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Dendritic spines are sites of synaptic plasticity and their head size correlates with the strength of the corresponding synapse. We recently showed that the distribution of spine head sizes follows a lognormal‐like distribution even after blockage of activity or plasticity induction. As the cytokine tumor necrosis factor (TNF) influences synaptic transmission and constitutive TNF and receptor (TNF‐R)‐deficiencies cause changes in spine head size distributions, we tested whether these genetic alterations disrupt the lognormality of spine head sizes. Furthermore, we distinguished between spines containing the actin‐modulating protein synaptopodin (SP‐positive), which is present in large, strong and stable spines and those lacking it (SP‐negative). Our analysis revealed that neither TNF‐deficiency nor the absence of TNF‐R1, TNF‐R2 or TNF‐R 1 and 2 (TNF‐R1/R2) degrades the general lognormal‐like, skewed distribution of spine head sizes (all spines, SP‐positive spines, SP‐negative spines). However, TNF, TNF‐R1 and TNF‐R2‐deficiency affected the width of the lognormal distribution, and TNF‐R1/2‐deficiency shifted the distribution to the left. Our findings demonstrate the robustness of the lognormal‐like, skewed distribution, which is maintained even in the face of genetic manipulations that alter the distribution of spine head sizes. Our observations are in line with homeostatic adaptation mechanisms of neurons regulating the distribution of spines and their head sizes. [ABSTRACT FROM AUTHOR]

Introduction: Repetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitates the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood. Consequently, achieving standardization of biological effects across different brain regions and subjects poses a significant challenge. Methods: This study compared the effects of 10 Hz repetitive magnetic stimulation (rMS) in entorhino-hippocampal tissue cultures from mice and rats, providing insights into the impact of the same stimulation protocol on similar neuronal networks under standardized conditions. Results: We observed the previously described plastic changes in excitatory and inhibitory synaptic strength of CA1 pyramidal neurons in both mouse and rat tissue cultures, but a higher stimulation intensity was required for the induction of rMS-induced synaptic plasticity in rat tissue cultures. Through systematic comparison of neuronal structural and functional properties and computational modeling, we found that morphological parameters of CA1 pyramidal neurons alone are insufficient to explain the observed differences between the groups. Although morphologies of mouse and rat CA1 neurons showed no significant differences, simulations confirmed that axon morphologies significantly influence individual cell activation thresholds. Notably, differences in intrinsic cellular properties were sufficient to account for the 10% higher intensity required for the induction of synaptic plasticity in the rat tissue cultures. Conclusion: These findings demonstrate the critical importance of axon morphology and intrinsic cellular properties in predicting the plasticity effects of rTMS, carrying valuable implications for the development of computer models aimed at predicting and standardizing the biological effects of rTMS. [ABSTRACT FROM AUTHOR]

Investigating and modelling the functionality of human neurons remains challenging due to the technical limitations, resulting in scarce and incomplete 3D anatomical reconstructions. Here we used a morphological modelling approach based on optimal wiring to repair the parts of a dendritic morphology that were lost due to incomplete tissue samples. In Drosophila, where dendritic regrowth has been studied experimentally using laser ablation, we found that modelling the regrowth reproduced a bimodal distribution between regeneration of cut branches and invasion by neighbouring branches. Interestingly, our repair model followed growth rules similar to those for the generation of a new dendritic tree. To generalise the repair algorithm from Drosophila to mammalian neurons, we artificially sectioned reconstructed dendrites from mouse and human hippocampal pyramidal cell morphologies, and showed that the regrown dendrites were morphologically similar to the original ones. Furthermore, we were able to restore their electrophysiological functionality, as evidenced by the recovery of their firing behaviour. Importantly, we show that such repairs also apply to other neuron types including hippocampal granule cells and cerebellar Purkinje cells. We then extrapolated the repair to incomplete human CA1 pyramidal neurons, where the anatomical boundaries of the particular brain areas innervated by the neurons in question were known. Interestingly, the repair of incomplete human dendrites helped to simulate the recently observed increased synaptic thresholds for dendritic NMDA spikes in human versus mouse dendrites. To make the repair tool available to the neuroscience community, we have developed an intuitive and simple graphical user interface (GUI), which is available in the TREES toolbox (www.treestoolbox.org). Author summary: Reconstructing neuronal dendrites by drawing their 3D branching structures in the computer has proved to be crucial for interpreting the flow of electrical signals and therefore the computations that dendrites perform on their inputs. These reconstructions are tedious and prone to disruptive limitations imposed by experimental procedures. In recent years, complementary computational procedures have emerged that reproduce the fine details of morphology in theoretical models. These models allow, for example, to populate large-scale neural networks and to study structure-function relationships. In this work we use a morphological model based on optimised wiring for signal conduction and material cost to repair faulty reconstructions, in particular those of human hippocampal dendrites, which are rare and precious but often cut due to technical limitations. Interestingly, we find that our synthetic repair mechanism reproduces the two distinct modes of repair observed in real dendrites: regeneration from the severed branch and invasion from neighbouring branches. Our model therefore provides both a useful tool for single-cell electrophysiological simulations and a useful theoretical concept for studying the biology of dendrite repair. [ABSTRACT FROM AUTHOR]

