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Biology subjects

Perets, N.

Publications and source records attributed to Perets, N..

2 recordsLinked to original sources

Patient-derived Brain Organoids Reveal Divergent Neuronal Activity Across Subpopulations of Autism Spectrum Disorder

Patient-derived brain organoids have emerged as a powerful model for investigating the mechanisms underlying neurological and psychiatric disorders. They provide novel insights into autism spectrum disorder (ASD), a heterogeneous neurodevelopmental condition whose underlying mechanisms remain poorly understood. Recent advancements in generating electrophysiological functional 3D brain organoids enable the study of molecular and network-level neuronal activity. Here, we aimed to characterize the neurophysiological underpinnings of ASD by comparing electrophysiological properties of brain organoids derived from eleven individuals diagnosed with autism spectrum disorder -10 with monogenic syndromic ASD across five genetic subtypes, and 1 with idiopathic ASD -to organoids derived from 4 neurotypical control individuals. We identified distinct differences in baseline activity (resting state) and evoked responses (synaptic plasticity and network dynamics) across ASD subgroups. To comprehensively assess these differences, we applied dimensionality reduction and machine learning (principal component analysis, PCA) to integrate multiple electrophysiological features into a unified framework. Our findings reveal subtype-specific neurophysiological alterations in ASD brain organoids, offering mechanistic insights into ASD heterogeneity and potential applications for early diagnostics, drug screening, and therapeutic development.

neuroscience↗

High order structure in mouse courtship vocalizations

Many complex motor behaviors can be decomposed into sequences of simple individual elements. Mouse ultrasonic vocalizations (USVs) are naturally divided into distinct syllables and thus are useful for studying the neural control of complex sequences production. However, little is known about the rules governing their temporal order. We recorded USVs during male-female courtship (460,000 USVs grouped into 44,000 sequences) and classified them using three popular algorithms. Modeling the sequences as Markov processes revealed a significant temporal structure which was dependent on the specific classification algorithm. To quantify how syllable misclassification obscures the true underlying sequence structure, we used information theory. We developed the Syntax Information Score and ranked the syllable classifications of the three algorithms. Finally, we derived a novel algorithm (Syntax Information Maximization) that utilized sequence statistics to improve the classification of individual USVs with respect to the underlying sequence structure.

bioinformatics↗