Recent Neuroscience Research Research Articles

Knowledge-driven multi-graph convolutional network for brain network analysis and potential biomarker discovery

In brain network analysis, individual-level data can provide biological features of individuals, while population-level data can provide demographic information of populations. However, existing methods mostly utilize either individual- or population-level features separately, inevitably neglecting the multi-level characteristics of brain disorders. To address this issue, we propose an end-to-end multi-graph neural network model called KMGCN. This model simultaneously leverages individual- and population-level features for brain network analysis. At the individual level, we construct multi-graph using both knowledge-driven and data-driven approaches. Knowledge-driven refers to constructing a knowledge graph based on prior knowledge, while data-driven involves learning a data graph from the data itself. At the population level, we construct multi-graph using both imaging and phenotypic data. Additionally, we devise a pooling method tailored for brain networks, capable of selecting brain regions that impact brain disorders. We evaluate the performance of our model on two large datasets, ADNI and ABIDE, and experimental results demonstrate that it achieves state-of-the-art performance, with 86.87% classification accuracy for ADNI and 86.40% for ABIDE, accompanied by around 10% improvements in all evaluation metrics compared to the state-of-the-art models. Additionally, the biomarkers identified by our model align well with recent neuroscience research, indicating the effectiveness of our model in brain network analysis and potential biomarker discovery. The code is available at https://github.com/GN-gjh/KMGCN.

Sense of Agency during Encoding Predicts Subjective Reliving.

Autonoetic consciousness (ANC), the ability to re-experience personal past events links episodic memory and self-consciousness by bridging awareness of oneself in a past event (i.e., during its encoding) with awareness of oneself in the present (i.e., during the reliving of a past event). Recent neuroscience research revealed a bodily form of self-consciousness, including the sense of agency (SoA) and the sense of body ownership (SoO) that are based on the integration of multisensory bodily inputs and motor signals. However, the relation between SoA and/or SoO with ANC is not known. Here, we used immersive virtual reality technology and motion tracking and investigated the potential association of SoA/SoO with ANC. For this, we exposed participants to different levels of visuomotor and perspectival congruency, known to modulate SoA and SoO, during the encoding of virtual scenes and collected ANC ratings 1 week after the encoding session. In a total of 74 healthy participants, we successfully induced systematic changes in SoA and SoO during encoding and found that ANC depended on the level of SoA experienced during encoding. Moreover, ANC was positively associated with SoA, but only for the scene encoded with preserved visuomotor and perspectival congruency, and such SoA-ANC coupling was absent for SoO and control questions. Collectively, these data provide behavioral evidence in a novel paradigm that links a key subjective component of bodily self-consciousness during encoding, SoA, to the subjective reliving of those encoded events from one's past, ANC.

Architectural Neuroimmunology: A Pilot Study Examining the Impact of Biophilic Architectural Design on Neuroinflammation

Recent research in architectural neuroscience has found that visual exposure to biophilic design may help reduce occupant physiological stress responses. However, there are still significant gaps in our understanding of the complex ways in which biophilic design impacts on building occupant neurophysiology. The relationship between visual exposure to biophilic design and neurophysiological responses such as neuroinflammation have yet to be directly investigated. This paper examines the results of a pilot study that was established to investigate the relationship between visual exposure to biophilic design and neuroinflammation, as mediated by physiological stress responses. The pilot study utilised a 32-channel quantitative electroencephalograph (qEEG) to assess the relative changes in neuroinflammatory markers (relative alpha and relative delta power band activity) of 10 participants while they were exposed to 2D digital images of buildings that visually expressed varying degrees of biophilic design. Participants exhibited a decrease in relative delta power when exposed to higher levels of biophilic design. No statistically significant changes in relative alpha power were observed. These findings suggest that exposure to buildings with higher degrees of biophilia may result in decreased neuroinflammatory activity. In doing so, this research works to further develop our understanding of the complex ways in which the built environment impacts on occupant neuroinflammation and physiological stress.

자폐스펙트럼의 차이와 다양성에 대한 존재론적 사유

This study aims to understand the neurological differences in the autism spectrum and to propose a neurodiversity ontology that enables people with autism and neurotypical people to coexist through human nature. To this end, I first present the neurological differences on the autism spectrum through recent neuroscience research. Based on this, I present the content and limitations of the theory of mind hypotheses related to autism and examine the causal relationship between the theory of mind and difficulties in social interaction and sensory perception. Next, neurodiversity is presented as a new discourse that can coexist with people with autism by leveraging human nature traits such as empathy, cooperative communication, and affinity, as claimed by evolutionary theory. Regardless of how well the individual functions, autism-related challenges should be supported rather than cured. The emphasis was on respecting people with autism for their individuality or identities. Finally, we are all neurodiverse individuals with cognitive and existential differences. Interdependence is described as a way of life in which a community of neurodiverse people can coexist. Neurodiversity is fundamentally an ontology in the sense that it recognizes human differences and respects diversity. Neurodiversity is fundamentally an ontology in the sense that it recognizes human differences and respects diversity. Neurodiversity can be a new discourse that contemplates the meaning of all beings with temporal differences and honors people with autism who live another way of life.