Pattern separation is a valuable computational function performed by neuronal circuits, such as the dentate gyrus, where dissimilarity between inputs is increased, reducing noise and increasing the storage capacity of downstream networks. Pattern separation is studied from both in vivo experimental and computational perspectives and, a number of different measures (such as orthogonalisation, decorrelation, or spike train distance) have been applied to quantify the process of pattern separation. However, these are known to give conclusions that can differ qualitatively depending on the choice of measure and the parameters used to calculate it. We here demonstrate that arbitrarily increasing sparsity, a noticeable feature of dentate granule cell firing and one that is believed to be key to pattern separation, typically leads to improved classical measures for pattern separation even, inappropriately, up to the point where almost all information about the inputs is lost. Standard measures therefore both cannot differentiate between pattern separation and pattern destruction, and give results that may depend on arbitrary parameter choices. We propose that techniques from information theory, in particular mutual information, transfer entropy, and redundancy, should be applied to penalise the potential for lost information (often due to increased sparsity) that is neglected by existing measures. We compare five commonly-used measures of pattern separation with three novel techniques based on information theory, showing that the latter can be applied in a principled way and provide a robust and reliable measure for comparing the pattern separation performance of different neurons and networks. We demonstrate our new measures on detailed compartmental models of individual dentate granule cells and a dentate microcircuit, and show how structural changes associated with epilepsy affect pattern separation performance. We also demonstrate how our measures of pattern separation can predict pattern completion accuracy. Overall, our measures solve a widely acknowledged problem in assessing the pattern separation of neural circuits such as the dentate gyrus, as well as the cerebellum and mushroom body. Finally we provide a publicly available toolbox allowing for easy analysis of pattern separation in spike train ensembles. Author summary: The hippocampus is a region of the brain strongly associated with spatial navigation and encoding of episodic memories. To perform these functions effectively it makes use of circuits that perform pattern separation, where redundant structure is removed from neural representations leaving only the most salient information. Pattern separation allows downstream pattern completion networks to better distinguish between similar situations. Pathological changes, caused by Alzheimer's, schizophrenia, or epilepsy, to the circuits that perform pattern separation are associated with reduced discriminative ability in both animal models and humans. Traditionally, pattern separation has been described alongside the complementary process of pattern completion, but more recent studies have focussed on the detailed neuronal and circuit features that contribute to pattern separation alone. We here show that traditional measures of pattern separation are inappropriate in this case, as they do not give consistent conclusions when parameters are changed and can confound pattern separation with the loss of important information. We show that directly accounting for the information throughput of a pattern separation circuit can provide new measures of pattern separation that are robust and consistent, and allow for nuanced analysis of the structure-function relationship of such circuits and how this may be perturbed by pathology. [ABSTRACT FROM AUTHOR]

