Neuroscientific Findings Research Articles

Epigenetic embedding of childhood adversity: mitochondrial metabolism and neurobiology of stress-related CNS diseases.

This invited article ad memoriam of Bruce McEwen discusses emerging epigenetic mechanisms underlying the long and winding road from adverse childhood experiences to adult physiology and brain functions. The conceptual framework that we pursue suggest multidimensional biological pathways for the rapid regulation of neuroplasticity that utilize rapid non-genomic mechanisms of epigenetic programming of gene expression and modulation of metabolic function via mitochondrial metabolism. The current article also highlights how applying computational tools can foster the translation of basic neuroscience discoveries for the development of novel treatment models for mental illnesses, such as depression to slow the clinical manifestation of Alzheimer's disease. Citing an expression that many of us heard from Bruce, while "It is not possible to roll back the clock," deeper understanding of the biological pathways and mechanisms through which stress produces a lifelong vulnerability to altered mitochondrial metabolism can provide a path for compensatory neuroplasticity. The newest findings emerging from this mechanistic framework are among the latest topics we had the good fortune to discuss with Bruce the day before his sudden illness when walking to a restaurant in a surprisingly warm evening that preluded the snowstorm on December 18th, 2019. With this article, we wish to celebrate Bruce's untouched love for Neuroscience.

Evaluating emotional and social interactions in museum liminal spaces: A case study of the National Museum of the Islamic Revolution and Holy Defense of Tehran, Iran

AbstractBackgroundThis study explores which architectural features of interstitial spaces improve visitors’ interactions with the overall museum space and with other visitors at the National Museum of the Islamic Revolution and Holy Defense in Tehran, Iran. Interstitial or liminal spaces are defined as “in‐between” spaces connecting primary spaces in a building that have a key role in relating humans to places.ObjectivesThis study asks which geometric features can increase the socio‐emotional interactions for visitors.MethodsTo answer the question, observations of behavior and body language were made at the study site. These data were analyzed based on body language science and neuroscience literature. Five in‐between spaces connecting exhibition halls were selected, and visitor behaviors were observed and analyzed using the Observer XT software by Noldus.ResultsResults showed that certain types of passages were associated with the highest levels of emotional interaction, while others are associated with the highest levels of social interaction.ConclusionsThe results of this study align with earlier neuroscience findings regarding brain activities in relation to certain geometries. The methods used in this study can be applied by architects and designers in their predesign planning and post design evaluation processes to improve socio‐emotional interactions for visitors.

Neuroscience Data Formats, Models, Repositories and Analytics: A Review

As neurotechnologies have gotten better, a lot of neuroscientific research has been done using these new technologies. Even though labs all over the world produce a lot of neuro-data, most of this data has not been shared to help people from different fields understand neuroscience. The neuro-data sharing is essential because it accelerates the pace of discovery in neuroscience. Effective data sharing will depend on the standardized use of file or data formats, highly reusable data analytics tools, and data storage formats. In this review paper, we review the four domains (data format, data model, data repository, and data analytics) that are currently in use in the neuroscience community. In the end, we are discussing several challenges associated with data sharing.

Physical, Social and Emotional Needs of Developing Children: Comparison of Neuroscientific Findings and Bertrand Russell's Ideas

Bu çalışmanın amacı Bertrand Russell’ın eğitim felsefesi görüşleri ile son dönemde giderek önem kazanan nörobilim araştırmalarında ve eğitsel nörobilim alanında ortaya çıkan bulgu ve felsefi görüşleri karşılaştırmaktır. Nörobilim çalışmalarının bulguları ve eğitsel nörobilim tartışmalarına göre öğrenme ve öğrenmenin davranışa dönüşmesini sağlayan en önemli unsurlar biyolojik (fiziksel ve kimyasal), ve sosyal unsurlardır. Bertrand Russell da eğitim hakkındaki görüşlerini bu unsurlara dayandırarak bir eğitim felsefesi geliştirmiştir. Nörobilimsel bulgular öğrenme ve diğer bilişsel faaliyetleri çoklu parametreler üzerinden detaylı şekilde açıklamaktadır. Russell’da özellikle modern toplumların sahip olduğu problemleri bu ayrıntılar ve gündelik dinamikler ile felsefi bir üslüpta tartışmıştır. Yapılan bu karşılaştırmada ortaya çıkan sonuçlara göre; öğrenme ve eylem ilişkisinde, insanın fizyolojik ve duygusal ihtiyaçlarının beraber değerlendirilmesinin önemi, eğitimde özerklik ve serbestinin rolü, cinsel eğitimi, sosyal güç dengeleri ve oyun temaları ön plana çıkan konu başlıkları olmuştur. Ortaya çıkan bu bulgulara göre biyolojik, duygusal ve sosyal unsurların iç içe geçmesi öğrenme ve bilişsel faaliyetler için temel oluşturmaktadır. Bu biyolojik, duygusal ve sosyal unsurların ortaya çıkardığı sosyal ve bireysel ihtiyaçlar da tartışma kapsamında değerlendirilmiştir.