Brain-inspired modular echo state network for EEG-based emotion recognition.

Previous studies have successfully applied a lightweight recurrent neural network (RNN) called Echo State Network (ESN) for EEG-based emotion recognition. These studies use intrinsic plasticity (IP) and synaptic plasticity (SP) to tune the hidden reservoir layer of ESN, yet they require extra training procedures and are often computationally complex. Recent neuroscientific research reveals that the brain is modular, consisting of internally dense and externally sparse subnetworks. Furthermore, it has been proved that this modular topology facilitates information processing efficiency in both biological and artificial neural networks (ANNs). Motivated by these findings, we propose Modular Echo State Network (M-ESN), where the hidden layer of ESN is directly initialized to a more efficient modular structure. In this paper, we first describe our novel implementation method, which enables us to find the optimal module numbers, local and global connectivity. Then, the M-ESN is benchmarked on the DEAP dataset. Lastly, we explain why network modularity improves model performance. We demonstrate that modular organization leads to a more diverse distribution of node degrees, which increases network heterogeneity and subsequently improves classification accuracy. On the emotion arousal, valence, and stress/calm classification tasks, our M-ESN outperforms regular ESN by 5.44, 5.90, and 5.42%, respectively, while this difference when comparing with adaptation rules tuned ESNs are 0.77, 5.49, and 0.95%. Notably, our results are obtained using M-ESN with a much smaller reservoir size and simpler training process.

The application of eXplainable artificial intelligence in studying cognition: A scoping review.

The rapid advancement of artificial intelligence (AI) has sparked renewed discussions on its trustworthiness and the concept of eXplainable AI (XAI). Recent research in neuroscience has emphasized the relevance of XAI in studying cognition. This scoping review aims to identify and analyze various XAI methods used to study the mechanisms and features of cognitive function and dysfunction. In this study, the collected evidence is qualitatively assessed to develop an effective framework for approaching XAI in cognitive neuroscience. Based on the Joanna Briggs Institute and preferred reporting items for systematic reviews and meta-analyses extension for scoping review guidelines, we searched for peer-reviewed articles on MEDLINE, Embase, Web of Science, Cochrane Central Register of Controlled Trials, and Google Scholar. Two reviewers performed data screening, extraction, and thematic analysis in parallel. Twelve eligible experimental studies published in the past decade were included. The results showed that the majority (75%) focused on normal cognitive functions such as perception, social cognition, language, executive function, and memory, while others (25%) examined impaired cognition. The predominant XAI methods employed were intrinsic XAI (58.3%), followed by attribution-based (41.7%) and example-based (8.3%) post hoc methods. Explainability was applied at a local (66.7%) or global (33.3%) scope. The findings, predominantly correlational, were anatomical (83.3%) or nonanatomical (16.7%). In conclusion, while these XAI techniques were lauded for their predictive power, robustness, testability, and plausibility, limitations included oversimplification, confounding factors, and inconsistencies. The reviewed studies showcased the potential of XAI models while acknowledging current challenges in causality and oversimplification, particularly emphasizing the need for reproducibility.

Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective.

It remains difficult for mobile robots to continue accurate self-localization when they are suddenly teleported to a location that is different from their beliefs during navigation. Incorporating insights from neuroscience into developing a spatial cognition model for mobile robots may make it possible to acquire the ability to respond appropriately to changing situations, similar to living organisms. Recent neuroscience research has shown that during teleportation in rat navigation, neural populations of place cells in the cornu ammonis-3 region of the hippocampus, which are sparse representations of each other, switch discretely. In this study, we construct a spatial cognition model using brain reference architecture-driven development, a method for developing brain-inspired software that is functionally and structurally consistent with the brain. The spatial cognition model was realized by integrating the recurrent state-space model, a world model, with Monte Carlo localization to infer allocentric self-positions within the framework of neuro-symbol emergence in the robotics toolkit. The spatial cognition model, which models the cornu ammonis-1 and -3 regions with each latent variable, demonstrated improved self-localization performance of mobile robots during teleportation in a simulation environment. Moreover, it was confirmed that sparse neural activity could be obtained for the latent variables corresponding to cornu ammonis-3. These results suggest that spatial cognition models incorporating neuroscience insights can contribute to improving the self-localization technology for mobile robots. The project website is https://nakashimatakeshi.github.io/HF-IGL/.

