Tool In Neuroscience Research Articles

Combining holistic and decomposition paradigms in preference modeling with the flexibility of FITradeoff

This paper focuses on the flexibility feature of the Flexible and Interactive Tradeoff (FITradeoff) multicriteria method for preference modeling. This method is based on the additive aggregation of criteria and using partial (incomplete; imprecise) information to be obtained from a Decision Maker (DM). The flexibility in FITradeoff for preference modeling has already considered two different perspectives: holistic evaluations and elicitation by decomposition based on the classical tradeoff procedure. This paper introduces a new feature in the flexibility of FITradeoff by combining and integrating these two paradigms: Holistic evaluations and elicitation by decomposition. This combination improves the preference modeling process, since it increases its efficiency and consistency. The use of results from behavioral studies is briefly presented. These results include those that arise from using neuroscience tools in order to modulate changes in the design of the Decision Support System and also from improving the decision process by supporting the way the analyst can interact with the DM.

Exploring cognitive aspects of FITradeoff method using neuroscience tools

Multiple decision support methods have been developed to support multi-criteria problems, offering a structured approach. One such method is FITradeoff, which is based on tradeoff, and has a strong axiomatic structure. Implemented in a Decision Support System, FITradeoff offers an experience with low cognitive effort and time demanded by the decision maker. Despite its advantages, some issues remain unclear. In this study, we sought to perform an analysis about the cognitive aspects during the elicitation process using FITradeoff in which the subjects solved decision problems developed by themselves. Two neuroscience tools, Eye-Tracker and a 14-channel EEG were used for neurological and psychophysiological data collection. From the results obtained, analyzes and considerations were made about the elicitation process with FITradeoff and the impact of different types of criteria on the performance of the decision maker. With these findings, it is expected to create direction for improvements in the Decision Support System.

Recent Developments in the Habit Hypothesis of OCD and Compulsive Disorders.

This chapter aims to familiarise the reader with a diverse and fast-growing literature concerning the role that habits play in obsessive-compulsive disorder (OCD). Core concepts will be introduced, including how the balance between habits and a more deliberate form of action selection (goal-directed control) has traditionally been measured and how cross-species translation, neuroscience tools, and computational modelling have been used to build on these basic principles and reveal core mechanisms under study today. Next, the application of these methods to the study of OCD and related disorders will be detailed, converging on a theory that enhanced habit expression, and indeed compulsions in OCD, might arise from deficits in goal-directed control systems. These basic findings will be contextualised in terms of major tide changes in the field, including the shift from categorical disease frameworks to dimensional ones. Mechanistically, recent research concerning how goal-directed deficits arise, perhaps through failures in the construction of a mental model, are discussed along with studies critically evaluating our ability to measure habits in humans, in a laboratory setting. The chapter ends with a nod to the future, focusing on the need for clinically oriented, longitudinal, and intervention-based research that aim to translate what is now a wealth of cross-sectional mechanistic insights to the clinic.

Planning an experiment in a virtual environment reality as a place of research on human behaviour using methods of neuroscience measurement – bibliometric analysis and methodological approach

The methods used in cognitive neuroscience allow through measurement methods to analyze the behavior of subjects. In the opinion of the authors, the application of the experiment in a virtual environment and the measurement of brain activity can obtain a detailed analysis of the information and knowledge of the subject. For this to happen, however, it is important that the entire course of the study is carried out in accordance with the assumptions, without errors that could affect the incorrect interpretation of the results obtained. Planning an experiment focuses on planning all the details involved in carrying out the study (Hicks, 1973, Goodwin, 2008). Therefore, in the course of the research, the authors sought information on the methodology of experimental research carried out in a virtual reality environment with the use of selected tools of cognitive neuroscience, including in particular the use of virtual reality (VR), electroencephalograph (EEG) and eye tracking. Valuable knowledge and information on this topic can be found, among others, in the publication of B Rey, M Alcañiz in the article "Research in neuroscience and virtual reality" [1] in which the importance of the implementation of research in virtual reality is highlighted. The authors believe that VR is an ideal tool to "generate controlled environments the can be used to observe human behavior. Different aspects can be analyzed from a neuroscience perspective, including perception, control of movement, learning, memory, and emotional aspects. " However, in order to assess the extent to which the experimental method in combination with technology allows for reliable information and knowledge management, it was necessary to look at considerations of the research methodology. Therefore, as the first stage, a bibliometric study was adopted, the analysis of the content contained in three databases such as WoS, Scopus, and Google Scholar. The analyses carried out showed a small percentage of publications presenting the methodology of implementation of an experimental study in VR treated as an environment in which research is carried out, within which the aforementioned methods of measurement from the field of neuroscience were applied. Based on the analysis, the authors, on the basis of selected materials in which the research processes have been well-grounded in methodology, as well as on their own research experience, will present the stages of design and implementation of an experimental study carried out in the virtual reality environment using the indicated methods of cognitive neuroscience. Based on a case study, they presented the activities carried out in the course of analyzed events, as well as pointed to potential errors that may occur in the course of research. A supermarket was taken as the virtual location of the study.

