Biology subjects

Escudero, J.

Publications and source records attributed to Escudero, J..

10 recordsLinked to original sources

bioRxiv2026

Towards Translational Sleep Staging: A Cross-Species Deep-Learning Model for Rodent and Human EEG

Study ObjectivesAutomated sleep staging underpins clinical sleep assessment and translational neuroscience, yet most data analyses work addresses human and animal data separately. We tested whether a seizure-oriented machine learning framework can be repurposed for three-state sleep staging in humans and rats, and whether models trained solely on rodent data can be applied directly to human recordings using an explicit cross-species montage. MethodsWe used the PySeizure, a standardised EEG preprocessing and seizure-detection framework, together with TinySleepNet as the core classifier. Models were trained and evaluated on the Sleep-EDF expanded Sleep Cassette subset (three classes: wake, non-rapid eye movement sleep, rapid eye movement sleep), then applied without fine-tuning to the Sleep Telemetry subset. The same pipeline was used on a SYNGAP1 rat dataset with analogous three-state labels. A novel human-rat electroencephalography montage mapped rat electrodes to putative human scalp homologues, enabling direct application of rat-trained models to Sleep Cassette. ResultsWithin Sleep Cassette, the accuracy in three-stage sleep classification was 0.95. Applying this model directly to Sleep Telemetry yielded an accuracy of 0.89. On the rodent dataset, accuracy was 0.78. When the rat-trained model was applied directly to Sleep Cassette, accuracy was 0.68. ConclusionsA single deep learning pipeline can support robust three-state sleep staging in humans and rodents and retains meaningful performance under both human cross-subset and rat-to-human transfer without any retraining or fine-tuning. The rat-trained models above-chance performance on human data, achieved without human training samples, shows that rodent-derived representations can contribute directly to human sleep staging when constrained by an anatomically informed montage, linking preclinical rodent recordings and clinical human sleep studies.

bioengineering↗

bioRxiv2025

Detecting infection-related mortality using dynamical statistical indicators of high-resolution activity time series

Predicting transitions between health, disease, and death across biological systems remains an important challenge with significant implications for both ecological management and medical intervention. Although the principles underlying these transitions are increasingly recognised, accurate and tractable dynamical indicators of health-to-disease transitions remain rare, especially at the level of individual organisms. Here, we use dynamical statistical indicators of high-resolution activity time series to predict infection-related mortality. By analysing locomotor activity data from infected Drosophila melanogaster flies, we find that individual dynamical indicators, such as the mean, variance, autocorrelation, and permutation entropy, differed between flies that survived and those that died during the experiment. When these indicators were used to train a Random Forest model, the classifier performed well (AUC = 0.94), demonstrating an accuracy of 87.9% in discriminating between infected flies that would die from infection and those that would survive, with the strongest discriminatory power detected over 12 hours prior to death. Our findings show that combining these easy-to-compute, dynamical statistical indicators with machine learning enhances the ability to predict health deterioration in the Drosophila model. Conceptually, our findings emphasize that the integration of dynamical statistical metrics from physiological or behavioural time-series with machine learning approaches may offer a promising avenue for real-time health monitoring in both ecological and clinical settings. HighlightsO_LIHigh-resolution locomotor activity time series distinguish infected flies that live or die C_LIO_LISimple dynamical indicators (mean, SD, CV, lag-1 autocorrelation) jointly improve outcome discrimination C_LIO_LIPermutation entropy declines over time and is reduced in flies approaching acute infection-related death C_LIO_LIRandom Forest models classify infection survival outcomes with high accuracy (AUC 0.94) C_LIO_LIDivergence in activity dynamics is detectable over 12-16 hours before death in flies that succumb to infection C_LI

ecology↗

bioRxiv2024

Capicua maintains anterior-posterior axis in Blattella germanica ovaries

The establishment of the symmetry axis is crucial for the development of all organisms. In insects, this process begins early in oogenesis with the correct distribution of the mRNAs and proteins in the oocyte. One protein that plays a role in organizing this distribution is the transcription factor Capicua (Cic). Cic has been studied in the context of oogenesis and embryonic development in Drosophila melanogaster. It is maternally expressed, begins essential for establishing the dorsoventral axis, and functions as a transcriptional repressor. Although the Cic sequences are conserved across species, their function in other types of insect ovaries is still little known. We wondered whether the function of Cic in insects has been maintained through evolution despite the ovary type or if it has been modified in parallel to the ovary evolution. To address this, we studied the Cic function in a phylogenetically basal insect, the cockroach Blattella germanica, a species with panoistic ovaries. Our findings show that B. germanica Cic is essential for oocyte development and the maturation of ovarian follicles. A loss of Cic function leads to disrupted cytoskeletal organization, defects in anterior-posterior polarity, and compromised follicle integrity. The conservation and functional divergence of Cic across different species suggest evolutionary adaptations in the mechanisms of insect oogenesis.

