Cellular and Synaptic Diversity of Layer 2-3 Pyramidal Neurons in Human Individuals

Abstract

Computation within cortical microcircuits is determined by functional properties of the neurons and their synaptic interactions. While heterogeneity of inhibitory interneurons is well established, the anatomical, physiological, and molecular differentiation of excitatory pyramidal neurons is not fully resolved. To identify functional subtypes within the pyramidal neuron population, we focused on human layer 2-3 cortex which greatly expanded during evolution. We performed multi-neuron patch-clamp recordings in brain slices from the temporal cortex of 22 epilepsy patients. We characterized the electrophysiological properties of up to 80 pyramidal neurons per patient, enabling us to assess inter- and intra-individual functional variability. Hierarchical clustering of the high-dimensional parameter space yielded functionally distinct clusters of pyramidal neurons which were present across individuals. This may represent a generic organizational principle converging with previously described transcriptomic heterogeneity. We further observed substantial heterogeneity in physiological parameters with intra-individual variability being severalfold larger than inter-individual variability. The phenotypic variability within and across pyramidal neuron subtypes has important implications for the computational capacity of the cortical microcircuit.

Resilience through diversity: Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony

“…As expected, the CV of the altered population is decreased, and continues to remain significantly lower than the CVs of the temporal (p=0.02, two-sampled coefficient of variance test) and frontal (p < 0.01) populations. These results precisely align with recent multi-patch data from human cortex (Planert et al, 2021) highlighting that between-individual variability in biophysical properties was smaller than within-individual variability. Therefore the CVs we measure here are more related to within-individual heterogeneity than between-individual heterogeneity, and thus increased CV is in fact more representative of increased intrinsic biophysical heterogeneity (Planert et al, 2021; Moradi Chameh et al, 2021), than between-subject variability.…”

“…Additionally, the significant difference between the standard deviations (SDs) of the DTTs in non-epileptogenic MTG and epileptogenic frontal lobe (p=0.03, Cohen's d effect size=0.5; F-test; SD=7.8 mV in non-epileptogenic MTG and SD=4.4 mV in epileptogenic frontal lobe) that is implemented in our models has a "moderate" effect size. Finally, we confirmed that the measured heterogeneities are not biased by variability between patients (Supplementary Figure S2), a finding supported by recent multi-patch data in human cortex showing that biophysical properties demonstrate smaller between-subject than within-subject variability (Planert et al, 2021).…”

“…Linearizing about the steady state values of Ūe,i yields the system the Kruskal-Wallis test is only performed on patients with > 2 cells). This difference would be expected to increase the heterogeneity in the frontal, epileptogenic population; nonetheless, its CV is still significantly lower than our two non-epileptogenic populations (see Figure 1 These results precisely align with recent multi-patch data from human cortex (Planert et al, 2021) highlighting that between-individual variability in biophysical properties was smaller than within-individual variability. Therefore the CVs we measure here are more related to within-individual heterogeneity than between-individual heterogeneity, and thus increased CV is in fact more representative of increased intrinsic biophysical heterogeneity (Planert et al, 2021;Moradi Chameh et al, 2021), than between-subject variability.…”

Self-supervised learning of probabilistic prediction through synaptic plasticity in apical dendrites: A normative model

“…It turns out that both types of neuron models support DLR, but facilitate learning of different types of probability landscapes. Since either type of probability landscape is useful in some scenarios and less in others, the resulting learning theory for apical dendrites supports the co-existence of different subtypes of pyramidal cells in layer 2/3 [Planert et al, 2021] with different interactions between apical dendrites, since this guarantees that good probabilistic predictions can be learnt in each scenario by at least some of the neurons.…”

Target cell-specific synaptic dynamics of excitatory to inhibitory neuron connections in supragranular layers of human neocortex

“…Advances in single cell genomics have demonstrated a generally conserved cell type organization from mouse to human, but with many changes in cellular gene expression that suggest differences in cellular physiology, anatomy and connectivity (Hodge et al, 2019; Bakken et al, 2021). Recently, work from several research groups has shown that electrophysiological properties and local synaptic connectivity can be studied in acute human neocortical slices derived from surgical resections (Molnar et al, 2008; Jiang et al, 2012; Testa-Silva et al, 2014; Kalmbach et al, 2018; Beaulieu-Laroche et al, 2018; Boldog et al, 2018; Seeman et al, 2018; Peng et al, 2019; Planert et al, 2021; Campagnola, Seeman et al, 2022). These studies have demonstrated many conserved features, but a variety of human specializations compared to rodents, including faster recovery from synaptic depression (Testa-Silva et al, 2014) and greater numbers of functional release sites (Molnar et al, 2016).…”