Neuroscientific Findings Research Articles

Cognitive neuroscience and music education: Relationships and interactions

This article examines the relationship between cognitive neuroscience and music education, evaluating the impact of cognitive neuroscience on musical learning, performance, and teaching strategies. The first section establishes the purpose and scope of the article, followed by an emphasis on the foundations of cognitive neuroscience in the second section. Cognitive neuroscience is explored in terms of music perception, mental processes, and fundamental concepts related to learning and memory. The third section, focusing on cognitive processes in music education, delves into the cognitive foundations of musical knowledge, addressing topics such as melody and rhythm perception and cognitive development in instrumental education. The fourth section discusses the neuroscientific analysis of musical performance, stage fright, brain activity, creativity, and mental processes. The fifth section discusses cognitive neuroscience-based teaching models, the neuroscientific foundations of student-centred approaches, and the role of technology in music education. The sixth section highlights future perspectives and research directions, recommendations for future studies, contributions of new cognitive neuroscience findings to music education, current developments in the intersection of music and cognitive neuroscience, and suggestions for future research. In the concluding section, the summarised article's significant findings and contributions are emphasised, drawing attention to potential areas for future examination and application. This article aims to serve as a comprehensive resource for readers interested in understanding the connections between cognitive neuroscience and music education, providing a foundation for further research in this field.

Evolutionary origin of the nervous system from Ctenophora prospective.

Nervous system is one of the key adaptations underlying the evolutionary success of the majority of animal groups. Ctenophores (or comb jellies) are gelatinous marine invertebrates that were probably the first lineage to diverge from the rest of animals. Due to the key phylogenetic position and multiple unique adaptations, the noncentralized nervous system of comb jellies has been in the center of the debate around the origin of the nervous system in the animal kingdom and whether it happened only once or twice. Here, we discuss the latest findings in ctenophore neuroscience and multiple challenges on the way to build a clear evolutionary picture of the origin of the nervous system.

La neurociencia como base para la mejora de las TIC educativas

Neuroscience, with its in-depth study of the human brain, has been identified as a vital tool for improving Information and Communication Technologies (ICT) in education. In order to provide more efficient and personalized didactic tools, the integration of neuroscientific findings into ICT design has been essential. Using qualitative methodologies involving documentary analysis, case studies, and systematic reviews, the importance of adapting ICT to the ways in which the brain naturally learns has been identified. Results indicate that adapting ICT based on neuroscientific principles leads to increased engagement, retention, and knowledge transfer among students. In addition, the use of digital tools aligned with neurodidactic strategies has proven to be more effective in creating inclusive and diversified learning environments, addressing the individual needs of each learner and promoting holistic learning.

What have we learned about artificial intelligence from studying the brain?

Neuroscience and artificial intelligence (AI) share a long, intertwined history. It has been argued that discoveries in neuroscience were (and continue to be) instrumental in driving the development of new AI technology. Scrutinizing these historical claims yields a more nuanced story, where AI researchers were loosely inspired by the brain, but ideas flowed mostly in the other direction.

TOTAL SELF‐TRANSFORMATION USING MINDFULNESS

AbstractThe author is a longtime corporate leader with extensive experience running executive development programs. He writes about the effective use of mindfulness for personal and professional development, including demystifying the term itself. He wants readers to “understand, explore, practice, get into the habit of and own the full extent of the amazing power of the dual approach of mindfulness meditation and of living mindfully.” He explains a “simple, practical, and powerful framework. It blends ancient Indian and Buddhist wisdom with current findings of neuroscience and my work in leadership development with corporate America. My dual approach has two key components. First, practicing 10 minutes of deep breathing every day to settle the mind, immediately followed by mindfulness meditation for 20 minutes, for a total of 30 minutes.” Noting that most of us take our minds for granted, he claims that “practicing mindfulness meditation every day is the equivalent to physical exercise, in terms of keeping the mind calm and functioning at its very peak. Living mindfully in the moment by observing our thoughts and feelings on an ongoing basis creates constant awareness. This awareness becomes sharper over time and is a prerequisite for personal change.”

