Mechanical thoughts and applications in cognitive neuroscience

The way the brain work and its principle of work has long been a big scientific question that scientists have dreamed of solving. However, as is known to all, the brain works at different levels, and the operation at different levels is interactional and mutually coupled. Unfortunately, until now, we still do not know how the nervous system at different levels is interacting and coupling with each other. This review provides some preliminary discussions on how to address these scientific questions, for which we propose a novel theory of the brain called neural energy. Such a theoretical and research approach can couple neural information with neural energy to address the interactions of the nervous system at various levels. Therefore, this review systematically summarizes the neural energy theories and methods proposed by our research in the field of brain science, as well as the internal relationship between mechanics and neural energy theory. Focuses on how to construct a Wang–Zhang (W–Z) neuron model equivalent to Hodgkin–Huxley (H–H) model by using the idea of analytical dynamics. Then, based on this model, we proposed a large-scale neural model and a theoretical framework of global neural coding of the brain in the field of neuroscience. It includes information processing of multiple sensory and perceptual nervous systems such as visual perception, neural mechanism of coupling between default mode network and functional network of brain, memory switching and brain state switching, brain navigation, prediction of new working mechanism of neurons, and interpretation of experimental phenomena that are difficult to be explained by neuroscience. It is proved that the new W–Z neuron model and neural energy theory have unique functions and advantages in neural modeling, neural information processing and methodology. The idea of large-scale neuroscience research with neural energy as the core will provide a potentially powerful research method for promoting the fusion of experimental neuroscience and theoretical neuroscience in the future, and propose a widely accepted brain theory system between experimental neuroscience and theoretical neuroscience. It is of great scientific significance to abandon the shortcomings of reductive and holism research methods in the field of neuroscience, and effectively integrate their respective advantages in methodology.

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods’ efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

Through their unique capabilities in analysing mental and behavioral data AI and ML transform the field of cognitive neuroscience. MRI and fMRI rely on AI to reliably identify memory troubles and also boost the early recognition of neurodegenerative disorders like Alzheimer's and Parkinson's. cognitive rehabilitation programs that utilize AI improve therapy results by responding instantly to individual needs. By using AI to power, them interfaces allow neural patterns to link with outside tools and offer fresh treatment methods for those who are mentally challenged. Psychological evaluations and treatments are upgrade their reliability as a result of AI's method to analyze emotional states and behaviours. Machine learning and AI are changing the field of cognitive neuroscience by improving diagnostics treatment and rehabilitation of many cognitive disorders.

This chapter provides an introduction to Bayesian models and their application in cognitive neuroscience. The central feature of Bayesian models, as opposed to other classes of models, is that Bayesian models represent the beliefs of an observer as probability distributions, allowing them to integrate information while taking its uncertainty into account. In the chapter, we will consider how the probabilistic nature of Bayesian models makes them particularly useful in cognitive neuroscience. We will consider two types of tasks in which we believe a Bayesian approach is useful: optimal integration of evidence from different sources, and the development of beliefs about the environment given limited information (such as during learning). We will develop some detailed examples of Bayesian models to give the reader a taste of how the models are constructed and what insights they may be able to offer about participants’ behavior and brain activity.

Applications in cognitive neuroscience: electroencephalography-based prediction of treatment response in schizophrenia and the effect of authenticity on emotion perception

Recently, cultural neuroscience has gained attention as a new, important, and interdisciplinary topic in the field of neuroscience. It helps us understand the interaction of cultural and biological factors over the course of life. This study aims to provide a comprehensive overview of the field to readers and potential researchers engaged in cultural neuroscience research. A bibliometric analysis was performed on 113 articles in the field of cultural neuroscience from 2008 to 2021 using data from the core collection of Web of Science. Network visualization software VOSviewer and ITGInsight were used for performance analysis and science mapping. Specifically, the performance analysis included countries, institutions, authors, papers, and journals, while science mapping analyzed the collaboration network, keyword network, bibliographic coupling network, and time series evolution. The results showed that the United States was the most productive country, Northwestern University was the most influential research institution, Chiao Jy was the most influential scholar, and “Social Cognitive and Affective Neuroscience” made the greatest contribution to publishing in the field of cultural neuroscience. Furthermore, collaboration is expected to be the development trend in the future. The key research topics in the field of cultural neuroscience included neuroimaging and psychiatric diseases, theoretical methods, interdisciplinary research, cultural differences (collectivism and individualism), and brain functions. Finally, future research will focus on cultural neuroscience, culture, and self, while adolescence will be the emerging research frontier.

