A simulation study investigating the impact of dendritic morphology and synaptic topology on neuronal firing patterns

Schizophrenia and psychedelic state: Dysconnection versus hyper-connection. A perspective on two different models of psychosis stemming from dysfunctional integration processes

Psychotic symptoms are a cross-sectional dimension affecting multiple diagnostic categories, despite schizophrenia represents the prototype of psychoses. Initially, dopamine was considered the most involved molecule in the neurobiology of schizophrenia. Over the next years, several biological factors were added to the discussion helping to constitute the concept of schizophrenia as a disease marked by a deficit of functional integration, contributing to the formulation of the Dysconnection Hypothesis in 1995. Nowadays the notion of dysconnection persists in the conceptualization of schizophrenia enriched by neuroimaging findings which corroborate the hypothesis. At the same time, in recent years, psychedelics received a lot of attention by the scientific community and astonishing findings emerged about the rearrangement of brain networks under the effect of these compounds. Specifically, a global decrease in functional connectivity was found, highlighting the disintegration of preserved and functional circuits and an increase of overall connectivity in the brain. The aim of this paper is to compare the biological bases of dysconnection in schizophrenia with the alterations of neuronal cyto-architecture induced by psychedelics and the consequent state of cerebral hyper-connection. These two models of psychosis, despite diametrically opposed, imply a substantial deficit of integration of neural signaling reached through two opposite paths.

Prediction of Neural Diameter From Morphology to Enable Accurate Simulation

Accurate neuron morphologies are paramount for computational model simulations of realistic neural responses. Over the last decade, the online repository NeuroMorpho.Org has collected over 140,000 available neuron morphologies to understand brain function and promote interaction between experimental and computational research. Neuron morphologies describe spatial aspects of neural structure; however, many of the available morphologies do not contain accurate diameters that are essential for computational simulations of electrical activity. To best utilize available neuron morphologies, we present a set of equations that predict dendritic diameter from other morphological features. To derive the equations, we used a set of NeuroMorpho.org archives with realistic neuron diameters, representing hippocampal pyramidal, cerebellar Purkinje, and striatal spiny projection neurons. Each morphology is separated into initial, branching children, and continuing nodes. Our analysis reveals that the diameter of preceding nodes, Parent Diameter, is correlated to diameter of subsequent nodes for all cell types. Branching children and initial nodes each required additional morphological features to predict diameter, such as path length to soma, total dendritic length, and longest path to terminal end. Model simulations reveal that membrane potential response with predicted diameters is similar to the original response for several tested morphologies. We provide our open source software to extend the utility of available NeuroMorpho.org morphologies, and suggest predictive equations may supplement morphologies that lack dendritic diameter and improve model simulations with realistic dendritic diameter.

Fundamentals of the modern theory of the phenomenon of "pain" from the perspective of a systematic approach. Neurophysiological basis. Part 1: A brief presentation of key subcellular and cellular ctructural elements of the central nervous system