Neuronal hyperexcitability is a pathological characteristic of Alzheimer's disease (AD). Three main mechanisms have been proposed to explain it: (i) dendritic degeneration leading to increased input resistance, (ii) ion channel changes leading to enhanced intrinsic excitability, and (iii) synaptic changes leading to excitation–inhibition (E/I) imbalance. However, the relative contribution of these mechanisms is not fully understood. Therefore, we performed biophysically realistic multi‐compartmental modelling of neuronal excitability in reconstructed CA1 pyramidal neurons from wild‐type and APP/PS1 mice, a well‐established animal model of AD. We show that, for synaptic activation, the excitability‐promoting effects of dendritic degeneration are cancelled out by decreased excitation due to synaptic loss. We find an interesting balance between excitability regulation and an enhanced degeneration in the basal dendrites of APP/PS1 cells, potentially leading to increased excitation by the apical but decreased excitation by the basal Schaffer collateral pathway. Furthermore, our simulations reveal three pathomechanistic scenarios that can account for the experimentally observed increase in firing and bursting of CA1 pyramidal neurons in APP/PS1 mice: scenario 1: enhanced E/I ratio; scenario 2: alteration of intrinsic ion channels (IAHP down‐regulated; INap, INa and ICaT up‐regulated) in addition to enhanced E/I ratio; and scenario 3: increased excitatory burst input. Our work supports the hypothesis that pathological network and ion channel changes are major contributors to neuronal hyperexcitability in AD. Overall, our results are in line with the concept of multi‐causality according to which multiple different disruptions are separately sufficient but no single particular disruption is necessary for neuronal hyperexcitability. Key points: This work presents simulations of synaptically driven responses in pyramidal cells (PCs) with Alzheimer's disease (AD)‐related dendritic degeneration.Dendritic degeneration alone alters PC responses to layer‐specific input but additional pathomechanistic scenarios are required to explain neuronal hyperexcitability in AD as follows.Possible scenario 1: AD‐related increased excitatory input together with decreased inhibitory input (E/I imbalance) can lead to hyperexcitability in PCs.Possible scenario 2: changes in E/I balance combined with altered ion channel properties can account for hyperexcitability in AD.Possible scenario 3: burst hyperactivity of the surrounding network can explain hyperexcitability of PCs during AD. [ABSTRACT FROM AUTHOR]

The perforant path provides the primary cortical excitatory input to the hippocampus. Because of its important role in information processing and coding, entorhinal projections to the dentate gyrus have been studied in considerable detail. Nevertheless, synaptic transmission between individual connected pairs of entorhinal stellate cells and dentate granule cells remains to be characterized. Here, we have used mouse organotypic entorhino-hippocampal tissue cultures of either sex, in which the entorhinal cortex (EC) to dentate granule cell (GC; EC–GC) projection is present, and EC–GC pairs can be studied using whole-cell patch-clamp recordings. By using cultures of wild-type mice, the properties of EC–GC synapses formed by afferents from the lateral and medial entorhinal cortex were compared, and differences in short-term plasticity were identified. As the perforant path is severely affected in Alzheimer’s disease, we used tissue cultures of amyloid precursor protein (APP)–deficient mice to examine the role of APP at this synapse. APP deficiency altered excitatory neurotransmission at medial perforant path synapses, which was accompanied by transcriptomic and ultrastructural changes. Moreover, presynaptic but not postsynaptic APP deletion through the local injection of Cre-expressing adeno-associated viruses in conditional APPflox/flox tissue cultures increased the neurotransmission efficacy at perforant path synapses. In summary, these data suggest a physiological role for presynaptic APP at medial perforant path synapses that may be adversely affected under altered APP processing conditions. [ABSTRACT FROM AUTHOR]

The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown, given that simpler models with fewer ion channels are also able to functionally reproduce the behaviour of some neurons. Here, we stochastically varied the ion channel densities of a biophysically detailed dentate gyrus granule cell model to produce a large population of putative granule cells, comparing those with all 15 original ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were dramatically more frequent at -6% vs. -1% in the simpler model. The full models were also more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve a target excitability. Author summary: Over the course of billions of years, evolution has led to a wide variety of biological systems. The emergence of the more complex among these seems surprising in the light of the high demands of searching for viable solutions in a correspondingly high-dimensional parameter space. In realistic neuron models with their inherently complex ion channel composition, we find a surprisingly large number of viable solutions when selecting parameters randomly. This effect is strongly reduced in models with fewer ion channel types but is recovered when inserting additional artificial ion channels. Because concepts from probability theory provide a plausible explanation for this improved distribution of valid model parameters, we propose that this effect may generalise to evolutionary selection in other complex biological systems. [ABSTRACT FROM AUTHOR]

