Biology subjects

Vanhatalo, S.

Publications and source records attributed to Vanhatalo, S..

8 recordsLinked to original sources

bioRxiv2026

Cortical bursts provide spatio-temporal frames for the large-scale phase synchrony emerging across the birth transition

Structural corticocortical connectivity grows rapidly around birth, but how it establishes the large-scale neuronal interactions needed for lifelong neurocognitive performance remains unclear. Using repeated high-density EEG from 233 preterm-born infants recorded at 33-45 weeks postmenstrual age, we characterized the development of three phase-based interaction modes with distinct temporal relationships to cortical bursts: burst-based, burst-framed, and general phase-based connectivity. Burst-framed connectivity was quantified with a novel metric, nested phase-phase correlation. All three modes declined globally with age but differed spatially: burst-based connectivity became more confined, burst-framed connectivity reorganized towards sensorimotor-association connectivity, whereas general phase-based connectivity retained a stable modular organization. General phase-based connectivity was initially concentrated along burst-framed connections, but this coupling weakened with age. Its greater persistence around term age was associated with poorer neurocognitive outcomes, suggesting delayed network maturation. These findings suggest that bursting may transiently scaffold the maturation of large-scale corticocortical phase synchrony across the birth transition.

neuroscience↗

bioRxiv2025

Capturing individual variation in children's oscillatory brain activity during nREM sleep

Human brain dynamics are highly unique between individuals: functional neuroimaging studies have recently described functional features that can be used as neural fingerprints. As such studies have generally been performed on adults, it is unclear if the results are applicable during brain development. Here we applied Bayesian reduced-rank regression (BRRR), to extract low-dimensional representations of electroencephalography (EEG) power spectra measured from normally developing children at different ages. The clinical EEG recordings represented different sleep stages in a pediatric population (N=782) of ages varying from 6 weeks to 19 years. The representations learned within specific sleep stages successfully separated between the subjects. Furthermore, a model trained with data across sleep stages generalized to unseen data, suggesting that informative neural fingerprints could be extracted. Fingerprint stability increased with the age of the subjects. In comparison to correlation-based measures in differentiating individuals from each other, the BRRR model performed better, highlighting the usefulness of the dimensionality reduction for data obtained in demanding clinical settings. While further studies are needed to address the possible non-linear maturation effects over developmental periods, our results demonstrate, for the first time, the existence of stable within-session neurofunctional fingerprints in pediatric populations.

neuroscience↗

bioRxiv2025

Longitudinal dynamics of gut plasmidome and antibiotic resistance during antibiotic therapy: a case report

The human gut microbiome is composed of diverse microbes, and its association with human health is well-recognized. Antibiotic therapies for treating infectious diseases often mediate adverse influences on gut microbial ecosystems. Further, ESBL (extended-spectrum beta-lactamase)-producing bacteria potentially residing in the gut benefit from antibiotic-induced environmental changes via positive selection for resistance traits. As antimicrobial resistance (AMR) genes are often harbored by mobile plasmids, the importance of these extrachromosomal mobile genetic elements during antibiotic exposure is evident. However, there is still a knowledge gap in how microbiomes respond to antibiotic treatment especially in terms of plasmid carriage and how gut plasmid populations evolve following and revive after antibiotic therapy. To address these questions, in this case report we investigated the changes in plasmid population in the gut microbiome of a single ESBL-carrying patient during antibiotic therapy for uncomplicated acute appendicitis. Employing longitudinal sampling, we collected E. coli strains and performed metagenomic analysis before, during, and after the treatment. Our findings indicate that the antibiotic treatment is associated with a transient alteration in the microbial composition, AMR profile, and plasmid population. An extensive but temporary domination of ESBL-E. coli within the gut microbiome was observed parallel to the ongoing antibiotic treatment. However, this was not sustained in the follow-up period, indicating a slight restoration of both the microbial composition and the plasmid population. The research underscores the temporary impact of antibiotic therapy on the dynamics of the gut plasmidome which essentially mediate the spread of AMR within the gut microbiome.