Toward A Neuroscientifically Informed “Reasonable Person” Test

Legal tests invoking the viewpoint of a so-called reasonable person play an important role in many domains of the law, ranging from intellectual property to free speech. In such cases, a central question involves determining how a hypothetical individual with “an ordinary or average level of care, prudence, or knowledge” would respond. Despite the seemingly commonsensical nature of these tests, their judicial application can be controversial due to concerns about subjectivity and vulnerability to explicit or implicit biases. Here we take a step toward addressing these concerns by using neuroscientific tools to observe, without the use of self-report, the nature of mental representations central to a set of disputes invoking the reasonable person. Specifically, using an fMRI-based measure, repetition suppression, to generate a neural index of subjective visual similarity, we sought to inform the application of the reasonable person test to a class of intellectual property law that evaluates whether a trademark is so similar to another as to generate consumer confusion. We show that, by leveraging well-established neuroscientific knowledge about visual processing, it is possible to construct a parsimonious neural index of subjective similarity using signals from object-sensitive brain regions identified a priori. Moreover, this neural index, aggregated across multiple participants, is sufficiently precise to detect instances of experimenter-induced bias in behavioral reports. Together these findings shed light on the potential evidentiary value of neuroscientific data to inform questions involving the reasonable person and suggest a novel domain for the use of neuroscience in law.

Repetitive transcranial magnetic stimulation: an innovative medical therapy.

Repetitive Transcranial Magnetic Stimulation (rTMS) is an innovative, non-invasive and well tolerated method that could be used as a treatment option for a variety of neuropsychiatric disorders. A large number of studies, for more than 30 years, have demonstrated that is a powerful neuroscience tool for diagnostic and therapeutic purposes. rTMS is based on the phenomenon of electromagnetic mutual induction, that firstly reported by Michael Faraday in 1831. Later, in 1985, Anthony Barker and his colleagues developed the first modern transcranial magnetic stimulation (TMS) device. rTMS uses brief electromagnetic pulses generated by an insulated coil, placed over the scalp. This technique has the ability to modulate the cortical activity of the brain. Daily rTMS stimulation for several weeks has been shown to be effective in reducing symptoms of many neuropsychiatric disorders. Moreover, studies have shown that dorsolateral prefrontal cortex (DLPFC) has a crucial role in improving cognitive performance and, as a result, is a commonly used target area for the treatment of many neuropsychiatric disorders such as depression. 2008 was a significant year for TMS history, as FDA approved for first time a TMS therapy device for the clinical treatment of depression. This approval as well as the National Institute for Health and Care Excellence (NICE) recommendation on TMS for the treatment of depression in 2015 lead to the establishment of rTMS as a first-line treatment for patients that failed in at least one prior antidepressant medication, followed by FDA approvals for the treatment of migraines and OCD. This paper aims to enhance further some of the clinical usefulness of rTMS.

