Biology subjects

Lauronen, L.

Publications and source records attributed to Lauronen, L..

4 recordsLinked to original sources

bioRxiv2026

A novel method for obtaining epileptic brain tissue for omic analyses using electrodes from a clinical stereoelectroencephalography study

Abstract Purpose: Developing a valid and practical method for obtaining brain tissue samples for transcriptomic and proteomic analyses using tissue adhered to stereoelectroencephalography (SEEG) electrodes, to enable investigation of the molecular mechanisms underlying chronic epilepsy. Method: Brain tissue samples adhered to SEEG electrodes were collected from six patients with drug-resistant epilepsy and preprocessed in a hospital environment after electrode removal. RNA extraction was initiated immediately, and proteomics samples were snap-frozen until subsequent analysis. The samples were categorized into three groups according to their electrophysiological profile: epileptogenic zone, propagation zone, and least-involved zone. The omic findings between these zones and anatomical brain areas were compared. Results: High-quality RNA and protein samples were obtained from tissue adhered to SEEG electrodes. Neuron- and brain-specific gene expression patterns and proteins were identified. Signs of activation of inflammatory mechanisms were most pronounced in the epileptogenic zone. Transcriptomic and proteomic findings demonstrated concordance. Conclusion: SEEG electrodes are a useful source for obtaining brain tissue for molecular characterization of chronic epilepsy. This method will enable the identification of shared and distinct molecular mechanisms in patients with varying etiologies of epilepsy.

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↗

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

Delineating the molecular and phenotypic spectrum of the SETD1B-related syndrome

Pathogenic variants in SETD1B have been associated with a syndromic neurodevelopmental disorder including intellectual disability, language delay and seizures. To date, clinical features have been described for eleven patients with (likely) pathogenic SETD1B sequence variants. We perform an in-depth clinical characterization of a cohort of 36 unpublished individuals with SETD1B sequence variants, describing their molecular and phenotypic spectrum. Selected variants were functionally tested using in vitro and genome-wide methylation assays. Our data present evidence for a loss-of-function mechanism of SETD1B variants, resulting in a core clinical phenotype of global developmental delay, language delay including regression, intellectual disability, autism and other behavioral issues, and variable epilepsy phenotypes. Developmental delay appeared to precede seizure onset, suggesting SETD1B dysfunction impacts physiological neurodevelopment even in the absence of epileptic activity. Interestingly, males are significantly overrepresented and more severely affected, and we speculate that sex-linked traits could affect susceptibility to penetrance and the clinical spectrum of SETD1B variants. Finally, despite the possibility of non-redundant contributions of SETD1B and its paralogue SETD1A to epigenetic control, the clinical phenotypes of the related disorders share many similarities, indicating that elucidating shared and divergent downstream targets of both genes will help to understand the mechanism leading to the neurobehavioral phenotypes. Insights from this extensive cohort will facilitate the counseling regarding the molecular and phenotypic landscape of newly diagnosed patients with the SETD1B-related syndrome.

genetics↗