microbiology↗

bioRxiv2024

Emergence of a synergistic scaffold in the brains of human infants

The human brain is a complex organ comprising billions of interconnected neurons which enables interaction with both physical and social environments. Neural dynamics of the whole brain go far beyond just the sum of its individual elements; a property known as "synergy". Previously it has been shown that synergy is crucial for many complex brain functions and cognition, however, it remains unknown how and when the large number of discrete neurons evolve into the unified system able to support synergistic interactions. Here we analysed high-density electroencephalography data from late fetal to early postnatal period. We found that the human brain transitions from redundancy-dominated to synergy-dominated system around birth. Frontal regions lead the emergence of a synergistic scaffold comprised of overlapping subsystems, while the integration of sensory areas developed gradually, from occipital to central regions. Strikingly, early developmental trajectories of brain synergy were modulated by environmental enrichment associated with enhanced mother-infant interactions, and the level of synergy near term equivalent age was associated with later neurocognitive development.

neuroscience↗

bioRxiv2023

A growth chart of brain function from infancy to adolescence based on electroencephalography

BackgroundIn children, objective, quantitative tools that determine functional neurodevelopment are scarce and rarely scalable for clinical use. Direct recordings of cortical activity using routinely acquired electroencephalography (EEG) offer reliable measures of brain function. MethodsWe developed and validated a measure of functional brain age (FBA) using a residual neural network-based interpretation of the paediatric EEG. In this cross-sectional study, we included 1056 children with typical development ranging in age from 1 month to 18 years. We analyzed a 10 to 15 minute segment of 18-channel EEG recorded during light sleep (N1 and N2 states). FindingsThe FBA obtained from EEG had a weighted mean absolute error (wMAE) of 0.85 years (95%CI: 0.69-1.02; n = 1056). A two-channel version of the FBA had a wMAE of 1.51 years (95%CI: 1.30-1.73; n = 1056) and was validated on an independent set of EEG recordings (wMAE = 2.27 years, 95%CI: 1.90-2.65; n = 723). Group-level maturational delays were also detected in a small cohort of children with Trisomy 21 (Cohens d = 0.36, p = 0.028). InterpretationAn FBA, based on EEG, is an accurate, practical and scalable automated tool to track brain function maturation throughout childhood with accuracy comparable to widely used physical growth charts. FundingThis research was supported by the National Health and Medical Research Council, Australia, Helsinki University Diagnostic Center Research Funds, Finnish Academy, Finnish Paediatric Foundation, and Sigrid Juselius Foundation. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSTools for objectively tracking neurodevelopment in paediatric populations using direct measurement of the brain are rare. Prior to conducting this study, we explored multiple databases (Google Scholar, PubMed, Web of Science) with search strategies that combined one or more of the terms "paediatric brain development", "brain age", "age estimation", "MRI measurements", "EEG measurements", "machine learning", "artificial intelligence", "advanced ageing", "neurodevelopmental delays" and "growth charts" with no restrictions on language and dates. In screening over 500 publications, 7 studies evaluated brain age in children using MRI and only a single study investigated maturation in EEG activity across discrete age bins. Added value of this studyWe formulated a measure of functional brain age (FBA) using state-of-the-art machine learning (ML) algorithms trained on a large, unique database consisting of multichannel clinical EEG recorded from N1/N2 sleep (n = 1056 children; 1 month to 17 years), with typical neurodevelopment confirmed at a 4-year follow-up. The FBA showed a high correlation with age and detected group-level differences associated with conditions of neurodevelopmental delay. Implications of all the available evidenceAge is prominent within EEG recordings of N1/N2 sleep and is readily extracted using ML. Public release of the FBA estimator and the use of EEG, commonly delivered in outpatient settings, as the basis of age prediction enables clear translation of measures of brain age to the clinic. Future work on EEG datasets across various neurodevelopmental profiles will enhance generalisability and user confidence in the clinical application of brain age.