Mitigating radiation-induced cognitive toxicity in brain metastases: More questions than answers.

The emergence of advanced systemic therapies added to the use of cranial radiation techniques has significantly improved outcomes for cancer patients with multiple brain metastases (BM), leading to a considerable increase in long-term survivors. In this context, the rise of radiation-induced cognitive toxicity (RICT) has become increasingly relevant. In this critical narrative review, we address the controversies arising from clinical trials aimed at mitigating RICT. We thoroughly examine interventions such as memantine, hippocampal avoidance irradiation during BM treatment or ina prophylactic setting, and the assessment of cognitive safety in stereotactic radiosurgery (SRS). Our focus extends to recent neuroscience research findings, emphasizing the importance of preserving not only the hippocampal cortex but also other cortical regions involved in neural dynamic networks and their intricate role in encoding new memories. Despite treatment advancements, effectively managing patients with multiple BM and determining the optimal timing and integration of radiation and systemic treatments remain areas requiring further elucidation. Future trials are required to delineate optimal indications and ensure SRS safety. Additionally, the impact of new systemic therapies and the potential effects of delaying irradiation on cognitive functioning also need to be addressed. Inclusive trial designs, encompassing patients with multiple BM and accounting for diverse treatment scenarios, are essential for advancing effective strategies in managing RICT and the treatment of BM patients.

Collective memory: between individual systems of consciousness and social systems.

Following a long period of neglect, research on different facets of collective memory is now developing apace in the human and social sciences, as well as at their interface with psychology and neuroscience. This resolutely multidisciplinary renewal of interest in memory sciences has given rise to a plethora of concepts with diverse meanings (e.g., social frameworks of memory, collective, shared, collaborative, social memory). The purpose of the present study was to provide a conceptual overview from a historical perspective, and above all to clarify concepts that are often used interchangeably, even though they refer to very different realities. Based on recent research in psychology and neuroscience, we use the concept of collective memory to refer to the operations of individual systems of consciousness. Collective memory is not the memory of a collective, but that of its individual members, either as members of social groups (shared memory) or as participants in social interactions (collaborative memory). Drawing on the contributions of contemporary sociology, we show that social memory is not collective memory, as it refers not to individual systems of consciousness, but to social systems. More specifically, it is the outcome of communication operations which, through redundancy and repetition, perform a continuous and selective re-imprinting of meaning that can be used for communication. Writing, printing and the new communication technologies constitute the three historical stages in the formation and development of an autonomous social memory, independent of living memories and social interactions. In the modern era, mass media fulfill an essential function of social memory, by sorting between forgetting and remembering on a planetary scale. When thinking about the articulation between collective memory and social memory, the concept of structural coupling allows us to identify two mechanisms by which individual systems of consciousness and social systems can interact and be mutually sensitized: schemas and scripts, and social roles. Transdisciplinary approach spearheads major methodological and conceptual advances and is particularly promising for clinical practice, as it should result in a better understanding of memory pathologies, including PTSD, but also cognitive disorders in cancer (chemobrain) or in neurodegenerative diseases.

Physical Education and Embodied Learning: A Review

Recent neuroscientific research highlighted interesting interactions between superior cognitive functions and the sensorimotor system. Overcoming the traditional views of the philosophy of mind and cognitive sciences, current empirical evidence shows that bodily states are the basis of information processing and that incarnation contributes to various aspects of mental phenomena. Starting from this theoretical framework, the purpose of this contribution is to outline, through exposure to the most significant discoveries in the world of neuroscience, the importance of Embodied Cognition as an emerging vision that considers the cognitive processes deeply rooted in the interaction of the body with the world. After careful selection and analysis of studies on neuroscience applied to teaching, the study focused on those who experienced in the school context embodied-teaching approaches aiming to promote transversal learning through movement and corporeality in action and interaction. The analysis highlights the strengths and critical points of the studies implemented in recent decades that focus on the integration and transversality of the body in learning. This is to understand whether embodied learning environments involving all spheres of personality can foster perception, knowledge, and conscious action in teaching and learning processes. An important result from the overview is the potential of theory in different educational environments and disciplines. The contemporary theoretical framework highlights the great potential of corporeality and physical education as actors in learning, but at the same time place in the foreground the need to experiment and disseminate new teaching approaches and perspectives.

