Biology subjects

Pastore, M.

Publications and source records attributed to Pastore, M..

5 recordsLinked to original sources

bioRxiv2026

Functional connectome harmonics and dynamic connectivity maps of the preadolescent brain

The maturation of large-scale functional brain networks during preadolescence underpins critical cognitive and behavioral development. However, the spatial and temporal organization of these networks in this age group remains incompletely characterized. Here we applied Functional Connectome Harmonics (FCH) and Leading Eigenvector Dynamics Analysis (LEiDA) to resting-state fMRI data from over 11,000 children aged 9-10 years in the ABCD Study. FCH revealed hierarchical spatial gradients spanning cortical and subcortical regions, while LEiDA identified recurrent dynamic brain states aligned with canonical intrinsic connectivity networks. Linking these spatial and temporal components, we established a low-dimensional harmonic scaffold constraining brain dynamics during this developmental window. These findings provide a large-scale spatiotemporal reference framework of preadolescent functional brain organization, offering a foundation for characterizing neurodevelopmental benchmarks and early neural markers relevant to adolescent mental health.

neuroscience↗

bioRxiv2024

Association between maternal depressive symptoms and hair cortisol concentration during pregnancy with corpus callosum integrity in newborns

BackgroundMaternal prenatal depressive symptoms are linked to neurodevelopmental impairments in offspring. Maternal cortisol levels are hypothesized to moderate this association, but its relationship with depressive symptoms is inconsistent. This study examined how maternal prenatal depressive symptoms and cortisol levels predict infant brain development, focusing on neonatal corpus callosum (CC) integrity. MethodsUsing data from the FinnBrain Birth Cohort Study, we analyzed 37 mother-infant dyads. MRI data were collected from 2 to 5 weeks old infants, and DTI imaging estimated fractional anisotropy (FA) in CC regions (Genu, Body, and Splenium). Maternal cortisol levels were assessed through hair cortisol concentration (HCC) from a 5cm hair segment, reflecting cortisol over the last five months of pregnancy. A factor score of maternal depressive symptoms was computed from EPDS questionnaire data collected at gestational weeks 14, 24, and 34. We employed multivariate regression models with a Bayesian approach for statistical testing, controlling for maternal and infant attributes. ResultsResults indicated that maternal prenatal depressive symptoms and HCC interact negatively in predicting infants FA across all CC regions. Infants exposed to high prenatal depressive symptoms and low HCC (1 SD below the mean) showed higher FA in all CC regions. ConclusionsThese findings highlight the complex dynamics between maternal prenatal cortisol levels and depressive symptoms, revealing a nuanced impact of those factors on the structural integrity of infants CC.

neuroscience↗

bioRxiv2024

Restoring data balance via generative models of T-cell receptors for antigen-binding prediction

Unveiling specificity in T cell recognition of antigens represents a major step to understand the immune system response. Many supervised machine learning approaches have been designed to build sequence-based predictive models of such specificity using binding and non-binding receptor-antigen data. Due to the scarcity of known specific T cell receptors for each antigen compared to the abundance of non-specific ones, available datasets are heavily imbalanced and make the goal of achieving solid predictive performances very challenging. Here, we propose to restore data balance through data augmentation using generative unsupervised models. We then use these augmented data to train supervised models for prediction of peptide-specific T cell receptors, or binding pairs of peptide and T cell receptor sequences. We show that our pipeline yields increased performance in prediction tasks of T cell receptors specificity. More broadly, our pipeline provides a general framework that could be used to restore balance in other computational problems involving biological sequence data.

bioinformatics↗

bioRxiv2024

Dynamic transient brain states in preschoolers mirror parental report of behavior and emotion regulation

The temporal dynamics of resting-state networks (RSNs) may represent an intrinsic functional repertoire supporting cognitive control performance across the lifespan (Kupis et al., 2021). However, little is known about brain dynamics during the preschool period, which is a sensitive time window for cognitive control development. The fast timescale of synchronization and switching characterizing cortical network functional organization gives rise to quasi-stable patterns (i.e., brain states) that recur over time. These can be inferred at the whole-brain level using Hidden Markov Models (HMMs), an unsupervised machine learning technique that allows the identification of rapid oscillatory patterns at the macro-scale of cortical networks (Vidaurre et al., 2018). The present study used a HMM technique to investigate dynamic neural reconfigurations and their associations with behavioral (i.e., parental questionnaires) and cognitive (i.e., neuropsychological tests) measures in typically developing preschoolers (4-6 years old). We used high density EEG to better capture the fast reconfiguration patterns of the HMM-derived metrics (i.e., switching rates, entropy rates, transition probabilities and fractional occupancies). Our results revealed that the HMM-derived metrics were reliable indices of individual neural variability and differed between boys and girls. However, only brain state transition patterns toward prefrontal and default-mode brain states, predicted differences on parental-report questionnaire scores. Overall, these findings support the importance of resting-state brain dynamics as functional scaffolds for behavior and cognition. Brain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers. KeypointsO_LIHMM-derived metrics are reliable hallmarks of individual neural variability and show gender-related differences. C_LIO_LIBrain state transition patterns toward prefrontal and default-mode brain states predict differences on parental-report questionnaires scores. C_LIO_LIBrain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers. C_LI

neuroscience↗

bioRxiv2019

Ubiquitin and SUMO conjugation as biomarkers of Acute Myeloid Leukemias response to chemotherapies

Ubiquitin and the ubiquitin-like SUMO are covalently conjugated to thousands of proteins to modulate their function and fate. Many of the enzymes involved in their conjugation are dysregulated in cancers and involved in cancer cells response to therapies. We describe here the identification of biomarkers of the activity of these enzymes and their use to predict Acute Myeloid Leukemias (AML) response to standard chemotherapy (daunorubicine-DNR and cytarabine-Ara-C). We compared the ability of extracts from chemosensitive and chemoresistant AML cells to conjugate ubiquitin or SUMO-1 on 9000 proteins spotted on protein-arrays. We identified 122 proteins whose conjugation by these post-translational modifiers marks AML resistance to DNR and/or Ara-C. Based on this modifomic signature, we defined a statistical score able to predict AML patient response to standard chemotherapy. We finally developed a miniaturized assay to easily assess the modification level of the selected biomarkers and validated it in patient cell extracts. Thus, our work identifies a new type of ubiquitin-based biomarkers that could be used to predict cancer patients response to treatments.\n\nSummary blurbThis study describes the identification of a new class of biomarkers of cancer response to therapies based on protein modification by Ubiquitin and SUMO and provides the tools to analyze them in Acute Myeloid Leukemia patient samples.

cancer biology↗