Cognitive and affective interaction with somatosensory afference in acupuncture-a specific brain response to compound stimulus.

Acupuncture is a clinical intervention consisting of multiple stimulus components, including somatosensory stimulation and manipulation of therapeutic context. Existing findings in neuroscience consolidated cognitive modulation to somatosensory afferent process, which could differ from placebo mechanism in brain. Here, we aimed to identify intrinsic process of brain interactions induced by compound stimulus of acupuncture treatment. To separately and comprehensively investigate somatosensory afferent and cognitive/affective processes in brain, we implemented a novel experimental protocol of contextual manipulation with somatosensory stimulation (real acupuncture: REAL) and only contextual manipulation (phantom acupuncture: PHNT) for fMRI scan, and conducted independent component (IC)-wise assessment with the concatenated fMRI data. By our double (experimentally and analytically) dissociation, two ICs (CA1: executive control, CA2: goal-directed sensory process) for cognitive/affective modulation (associated with both REAL and PHNT) and other two ICs (SA1: interoceptive attention and motor-reaction, SA2: somatosensory representation) for somatosensory afference (associated with only REAL) were identified. Moreover, coupling between SA1 and SA2 was associated with a decreased heart rate during stimulation, whereas CA1 was associated with a delayed heart rate decrease post-stimulation. Furthermore, partial correlation network for these components demonstrated a bi-directional interaction between CA1 and SA1/SA2, suggesting the cognitive modulation to somatosensory process. The expectation for the treatment negatively affected CA1 but positively affected SA1 in REAL, whereas the expectation positively affected CA1 in PHNT. These specific cognitive-somatosensory interaction in REAL were differed from vicarious sensation mechanism in PHNT; and might be associated with a characteristic of acupuncture, which induces voluntary attention for interoception. Our findings on brain interactions in acupuncture treatment elucidated the underlying brain mechanisms for compound stimulus of somatosensory afferent and therapeutic contextual manipulation, which might be a specific response to acupuncture.

Canonical circuit computations for computer vision.

Advanced computer vision mechanisms have been inspired by neuroscientific findings. However, with the focus on improving benchmark achievements, technical solutions have been shaped by application and engineering constraints. This includes the training of neural networks which led to the development of feature detectors optimally suited to the application domain. However, the limitations of such approaches motivate the need to identify computational principles, or motifs, in biological vision that can enable further foundational advances in machine vision. We propose to utilize structural and functional principles of neural systems that have been largely overlooked. They potentially provide new inspirations for computer vision mechanisms and models. Recurrent feedforward, lateral, and feedback interactions characterize general principles underlying processing in mammals. We derive a formal specification of core computational motifs that utilize these principles. These are combined to define model mechanisms for visual shape and motion processing. We demonstrate how such a framework can be adopted to run on neuromorphic brain-inspired hardware platforms and can be extended to automatically adapt to environment statistics. We argue that the identified principles and their formalization inspires sophisticated computational mechanisms with improved explanatory scope. These and other elaborated, biologically inspired models can be employed to design computer vision solutions for different tasks and they can be used to advance neural network architectures of learning.

Decoding and synthesizing tonal language speech from brain activity.