Using auditory texture statistics for domain-neutral removal of background sounds

IntroductionHuman communication often occurs under adverse acoustical conditions, where speech signals mix with interfering background noise. A substantial fraction of interfering noise can be characterized by a limited set of statistics and has been referred to as auditory textures. Recent research in neuroscience has demonstrated that humans and animals utilize these statistics for recognizing, classifying, and suppressing textural sounds.MethodsHere, we propose a fast, domain-free noise suppression method exploiting the stationarity and spectral similarity of sound sources that make up sound textures, termed Statistical Sound Filtering (SSF). SSF represents a library of spectrotemporal features of the background noise and then compares this against instants in speech-noise-mixtures to subtract contributions that are statistically consistent with the interfering noise.ResultsWe evaluated the performance of SSF using multiple quality measures and human listeners on the standard TIMIT corpus of speech utterances. SSF improved the sound quality across all performance metrics, capturing different aspects of the sound. Additionally, human participants reported reduced background noise levels as a result of filtering, without any significant damage to speech quality. SSF executes rapidly (~100× real-time) and can be retrained rapidly and continuously in changing acoustic contexts.DiscussionSSF is able to exploit unique aspects of textural noise and therefore, can be integrated into hearing aids where power-efficient, fast, and adaptive training and execution are critical.

Worldviews and environmental ethics: Contributions of brain processing networks.

Research suggests that worldviews define our relationship to the environment, including our responsibility to the environment and our planet. This paper examines two specific worldviews and their potential environmental impact: the materialist worldview, considered to be the dominant worldview of Western society, and the so-called post-materialist worldview. We believe that changing the worldview of both individuals and society is key to changing environmental ethics, specifically attitudes, beliefs, and actions towards the environment. Recent neuroscience research suggests that brain filters and networks contribute to concealing an expanded nonlocal awareness. This creates self-referential thinking and contributes to the limited conceptual framework characteristic of a materialist worldview. We discuss the underlying concepts of both materialist and post-materialist worldviews including their impact on environmental ethics, then explore the various types of neural filters and processing networks that contribute to a materialist worldview, and finally explore methods for modifying neural filters and changing worldviews.

Spiking neural network with working memory can integrate and rectify spatiotemporal features.

In the real world, information is often correlated with each other in the time domain. Whether it can effectively make a decision according to the global information is the key indicator of information processing ability. Due to the discrete characteristics of spike trains and unique temporal dynamics, spiking neural networks (SNNs) show great potential in applications in ultra-low-power platforms and various temporal-related real-life tasks. However, the current SNNs can only focus on the information a short time before the current moment, its sensitivity in the time domain is limited. This problem affects the processing ability of SNN in different kinds of data, including static data and time-variant data, and reduces the application scenarios and scalability of SNN. In this work, we analyze the impact of such information loss and then integrate SNN with working memory inspired by recent neuroscience research. Specifically, we propose Spiking Neural Networks with Working Memory (SNNWM) to handle input spike trains segment by segment. On the one hand, this model can effectively increase SNN's ability to obtain global information. On the other hand, it can effectively reduce the information redundancy between adjacent time steps. Then, we provide simple methods to implement the proposed network architecture from the perspectives of biological plausibility and neuromorphic hardware friendly. Finally, we test the proposed method on static and sequential data sets, and the experimental results show that the proposed model can better process the whole spike train, and achieve state-of-the-art results in short time steps. This work investigates the contribution of introducing biologically inspired mechanisms, e.g., working memory, and multiple delayed synapses to SNNs, and provides a new perspective to design future SNNs.

How Unexpected Events are Processed in Theory of Mind Regions: A Conceptual Replication

ABSTRACT Recent research in social neuroscience has postulated that Theory of Mind (ToM) regions play a role in processing social prediction error (PE: the difference between what was expected and what was observed). Here, we tested whether PE signal depends on the type of prior information people use to make predictions—an agent’s prior mental states (e.g. beliefs, desires, preferences) or an agent’s prior behavior—as well as the type of information that confirms or violates such predictions. That is, does prior information about mental states (versus behavior) afford stronger predictions about an agent’s subsequent mental states or behaviors? Additionally, when information about an agent’s prior mental states or behavior is available, is PE signal strongest when information about an agent’s subsequent mental state (vs behavior) is revealed? In line with prior research, results suggest that DMPFC, LTPJ, and RTPJ are recruited more for unexpected than expected outcomes. However, PE signal does not seem to discriminate on the basis of prior or outcome information type. These findings suggest that ToM regions may flexibly incorporate any available information to make predictions about, monitor, and perhaps explain, inconsistencies in social agents.