A Multimodal Neural Activity Readout Integrated Circuit for Recording Fluorescence and Electrical Signals

Monitoring the electrical neural signals is an important method for understanding the neuronal mechanism. In particular, in order to perform a cell-type-specific study, it is necessary to observe the concentration of calcium ions using fluorescent indicators in addition to measuring the electrical neural signal. This paper presents a multimodal multichannel neural activity readout integrated circuit that can perform not only electrical neural recording but also fluorescence recording of neural activity for the cell-type-specific study of heterogeneous neuronal cell populations. For monitoring the calcium ions, the photodiode generates the current according to the fluorescence expressed by the reaction between the genetically encoded calcium indicators and calcium ions. The time-based fluorescence recording circuit then records the photodiode current. The electrical neural signal captured by the microelectrode is recorded through the low-noise amplifier, variable gain amplifier, and analog-to-digital converter. The proposed integrated circuit is fabricated in a 1-poly 6-metal (1P6M) 0.18-μm CMOS process. The fluorescence recording circuit achieves a recording range of 81 dB (75 pA to 860 nA) and consumes a power of 724 nW/channel. The electrical recording circuit achieves an input-referred noise of 2.7 μVrms over the bandwidth of 10 kHz, while consuming the power of 4.9 μW/channel. The functionality of the proposed circuits is verified through the in vivo and in vitro experiments. Compared to the conventional neuroscience tools, which consist of bulky off-chip components, this neural interface is implemented in a compact size to perform multimodal neural recording while consuming low power.

Deep learning approaches for neural decoding across architectures and recording modalities.

Decoding behavior, perception or cognitive state directly from neural signals is critical for brain-computer interface research and an important tool for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. We describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to functional magnetic resonance imaging. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.

Signal-Space Projection Suppresses the tACS Artifact in EEG Recordings.

BackgroundTo probe the functional role of brain oscillations, transcranial alternating current stimulation (tACS) has proven to be a useful neuroscientific tool. Because of the excessive tACS-caused artifact at the stimulation frequency in electroencephalography (EEG) signals, tACS + EEG studies have been mostly limited to compare brain activity between recordings before and after concurrent tACS. Critically, attempts to suppress the artifact in the data cannot assure that the entire artifact is removed while brain activity is preserved. The current study aims to evaluate the feasibility of specific artifact correction techniques to clean tACS-contaminated EEG data.New MethodIn the first experiment, we used a phantom head to have full control over the signal to be analyzed. Driving pre-recorded human brain-oscillation signals through a dipolar current source within the phantom, we simultaneously applied tACS and compared the performance of different artifact-correction techniques: sine subtraction, template subtraction, and signal-space projection (SSP). In the second experiment, we combined tACS and EEG on one human subject to demonstrate the best-performing data-correction approach in a proof of principle.ResultsThe tACS artifact was highly attenuated by SSP in the phantom and the human EEG; thus, we were able to recover the amplitude and phase of the oscillatory activity. In the human experiment, event-related desynchronization could be restored after correcting the artifact.Comparison With Existing MethodsThe best results were achieved with SSP, which outperformed sine subtraction and template subtraction.ConclusionOur results demonstrate the feasibility of SSP by applying it to a phantom measurement with pre-recorded signal and one human tACS + EEG dataset. For a full validation of SSP, more data are needed.

The use of consumer neuroscience in aroma marketing of a service company

In today's highly competitive era, it is becoming increasingly difficult for businesses to attract and keep customers. Modern marketing strategies focused on examining customer behavior, and possibilities of the business sector in influencing them are becoming increasingly beneficial. Consumer neuroscience has great potential here, as it reveals internal consumer preferences by using innovative techniques. Human senses play an important role in affecting these preferences, and smell has the greatest potential to influence customers subconsciously, and thus support the sale of products, keep customers in the store longer, or build a brand. Aroma marketing is the continuously developing field of consumer neuroscience. The paper aims to examine the use of consumer neuroscience in aroma marketing of a service company. After theoretical analysis, we carried out practical research aimed at examining the impact of aroma marketing in a selected service company – Sport Café in Nitra – to increase the sales of a particular product, and its real impact on the economic indicators of this company through the use of consumer neuroscience tools and questionnaire survey. In aromatization, choosing the right aroma is of the utmost importance, therefore in our research, this selection was done in the Laboratory of Consumer Studies at the Faculty of Economics and Management, SUA in Nitra. The research was carried out through conscious and unconscious testing of selected aromas by a randomly chosen sample of customers. Conscious testing included using a questionnaire method and unconscious testing was done using a face-reading device that provides an objective assessment of emotions based on facial expressions. The choice of aroma was followed by testing in real conditions, by diffusing the aroma in the café. Based on the collected data, we confirmed the positive impact of aromatization on coffee sales. In conclusion, we present some recommendations for service companies, focused mainly on cafés, with emphasis on the importance of aromatization for attracting and keeping customers and improving profitability.

