Tool In Neuroscience Research Articles

Shape Mediation Analysis in Alzheimer's Disease Studies.

As a crucial tool in neuroscience, mediation analysis has been developed and widely adopted to elucidate the role of intermediary variables derived from neuroimaging data. Typically, structural equation models (SEMs) are employed to investigate the influences of exposures on outcomes, with model coefficients being interpreted as causal effects. While existing SEMs have proven to be effective tools for mediation analysis involving various neuroimaging-related mediators, limited research has explored scenarios where these mediators are derived from the shape space. In addition, the linear relationship assumption adopted in existing SEMs may lead to substantial efficiency losses and decreased predictive accuracy in real-world applications. To address these challenges, we introduce a novel framework for shape mediation analysis, designed to explore the causal relationships between genetic exposures and clinical outcomes, whether mediated or unmediated by shape-related factors while accounting for potential confounding variables. Within our framework, we apply the square-root velocity function to extract elastic shape representations, which reside within the linear Hilbert space of square-integrable functions. Subsequently, we introduce a two-layer shape regression model to characterize the relationships among neurocognitive outcomes, elastic shape mediators, genetic exposures, and clinical confounders. Both estimation and inference procedures are established for unknown parameters along with the corresponding causal estimands. The asymptotic properties of estimated quantities are investigated as well. Both simulated studies and real-data analyses demonstrate the superior performance of our proposed method in terms of estimation accuracy and robustness when compared to existing approaches for estimating causal estimands.

Neurorights in the Constitution: from neurotechnology to ethics and politics.

Neuroimaging technologies such as brain-computer interfaces and neurofeedback have evolved rapidly as new tools for cognitive neuroscience and as potential clinical interventions. However, along with these developments, concern has grown based on the fear of the potential misuse of neurotechnology. In October 2021, Chile became the first country to include neurorights in its Constitution. The present article is divided into two parts. In the first section, we describe the path followed by neurorights that led to its inclusion in the Chilean Constitution, and the neurotechnologies usually involved in neurorights discussions. In the second part, we discuss two potential problems of neurorights. We begin by pointing out some epistemological concerns regarding neurorights, mainly referring to the ambiguity of the concepts used in neurolegislations, the difficult relationship between neuroscience and politics and the weak reasons for urgency in legislating. We then describe the dangers of overprotective laws in medical research, based on the detrimental effect of recent legislation in Chile and the potential risk posed by neurorights to the benefits of neuroscience development. This article aims to engage with the scientific community interested in neurotechnology and neurorights in an interdisciplinary reflection of the potential consequences of neurorights.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Mechanisms of brain self-regulation: psychological factors, mechanistic models and neural substrates.

While neurofeedback represents a promising tool for neuroscience and a brain self-regulation approach to psychological rehabilitation, the field faces several problems and challenges. Current research has shown great variability and even failure among human participants in learning to self-regulate target features of brain activity with neurofeedback. A better understanding of cognitive mechanisms, psychological factors and neural substrates underlying self-regulation might help improve neurofeedback's scientific and clinical practices. This article reviews the current understanding of the neural mechanisms of brain self-regulation by drawing on findings from human and animal studies in neurofeedback, brain-computer/machine interfaces and neuroprosthetics. In this article, we look closer at the following topics: cognitive processes and psychophysiological factors affecting self-regulation, theoretical models and neural substrates underlying self-regulation, and finally, we provide an outlook on the outstanding gaps in knowledge and technical challenges. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Optogenetic stimulation recruits cortical neurons in a morphology-dependent manner.

Single-photon optogenetics enables precise, cell-type-specific modulation of neuronal circuits, making it a crucial tool in neuroscience. Its miniaturization in the form of fully implantable wide-field stimulator arrays enables long-term interrogation of cortical circuits and bares promise for Brain-Machine Interfaces for sensory and motor function restoration. However, achieving selective activation of functional cortical representations poses a challenge, as studies show that targeted optogenetic stimulation results in activity spread beyond one functional domain. While recurrent network mechanisms contribute to activity spread, here we demonstrate with detailed simulations of isolated pyramidal neurons from cat of unknown sex that already neuron morphology causes a complex spread of optogenetic activity at the scale of one cortical column. Since the shape of a neuron impacts its optogenetic response, we find that a single stimulator at the cortical surface recruits a complex spatial distribution of neurons that can be inhomogeneous and vary with stimulation intensity and neuronal morphology across layers. We explore strategies to enhance stimulation precision, finding that optimizing stimulator optics may offer more significant improvements than preferentially somatic expression of the opsin through genetic targeting. Our results indicate that, with the right optical setup, single-photon optogenetics can precisely activate isolated neurons at the scale of functional cortical domains spanning several hundred micrometers.Significance Statement Sensory features, such as the position or orientation of a visual stimulus, are mapped onto the surface of cortex as functional domains. Their selective activation, that may enable eliciting complex percepts, is intensively pursued for basic science and clinical applications. However, delivery of light into one functional domain in optogenetically transfected cortex results in complex, widespread neuronal activity, spreading beyond the targeted domain. Our computational study reveals that neuron morphology contributes to this diffuse response in a cortical-layer and intensity-dependent manner. We show that enhancing the stimulator optics is more effective than soma-targeting of the opsin in increasing spatial precision of stimulation. Our simulations provide insights for designing optogenetic stimulation protocols and hardware to achieve selective activation of functional domains.

