood profile. More importantly, we show that subgroups with different profiles have a different relationship between brain phenotypes detected in magnetic resonance imaging and disease condition.

bioinformatics↗