Recent studies have shown that the feasibility of speech brain-computer interfaces (BCIs) as a clinically valid treatment in helping nontonal language patients with communication disorders restore their speech ability. However, tonal language speech BCI is challenging because additional precise control of laryngeal movements to produce lexical tones is required. Thus, the model should emphasize the features from the tonal-related cortex. Here, we designed a modularized multistream neural network that directly synthesizes tonal language speech from intracranial recordings. The network decoded lexical tones and base syllables independently via parallel streams of neural network modules inspired by neuroscience findings. The speech was synthesized by combining tonal syllable labels with nondiscriminant speech neural activity. Compared to commonly used baseline models, our proposed models achieved higher performance with modest training data and computational costs. These findings raise a potential strategy for approaching tonal language speech restoration.

BE-ELM: Biological ensemble Extreme Learning Machine without the need of explicit aggregation

Extreme learning machines (ELMs) are commonly adopted as base learners in ensemble methods due to unstable results and fast learning speed. However, most existing ensemble structures require explicit aggregation of ELM base learners’ results before the final decision. This study proposes a novel Biological Ensemble ELMs called BE-ELM from a biological perspective for the first time and exploit the superiority of assembling multiple ELM base learners in parallel without the need for explicit aggregation, and thus simplifying the learning procedure. BE-ELM’s structure is inspired by recent MIT neuroscience findings of brain learning mechanisms. The expression of the analytical solution of BE-ELM is similar to that of basic ELMs, which allows it to inherit their fast learning speed in the ensemble structure. Moreover, BE-ELM also has the added advantage of superior performance. Our theoretical analysis shows that BE-ELM consisting of multiple ELM base learners built on subsets of the original features is equivalent to a more complex ELM on the original feature space. We prove that BE-ELM, even without any explicit aggregation, can guarantee enhanced generalization capabilities. To confirm our findings, we conducted extensive experiments on various datasets. The results demonstrate that BE-ELM outperforms traditional ELMs and other state-of-the-art ensemble ELMs in terms of generalization performance on most datasets. These findings suggest that BE-ELM has the potential to improve prediction outcomes in practical applications.

ReX: an integrative tool for quantifying and optimizing measurement reliability for the study of individual differences.

Characterizing multifaceted individual differences in brain function using neuroimaging is central to biomarker discovery in neuroscience. We provide an integrative toolbox, Reliability eXplorer (ReX), to facilitate the examination of individual variation and reliability as well as the effective direction for optimization of measuring individual differences in biomarker discovery. We also illustrate gradient flows, a two-dimensional field map-based approach to identifying and representing the most effective direction for optimization when measuring individual differences, which is implemented in ReX.

How Digital Role Play Can Enhance Performance Leveraging Corporate Capabilities

Performance management isn’t just a matter of defining and following a process. Instead, it’s above all a lively and intense interaction between the managers and their reports. Skills and technique underlying an effective and satisfying relationship is essential to ensure sustainable results with involved and motivated teams. We propose a role-play game based on Artificial Intelligence that allows to put capabilities into actionable, measurable behaviors, throughout communication. This method makes it possible to practice critical conversations in working environment, with different characters based on personality psychology and neuroscientific findings. The training method can be used to practice key skills in the processes of performance management, such as: setting goals, monitoring results, give feedback and reward. The method allows to improve performance leveraging corporate capabilities. In this statement the architecture of the method will be illustrated, to specifically explain how capabilities have been directly related to skills, behaviors and then conversations. The specific performance management training will be explored.

The challenge of clinical reasoning in chronic multimorbidity: time and interactions in the Health Issues Network model.

The increasing prevalence of multimorbidity requires new theoretical models and educational approaches to develop physicians' ability to manage multimorbidity patients. The Health Issues Network (HIN) is an educational approach based on a graphical depiction of the evolutions over time of the concurrent health issues of a patient andoftheir interactions. From a theoretical point of view,theHINapproach is rooted in Prigogine's vision ofthe "becoming" of the events and in the concept ofknowledge organization, intended as the process of storingand structuring of information in a learner's mind.The HINapproach allows to design clinical exercises tofosterlearners' ability to detect evolutionary paths and interactions among health issues. Recent findings of neuroscience support the expectation that interpreting, completing, and creating diagrams depicting complex clinical cases improves the "sense of time", as a fundamental competence inthe management of multimorbidity. The application of the HIN approach is expected to decrease the risk of errors in themanagement of multimorbidity patients. The approach isstill under validation, both for undergraduate students and for the continuous professional development of physicians.