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research.

The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.

Control for multifunctionality: bioinspired control based on feeding in Aplysia californica.

Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source of bioinspiration for robotic controllers. However, many existing approaches to modeling biological neural networks rely on computationally expensive models and tend to focus solely on the nervous system, often neglecting the biomechanics of the periphery. As a consequence, while these models are excellent tools for neuroscience, they fail to predict functional behavior in real time, which is a critical capability for robotic control. To meet the need for real-time multifunctional control, we have developed a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. Using this approach, we present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors (biting, swallowing, and rejection), demonstrates behavioral switching in response to external sensory cues, and incorporates both known neural connectivity and a simple bioinspired mechanical model of the feeding apparatus. We demonstrate that the model can be used for formulating testable hypotheses and discuss the implications of this approach for robotic control and neuroscience.

The claustrum

The claustrum is a brain region that has been investigated for over 200 years, yet its precise function remains unknown. In the final posthumously released article of Francis Crick, written with Christof Koch, the claustrum was suggested to be critically linked to consciousness. Though the claustrum remained relatively obscure throughout the last half century, it has enjoyed a renewed interest in the last 15 years since Crick and Koch's article. During this time, the claustrum, like many other brain regions, has been studied with the myriad of modern systems neuroscience tools that have been made available by the intersection of genetic and viral technologies. This has uncovered new information about its anatomical connectivity and physiological properties and begun to reveal aspects of its function. From these studies, one clear consensus has emerged which supports Crick and Koch's primary interest in the claustrum: the claustrum has widespread extensive connectivity with the entire cerebral cortex, suggesting a prominent role in 'higher order processes'.

Optical Tweezers Exploring Neuroscience.

Over the past decade, optical tweezers (OT) have been increasingly used in neuroscience for studies of molecules and neuronal dynamics, as well as for the study of model organisms as a whole. Compared to other areas of biology, it has taken much longer for OT to become an established tool in neuroscience. This is, in part, due to the complexity of the brain and the inherent difficulties in trapping individual molecules or manipulating cells located deep within biological tissue. Recent advances in OT, as well as parallel developments in imaging and adaptive optics, have significantly extended the capabilities of OT. In this review, we describe how OT became an established tool in neuroscience and we elaborate on possible future directions for the field. Rather than covering all applications of OT to neurons or related proteins and molecules, we focus our discussions on studies that provide crucial information to neuroscience, such as neuron dynamics, growth, and communication, as these studies have revealed meaningful information and provide direction for the field into the future.

Personality and psychopathology: In defense of a practical path toward integrating psychometric and biological approaches to advance a comprehensive model.

Personality and psychopathology each reflect patterns of internal experience and outward behavior that differ between people and affect functioning. Drawing strict distinctions between the two concepts is not only difficult, but it may prove unnecessary for advancing an integrated model of psychological experiences associated with mental illness. We argue that developing such a model will be critical for improving treatment outcomes, and we discuss a practical path forward. Proponents of psychometric approaches to developing models of psychological experience focus on observable phenotypes and utilize statistical methods to describe patterns of covariation among a broad range of symptoms and dispositions. Advocates of biologically based approaches emphasize neuroscientific tools for identifying abnormalities in brain function that give rise to an individual's experience. There is substantial evidence that measures of personality and measures of symptoms capture nonoverlapping, clinically important information for understanding how and for whom treatments for mental illness work. In this article, we highlight the importance of combining psychometric and neurobiological approaches in order to understand which features of an individual those measures reflect, which aspects of neurobiology generate and maintain those features, how they relate to each other, and critically, how best to alter them to reduce distress and dysfunction.

High throughput instrument to screen fluorescent proteins under two-photon excitation.