Impact of Problem‐Based Learning Coaching and Neuroeducation in the Development of 21st Century Lifelong Learners

AbstractOver the past century, health knowledge has advanced dramatically, so it is expected that future health professionals will need to learn effectively in the workplace and adapt to novel situations that cannot yet be predicted. Simultaneously, the demographics of university students have changed significantly in regard to age, gender, and socioeconomic background. Health educators must adapt their teaching methods to foster in each student the development of lifelong learning skills. This perspective article examines the concept of neuroeducation through the lens of Problem‐Based Learning (PBL), with an emphasis on PBL coaching. Furthermore, it demonstrates the benefit of implementing a PBL coaching approach, using knowledge from neuroscience findings to enhance students' learning and well‐being. Following targeted professional development aiming to consolidate the understanding of the brain's structures and functions that increase student learning capacity and build group coaches' skills, existing PBL academics could be an integral part of such a program.

A multi-stage anticipated surprise model with dynamic expectation for economic decision-making

There are many modeling works that aim to explain people’s behaviors that violate classical economic theories. However, these models often do not take into full account the multi-stage nature of real-life problems and people’s tendency in solving complicated problems sequentially. In this work, we propose a descriptive decision-making model for multi-stage problems with perceived post-decision information. In the model, decisions are chosen based on an entity which we call the ‘anticipated surprise’. The reference point is determined by the expected value of the possible outcomes, which we assume to be dynamically changing during the mental simulation of a sequence of events. We illustrate how our formalism can help us understand prominent economic paradoxes and gambling behaviors that involve multi-stage or sequential planning. We also discuss how neuroscience findings, like prediction error signals and introspective neuronal replay, as well as psychological theories like affective forecasting, are related to the features in our model. This provides hints for future experiments to investigate the role of these entities in decision-making.

Radiomic tractometry reveals tract-specific imaging biomarkers in white matter

Tract-specific microstructural analysis of the brain’s white matter (WM) using diffusion MRI has been a driver for neuroscientific discovery with a wide range of applications. Tractometry enables localized tissue analysis along tracts but relies on bare summary statistics and reduces complex image information along a tract to few scalar values, and so may miss valuable information. This hampers the applicability of tractometry for predictive modelling. Radiomics is a promising method based on the analysis of numerous quantitative image features beyond what can be visually perceived, but has not yet been used for tract-specific analysis of white matter. Here we introduce radiomic tractometry (RadTract) and show that introducing rich radiomics-based feature sets into the world of tractometry enables improved predictive modelling while retaining the localization capability of tractometry. We demonstrate its value in a series of clinical populations, showcasing its performance in diagnosing disease subgroups in different datasets, as well as estimation of demographic and clinical parameters. We propose that RadTract could spark the establishment of a new generation of tract-specific imaging biomarkers with benefits for a range of applications from basic neuroscience to medical research.

NeuroDecodeR: a package for neural decoding in R.

Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement a range of different analyses. We also discuss how to format data to be able to use the package, and we give two examples of how to use the package to analyze real data. We believe that this package, combined with the rich data analysis ecosystem in R, will make it significantly easier for researchers to create reproducible decoding analyses, which should help increase the pace of neuroscience discoveries.