Bridging brain and body in cancer.

Recent work has highlighted the central role the brain-body axis plays in not only maintaining organismal homeostasis but also coordinating the body's response to immune and inflammatory insults. Here, we discuss how science is poised to address the many ways that our brain is directly involved with disease. In particular, we feel that combining cutting-edge tools in neuroscience with translationally relevant models of cancer will be critical to understanding how the brain and tumors communicate and modulate each other's behavior.

Special Section Guest Editorial: Open-source neurophotonic tools for neuroscience.

The editorial completes the Neurophotonics special series on open-source neurophotonic tools for neuroscience.

Online functional connectivity analysis of large all-to-all networks in MNE Scan

Abstract The analysis of electroencephalography (EEG)/magnetoencephalography (MEG) functional connectivity has become an important tool in neuroscience. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, that is, after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved by enforcing multithreading and high efficiency for all computations, so that repeated computations were avoided wherever possible, which allows for a major speed-up in the case of overlapping intervals. We present comprehensive performance evaluations of these implementations proving the online capability for the computation of large all-to-all functional connectivity networks. As a proof of principle, we demonstrate the feasibility of online functional connectivity estimation in the evaluation of somatosensory evoked brain activity

Optimizing magnetometers arrays and analysis pipelines for multivariate pattern analysis

BackgroundMultivariate pattern analysis (MVPA) has proven an excellent tool in cognitive neuroscience. It also holds a strong promise when applied to optically-pumped magnetometer-based magnetoencephalography. New methodTo optimize OPM-MEG systems for MVPA experiments this study examines data from a conventional MEG magnetometer array, focusing on appropriate noise reduction techniques for magnetometers. We determined the least required number of sensors needed for robust MVPA for image categorization experiments. ResultsWe found that the use of signal space separation (SSS) without a proper regularization significantly lowered the classification accuracy considering a sub-array of 102 magnetometers or a sub-array of 204 gradiometers. We also found that classification accuracy did not improve when going beyond 30 sensors irrespective of whether SSS has been applied. Comparison with existing methodsThe power spectra of data filtered with SSS has a substantially higher noise floor that data cleaned with SSP or HFC. Consequently, MVPA decoding results obtained from the SSS-filtered data are significantly lower compared to all other methods employed. ConclusionsWhen designing MEG system based on SQUID magnetometers optimized for multivariate analysis for image categorization experiments, about 30 magnetometers are sufficient. We advise against applying SSS filters without a proper regularization to data from MEG and OPM systems prior to performing MVPA as this method, albeit reducing low-frequency external noise contributions, also introduces an increase in broadband noise. We recommend employing noise reduction techniques that either decrease or maintain the noise floor of the data like signal-space projection, homogeneous field correction and gradient noise reduction.

The Study of Sensorimotor Circuit Assembly in Drosophila melanogaster Embryos and Larvae.

In animals, movement is generated by the activity of motor circuits housed in the vertebrate spinal cord or the arthropod nerve cord. How motor circuits form is a fundamental question, with wide-ranging impacts on the fields of development, neurobiology, medicine, evolution, and beyond. Until recently, studying circuit assembly had been experimentally difficult, with a paucity of suitable models. Due to the introduction of novel neuroscience tools (calcium imaging, optogenetics, connectomics), Drosophila embryos and larvae can be used as models to study motor circuit assembly. Here, we briefly review the knowledge relevant to motor circuit assembly in Drosophila larvae. We discuss the larval body and its movements, larval neurons and circuits in the motor system, and how the generation of neural diversity starting from stem cells relates to circuit formation. The long-term goal of Drosophila research in this field is to identify developmental rules, determine when the rules apply, generate an integrated understanding of motor circuit development, and uncover molecular mechanisms driving the assembly process. Motor circuits are an ancient part of the nervous system, and so far, the developmental programs guiding motor circuit assembly appear to be largely conserved across phyla. Thus, as methods improve in other systems, findings in Drosophila will provide foundational concepts that will inspire hypotheses in those systems.