Two-photon microscopy together with fluorescent proteins and fluorescent protein-based biosensors are commonly used tools in neuroscience. To enhance their experimental scope, it is important to optimize fluorescent proteins for two-photon excitation. Directed evolution of fluorescent proteins under one-photon excitation is common, but many one-photon properties do not correlate with two-photon properties. A simple system for expressing fluorescent protein mutants is E. coli colonies on an agar plate. The small focal volume of two-photon excitation makes creating a high throughput screen in this system a challenge for a conventional point-scanning approach. We present an instrument and accompanying software that solves this challenge by selectively scanning each colony based on a colony map captured under one-photon excitation. This instrument, called the GIZMO, can measure the two-photon excited fluorescence of 10,000 E. coli colonies in 7 hours. We show that the GIZMO can be used to evolve a fluorescent protein under two-photon excitation.

Regional variation in neurovascular coupling and why we still lack a Rosetta Stone.

Functional magnetic resonance imaging (fMRI) is the dominant tool in cognitive neuroscience although its relation to underlying neural activity, particularly in the human brain, remains largely unknown. A major research goal, therefore, has been to uncover a 'Rosetta Stone' providing direct translation between the blood oxygen level-dependent (BOLD) signal, the local field potential and single-neuron activity. Here, I evaluate the proposal that BOLD signal changes equate to changes in gamma-band activity, which in turn may partially relate to the spiking activity of neurons. While there is some support for this idea in sensory cortices, findings in deeper brain structures like the hippocampus instead suggest both regional and frequency-wise differences. Relatedly, I consider four important factors in linking fMRI to neural activity: interpretation of correlations between these signals, regional variability in local vasculature, distributed neural coding schemes and varying fMRI signal quality. Novel analytic fMRI techniques, such as multivariate pattern analysis (MVPA), employ the distributed patterns of voxels across a brain region to make inferences about information content rather than whether a small number of voxels go up or down relative to baseline in response to a stimulus. Although unlikely to provide a Rosetta Stone, MVPA, therefore, may represent one possible means forward for better linking BOLD signal changes to the information coded by underlying neural activity. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography.

Brain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate "regions" rather than as white matter "bundles" or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different automated state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

Viral tools for neuroscience

Recombinant viruses are the workhorse of modern neuroscience. Whether one would like to understand a neuron's morphology, natural activity patterns, molecular composition, connectivity or behavioural and physiologic function, most studies begin with the injection of an engineered virus, often an adeno-associated virus or herpes simplex virus, among many other types. Recombinant viruses currently enable some combination of cell type-specific, circuit-selective, activity-dependent and spatiotemporally resolved transgene expression. Viruses are now used routinely to study the molecular and cellular functions of a gene within an identified cell type in the brain, and enable the application of optogenetics, chemogenetics, calcium imaging and related approaches. These advantageous properties of engineered viruses thus enable characterization of neuronal function at unprecedented resolution. However, each virus has specific advantages and disadvantages, which makes viral tool selection paramount for properly designing and executing experiments within the central nervous system. In the current Review, we discuss the key principles and uses of engineered viruses and highlight innovations that are needed moving forward.

Magnetoresistive Sensor in Two-Dimension on a 25 μm Thick Silicon Substrate for In Vivo Neuronal Measurements.

Neuronal electrical activity is widely studied in vivo, and the ability to measure its magnetic equivalent to obtain an undisturbed signal with both amplitude and direction information leading to neuronal signal mapping would be a promising tool for neuroscience. To provide such a tool, a probe with spin-electronics-based magnetic sensors with orthogonal axes of sensitivity for two directions of measurement is realized, thanks to a local magnetization re-orientation technique induced by Joule heating. This probe is tested under in vivo measurement conditions in the brain of an anesthetized rat. To be as close as possible to neurons and to create minimal damage during the probe's insertion, the tip thickness has been drastically decreased using a silicon-on-insulator substrate. Our probes provide the ability to perform in vivo magnetic measurements on two orthogonal axes on a 25 μm thick silicon tip with a sensitivity of 1.7%/mT along one axis and 0.9%/mT along the perpendicular axis in the sensor plane, for a limit of detection at 1 kHz of 1.0 and 1.3 nT, respectively. These probes have been tested through a phantom study and during an in vivo experiment. The robustness and stability over one year are demonstrated.