Neurobiological Experiments Research Articles

Neurotoxin-Derived Optical Probes for Elucidating Molecular and Developmental Biology of Neurons and Synaptic Connections. Toxin-Derived Optical Probes for Neuroimaging.

Botulinum neurotoxins (BoNTs) and tetanus toxin (TeTX) are the deadliest biological substances that cause botulism and tetanus, respectively. Their astonishing potency and capacity to enter neurons and interfere with neurotransmitter release at presynaptic terminals have attracted much interest in experimental neurobiology and clinical research. Fused with reporter proteins or labelled with fluorophores, BoNTs and TeTX and their non-toxic fragments also offer remarkable opportunities to visualize cellular processes and functions in neurons and synaptic connections. This study presents the state-of-the-art optical probes derived from BoNTs and TeTX and discusses their applications in molecular and synaptic biology and neurodevelopmental research. It reviews the principles of the design and production of probes, revisits their applications with advantages and limitations and considers prospects for future improvements. The versatile characteristics of discussed probes and reporters make them an integral part of the expanding toolkit for molecular neuroimaging, promoting the discovery process in neurobiology and translational neurosciences.

Neurophysiological measurements of planarian brain activity: a unique model for neuroscience research.

Planarians are well-known model organisms for regeneration and developmental biology research due to their remarkable regenerative capacity. Here, we aim to advocate for the use of planaria as a valuable model for neurobiology, as well. Planarians have most of the major qualities of more developed organisms, including a primal brain. These traits combined with their exceptional regeneration capabilities, allow neurobiological experiments not possible in any other model organism, as we demonstrate by electrophysiological recording from planaria with two heads that control a shared body. To facilitate planarian neuroscience research, we developed an extracellular multi-unit recording procedure for the planarians fragile brain (Dugesia japonica). We created a semi-intact preparation restrained with fine dissection pins, enabling hours of reliable recording, via a suction electrode. Here, we demonstrate the feasibility and potential of planarian neurophysiological research by characterizing the neuronal activity during simple learning processes and responses to various stimuli. In addition, we examined the use of linalool as anesthetic agent to allows recordings from an intact, large worm and for fine electrophysiological approaches such as intracellular recording. The demonstrated ability for neurophysiological measurements, along with the inherent advantages of planarians, promotes this exceptional model organism for neuroscience research.

Effects of optogenetic astroglia activation in modulation of synaptic transmission and rhythmogenesis of the hippocampus

In the field of neuronal signal transduction research, astroglia have become increasingly important. Astrocytes, or astroglial cells, are recognized as the third element in synaptic transmission regulation. This complex is conventionally known as a tripartite synapse. A multitude of mathematical models are being developed to investigate the function of astroglia in the tripartite synapse. Neurobiological experiments were carried out to confirm the dynamics observed in the previously proposed mean field activity mathematical model [1]. The experiments focused on evaluating the effect of optogenetic activation of astrocytes on synaptic transmission in C57BL/6 inbred mouse hippocampal slices. One month prior to the experiments, the AAV GFAP ChR2 eYFP virus was injected into the lateral ventricles of the brains of the experimental mice. This procedure was crucial for the expression of astrocyte-specific photosensitive channels, also known as channel rhodopsin. The patch-clamp method was used to conduct experiments on both the experimental mice and a control group of mice that did not receive virus injections. Spontaneous neuronal activity (local field potentials) and synaptic currents (GABA currents) were simultaneously recorded in the experiments. After activating astrocytes that expressed the photosensitive channel, an increase in GABAergic currents became evident during the transmission of synaptic signals between neurons. This suggests that astrocytes likely modulate synaptic transmission by releasing a gliotransmitter into the synaptic cleft. Thus, the study confirmed the presence of mean field activity patterns in a phenomenological model that describes the dynamics of a population of neurons. This model was developed based on the Tsodyks–Markram model and considers the primary attributes of neuron-glial interaction through a tripartite synapse. The model includes the short-term synaptic plasticity of the Tsodyks–Markram model and the astrocyte potential of synaptic transmission. The activation of astrocytes results in a diverse range of dynamic modes that describe various patterns of network activity under the mean field approach framework. Currently, multiple experimental hypotheses exist regarding astrocytes’ release of a gliotransfer into the synaptic cleft and its specific type. Alternatively, a complex cascade of sequential activation of glutamateergic and GABAergic receptors may occur. Additional experimental work is necessary to assess the pharmacological contribution of channels and transporters involved in modulating synaptic transmission during astrocyte optogenetic activation. The obtained results will refine the mathematical model of mean field neuronal activity to increase its biological plausibility. The methodology used involves identifying sparse nonlinear dynamical systems from data by solving for the system’s equations of motion [2]. These equations are reconstructed from noisy measurement data, allowing for a more accurate representation of the dynamic system. The only assumption regarding the arrangement of a dynamical system is that there exist only a handful of significant factors regulating the dynamics, leading to equations that are sparse within the realm of potential functions. Sparse regression is used to ascertain the minimum number of terms required in dynamic equations for precise data representation. This strategy enables the construction of mathematical models that are both as accurate as feasible, and maximally uncomplicated, eliminating the need for retraining. Notably, this method is suitable for parameterized systems and systems subjected to external influences or changes over time. The two approaches were used to forecast the dynamics of the mean field of a neural population. The accuracy of the forecast was subsequently evaluated.