The cation-chloride cotransporters KCC2 and NKCC1 regulate the intracellular Cl- concentration and cell volume of neurons and/or glia. The Cl- extruder KCC2 is expressed at higher levels than the Cl- transporter NKCC1 in mature compared to immature neurons, accounting for the developmental shift from high to low Cl- concentration and from depolarizing to hyperpolarizing currents through GABA-A receptors. Previous studies have shown that KCC2 expression is downregulated following central nervous system injury, returning neurons to a more excitable state, which can be pathological or adaptive. Here, we show that deafferentation of the dendritic segments of granule cells in the outer (oml) and middle (mml) molecular layer of the dentate gyrus via entorhinal denervation in vivo leads to cell-type- and layer-specific changes in the expression of KCC2 and NKCC1. Microarray analysis validated by reverse transcription-quantitative polymerase chain reaction revealed a significant decrease in Kcc2 mRNA in the granule cell layer 7 days post-lesion. In contrast, Nkcc1 mRNA was upregulated in the oml/mml at this time point. Immunostaining revealed a selective reduction in KCC2 protein expression in the denervated dendrites of granule cells and an increase in NKCC1 expression in reactive astrocytes in the oml/mml. The NKCC1 upregulation is likely related to the increased activity of astrocytes and/or microglia in the deafferented region, while the transient KCC2 downregulation in granule cells may be associated with denervation-induced spine loss, potentially also serving a homeostatic role via boosting GABAergic depolarization. Furthermore, the delayed KCC2 recovery might be involved in the subsequent compensatory spinogenesis. [ABSTRACT FROM AUTHOR]

The axon initial segment (AIS) is the site of action potential initiation and important for the integration of synaptic input. Length and localization of the AIS are dynamic, modulated by afferent activity and contribute to the homeostatic control of neuronal excitability. Synaptopodin is a plasticity-related protein expressed by the majority of telencephalic neurons. It is required for the formation of cisternal organelles within the AIS and an excellent marker to identify these enigmatic organelles at the light microscopic level. Here we applied 2 h of high frequency stimulation of the medial perforant path in rats in vivo to induce a strong long-term potentiation of dentate gyrus granule cells. Immunolabeling for bIV-spectrin and synaptopodin were performed to study structural changes of the AIS and its cisternal organelles. Three-dimensional analysis of the AIS revealed a shortening of the AIS and a corresponding reduction of the number of synaptopodin clusters. These data demonstrate a rapid structural plasticity of the AIS and its cisternal organelles to strong stimulation, indicating a homeostatic response of the entire AIS compartment. [ABSTRACT FROM AUTHOR]

Transcranial magnetic stimulation (TMS) can depolarize cortical neurons through the intact skin and skull. The characteristics of the induced electric field (E-field) have a major impact on specific outcomes of TMS. Using multi-scale computational modeling, we explored whether the stimulation parameters derived from the primary motor cortex (M1) induce comparable macroscopic E-field strengths and subcellular/cellular responses in the dorsolateral prefrontal cortex (DLPFC). To this aim, we calculated the TMS-induced E-field in 16 anatomically realistic head models and simulated the changes in membrane voltage and intracellular calcium levels of morphologically and biophysically realistic human pyramidal cells in the M1 and DLPFC. We found that the conventional intensity selection methods (i.e., motor threshold and fixed intensities) produce variable macroscopic E-fields. Consequently, it was challenging to produce comparable subcellular/cellular responses across cortical regions with distinct folding characteristics. Prospectively, personalized stimulation intensity selection could standardize the E-fields and the subcellular/cellular responses to repetitive TMS across cortical regions and individuals. The suggested computational approach points to the shortcomings of the conventional intensity selection methods used in clinical settings. We propose that multi-scale modeling has the potential to overcome some of these limitations and broaden our understanding of the neuronal mechanisms for TMS. [ABSTRACT FROM AUTHOR]