The phenomenon of “pain” is a psychophysiological phenomenon that is actualized in the mind of a person as a result of the systemic response of his body to certain external and internal stimuli. The heart of the corresponding mental processes is certain neurophysiological processes, which in turn are caused by a certain form of the systemic structural and functional organization of the central nervous system (CNS). Thus, the systemic structural and functional organization of the central nervous system of a person, determining the corresponding psychophysiological state in a specific time interval, determines its psycho-emotional states or reactions manifested by the pain phenomenon. The nervous system of the human body has a hierarchical structure and is a morphologically and functionally complete set of different, interconnected, nervous and structural formations. The basis of the structural formations of the nervous system is nervous tissue. It is a system of interconnected differentials of nerve cells, neuroglia and glial macrophages, providing specific functions of perception of stimulation, excitation, generation of nerve impulses and its transmission. The neuron and each of its compartments (spines, dendrites, catfish, axon) is an autonomous, plastic, active, structural formation with complex computational properties. One of them – dendrites – plays a key role in the integration and processing of information. Dendrites, due to their morphology, provide neurons with unique electrical and plastic properties and cause variations in their computational properties. The morphology of dendrites: 1) determines – a) the number and type of contacts that a particular neuron can form with other neurons; b) the complexity, diversity of its functions; c) its computational operations; 2) determines – a) variations in the computational properties of a neuron (variations of the discharges between bursts and regular forms of pulsation); b) back distribution of action potentials. Dendritic spines can form synaptic connection – one of the main factors for increasing the diversity of forms of synaptic connections of neurons. Their volume and shape can change over a short period of time, and they can rotate in space, appear and disappear by themselves. Spines play a key role in selectively changing the strength of synaptic connections during the memorization and learning process. Glial cells are active participants in diffuse transmission of nerve impulses in the brain. Astrocytes form a three-dimensional, functionally “syncytia-like” formation, inside of which there are neurons, thus causing their specific microenvironment. They and neurons are structurally and functionally interconnected, based on which their permanent interaction occurs. Oligodendrocytes provide conditions for the generation and transmission of nerve impulses along the processes of neurons and play a significant role in the processes of their excitation and inhibition. Microglial cells play an important role in the formation of the brain, especially in the formation and maintenance of synapses. Thus, the CNS should be considered as a single, functionally “syncytia-like”, structural entity. Because the three-dimensional distribution of dendritic branches in space is important for determining the type of information that goes to a neuron, it is necessary to consider the three-dimensionality of their structure when analyzing the implementation of their functions.

Spatial registration of neuron morphologies based on maximization of volume overlap

BACKGROUND

Morphological features are widely used in the study of neuronal function and pathology. Invertebrate neurons are often structurally stereotypical, showing little variance in gross spatial features but larger variance in their fine features. Such variability can be quantified using detailed spatial analysis, which however requires the morphologies to be registered to a common frame of reference.

RESULTS

We outline here new algorithms - Reg-MaxS and Reg-MaxS-N - for co-registering pairs and groups of morphologies, respectively. Reg-MaxS applies a sequence of translation, rotation and scaling transformations, estimating at each step the transformation parameters that maximize spatial overlap between the volumes occupied by the morphologies. We test this algorithm with synthetic morphologies, showing that it can account for a wide range of transformation differences and is robust to noise. Reg-MaxS-N co-registers groups of more than two morphologies by iteratively calculating an average volume and registering all morphologies to this average using Reg-MaxS. We test Reg-MaxS-N using five groups of morphologies from the Droshophila melanogaster brain and identify the cases for which it outperforms existing algorithms and produce morphologies very similar to those obtained from registration to a standard brain atlas.

CONCLUSIONS

We have described and tested algorithms for co-registering pairs and groups of neuron morphologies. We have demonstrated their application to spatial comparison of stereotypic morphologies and calculation of dendritic density profiles, showing how our algorithms for registering neuron morphologies can enable new approaches in comparative morphological analyses and visualization.

Models and simulation of 3D neuronal dendritic trees using Bayesian networks

Neuron morphology is crucial for neuronal connectivity and brain information processing. Computational models are important tools for studying dendritic morphology and its role in brain function. We applied a class of probabilistic graphical models called Bayesian networks to generate virtual dendrites from layer III pyramidal neurons from three different regions of the neocortex of the mouse. A set of 41 morphological variables were measured from the 3D reconstructions of real dendrites and their probability distributions used in a machine learning algorithm to induce the model from the data. A simulation algorithm is also proposed to obtain new dendrites by sampling values from Bayesian networks. The main advantage of this approach is that it takes into account and automatically locates the relationships between variables in the data instead of using predefined dependencies. Therefore, the methodology can be applied to any neuronal class while at the same time exploiting class-specific properties. Also, a Bayesian network was defined for each part of the dendrite, allowing the relationships to change in the different sections and to model heterogeneous developmental factors or spatial influences. Several univariate statistical tests and a novel multivariate test based on Kullback-Leibler divergence estimation confirmed that virtual dendrites were similar to real ones. The analyses of the models showed relationships that conform to current neuroanatomical knowledge and support model correctness. At the same time, studying the relationships in the models can help to identify new interactions between variables related to dendritic morphology.