Eview: an open source software for converting and visualizing of multichannel electrophysiological signals

BACKGROUND: Methods and tools for operating with multichannel electrophysiological signals need to develop and correspond to the speed of data traffic in contemporary experiments. Analyzing and visualizing experimental data with minimal delay and with minimized experimenter effort is a pressing task in the field of neurobiology and requires the use of complex approaches specifically selected for each specific type of experiment. Creating open-source programs that can be promptly adapted for different tasks is one of the approaches that provide the ability to perform complex scientific experiments with high quality. AIM: This work is aimed at creating open-source software for analytical and visualization support of neurobiological experiments. METHODS: Software development was performed in MATLAB environment. The program is built on a modular principle and includes an intuitive graphical interface that facilitates control of the signal processing and display. RESULTS: A software tool was created that allows to optimize and accelerate various stages of electrophysiological research, including preliminary analysis of the quality of the experiment being prepared, in-depth analysis of recorded signals, and preparation of illustrative material for publications. CONCLUSION: The resulting program has a number of advantages in comparison with similar products in terms of versatility, speed, and availability, and can be used to solve a wide class of research problems.

Construction of feature analysis model for demeanor evidence investigation based on data mining algorithm

Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development of modern technology, the observation of demeanor has entered a new stage of multimodal quantification. The quantifiable results of demeanor observation with the help of scientific and technological instruments play an important role in judicial work such as search, interrogation, and evidence review. The use of man-made reasoning in the field of legal settling has become increasingly broad. Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments. The model can improve the accuracy of evidence use, and the correct use of demeanor evidence can realize the true litigation value of intelligent justice and the concept of human rights protection, and promote the construction of intelligent justice.

Free Will:Convergence and Divergence of Libet and Wegner

Int the light of the advancement in cognitive neuroscientific investigations of free will, from rCBF to EEG scan techniques and eventually fMRI scan techniques, such advanced studies on free will arguing for human determinism centre their research on the works of Libet and Wegner. The neurobiological experiment of Libet and the cognitive assessment by Wegner could be argued to eliminate the possibility of free will but Libet and Wegner’s views are in tandem, non-identical and intolerant of human freedom. This article exposes the incoherency intercepting Libet and Wegner’s studies and the philosophical imports that make them not coherent. Keywords: initiation, control, readiness potential, conscious will, illusion DOI: 10.7176/JPCR/56-01 Publication date: March 31 st 2023

Bringing Bayes and Shannon to the Study of Behavioural and Neurobiological Timing and Associative Learning