НЕЙРО-ЭКОНОМИЧЕСКИЕ И КОГНИТИВНО-ПСИХОЛОГИЧЕСКИЕ АСПЕКТЫ ТЕХНОЛОГИЙ МЕРЧАНДАЙЗИНГА

This article is of a review and analytical nature and is devoted to the problems of applying modern discoveries of neuroscience (neurobiology, neurophysiology, neuroeconomics) in managing customer behavior. In particular, a multipolar model of customer behavior is proposed, combining the emotional-sensory and cognitive-psychological fields of irrationally rational consumer behavior. The authors found that purchasing decisions by visitors to points of sale can be made at three levels: 1) at the emotional and sensory level, dictated by neurobiological processes and being irrational, which are taken unconsciously during the direct neuro-communication interaction of the product and the visitor in the trading halls; 2) at the level of automatic decisions based on empirical experience that are rational – consciously reflective (objective experience) or irrational - unconsciously reflective (subjective experience), while the constancy of the product’s place in the space of the trading floor is a necessary condition for the formation of experience and the manifestation of such behavior; c) at a high cognitive level based on complex cognitive-psychological processes caused by the need to think about and justify rational decisions. They are aimed at reducing the qualitative, financial or other risk that can cause cognitive dissonance, and visitors are characterized by resistance to merchandising tools used at points of sale. The obtained results make a definite contribution to the development of the theory of neuromarketing and contribute to the formation of the neuro-cognitive concept of merchandising. The main provisions of the article may be of practical importance in applied research in the development of new neuromarketing and merchandising tools.

Multi-Scale Spatio-Temporal Fusion with Adaptive Brain Topology Learning for fMRI Based Neural Decoding.

Neural decoding aims to extract information from neurons' activities to reveal how the brain functions. Due to the inherent spatial and temporal characteristics of brain signals, spatio-temporal computing has become a hot topic for neural decoding. However, the extant spatio-temporal decoding methods usually use static brain topology, ignoring the dynamic patterns of the interaction between brain regions. Further, they do not identify the hierarchical organization of brain topology, leading to only superficial insight into brain spatio-temporal interactions. Therefore, here we propose a novel framework, the Multi-Scale Spatio-Temporal framework with Adaptive Brain Topology Learning (MSST-ABTL), for neural decoding. It includes two new capabilities to enhance spatio-temporal decoding: i) ABTL module, which learns dynamic brain topology while updating specific patterns of brain regions, ii) MSST module, which captures the association of spatial pattern and temporal evolution, and further enhances the interpretability of the learned dynamic topology from multi-scale perspective. We evaluated the framework on the public Human Connectome Project (HCP) dataset (resting-state and task-related fMRI data). The extensive experiments show that the proposed MSST-ABTL outperforms state-of-the-art methods on four evaluation metrics, and also can renew the neuroscientific discoveries in the brain's hierarchical patterns.

Multitrial free recall for evaluating memory.

Much of our knowledge concerning the neural basis of human memory derives from lab-based verbal recall tasks. Outside of the lab, clinicians use validated and normed neuropsychological tests to assess patients' memory function and to evaluate clinical interventions. Here we sought to establish the clinical validity of examining memory through multitrial free recall of semantically organized and unrelated word lists. We compare memory performance in multitrial free recall tasks with the Rey Auditory Verbal Learning Test and the California Verbal Learning Test, two common neuropsychological tests aimed at evaluating memory function in clinical settings. We compare predictive validity between the tasks by evaluating deficits in a patient sample and examining age-related declines in memory. We additionally compare test-retest reliability, establish convergent validity, and show the emergence of common recall dynamics between the tasks. We demonstrate that both laboratory free recall tasks have better predictive validity and test-retest reliability than the established neuropsychological tests. We further show that all tasks have good convergent validity and reveal core memory processes, including temporal and semantic organization. However, we also demonstrate the benefits of repeated trials for evaluating the dynamics of memory search and their neuropsychological sequelae. These results provide evidence for the clinical validity of lab-based multitrial free recall tasks and highlight their psychometric benefits over neuropsychological measures. Based on these results, we discuss the need to bridge the gap between clinical understanding of putative mechanisms underlying memory disorders and neuroscientific findings obtained using lab-based free recall tasks. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Regional-asymmetric Adaptive Graph Convolutional Neural Network for Diagnosis of Autism in Children with Resting-state EEG.