The reported effects of neuroscience literacy and belief in neuromyths among parents of adolescents

Neuroscience research has increased our understanding of brain development, but little is known about how parents of adolescents engage with this neuroscientific information. Dutch parents completed a digital survey on neuromyths, neuroscience literacy and views of the adolescent brain and behaviour. These parents believed 44.7% of neuromyths and showed reasonable neuroscience literacy (79.8%). Stronger neuromyth belief predicted a more negative view on adolescent brain development. About 68% of the parents reported that they had changed their parenting behaviour based on their understanding of neuroscientific findings. These self-reported changes most often reflected changes to parents' own behaviour. The results of this study underline the importance for scientists and parents to engage in scientific activities to promote respectful and trusting relationships between them. These relationships have the potential to make communication about adolescent brain development between scientists and parents more effective and will empower parents to use correct information as a basis for their decisions around raising their adolescents.

From helplessness to controllability: toward a neuroscience of resilience.

"Learned helplessness" refers to debilitating outcomes, such as passivity and increased fear, that follow an uncontrollable adverse event, but do not when that event is controllable. The original explanation argued that when events are uncontrollable the animal learns that outcomes are independent of its behavior, and that this is the active ingredient in producing the effects. Controllable adverse events, in contrast, fail to produce these outcomes because they lack the active uncontrollability element. Recent work on the neural basis of helplessness, however, takes the opposite view. Prolonged exposure to aversive stimulation per se produces the debilitation by potent activation of serotonergic neurons in the brainstem dorsal raphe nucleus. Debilitation is prevented with an instrumental controlling response, which activates prefrontal circuitry detecting control and subsequently blunting the dorsal raphe nucleus response. Furthermore, learning control alters the prefrontal response to future adverse events, thereby preventing debilitation and producing long-term resiliency. The general implications of these neuroscience findings may apply to psychological therapy and prevention, in particular by suggesting the importance of cognitions and control, rather than habits of control.

Ethical Issues in Neuroscience and Neuropsychiatric: A Review of Major Issues

<div><table cellspacing="0" cellpadding="0" align="left"><tbody><tr><td align="left" valign="top"><p><em>Adherence to ethical principles is essential nowadays in neuroscience and neuropsychiatric research. observance of ethical principles is essential in the case of neurology and neuroscience research, as it is in all the other medical and surgical fields. as neuroscience research advances, researchers, clinicians, and other stakeholders will face a host of ethical challenges. Ethical problems of neuroscience-based diagnosis and interventions. This section concerns problems resulting from neuroimaging, pharmacological brain enhancement, and new technical interventions in the brain. The research is based on a literature review, so data will be collected from literary sources such as scientific journals, books, and articles in January 2023 relating to the research topic. The literature search process must be systematically documented, including keywords, databases used, and search dates. Data analysis will be carried out by reading and understanding the content of the literature, identifying ethical problems encountered in the field of neuroscience and neuropsychiatry, and analyzing different perspectives in addressing these ethical problems. In this article, we also review the major ethical issues raised by advances in neuropsychiatric genetics. Advances in neuroscience raise ethical, social, and legal issues about the human person and the brain. The potential benefits of applying neuroimaging, psychopharmacology, and neurotechnology to support mentally ill and healthy persons have to be carefully weighed against their potential harm. Questions concerning underlying concepts of humans should be actively dealt with by interdisciplinary and public debate.</em><em> </em> <em>t is therefore essential to develop bioethics and encourage discoveries in neuroscience to firmly respect the ethical principles available at the time of their publication.</em></p><p><em><br /></em></p></td></tr></tbody></table></div><strong><em>Keyword: Ethical Issues, Neuroscience, Neuropsychiatric</em></strong>

Transforming Empathy-Based Stress to Compassion: Skillful Means to Preventing Teacher Burnout.

