Biology subjects

de Kock, C. P.

Publications and source records attributed to de Kock, C. P..

6 recordsLinked to original sources

bioRxiv2026

Uncovering the Basis of Human ConnectomeComplexity: The Role of Neuronal Morphology

Comparative studies have established differences between the electrophysiology and anatomy of human and rodent cortical circuits. A consistent finding is that human neuronal morphologies display more elaborate neurite shapes than those of rodents, a feature that cannot be accounted for merely by their larger size according to recent findings. Here, we study the impact of these neurite shapes on the structure of synaptic connectivity in their local microcircuitry. Our approach is based on the idea that axonal and dendritic geometries constrain the locations of afferent and efferent synaptic contacts (potential connectivity). Although the mechanisms by which potential connectivity translates into actual synaptic connectivity are manifold and complex, the potential connectivity is nevertheless highly informative for the final structure of a biological connectome. We found that connectomes predicted from human reconstructed morphologies have higher complexity according to several measures that have been demonstrated to be functionally relevant. Going beyond a simple comparison, we demonstrate mechanistically how the shapes of neuron morphologies give rise to non-random and clustered structures observed in experimentally measured connectomes, and how the specific shapes of human neurons strengthen the process. Finally, we conceptually examine how synapse formation processes may interact with potential connectivity, showing that a process compatible with Hebbian plasticity leads to the highest complexity and best match experimentally observed patterns.

neuroscience↗

bioRxiv2025

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↗

bioRxiv2024

What makes human cortical pyramidal neurons functionally complex

Humans exhibit unique cognitive abilities within the animal kingdom, but the neural mechanisms driving these advanced capabilities remain poorly understood. Human cortical neurons differ from those of other species, such as rodents, in both their morphological and physiological characteristics. Could the distinct properties of human cortical neurons help explain the superior cognitive capabilities of humans? Understanding this relationship requires a metric to quantify how neuronal properties contribute to the functional complexity of single neurons, yet no such standardized measure currently exists. Here, we propose the Functional Complexity Index (FCI), a generalized, deep learning-based framework to assess the input-output complexity of neurons. By comparing the FCI of cortical pyramidal neurons from different layers in rats and humans, we identified key morpho-electrical factors that underlie functional complexity. Human cortical pyramidal neurons were found to be significantly more functionally complex than their rat counterparts, primarily due to differences in dendritic membrane area and branching pattern, as well as density and nonlinearity of NMDA-mediated synaptic receptors. These findings reveal the structural-biophysical basis for the enhanced functional properties of human neurons.

neuroscience↗

bioRxiv2023

Of mice and men: Increased dendritic complexity gives rise to unique human networks

The organizational principles that distinguish the human brain from other species have been a long-standing enigma in neuroscience. Focusing on the uniquely evolved human cortical layers 2 and 3, we computationally reconstruct the cortical architecture for mice and humans. We show that human pyramidal cells form highly complex networks, demonstrated by the increased number and simplex dimension compared to mice. This is surprising because human pyramidal cells are much sparser in the cortex. We show that the number and size of neurons fail to account for this increased network complexity, suggesting that another morphological property is a key determinant of network connectivity. Topological comparison of dendritic structure reveals much higher perisomatic (basal and oblique) branching density in human pyramidal cells. Using topological tools we quantitatively show that this neuronal structural property directly impacts network complexity, including the formation of a rich subnetwork structure. We conclude that greater dendritic complexity, a defining attribute of human L2 and 3 neurons, may provide the human cortex with enhanced computational capacity and cognitive flexibility. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/557170v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@c0adadorg.highwire.dtl.DTLVardef@1ca4aaeorg.highwire.dtl.DTLVardef@1ecf1b5org.highwire.dtl.DTLVardef@f0d44d_HPS_FORMAT_FIGEXP M_FIG C_FIG A. A multiscale analysis was performed to compare the mouse and human brains: from the anatomical properties of brain regions to the morphological details of single neurons. B. Human circuits are larger than mice in terms of size and number of neurons, but present decreased neuron density, resulting in increased distances between neurons, particularly among pyramidal cells. C. Greater network complexity emerges within the human brain. Network complexity is defined by larger groups of neurons forming complex interconnections throughout the network. D. The topological analysis of layer 2/3 pyramidal cells in the temporal cortex reveals an intriguing difference: human neurons exhibit a significantly larger number of dendritic branches, especially near the cell body compared to mice. This phenomenon is termed "higher topological complexity" in dendrites. Our findings suggest that dendritic complexity wields a more substantial influence on network complexity than neuron density.

neuroscience↗

bioRxiv2023

Interpretable modelling of input-output computations in cortex

Neurons receive input from thousands of synapses, which they transform into action potentials (APs) via their complex dendrites. How the dendritic location of these inputs, their timing, strength, and presynaptic origin impact AP output remains generally unknown. Here we demonstrate how to reveal which properties of the input causally underlie AP output, and how this input-output computation is influenced by the morphology and biophysical properties of the dendrites. For this purpose, we derive analytically tractable, interpretable models of the input-output computation that layer 5 pyramidal tract neurons (L5PTs) - the major output cell type of the cerebral cortex - perform upon sensory stimulation. We find that this input-output computation is preserved across L5PTs despite morphological and biophysical diversity. We show that three features are sufficient to explain in vivo observed sensory responses and receptive fields of L5PTs with high accuracy: the count of active excitatory versus inhibitory synapses preceding the response, their spatial distribution on the dendrites, and the AP history. Based on this analytically tractable and interpretable description of the input-output computation, we show how to dissect the contributions of different input populations in thalamus and cortex to sensory responses of L5PTs. Thus, our approach provides a roadmap for revealing cellular input-output computations across different in vivo conditions. Author SummaryRevealing how synaptic inputs drive action potential output is one of the major challenges in neuroscience research. An increasing number of approaches therefore seek to combine detailed measurements at synaptic, cellular and network scales into biologically realistic brain models. Indeed, such models have started to make empirically testable predictions about the inputs that underlie in vivo observed activity patterns. However, the enormous complexity of these models generally prevents the derivation of interpretable descriptions that explain how neurons transform synaptic input into action potential output, and how these input-output computations depend on synaptic, cellular and network properties. Here we introduce an approach to reveal input-output computations that neurons in the cerebral cortex perform upon sensory stimulation. We reduce a realistic multi-scale cortex model to the minimal description that accounts for in vivo observed responses. Thereby, we identify the input-output computation that these cortical neurons perform under this in vivo condition, and we show that this computation is preserved across neurons despite morphological and biophysical diversity. Our approach provides analytically tractable and interpretable descriptions of neuronal input-output computations during specific in vivo conditions.

neuroscience↗

bioRxiv2021

Neuronal morphology and physiology, functional brain networks and memory in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of therapy-resistant TLE patients, who underwent fMRI, MEG and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.

neuroscience↗