Abstract Bayesian parameter estimation and Shannon’s theory of information provide tools for analysing and understanding data from behavioural and neurobiological experiments on interval timing—and from experiments on Pavlovian and operant conditioning, because timing plays a fundamental role in associative learning. In this tutorial, we explain basic concepts behind these tools and show how to apply them to estimating, on a trial-by-trial, reinforcement-by-reinforcement and response-by-response basis, important parameters of timing behaviour and of the neurobiological manifestations of timing in the brain. These tools enable quantification of relevant variables in the trade-off between acting as an ideal observer should act and acting as an ideal agent should act, which is also known as the trade-off between exploration (information gathering) and exploitation (information utilization) in reinforcement learning. They enable comparing the strength of the evidence for a measurable association to the strength of the behavioural evidence that the association has been perceived. A GitHub site and an OSF site give public access to well-documented Matlab and Python code and to raw data to which these tools have been applied.

Intracerebral Transplantation and <em>In Vivo</em> Bioluminescence Tracking of Human Neural Progenitor Cells in the Mouse Brain

Cell therapy has long been an emerging treatment paradigm in experimental neurobiology. However, cell transplantation studies often rely on end-point measurements and can therefore only evaluate longitudinal changes of cell migration and survival to a limited extent. This paper provides a reliable, minimally invasive protocol to transplant and longitudinally track neural progenitor cells (NPCs) in the adult mouse brain. Before transplantation, cells are transduced with a lentiviral vector comprising a bioluminescent (firefly-luciferase) and fluorescent (green fluorescent protein [GFP]) reporter. The NPCs are transplanted into the right cortical hemisphere using stereotaxic injections in the sensorimotor cortex. Following transplantation, grafted cells were detected through the intact skull for up to five weeks (at days 0, 3, 14, 21, 35) with a resolution limit of 6,000 cells using in vivo bioluminescence imaging. Subsequently, the transplanted cells are identified in histological brain sections and further characterized with immunofluorescence. Thus, this protocol provides a valuable tool to transplant, track, quantify, and characterize cells in the mouse brain.

Intracerebral Transplantation and <em>In Vivo</em> Bioluminescence Tracking of Human Neural Progenitor Cells in the Mouse Brain

Cell therapy has long been an emerging treatment paradigm in experimental neurobiology. However, cell transplantation studies often rely on end-point measurements and can therefore only evaluate longitudinal changes of cell migration and survival to a limited extent. This paper provides a reliable, minimally invasive protocol to transplant and longitudinally track neural progenitor cells (NPCs) in the adult mouse brain. Before transplantation, cells are transduced with a lentiviral vector comprising a bioluminescent (firefly-luciferase) and fluorescent (green fluorescent protein [GFP]) reporter. The NPCs are transplanted into the right cortical hemisphere using stereotaxic injections in the sensorimotor cortex. Following transplantation, grafted cells were detected through the intact skull for up to five weeks (at days 0, 3, 14, 21, 35) with a resolution limit of 6,000 cells using in vivo bioluminescence imaging. Subsequently, the transplanted cells are identified in histological brain sections and further characterized with immunofluorescence. Thus, this protocol provides a valuable tool to transplant, track, quantify, and characterize cells in the mouse brain.

New Open-Source Tools: Using Bonsai for Behavioral Tracking and Closed-Loop Experiments.

The ability to dynamically control a behavioral task based on real-time animal behavior is an important feature for experimental neuroscientists. However, designing automated boxes for behavioral studies requires a coordinated combination of mechanical, electronic, and software design skills which can challenge even the best engineers, and for that reason used to be out of reach for the majority of experimental neurobiology and behavioral pharmacology researchers. Due to parallel advances in open-source hardware and software developed for neuroscience researchers, by neuroscience researchers, the landscape has now changed significantly. Here, we discuss powerful approaches to the study of behavior using examples and tutorials in the Bonsai visual programming language, towards designing simple neuroscience experiments that can help researchers immediately get started. This language makes it easy for researchers, even without programming experience, to combine the operation of several open-source devices in parallel and design their own integrated custom solutions, enabling unique and flexible approaches to the study of behavior, including video tracking of behavior and closed-loop electrophysiology.