Classics in Chemical Neuroscience: Muscimol.

Muscimol (3) is a psychoactive isoxazole present in various Amanita mushrooms, along with ibotenic acid and muscarine. It is structurally related to GABA and acts as a GABAA agonist with great affinity. Muscimol use dates back to Siberian shamanic cultures as an entheogen, where it was ingested orally to exert psychoactive effects. Although not approved for clinical use, its potential and use as a research tool in neuroscience is of immense value, with 3H-muscimol being used as a radioligand in GABA receptor research. Since its discovery in the early 60s, many research groups have worked on the synthesis of the compound. Recent research suggests the potential use of muscimol in neuropathic pain relief and other potential uses are also being studied. In this review, we will cover the history, chemistry, pharmacology and overall importance of the compound.

Intelligent systems and consumer neuroscience in the age of computational advertising

Abstract The main goal of this article is to provide an overview of the use and characteristics of intelligent systems and neuroscience tools applicable in the field of contemporary advertising. The newly emerging field of computational advertising is undergoing dynamic development, and this concept is now placed in the context of advanced intelligent systems, artificial intelligence, and virtual reality. According to the specified parameters, a systematic literature search of scientific publications was carried out and subsequently evaluated. The research questions are focused on the identification of intelligent systems and current consumer neuroscience tools finding application in the current trend of computational advertising. It follows from the processed systematic literature review that there are currently a number of intelligent systems and also a number of tools in the field of consumer neuroscience that can find application within the broader concept of computational advertising. These more or less intelligent systems and neuroscientific tools are already affecting all phases of the advertising life cycle. At the same time, a number of ethical issues associated with the use of both these technologies and tools have been found, which still need to be explored. The article attempts to fill the gap in the lack of literature dealing with this issue. Last but not least, the article contains a critical view of these new technological possibilities and also describes a number of new ethical challenges arising in this area.

Channelrhodopsins with distinct chromophores and binding patterns

Channelrhodopsins are popular optogenetic tools in neuroscience, but remain poorly understood mechanistically. Here we report the cryo-EM structures of channelrhodopsin-2 (ChR2) from Chlamydomonas reinhardtii and H. catenoides kalium channelrhodopsin (KCR1). We show that ChR2 recruits an endogenous N-retinylidene-PE-like molecule to a previously unidentified lateral retinal binding pocket, exhibiting a reduced light response in HEK293 cells. In contrast, H. catenoides kalium channelrhodopsin (KCR1) binds an endogenous retinal in its canonical retinal binding pocket under identical condition. However, exogenous ATR reduces the photocurrent magnitude of wild type KCR1 and also inhibits its leaky mutant C110T. Our results uncover diverse retinal chromophores with distinct binding patterns for channelrhodopsins in mammalian cells, which may further inspire next generation optogenetics for complex tasks such as cell fate control.

Comparison of Studies Conducted in the Field of Neuromarketing and Artificial Intelligence Using Bibliometric Method

Abstract Neuromarketing research focuses on consumer purchase intention, decision-making processes, purchase behavior, brand awareness, brand loyalty, and repeat purchase behavior. In these studies, consumer behavior has been analyzed using neuroscientific methods and tools. The most commonly used tools include Functional Magnetic Resonance Imaging (fMRI), Eye Tracking, Electroencephalogram (EEG), Positron Emission Tomography (PET), Transcranial Magnetic Stimulation (TMS), Magnetoencephalogram (MEG), Steady State Topography (SST), Implicit Association Test (IAT), Facial Electromyography (fEMG), Automatic Face Coding (AFC), Skin Conductance Response (SCR), and other methods for measuring physiological responses. However, the use of these neuroscientific tools is not always possible due to economic constraints and lack of experimental design. Neuroscientific imaging and measurement methods are not preferred in every study due to their high costs and expertise requirements. However, when neuromarketing studies are examined, it is seen that methods such as Eye Tracking, EEG and fMRI are used more widely. These tools contribute to a deeper understanding of consumer behavior. In order to better analyze consumer behavior, it is of great importance to convey marketing stimuli and messages correctly. In the field of marketing, the effect of stimuli conveyed to consumers using the five senses is one of the focal points of neuromarketing. More than one neuroscientific method should be used together to understand consumer intentions, thoughts and purchasing behaviors. In this way, the obtained data can be analyzed more comprehensively and clearer insights can be provided about neuromarketing. The aim of this study is to present a comprehensive assessment of the use of neuroscientific tools by examining the publications in the field of neuromarketing in the Web of Science database between 2010-2024 with bibliometric analysis. The study will address the limitations of not using more than one neuroscientific tool together in neuromarketing research and the inadequacy of analyses supported by artificial intelligence. A more holistic approach will be proposed to address these shortcomings and a guiding resource for future research will be created.

