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Results for “developmental noise”

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4 recordsLinked to original sources
PubMed2026

Complex genotype-phenotype relationships in neurodevelopmental disorders.

With the advent of sequencing technologies in recent years, hundreds of high-confidence risk genes have been implicated in neurodevelopmental disorders (NDDs). However, individuals carrying pathogenic variants in the same gene frequently exhibit diverse clinical presentations, including varied symptoms and diagnoses. We propose that this heterogeneity arises from different interacting factors that modulate the phenotypic outcomes of pathogenic variants, including variant-level features, modifying variation across the genome, prenatal and early-life environmental exposures, and developmental noise. Resolving these factors requires integrative approaches that combine population-scale genetics and functional genomics with environmental monitoring and quantitative assessments of stochastic developmental variation. Advancing our understanding of these factors is critical to elucidating the etiology of NDDs and improving diagnostic and personalized therapeutic strategies.

Humans↗
PubMed2025

Tracking Somatic Mutations for Lineage Reconstruction.

The human genome is composed of distinct genomic regions that are susceptible to various types of somatic mutations. Among these, Short Tandem Repeats (STRs) stand out as the most mutable genetic elements. STRs are short repetitive polymorphic sequences, predominantly situated within noncoding sectors of the genome. The intrinsic repetition characterizing these sequences makes them highly mutable in vivo. Consequently, this characteristic provides the chance to unravel the natural developmental history of human viable cells retrospectively. However, STRs also introduce stutter noise in vitro amplification, which makes their analysis challenging. Here we describe our integrated biochemical-computational platform for single-cell lineage analysis. It consists of a pipeline whose inputs are single cells and whose output is a lineage tree of input cells.

Humans↗
PubMed2026

GT-Mamba: a Topology-Aware Graph-State space model for robust and interpretable epigenetic age prediction.

MOTIVATION: Current epigenetic clocks face a trade-off between predictive accuracy and biological interpretability, often relying on dataset-specific correction to generalize across cohorts. We propose GT-Mamba, a novel architecture that integrates a Structure-Aware Graph Transformer with the Mamba state space model. This design captures CpG topological correlations and genome-wide long-range dependencies. RESULTS: GT-Mamba demonstrates strong out-of-the-box robustness across heterogeneous independent validation cohorts, achieving a weighted average MAE of 4.43 years. Notably, it effectively generalizes to EPIC 850k arrays despite partial feature missingness, and maintains consistent performance across homologous age distribution shifts (MAE 2.94 years in a young cohort). Ablation studies confirm that graph topology contributes to improved robustness against noise. Mechanistic analysis suggests that the model captures methylation patterns associated with both developmental and functional processes. AVAILABILITY: Source code and pre-trained models are freely available at https://github.com/NENUBioCompute/GT-Mamba and archived on Zenodo (DOI: 10.5281/zenodo.19703155).

Epigenesis, Genetic↗
PubMed2026

Disentangling oscillatory and aperiodic neural activity in autism: A spectral parameterization analysis of neurofeedback intervention.

BACKGROUND: Autism Spectrum Disorder (ASD) is characterized by atypical neural oscillations and heterogeneous alterations in excitation/inhibition (E/I) balance, the directionality of which varies across individuals, neural circuits, and developmental stages. While Alpha-band neurofeedback (NFB) is a promising intervention, its underlying neurophysiological mechanisms remain unclear, partly due to the conflation of periodic and aperiodic signals in traditional EEG analysis. METHODS: This randomized controlled trial recruited 40 children with ASD, assigned to either an experimental group (Alpha-training NFB) or a no-feedback group. Resting-state EEG and behavioral assessments (SRS, ABC) were collected pre- and post-intervention. We employed spectral parameterization to decompose neural activity into aperiodic (1/f slope, offset) and periodic (periodic alpha power, center frequency) components. RESULTS: NFB training yielded significant behavioral improvements in social cognition and relating skills. Physiologically, the experimental group exhibited a significant steepening of the aperiodic slope (increased exponent), reflecting a reduction in neural noise and potential optimization of inhibitory modulation. Furthermore, we observed enhanced periodic alpha power and an acceleration of the alpha center frequency (ACF), indicative of improved neural efficiency and maturation. These physiological shifts in frontal and occipital regions were significantly correlated with improvements in behavioral scores. CONCLUSION: Alpha-training NFB was associated with improvements in caregiver-rated behavioral scores and modulated spectral features of resting-state EEG in children with ASD. These findings validate the utility of spectral parameterization markers in evaluating neuromodulatory interventions.

Humans↗