From population to neuron: exploring common mediators for metabolic problems and mental illnesses.

Major mental illnesses such as schizophrenia (SZ) and bipolar disorder (BP) frequently accompany metabolic conditions, but their relationship is still unclear, in particular at the mechanistic level. We implemented an approach of “from population to neuron”, combining population-based epidemiological analysis with neurobiological experiments using cell and animal models based on a hypothesis built from the epidemiological study. We characterized high-quality population data, olfactory neuronal cells biopsied from patients with SZ or BP, and healthy subjects, as well as mice genetically modified for insulin signaling. We accessed the Danish Registry and observed (1) a higher incidence of diabetes in people with SZ or BP and (2) higher incidence of major mental illnesses in people with diabetes in the same large cohort. These epidemiological data suggest the existence of common pathophysiological mediators in both diabetes and major mental illnesses. We hypothesized that molecules associated with insulin resistance might be such common mediators, and then validated the hypothesis by using two independent sets of olfactory neuronal cells biopsied from patients and healthy controls. In the first set, we confirmed an enrichment of insulin signaling-associated molecules among the genes that were significantly different between SZ patients and controls in unbiased expression profiling data. In the second set, olfactory neuronal cells from SZ and BP patients who were not pre-diabetic or diabetic showed reduced IRS2 tyrosine phosphorylation upon insulin stimulation, indicative of insulin resistance. These cells also displayed an upregulation of IRS1 protein phosphorylation at serine-312 at baseline (without insulin stimulation), further supporting the concept of insulin resistance in olfactory neuronal cells from SZ patients. Finally, Irs2 knockout mice showed an aberrant response to amphetamine, which is also observed in some patients with major mental illnesses. The bi-directional relationships between major mental illnesses and diabetes suggest that there may be common pathophysiological mediators associated with insulin resistance underlying these mental and physical conditions.

Learning allocentric representations of space for navigation

The hippocampus of the mammalian brain supports spatial navigation by building cognitive maps of the environments in which the animal explores. Currently, there is little neurocomputational work investigating the encoding and decoding mechanisms of hippocampal neural representations in large-scale environments. We propose a biologically-inspired hierarchical neural network architecture to learn the transformation of egocentric sensorimotor inputs into allocentric spatial representation for navigation. The hierarchical network is composed of two parallel subnetworks mimicking the lateral entorhinal cortex (LEC) and medial entorhinal cortex (MEC), and one convergent subnetwork mimicking the hippocampus. LEC relays time-related visual information and MEC supplies space-related information in the form of multi-resolution grid codes as resulted from integrating movement information. The convergent subnetwork integrates all information from the parallel subnetworks and predicts the position of the agent in the environment. Synaptic weights of the vision-to-place and grid-to-place connections are learned based on the stochastic gradient descent algorithm. Simulations in a large virtual maze demonstrate that hippocampal place units in the model form multiple and irregularly-spaced place fields, similar to those observed in neurobiological experiments. The model is able to accurately decode the positions of the agent from the learned spatial representations. Moreover, the model is capable of adaptation to degraded visual inputs, and therefore is robust against perturbations. When the motion inputs are deprived, the model meets with localization difficulty, suffering from less accuracy in position predictions.

A brain-inspired compact cognitive mapping system.