Free energy and inference in living systems.

Organisms are non-equilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy (FE) principle describes an organism's homeostasis as the regulation of biochemical work constrained by the physical FE cost. By contrast, recent research in neuroscience and theoretical biology explains a higher organism's homeostasis and allostasis as Bayesian inference facilitated by the informational FE. As an integrated approach to living systems, this study presents an FE minimization theory overarching the essential features of both the thermodynamic and neuroscientific FE principles. Our results reveal that the perception and action of animals result from active inference entailed by FE minimization in the brain, and the brain operates as a Schrödinger's machine conducting the neural mechanics of minimizing sensory uncertainty. A parsimonious model suggests that the Bayesian brain develops the optimal trajectories in neural manifolds and induces a dynamic bifurcation between neural attractors in the process of active inference.

An updated classification of meditation methods using principles of taxonomy and systematics.

This paper revisits the proposal for the classification of meditation methods which we introduced in our initial 2013 publication, "Toward a Universal Taxonomy and Definition of Meditation". At that time, we advanced the thesis that meditation methods could be effectively segregated into three orthogonal categories by integrating the taxonomic principle of functional essentialism and the paradigm of Affect and Cognition; and we presented relevant research findings which supported that assertion. This iteration expands upon those theoretical and methodological elements by articulating a more comprehensive Three Tier Classification System which accounts for the full range of meditation methods; and demonstrates how recent neuroscience research continues to validate and support our thesis. This paper also introduces a novel criterion-based protocol for formulating classification systems of meditation methods, and demonstrates how this model can be used to compare and evaluate various other taxonomy proposals that have been published over the past 15 years.

Neuroeducation and Mathematics: The Formation of New Educational Practices.

Recent research in educational neuroscience has established the correlation between the way the human brain works and the process of perceiving and learning mathematical concepts. In this chapter, a research approach is proposed, based on the principles of educational neuroscience, and focuses on the way students deal with new knowledge in mathematics. Initially, using neuroscientific techniques and a multidimensional approach to new knowledge, data will be collected from students. By collecting neurophysiological measurements and analyzing the data, an attempt will be made to formulate learning paths for a better understanding of fractional concepts, based on the needs of each student.

Sustaining Ireland, Body and Soul: A Woman Leader's Story of the Cooperative Movement

This article tells the story of the Cooperative Movement in Ireland during the late nineteenth and early twentieth centuries from the perspective of one of its woman leaders. It does so in order to distil lessons for the contemporary thought leadership of sustainability from a period before the term was coined. It does so with the warrant of Albert Einstein: The distinction between the past, the present and the future is only a stubbornly persistent illusion. Its evidence base is historical literature, but its argument and analysis draw on recent research in leadership studies, neuroscience and theology.

Organization Development and Effectiveness: Essential Female Leadership Competencies for Industry 4.0 Transformation

This paper summarizes and synthesizes research evidence on different women leadership characteristics and behaviors from men and identifies essential female leadership competencies that are necessary to address the new challenges of today’s workplace and the paradigm shifts for female leaders to remain competitive in Industry 4.0. Recent neuroscience and social science research findings regarding women’s leadership characteristics and behaviors were reviewed and discussed. Critical leadership traits, abilities, and skills for (international) business success were studied. Six essential female leadership skill sets for Industry 4.0 were then proposed to provide human resource development practitioners a strategic framework that will be necessary for women leadership development and to promote gender equality in the workplace. Some feminine traits can be women’s leadership strengths such as sensing, listening, accepting, approaching, collaborating, supporting, and encouraging skills. Finally, implications for human resource development and recommendations for future research were provided.

BreakThrough Communication in a Hybrid World: Amplifying Interactive, Experiential Learning

This case study document describes the research foundation and instructional design of BreakThrough Communication, an evidence-based hybrid learning enterprise that builds organizational capacity by boosting communication skill in individuals and teams. BTC’s tiered talent development program combines asynchronous, self-paced video instruction in foundational knowledge; live virtual workshops customized to engage learners in practicing new skills on organizational challenges they face; and change-sustaining follow-through to embed new skills through assessment, video reinforcers, and real-time practice sessions. A certificate program that prepares internal trainers to assume communication coach roles completes the process. The BTC learning system is grounded in the traditional pedagogy of the communication field, aligns with adult learning theory, and reflects recent neuroscience research on how the human brain most efficiently learns. Three distinct areas of communication skill comprise the program’s subject material: inter-personal conflict; teamwork; persuasive speaking. The communication models within each area have been validated in both experimental and field studies, shown to change behavior and affect outcomes.