developmental biology↗

bioRxiv2024

A piRNA regulating oogenesis and embryo development in cockroaches

PIWI-interacting RNAs (piRNAs) are small non-coding RNAs, typically 26 to 31 nucleotides long, originally known for silencing transposable elements (TEs), thus maintaining genomic stability. However, recent research has revealed additional regulatory roles. In this study, we investigate piRNA-305221, which is highly expressed in the ovaries of the German cockroach, Blattella germanica, to understand its involvement in oogenesis and reproduction. piRNA-305221 is found in germinal and somatic cells during the gonadotropic cycle, and is maternally provided to the egg. Its expression correlates with critical ovarian events, such as endoreplication and follicular cell differentiation, suggesting regulatory functions beyond TE silencing. Functional knockdown using antisense oligonucleotides (ASOs) resulted in delayed oviposition, malformed oothecae, and reduced offspring viability. Gene expression analysis revealed that the reduction of piRNA-305221 decreased shade mRNA levels, impairing the conversion of ecdysone to its active form, 20-hydroxyecdysone, and a concomitant increase in expression of upstream steroidogenic genes (spook, phantom, disembodied). These results indicate that piRNA-305221 may regulate steroidogenesis through direct or indirect control of mRNA targets. This study highlights the broader regulatory functions of piRNAs and demonstrates the utility of ASO-mediated knockdown in functional studies of non-coding RNAs.

developmental biology↗

bioRxiv2024

Absence seizures and sleep abnormalities in a rat model of GRIN2B neurodevelopmental disorder

Pathogenic mutations in GRIN2B are an important cause of severe neurodevelopmental disorders resulting in epilepsy, autism and intellectual disability. GRIN2B encodes the GluN2B subunit of N-methyl-D-aspartate receptors (NMDARs), which are ionotropic glutamate receptors critical for normal development of the nervous system and synaptic plasticity. Here, we characterized a novel Grin2b heterozygous knockout rat model with 24-hour EEG recordings. We found rats heterozygous for the deletion (Grin2b+/-) had a higher incidence of spontaneous spike and wave discharges, the electrographic correlate of absence seizures, than wild-type rats (Grin2b+/+). Spike and wave discharges were longer in duration and displayed higher overall spectral power in Grin2b+/- when compared to those in Grin2b+/+ animals. Heterozygous mutant rats also had abnormal sleep-wake brain state dynamics over the circadian cycle. Specifically, we identified a reduction in total rapid eye movement sleep and, altered distributions of non-rapid eye movement sleep and wake epochs, when compared to controls. This was accompanied by an increase in overall spectral power during non-rapid eye movement sleep in Grin2b+/-. The sleep-wake phenotypes were largely uncorrelated to the incidence of spike and wave discharges. We then tested the antiseizure efficacy of ethosuximide, a T-type voltage-gated calcium channel blocker used in the treatment of absence seizures, and memantine, a noncompetitive NMDAR antagonist currently explored as a mono or adjunctive treatment option in NMDAR related neurodevelopmental disorders. Ethosuximide reduced the number and duration of spike and wave discharges, while memantine did not affect the number of spike and wave discharges but reduced their duration. These results highlight two potential therapeutic options for GRIN2B related epilepsy. Our data shows the new rat Grin2b haploinsufficiency model exhibits clinically relevant phenotypes. As such, it could prove crucial in deciphering underlying pathological mechanisms and developing new therapeutically translatable strategies for GRIN2B neurodevelopmental disorders.

neuroscience↗

bioRxiv2023

Single-Channel EEG Artifact Identification with the Spectral Slope

Electroencephalogram (EEG) signals are a valuable recording technique to diagnose neurological disorders and identify noninvasive biomarkers for clinical application, however, they are vulnerable to various artifacts. It is difficult to define exact parameters which efficiently distinguish artifacts from neural activity, and thus cleaning EEG data often relies on labor-intensive visual scoring methods. While signal processing techniques to remove artifacts exist, many state-of- the-art techniques are designed for multivariate signals, which can be challenging to implement in recording setups with few electrodes. We demonstrate how the spectral slope - a method previously used to distinguish between conscious states by linear regression of the logarithmic EEG power spectra - can also be used to identify epochs contaminated by recording artifacts in rat EEG recordings and propose this as a first pass artifact detection method. We computed the mean spectral slope for both clean and noisy epochs and compared the distributions among individual recordings to determine whether the decision threshold should be dynamic or fixed. We found no significant difference between the mean of these distributions and determined that a spectral slope threshold of -8 V 2/Hz was effective at identifying noisy epochs across all recordings. The accuracy of our method was evaluated against visually scored recordings and obtained an average accuracy, F1 and Cohen Kappa score of 94.2%, 86.4%, and 83%, respectively, across all epochs. Our study contributes to the automation of EEG artifact detection by presenting a straightforward initial method for identifying contaminated epochs based on the spectral slope of a single EEG channel in rodent recordings.