Optogenetic Brain-Computer Interfaces.

The brain-computer interface (BCI) is one of the most powerful tools in neuroscience and generally includes a recording system, a processor system, and a stimulation system. Optogenetics has the advantages of bidirectional regulation, high spatiotemporal resolution, and cell-specific regulation, which expands the application scenarios of BCIs. In recent years, optogenetic BCIs have become widely used in the lab with the development of materials and software. The systems were designed to be more integrated, lightweight, biocompatible, and power efficient, as were the wireless transmission and chip-level embedded BCIs. The software is also constantly improving, with better real-time performance and accuracy and lower power consumption. On the other hand, as a cutting-edge technology spanning multidisciplinary fields including molecular biology, neuroscience, material engineering, and information processing, optogenetic BCIs have great application potential in neural decoding, enhancing brain function, and treating neural diseases. Here, we review the development and application of optogenetic BCIs. In the future, combined with other functional imaging techniques such as near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI), optogenetic BCIs can modulate the function of specific circuits, facilitate neurological rehabilitation, assist perception, establish a brain-to-brain interface, and be applied in wider application scenarios.

History and Evolution of the Electroencephalogram.

This paper summarizes the history and evolution of the electroencephalogram (EEG). The EEG, used to record the electrical activity of the brain, is a pivotal tool in neuroscience and medicine. Its history and evolution reflect significant advancements in our understanding of brain function and our ability to diagnose and treat neurological conditions. This tool has revolutionized our understanding of the brain's electrical activity and is the cornerstone for the diagnosis and treatment of epilepsy and related disorders.The evolution of the EEGfrom early experimental observations to sophisticated modern applications highlights the profound progress in our ability to monitor and interpret brain activity. The EEG remains an invaluable tool in clinical and research settings, continually evolving with technological advancements to expand our understanding of the human brain. This review traces the journey of this iconic tool.

Aberrant hippocampal Ca2+ microwaves following synapsin-dependent adeno-associated viral expression of Ca2+ indicators.

Genetically encoded calcium indicators (GECIs) such as GCaMP are invaluable tools in neuroscience to monitor neuronal activity using optical imaging. The viral transduction of GECIs is commonly used to target expression to specific brain regions, can be conveniently used with any mouse strain of interest without the need for prior crossing with a GECI mouse line, and avoids potential hazards due to the chronic expression of GECIs during development. A key requirement for monitoring neuronal activity with an indicator is that the indicator itself minimally affects activity. Here, using common adeno-associated viral (AAV) transduction procedures, we describe spatially confined aberrant Ca2+ microwaves slowly travelling through the hippocampus following expression of GCaMP6, GCaMP7, or R-CaMP1.07 driven by the synapsin promoter with AAV-dependent gene transfer in a titre-dependent fashion. Ca2+ microwaves developed in hippocampal CA1 and CA3, but not dentate gyrus nor neocortex, were typically first observed at 4 wk after viral transduction, and persisted up to at least 8 wk. The phenomenon was robust and observed across laboratories with various experimenters and setups. Our results indicate that aberrant hippocampal Ca2+ microwaves depend on the promoter and viral titre of the GECI, density of expression, as well as the targeted brain region. We used an alternative viral transduction method of GCaMP which avoids this artefact. The results show that commonly used Ca2+-indicator AAV transduction procedures can produce artefactual Ca2+ responses. Our aim is to raise awareness in the field of these artefactual transduction-induced Ca2+ microwaves, and we provide a potential solution.