Artificial neural networks, taking inspiration from biological neurons, have become an invaluable tool for machine learning applications. Recent studies have developed techniques to effectively tune the connectivity of sparsely-connected artificial neural networks, which have the potential to be more computationally efficient than their fully-connected counterparts and more closely resemble the architectures of biological systems. We here present a normalisation, based on the biophysical behaviour of neuronal dendrites receiving distributed synaptic inputs, that divides the weight of an artificial neuron's afferent contacts by their number. We apply this dendritic normalisation to various sparsely-connected feedforward network architectures, as well as simple recurrent and self-organised networks with spatially extended units. The learning performance is significantly increased, providing an improvement over other widely-used normalisations in sparse networks. The results are two-fold, being both a practical advance in machine learning and an insight into how the structure of neuronal dendritic arbours may contribute to computation. Author summary: Neurons receive contacts from other cells on extensively branched processes known as dendrites. When a contact is formed, activity in one cell is communicated to another by altering the conductance of the receiving cell's membrane and allowing an ionic current to flow. A neuron with longer dendrites is intrinsically less excitable as these currents can more easily dissipate both across the larger cell membrane and along the dendrites themselves. We have recently shown that, in real neurons, this effect is precisely cancelled by the increased number of contacts allowed by longer dendrites. This in turn implies that the ability of a single synapse to influence a neuron is likely to be inversely proportional to the total number of synapses that that neuron receives. Here we study the computational implications of this effect using the well-established framework of artificial neural networks. Sparsely-connected artificial neural networks adapt their connectivity to solve defined computational tasks such as classifying inputs and are at the forefront of modern machine learning. We apply the normalisation implied by dendritic structure to such networks: artificial neurons receiving more contacts require larger dendrites and so each individual contact will have proportionately less influence. Our normalisation allows networks to learn desired tasks faster and more consistently. Our result is both a practical advance in machine learning and a previously unappreciated way in which intrinsic properties of neurons may contribute to their computational function. [ABSTRACT FROM AUTHOR]

Background: Aging is the main risk factor for Alzheimer's disease (AD) characterized by a cognitive decline related to a synaptic disfunction (Chen et al., 2019). In AD brains, tau abnormal phosphorylation coexists with heparan sulfates (HS), suggesting that HS could lead to the formation of neurofibrillary tangles (NFT) (Goedert et al., 1996). In vitro, the aggregation of tau is not possible in the absence of heparin, a highly sulfated HS carrying 3‐O‐sulfation (3S‐HS). 3S‐HS are the product of HS 3‐O Sulfotransferase (HS3ST) enzymes (Sepulveda‐Diaz et al., 2015). In AD, Heparan Sulfate‐Glucosamine 3‐Sulfotransferase 2 (HS3ST2) expression is higher in tauopathy vulnerable brain regions, such as the hippocampus and cortex (Huynh et al., 2019). Previously, we showed in a cellular model that HS3ST2 induces cell autonomous aggregation of tau (Huynh et al., 2022). However, the mechanism leading to an increased expression of HS3ST2, and therefore increase in 3S‐HS synthesis are unknown. Additionally, it is not known the effect of reducing the levels of HS3ST2 in tau oligomerization and aggregation in neurons. Method: Primary hippocampal neurons (PHN) from the rTg4510 mice model were dissected and maintained in vitro for 20 days (DIV20). All treatments (ALK5inh, Tgfb1) and lentiviral transduction (Lv‐NonTarget, Lv‐shHs3st2) were done at day in vitro 15 (DIV15). Result: In brain of aged individuals, transforming growth factor TGFB1 is upregulated, modulating gene expression (Doyle et al., 2010). SMAD4 transcription factor is activated upon the dimerization of TGFB receptor targeting specific genes (Martin‐Malpartida et al., 2017). SMAD4 contains genome wide recognition motifs that are present in the Hs3st2 promoter, suggesting a possible regulation. We showed that Hs3st2 is under regulation of Tgfb1 and blocking pSmad2/3 reduces its levels. Contrarily, Hs3st2 levels were increased by increasing pSmad2/3. The importance of Tgfb1 coregulation with Hs3st2 was assessed by blocking the expression of Hs3st2, leading to a decrease in tau oligomerization and aggregation. Finally, we found that the reduction of 3S‐HS enhances synaptic size, and presynaptic/postsynaptic connectivity. Conclusion: Overall, we depicted the regulation of Hs3st2, and demonstrate its involvement in synaptic connectivity and tau aggregation, confirming the importance of 3S‐HS in the physiopathology of AD. [ABSTRACT FROM AUTHOR]