Teachers play a critical role in preparing our children and adolescents for a successful future. However, despite thelarge number of students impacted by trauma and adversity, teachers are often not well prepared to provide trauma-sensitivesupport. Furthermore, while working to support students exposed to trauma and adversity, teachers may experience empathy-basedstress exacerbating already high levels of stress among them. This narrative review explores the issue of empathy-basedstress within the context of the prosocial classroom model which proposes that teachers' social and emotional competence andwell-being are key to their ability to create and maintain supportive learning environments critical to student academic andbehavioral outcomes. Recent findings in neuroscience and education researchare applied to support teachers'development of these competencies. We propose that shifting from empathy-based stress to compassionate respondingmay be one such competency to help teachers' respond effectively to their students' needs while protecting their own wellbeing. We review research that supports this proposition and explore implications for teacher professional learning,educational policy, and further research.

Listening speaks to our intuition while reading promotes analytic thought.

It is widely assumed that thinking is independent of language modality because an argument is either logically valid or invalid regardless of whether we read or hear it. This is taken for granted in areas such as psychology, medicine, and the law. Contrary to this assumption, we demonstrate that thinking from spoken information leads to more intuitive performance compared with thinking from written information. Consequently, we propose that people think more intuitively in the spoken modality and more analytically in the written modality. This effect was robust in five experiments (N = 1,243), across a wide range of thinking tasks, from simple trivia questions to complex syllogisms, and it generalized across two different languages, English and Chinese. We show that this effect is consistent with neuroscientific findings and propose that modality dependence could result from how language modalities emerge in development and are used over time. This finding sheds new light on the way language influences thought and has important implications for research that relies on linguistic materials and for domains where thinking and reasoning are central such as law, medicine, and business. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Neuroscience for a new concept of decision-making style

Decision-making styles refer to the style through which individuals identify and evaluate the information needed to make a decision and how they usually consider the various possible alternatives. While decision-making styles have been explored from several research perspectives, no neuroscientific studies addressed this topic before. Considering the neuroscientific findings on the study of the decision-making process, the purpose of this article is to go beyond the traditional models conceptualizing the styles of decisionmaking and to provide a novel insight and definition of this concept, through the identification of some of the prerogatives most closely associated with the notion of style: these include self-representation, adaptability, and risk-taking. Within this conceptual framework, it is proposed that the style of a decision-maker is strictly linked to the physiological tendency to tolerate and regulate stress, to the ability to self-represent one’s goals and be able to prioritize them, to the adaptability level, and to risk-taking and management traits. To fully understand the construct of decision-making styles, this theoretical contribution also underlines the importance of adopting a conceptual perspective that takes into account behavioural, self-report and neuroscientific measures to profile decision-makers.

Philosophical Pragmatism and the Challenges of Information Technologies

Philosophical Pragmatism and the Challenges of Information Technologies

Information Processing in the Brain and Financial Innovations

Based on recent neuroscience findings, we consider the brain to be a Bayesian prediction engine operating under a resource constraint. It meets incoming information with predictions based on an internal model of the world. Finite brain resources are then spent on processing the gaps between incoming information and these predictions. In addition, adverse information may trigger a stress response, exacting a cost on the brain and the body. We argue that the brain, which ultimately is the seat of all decision-making, considers these costs while choosing among alternatives. We show that incorporating such costs enriches our understanding of the financial market behavior by providing a new approach to recent financial innovations, as well as pushing up the equity premium and making it countercyclical. Ignoring such costs may make certain and uncertain utility appear different from each other consistent with recent empirical findings.

Will the real resource theory please stand up! Vigilance is a renewable resource and should be modeled as such

The vigilance decrement or decline in signal detection performance with time on task is one of the most reliable findings in the cognitive neuroscience and psychology literatures. The majority of theories proposed to explain the decrement are limited cognitive or attention resource based theories; the central nervous system is a limited capacity processor. The decrement in performance is then due to resource reallocation (or misallocation), resource depletion or some combination of both mechanisms. The role of resource depletion, in particular, is hotly debated. However, this may be due to a lack of understanding of the renewable nature of the vigilance resources and how this renewal process impacts performance during vigilance tasks. In the present paper, a simple quantitative model of vigilance resource depletion and renewal is described and shown to generate performance data similar to results seen in both humans and spiders. This model clarifies the role resource depletion and resource renewal may play in vigilance in both people and other animals.