In many simultaneous localization and mapping (SLAM) systems, the map of the environment grows over time as the robot explores the environment. The ever-growing map prevents long-term mapping, especially in large-scale environments. In this paper, we develop a compact cognitive mapping approach inspired by neurobiological experiments. Mimicking the firing activities of neighborhood cells, neighborhood fields determined by movement information, i.e. translation and rotation, are modeled to describe one of the distinct segments of the explored environment. The vertices with low neighborhood field activities are avoided to be added into the cognitive map. The optimization of the cognitive map is formulated as a robust non-linear least squares problem constrained by the transitions between vertices, and is numerically solved efficiently. According to the cognitive decision-making of place familiarity, loop closure edges are clustered depending on time intervals, and then batch global optimization of the cognitive map is performed to satisfy the combined constraint of the whole cluster. After the loop closure process, scene integration is performed, in which revisited vertices are removed subsequently to further reduce the size of the cognitive map. The compact cognitive mapping approach is tested on a monocular visual SLAM system in a naturalistic maze for a biomimetic animated robot. Our results demonstrate that the proposed method largely restricts the growth of the size of the cognitive map over time, and meanwhile, the compact cognitive map correctly represents the overall layout of the environment. The compact cognitive mapping method is well suitable for the representation of large-scale environments to achieve long-term robot navigation.

Immunohistochemical markers for neurobiology

In neurobiological studies, crucial is the selection of most appropriate and informative experimental methods, one of which is immunohistochemistry. This review briefly summarizes the experience of adaptation of immunohistochemical methods to nervous system studies accumulated over years the Laboratory of Functional Morphology of the Central and Peripheral Nervous System (Institute of Experimental Medicine). The aim of this work was to determine the most effective and reliable immunomarkers for neurobiological studies. The article contains theoretical basis and practical recommendations for use of key cytospecific and functional markers used in studies of structural and functional organization of brain and spinal cord of mammalian animals and human. In particular, the results of immunohistochemical reactions to neural markers (NeuN, neurofilament proteins, alpha-tubulin, alpha-synuclein), neurotransmitter synthesizing enzymes (tyrosine hydroxylase, glutamate decarboxylase, choline acetyltransferase, NO synthase) and glial markers (GFAP, glutamine synthetase, Iba-1, vimentin) are demonstrated. The presented methodology is useful for experimental neurobiology and clinical morphological diagnostics.

Субъект в информационном обществе: свобода воли или «сумма технологий»?

Neuroscience development emphasizes the problem of the human’s free will. Results of neurobiological experiments are often interpreted as justification of the human’s lack of free will; S. Harris shares this viewpoint. However, analysis of discussions on free will that have unfolded in neuroscience and cognitive studies as well as critical analysis of arguments provided by neuroscience representatives - opponents of the free will doctrine - show that findings of modern neuroscience and cognitive studies do not contradict the postulate of free will as the subject’s inherent quality.

OCIAD1 contributes to neurodegeneration in Alzheimer's disease by inducing mitochondria dysfunction, neuronal vulnerability and synaptic damages.

BackgroundHyperamyloidosis in the brain is known as the earliest neuropathological change and a unique etiological factor in Alzheimer's disease (AD), while progressive neurodegeneration in certain vulnerable brain regions forms the basis of clinical syndromes. It is not clear how early hyperamyloidosis is implicated in progressive neurodegeneration and what factors contribute to the selective brain vulnerability in AD.MethodsBioinformatics and experimental neurobiology methods were integrated to identify novel factors involved in the hyperamyloidosis-induced brain vulnerability in AD. We first examined neurodegeneration-specific gene signatures from sporadic AD patients and synaptic protein changes in young transgenic AD mice. Then, we systematically assessed the association of a top candidate gene with AD and investigated its mechanistic role in neurodegeneration.FindingsWe identified the ovary-orientated protein OCIAD1 (Ovarian-Carcinoma-Immunoreactive-Antigen-Domain-Containing-1) as a neurodegeneration-associated factor for AD. Higher levels of OCIAD1, found in vulnerable brain areas and dystrophic neurites, were correlated with disease severity. Multiple early AD pathological events, particularly Aβ/GSK-3β signaling, elevate OCIAD1, which in turn interacts with BCL-2 to impair mitochondrial function and facilitates mitochondria-associated neuronal injury. Notably, elevated OCIAD1 by Aβ increases cell susceptibility to other AD pathological challenges.InterpretationOur findings suggest that OCIAD1 contributes to neurodegeneration in AD by impairing mitochondria function, and subsequently leading to neuronal vulnerability, and synaptic damages.FundingTing Tsung & Wei Fong Chao Foundation, John S Dunn Research Foundation, Cure Alzheimer's Fund, and NIH R01AG057635 to STCW.

