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Hattingh, R. M.

Publications and source records attributed to Hattingh, R. M..

2 recordsLinked to original sources

Human neurons undergo protracted functional maturation into adulthood

Summary ParagraphHuman cognitive development is uniquely prolonged1-3, reflecting the extended postnatal maturation of the cerebral cortex where cell-type differentiation4,5, synaptogenesis6,7, myelination8 and transcriptional regulation5,9,10 all follow protracted developmental timelines. However, when human cortical neurons reach functional electrophysiological maturity and how their developmental trajectory compares to other species remains unknown. Here we show through patch-clamp recordings of human temporal cortex from infancy to adulthood that supragranular pyramidal neurons exhibit pronounced neoteny of their functional properties, with physiological maturation continuing well into adulthood. Comparing human and mouse developmental trajectories reveals human neurons are on a much slower developmental timeline, maturing physiologically hundreds of times slower than mouse and 2-6 times slower than would be predicted from anatomical brain growth differences between species. This reflects a fundamentally different allometric relationship between physiological and anatomical maturation; while mouse neuronal physiology closely tracks brain growth, human physiological development follows its own extended timeline. This slow maturation results in different stages of cognitive development being supported by functionally distinct neuronal populations, with the progression from infancy to middle age characterized by specific electrophysiological profiles. Notably, a neuronal subtype thought to be human-specific, with electrophysiological traits that enhance computational capacity, appears only in late adolescence or early adulthood. This extreme protraction of neurophysiological development provides a cellular basis for prolonged human cognitive maturation, demonstrating that neuronal physiological neoteny represents a fundamental evolutionary adaptation in human brain development.

neuroscience↗

Comprehensive Analysis of Human Dendritic Spine Morphology and Density: Exploring Diversity of Human Dendritic Spines

Dendritic spines, small protrusions on neuronal dendrites, play a crucial role in brain function by changing shape and size in response to neural activity. So far, in depth analysis of dendritic spines in human brain tissue is lacking. This study presents a comprehensive analysis of human dendritic spine morphology and density using a unique dataset from human brain tissue from 27 patients (8 females, 19 males, aged 18-71) undergoing tumor or epilepsy surgery at three neurosurgery sites. We used acute slices and organotypic brain slice cultures to examine dendritic spines, classifying them into the three main morphological subtypes: Mushroom, Thin, and Stubby, via 3D reconstruction using ZEISS arivis Pro software. A deep learning model, trained on 39 diverse datasets, automated spine segmentation and 3D reconstruction, achieving a 74% F1-score and reducing processing time by over 50%. We show significant differences in spine density by sex, dendrite type, and tissue condition. Females had higher spine densities than males, and apical dendrites were denser in spines than basal ones. Acute tissue showed higher spine densities compared to cultured human brain tissue. With time in culture, Mushroom spines decreased, while Stubby and Thin spine percentages increased, particularly from 7-9 to 14 days in vitro, reflecting potential synaptic plasticity changes. Our study underscores the importance of using human brain tissue to understand unique synaptic properties and shows that integrating deep learning with traditional methods enables efficient large-scale analysis, revealing key insights into sex- and tissue-specific dendritic spine dynamics relevant to neurological diseases. New and NoteworthyThis study presents a dataset of nearly 4,000 morphologically reconstructed human dendritic spines across different ages, gender, and tissue conditions. The dataset was further used to evaluate a deep learning algorithm for three-dimensional spine reconstruction, offering a scalable method for semi-automated spine analysis across various tissues and microscopy setups. The findings enhance understanding of human neurology, indicating potential connections between spine morphology, brain function, and the mechanisms of neurological and psychiatric diseases.

neuroscience↗