Currently, resting-state electroencephalography (rs-EEG) has become an effective and low-cost evaluation way to identify autism spectrum disorders (ASD) in children. However, it is of great challenge to extract useful features from raw rs-EEG data to improve diagnosis performance. Traditional methods mainly rely on the design of manual feature extractors and classifiers, which are separately performed and cannot be optimized simultaneously. To this end, this paper proposes a new end-to-end diagnostic method based on a recently emerged graph convolutional neural network for the diagnosis of ASD in children. Inspired by related neuroscience findings on the abnormal brain functional connectivity and hemispheric asymmetry characteristics observed in autism patients, we design a new Regional-asymmetric Adaptive Graph Convolutional Neural Network (RAGNN). It utilizes a hierarchical feature extraction and fusion process to learn separable spatiotemporal EEG features from different brain regions, two hemispheres, and a global brain. In the temporal feature extraction section, we utilize a convolutional layer that spans from the brain area to the hemisphere. This allows for effectively capturing temporal features both within and between brain areas. To better capture spatial characteristics of multi-channel EEG signals, we employ adaptive graph convolutional learning to capture non-Euclidean features within the brain's hemispheres. Additionally, an attention layer is introduced to highlight different contributions of the left and right hemispheres, and the fused features are used for classification. We conducted a subject-independent cross-validation experiment on rs-EEG data from 45 children with ASD and 45 typically developing (TD) children. Experimental results have shown that our proposed RAGNN model outperformed several existing deep learning-based methods (ShaollowNet, EEGNet, TSception, ST-GCN, and CGRU-MDGN).

An in-depth analysis of the polyvagal theory in light of current findings in neuroscience and clinical research.

The polyvagal theory has led to the understanding of the functions of the autonomic nervous system in biological development in humans, since the vagal system, a key structure within the polyvagal theory, plays a significant role in addressing challenges of the mother-child dyad. This article aims to summarize the neurobiological aspects of the polyvagal theory, highlighting some of its strengths and limitations through the lens of new evidence emerging in several research fields-including comparative anatomy, embryology, epigenetics, psychology, and neuroscience-in the 25years since the theory's inception. Rereading and incorporating the polyvagal idea in light of modern scientific findings helps to interpret the role of the vagus nerve through the temporal dimension (beginning with intrauterine life) and spatial dimension (due to the numerous connections of the vagus with various structures and systems) in the achievement and maintenance of biopsychosocial well-being, from the uterus to adulthood.

Establishment of a high-content imaging assay for tau aggregation in hiPSC-derived neurons differentiated from two protocols to routinely evaluate compounds and genetic perturbations

Aberrant protein aggregation is a pathological cellular hallmark of many neurodegenerative diseases, such as Alzheimer's disease (AD) and frontotemporal dementia (FTD), where the tau protein is aggregating, forming neurofibrillary tangles (NFTs), and propagating from neuron to neuron. These processes have been linked to disease progression and a decline in cognitive function. Various therapeutic approaches aim at the prevention or reduction of tau aggregates in neurons. Human induced pluripotent stem cells (hiPSCs) are a very valuable tool in neuroscience discovery, as they offer access to potentially unlimited amounts of cell types that are affected in disease, including cortical neurons, for in vitro studies. We have generated an in vitro model for tau aggregation that uses hiPSC – derived neurons expressing an aggregation prone, fluorescently tagged version of the human tau protein after lentiviral transduction. Upon addition of tau seeds in the form of recombinant sonicated paired helical filaments (sPHFs), the neurons show robust, disease-like aggregation of the tau protein. The model was developed as a plate-based high content screening assay coupled with an image analysis algorithm to evaluate the impact of small molecules or genetic perturbations on tau. We show that the assay can be used to evaluate small molecules or screen targeted compound libraries. Using siRNA-based gene knockdown, genes of interest can be evaluated, and we could show that a targeted gene library can be screened, by screening nearly 100 deubiquitinating enzymes (DUBs) in that assay. The assay uses an imaging-based readout, a relatively short timeline, quantifies the extent of tau aggregation, and also allows the assessment of cell viability. Furthermore, it can be easily adapted to different hiPSC lines or neuronal subtypes. Taken together, this complex and highly relevant approach can be routinely applied on a weekly basis in the screening funnels of several projects and generates data with a turnaround time of approximately five weeks.

Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection

ObjectiveThe progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. MethodsTo tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. ResultsUnlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. ConclusionThis research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. SignificanceThe findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.

Neuroscience in pictures:1. History of psychiatric neuroscience

Our understanding of the brain basis of mental illness has evolved over three and half millennia. Early insights into the role of the brain in relation to the mind faded during the middle ages as mental illness became the province of religion, spirituality, and philosophy. Psychiatry became a medical discipline again as medical and scientific thinking evolved during the 17th century. However, progress in neuroscience and astute clinical observations were punctuated by setbacks due to lingering dualism, reductionistic thinking, and dogma. Accelerating neuroscience discoveries and methodological innovations are beginning to bring neuroscience and psychiatry closer than ever as we begin the 21st century, This pictorial article seeks to briefly highlight this journey for an early trainee in psychiatry and related professions in mind.

Behavioral and cognitive neuroscience findings regarding assumptions of the evidence accumulation model

Behavioral and cognitive neuroscience findings regarding assumptions of the evidence accumulation model

Supervised Learning in Multilayer Spiking Neural Networks With Spike Temporal Error Backpropagation.

The brain-inspired spiking neural networks (SNNs) hold the advantages of lower power consumption and powerful computing capability. However, the lack of effective learning algorithms has obstructed the theoretical advance and applications of SNNs. The majority of the existing learning algorithms for SNNs are based on the synaptic weight adjustment. However, neuroscience findings confirm that synaptic delays can also be modulated to play an important role in the learning process. Here, we propose a gradient descent-based learning algorithm for synaptic delays to enhance the sequential learning performance of single spiking neuron. Moreover, we extend the proposed method to multilayer SNNs with spike temporal-based error backpropagation. In the proposed multilayer learning algorithm, information is encoded in the relative timing of individual neuronal spikes, and learning is performed based on the exact derivatives of the postsynaptic spike times with respect to presynaptic spike times. Experimental results on both synthetic and realistic datasets show significant improvements in learning efficiency and accuracy over the existing spike temporal-based learning algorithms. We also evaluate the proposed learning method in an SNN-based multimodal computational model for audiovisual pattern recognition, and it achieves better performance compared with its counterparts.

Consciousness, 4E cognition and Aristotle: a few conceptual and historical aspects

The new approach in cognitive science largely known as “4E cognition” (embodied/embedded/enactive/extended cognition), which sheds new light on the complex dynamics of human consciousness, seems to revive some of Aristotle's views. For instance, the concept of “nature” (phusis) and the discussion on “active intellect” (nous poiêtikos) may be particularly relevant in this respect. Out of the various definitions of “nature” in Aristotle's Physics, On the Parts of Animals and Second Analytics, I will concentrate on nature defined as an inner impulse to movement, neither entirely “corporeal,” nor entirely “incorporeal,” and neither entirely “substantial,” nor entirely “accidental.” Related to that, I will consider the distinction in On the Soul between the “active” and the “passive” intellect, which Aristotle asserted as generally present in “nature” itself. By offering a conceptual and historical analysis of these views, I intend to show how the mind–body problem, which is essential for the explanation of consciousness, could be somewhat either eluded or transcended by both ancients and contemporaries on the basis of a subtle account of causation. While not attempting to diminish the impact of the Cartesian paradigm, which led to the so-called “hard problem of consciousness,” I suggest that the most recent neuroscience discoveries on the neurophysiological phenomena related to human consciousness could be better explained and understood if interpreted within a 4E cognition paradigm, inspired by some Aristotelian views.