Measuring musical preferences from listening behavior: Data from one million people and 200,000 songs

Musical preferences are a fundamental individual difference predicting a multitude of listening behaviors. For decades, researchers have investigated how musical preferences are organized but have been hindered by genre-based and self-report methodologies. Recently, researchers have begun to investigate musical preferences at the feature-level using stimuli, rather than at the genre-level using self-reports. However, these new methods have been experimental and limited in their ecological validity. To address these recent limitations, we use an ecologically valid behavioral approach based on one million people who listened to more than 200,000 songs from streaming services, which is to our knowledge the largest study to date on the structure of musical preferences. Individual musical preference was measured from song playback counts and analyzed using principal components analysis on the psychological and sonic music features. Our results showed that music-feature preferences had a three-dimensional structure confirming previous theory and research. These dimensions are Arousal (level of energy in music), Valence (spectrum of negative to positive emotions), and Depth (intellectual and emotional depth in music). These findings lay firm ground for future research on music-feature preferences and pave the way for social-psychological and neurobiological experiments with music.

From Microscopes to Optogenetics: Ian Hacking Vindicated

AbstractI introduce two new tools in experimental neurobiology, optogenetics and DREADDs (designer receptors exclusively activated by designer drugs). These tools permit unprecedented control over activity in specific neurons in behaving animals. In addition to their inherent scientific interest, these tools make an important contribution to philosophy of science. They illustrate the very premises of Ian Hacking’s “microscope” argument for the relative independence of experiment from theory. This new example is important for generalizing Hacking’s argument because the background sciences (optics for microscopes, molecular biology for optogenetics, and DREADDs) and the fields of engineering producing these tools differ significantly.

Connection experiments in neurobiology

Accounts of causal explanation are standard in philosophy of science. Less common are accounts of experimentation to investigate causal relations: detailed discussions of the specific kinds of experiments scientists design and run. Silva, Landreth, and Bickle’s (SLB) (Engineering the next revolution in neuroscience: the new science of experiment planning, Oxford University Press, New York, 2014) account of “connection experiments” derives directly from landmark experiments in “molecular and cellular cognition.” We start with its key components, and then using a detailed case study from recent social neuroscience we emphasize and extend three features of SLB’s account: (1) a division of distinct types of connection experiments, each providing a different type of evidence for a hypothesized causal relationship; (2) the typically downward-looking nature (in the sense of componentry) of the experimental search for mediating causes in mainstream neurobiology; and most importantly, (3) the centrality of multiple-experiment research programs, with each experiment designed such that if successful its results can be integrated with the others, toward the goal of confirming multiple-phenomena causal pathways. Our extension of SLB’s account complements existing philosophical work on experimentation in neurobiology.

The Value of Duane Rumbaugh’s Comparative Perspective—in Neurobiology

One commonality across the wide-ranging influences Duane Rumbaugh had on late-20th century science was his commitment to the comparative perspective in psychology. I argue here that a commitment similar in force to Rumbaugh’s also infuses mainstream experimental neurobiology. This connection is ironic because Rumbaugh eschewed brain intervention experimentation in vivo throughout his scientific career. Still, the influence and value of a perspective similar to Rumbaugh’s can be found in neurobiology in at least three places. First, recent neurobiology has made good on one of Rumbaugh’s predictions, that rearing and early environment will be shown to influence behavior and cognition in nonprimate animals. Second, the epistemologically justified use of animal models in experimental neurobiology to investigate human brain mechanism presupposes a strong commitment to the comparative perspective. Third, commitment to the comparative perspective raises the most pressing ethical concern in neurobiology, namely, how is it ethical to perform brain intervention experiments on animal models if their brain mechanisms and behaviors compare closely enough with ours to justifiably generalize these experiments’ results?