Aberrant hippocampal Ca2+ microwaves following synapsin-dependent adeno-associated viral expression of Ca2+ indicators

Genetically encoded calcium indicators (GECIs) such as GCaMP are invaluable tools in neuroscience to monitor neuronal activity using optical imaging. The viral transduction of GECIs is commonly used to target expression to specific brain regions, can be conveniently used with any mouse strain of interest without the need for prior crossing with a GECI mouse line, and avoids potential hazards due to the chronic expression of GECIs during development. A key requirement for monitoring neuronal activity with an indicator is that the indicator itself minimally affects activity. Here, using common adeno-associated viral (AAV) transduction procedures, we describe spatially confined aberrant Ca2+ microwaves slowly travelling through the hippocampus following expression of GCaMP6, GCaMP7, or R-CaMP1.07 driven by the synapsin promoter with AAV-dependent gene transfer in a titre-dependent fashion. Ca2+ microwaves developed in hippocampal CA1 and CA3, but not dentate gyrus nor neocortex, were typically first observed at 4 wk after viral transduction, and persisted up to at least 8 wk. The phenomenon was robust and observed across laboratories with various experimenters and setups. Our results indicate that aberrant hippocampal Ca2+ microwaves depend on the promoter and viral titre of the GECI, density of expression, as well as the targeted brain region. We used an alternative viral transduction method of GCaMP which avoids this artefact. The results show that commonly used Ca2+-indicator AAV transduction procedures can produce artefactual Ca2+ responses. Our aim is to raise awareness in the field of these artefactual transduction-induced Ca2+ microwaves, and we provide a potential solution.

Validation of the number of pulses required for TMS-EEG in the prefrontal cortex considering test feasibility

BackgroundTranscranial magnetic stimulation (TMS) combined with electroencephalography (EEG), TMS-EEG, is a useful neuroscientific tool for the assessment of neurophysiology in the human cerebral cortex. Theoretically, TMS-EEG data is expected to have a better data quality as the number of stimulation pulses increases. However, since TMS-EEG testing is a modality that is examined on human subjects, the burden on the subject and tolerability of the test must also be carefully considered. MethodIn this study, we aimed to determine the number of stimulation pulses that satisfy the reliability and validity of data quality in single-pulse TMS (spTMS) for the dorsolateral prefrontal cortex (DLPFC). TMS-EEG data for (1) 40-pulse, (2) 80-pulse, (3) 160-pulse, and (4) 240-pulse conditions were extracted from spTMS experimental data for the left DLPFC of 20 healthy subjects, and the similarities between TMS-evoked potentials (TEP) and oscillations across the conditions were evaluated. ResultsAs a result, (2) 80-pulse and (3) 160-pulse conditions showed highly equivalent to the benchmark condition of (4) 240-pulse condition. However, (1) 40-pulse condition showed only weak to moderate equivalence to the (4) 240-pulse condition. Thus, in the DLPFC TMS-EEG experiment, 80 pulses of stimulations was found to be a reasonable enough number of pulses to extract reliable TEPs, compared to 160 or 240 pulses. ConclusionsThis is the first substantial study to examine the appropriate number of stimulus pulses that are reasonable and feasible for TMS-EEG testing of the DLPFC.

Pulsatile electrical stimulation creates predictable, correctable disruptions in neural firing

Electrical stimulation is a key tool in neuroscience, both in brain mapping studies and in many therapeutic applications such as cochlear, vestibular, and retinal neural implants. Due to safety considerations, stimulation is restricted to short biphasic pulses. Despite decades of research and development, neural implants lead to varying restoration of function in patients. In this study, we use computational modeling to provide an explanation for how pulsatile stimulation affects axonal channels and therefore leads to variability in restoration of neural responses. The phenomenological explanation is transformed into equations that predict induced firing rate as a function of pulse rate, pulse amplitude, and spontaneous firing rate. We show that these equations predict simulated responses to pulsatile stimulation with a variety of parameters as well as several features of experimentally recorded primate vestibular afferent responses to pulsatile stimulation. We then discuss the implications of these effects for improving clinical stimulation paradigms and electrical stimulation-based experiments.

Eye-tracker and fNIRS: Using neuroscientific tools to assess the learning experience during children's educational robotics activities

In technology education, there has been a paradigmatic shift towards student-centered approaches such as learning by doing, constructionism, and experiential learning. Educational robotics allows students to experiment with building and interacting with their creations while also fostering collaborative work. However, understanding the student's response to these approaches is crucial to adapting them during the teaching-learning process. In this sense, neuroscientific tools such as Functional Near-Infrared Spectroscopy and Eye-tracker could be useful, allowing the investigation of relevant states experienced by students. Although they have already been used in educational research, their practical relevance in the teaching-learning process has not been extensively investigated. In this perspective article expressing our position, we bring four examples of learning experiences in a robotics class with children, in which we illustrate the usefulness of these tools.