neuroscience↗

bioRxiv2021

Automated detection of artefacts in neonatal EEG with residual neural networks

Background and ObjectiveTo develop a computational algorithm that detects and identifies different artefact types in neonatal electroencephalography (EEG) signals. MethodsAs part of a larger algorithm, we trained a Residual Deep Neural Network on expert human annotations of EEG recordings from 79 term infants recorded in a neonatal intensive care unit (112 h of 18-channel recording). The network was trained using 10 fold cross validation in Matlab. Artefact types included: device interference, EMG, movement, electrode pop, and non-cortical biological rhythms. Performance was assessed by prediction statistics and further validated on a separate independent dataset of 13 term infants (143 h of 3-channel recording). EEG pre-processing steps, and other post-processing steps such as averaging probability over a temporal window, were also included in the algorithm. ResultsThe Residual Deep Neural Network showed high accuracy (95%) when distinguishing periods of clean, artefact-free EEG from any kind of artefact, with a median accuracy for individual patient of 91% (IQR: 81%-96%). The accuracy in identifying the five different types of artefacts ranged from 57%-92%, with electrode pop being the hardest to detect and EMG being the easiest. This reflected the proportion of artefact available in the training dataset. Misclassification as clean was low for each artefact type, ranging from 1%-11%. The detection accuracy was lower on the validation set (87%). We used the algorithm to show that EEG channels located near the vertex were the least susceptible to artefact. ConclusionArtefacts can be accurately and reliably identified in the neonatal EEG using a deep learning algorithm. Artefact detection algorithms can provide continuous bedside quality assessment and support EEG review by clinicians or analysis algorithms. HighlightsO_LIWe applied a Residual Deep Neural Network as part of an artefact detection algorithm in neonatal electroencephalograms. C_LIO_LIThe algorithm shows high accuracy in identifying artefactual data in general and for specific artefact types. C_LIO_LIEEG channels near the top of the head are less prone to artefact. C_LI

neuroscience↗

bioRxiv2021

Phase-based cortical synchrony is affected by prematurity

Inter-areal synchronization by phase-phase correlations (PPC) of cortical oscillations mediates many higher neurocognitive functions, which are often affected by prematurity, a globally prominent neurodevelopmental risk factor. Here, we used electroencephalography (EEG) to examine brain-wide cortical PPC networks at term-equivalent age, comparing human infants after early prematurity to a cohort of healthy controls. We found that prematurity affected these networks in a sleep state-specific manner, and the differences between groups were also frequency-selective, involving brain-wide connections. The strength of synchronization in these networks was predictive of clinical outcomes in the preterm infants. These findings show that prematurity affects PPC networks in a clinically significant manner suggesting early functional biomarkers of later neurodevelopmental compromise to be used in clinical and translational studies after early neonatal adversity.

neuroscience↗

bioRxiv2019

Automated cot-side tracking of functional brain age in preterm infants with routine EEG monitoring

ObjectiveA major challenge in the care of preterm infants is the early identification of compromised neurological development. While several measures are routinely used to track anatomical growth, there is a striking lack of reliable and objective tools for tracking maturation of early brain function; a cornerstone of lifelong neurological health. We present a cot-side method for measuring the functional maturity of the newborn brain based on routinely-available neurological monitoring with electroencephalography (EEG). MethodsWe used a dataset of 177 EEG recordings from 65 preterm infants to train a multivariable prediction of functional brain age (FBA) from EEG. The FBA was validated on an independent set of 99 EEG recordings from 42 preterm infants. The difference between FBA and postmenstrual age (PMA) was evaluated as a predictor for neurodevelopmental outcome. ResultsThe FBA correlated strongly with the PMA of an infant, with a median prediction error of less than 1 week. Moreover, individual babies follow well-defined individual trajectories. The accuracy of the FBA applied to the validation set was statistically equivalent to the training set accuracy. In a subgroup of infants with repeated EEG recordings, a persistently negative predicted age difference was associated with poor neurodevelopmental outcome. InterpretationThe FBA enables the tracking of functional neurodevelopment in preterm infants. This establishes proof of principle for growth charts for brain function, a new tool to assist clinical management and identify infants who will benefit most from early intervention.

neuroscience↗