neuroscience↗

bioRxiv2022

Abnormal brain state distribution and network connectivity in a SYNGAP1 rat model

Mutations in the SYNGAP1 gene are one of the common predictors of neurodevelopmental disorders, commonly resulting in individuals developing autism, intellectual disability, epilepsy, and sleep deficits. EEG recordings in neurodevelopmental disorders show potential to identify clinically translatable biomarkers to both diagnose and track the progress of novel therapeutic strategies, as well as providing insight into underlying pathological mechanisms. In a rat model of SYNGAP1 haploinsufficiency in which the exons encoding the calcium/lipid binding and GTPase activating protein (GAP) domains have been deleted (Syngap+/{Delta}-GAP) we analysed the duration and occurrence of wake, non rapid eye movement (NREM) and rapid eye movement (REM) brain states during 6 hour multi-electrode EEG recordings. We find that although Syngap+/{Delta}-GAP animals spend an equivalent percent time in wake and sleep states, they have an abnormal brain state distribution as the number of wake and NREM bouts are reduced and there is an increase in the average duration of both wake and NREM epochs. We perform connectivity analysis by calculating the average imaginary coherence between electrode pairs at varying distance thresholds during these states. In group averages from pairs of electrodes at short distances from each other, a clear reduction in connectivity during NREM is present between 11.5 Hz and 29.5 Hz, a frequency range that overlaps with sleep spindles, oscillatory phenomena thought to be important for normal brain function and memory consolidation. Sleep spindles occurrence, amplitude, power and spread across multiple electrodes were not reduced in Syngap+/{Delta}-GAP rats, with only a small decrease in duration detected. Nonetheless, by analysing the dynamic imaginary coherence during sleep spindles, we found a reduction in high connectivity instances between short-distance electrode pairs. Finally, by comparing the dynamic imaginary coherence during sleep spindles between individual electrode pairs, we identified a group of channels over the right somatosensory, association and visual cortices that have a significant reduction in connectivity during sleep spindles in mutant animals. These data suggest that Syngap+/{Delta}-GAP rats have altered brain state dynamics and EEG connectivity, which may have clinical relevance for SYNGAP haploinsufficiency in humans.

neuroscience↗

bioRxiv2020

Hierarchical complexity of the macro-scale neonatal brain

The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period; assess correspondences with hierarchical complexity in adulthood; and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers, and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.

neuroscience↗

bioRxiv2018

Hierarchical Complexity of the Adult Human Structural Connectome

The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology.

systems biology↗

bioRxiv2017

An analysis of EEG networks and their correlation with cognitive impairment in preschool children with epilepsy

Objective: Cognitive impairment (CI) is common in children with epilepsy and can have devastating effects on their quality of life and that of their family. Early identification of CI is a priority to improve outcomes, but the current gold standard of detection with psychometric assessment is resource intensive and not always available. This paper proposes a novel technique of network analysis using routine clinical electroencephalography (EEG) to help identify CI in children with early-onset epilepsy (CWEOE) (0-5 y.o.).\n\nMethods: We analyzed functional networks from routinely acquired EEGs of 51 newly diagnosed CWEOE from a prospective population-based study. Combinations of connectivity metrics (e.g. phase-slope index (PSI)) with sub-network analysis (e.g. cluster-span threshold (CST)) identified significant correlations between network properties and cognition scores via rank correlation analysis with Kendalls{tau} . Predictive properties were investigated using a 5-fold cross-validated K-Nearest Neighbor classification model with normal cognition, mild/moderate CI and severe CI classes.\n\nResults: Phase-dependent connectivity metrics had higher sensitivity to cognition scores, with sub-networks identifying significant functional network changes over a broad range of spectral frequencies. Approximately 70.5% of all children were appropriately classified as normal cognition, mild/moderate CI or severe CI using CST network features. CST classification predicted CI classes 55% better than chance, and reduced misclassification penalties by half.\n\nConclusions: CI in CWEOE can be detected with sensitivity at 85% (with respect to identifying either mild/moderate or severe CI) and specificity of 84%, by EEG network analysis.\n\nSignificance: This study outlines a data-driven methodology for identifying candidate biomarkers of CI in CWEOE from network features. Following additional replication, the proposed method and its use of routinely acquired EEG forms an attractive proposition for supporting clinical assessment of CI.

neuroscience↗