Basic and Clinical Neuroscience (BCN) is an open-access journal, which is the official publication of Iran University of Medical Sciences and the Iranian Neuroscience Society. BCN is an international multidisciplinary peer-reviewed journal that publishes editorials, original full‐length research articles, short communications, reviews, methodological papers, commentaries, case-reports and perspectives in the broad fields of developmental, molecular, cellular, systems, computational, behavioral, cognitive, and clinical neuroscience. Manuscripts submitted to BCN should describe novel results generated by experiments that were guided by clearly defined aims or hypotheses and should not have appeared or submitted to any other journals. BCN’s aim is to provide serious ties in interdisciplinary communication, accessibility to a broad readership internationally, the effective peer review process, and independence from all possible non-scientific interests. BCN also tries to empower national, regional and international collaborative networks in the field of neuroscience and to be the voice of the Iranian and regional neuroscience community in the world of neuroscientists. BCN is looking for acquiring world-class quality standards as a highly visible and cited peer-reviewed journal. To achieve this aim, BCN asks for support from all Iranian and non-Iranian neuroscientists doing research around the world for submitting manuscripts in all aspects of neuroscience. Meanwhile, we encourage authors to submit manuscripts that cover translational aspects between basic and clinical neuroscience. Please join us in this endeavor. We are looking forward to your active contribution, reviews, comments, and valuable inputs.

Color as a unique sentience human has is ubiquitous in graphic design and art. It can make graphic design more gorgeous with an ingenious idea. This paper aims to discover the relationship between color composition and cognitive neuroscience in attempt to fully exert the advantages of colors in graphic design. Here the important role of colors in graphic design and the relationship between color composition and cognitive neuroscience are first elaborated. Next, several impact factors affecting graphic design is blended with the color concept to explore how they impact each other.

BackgroundAccurate prediction of neural activity during task‐based paradigms is essential for advancing computational neuroscience and improving clinical applications. Leveraging publicly available datasets, this study developed a custom machine learning model to analyze task‐specific brain activity by aggregating functional MRI (fMRI) data into 360 regions of interest (ROIs) based on the Glasser parcellation.MethodFunctional data from the Human Connectome Project (HCP), involving 100 participants, was utilized for this analysis. Participants performed seven cognitive tasks, including motor, working memory, emotion, language, social cognition, gambling, and relational reasoning, with each task collected over two acquisition runs (LR and RL) at a temporal resolution of 0.72 seconds. The data were preprocessed to extract ROI‐level information, and a custom‐built neural network was trained to predict brain activity across conditions for each task.ResultThe model achieved high predictive accuracy, successfully identifying task‐specific neural activation patterns across diverse cognitive paradigms. Key insights revealed robust hemispheric symmetry during motor tasks and distinct activation profiles for relational reasoning and social cognition tasks. The use of ROI‐level aggregation provided a balance between computational efficiency and spatial resolution, enhancing the interpretability of the results.Conclusionhis work demonstrates the potential of machine learning models in analyzing publicly available neuroimaging datasets to predict and characterize task‐specific brain activity. The findings underscore the importance of data‐driven approaches in uncovering neural dynamics and pave the way for applications in cognitive neuroscience and clinical interventions.

BackgroundAccurate prediction of neural activity during task‐based paradigms is essential for advancing computational neuroscience and improving clinical applications. Leveraging publicly available datasets, this study developed a custom machine learning model to analyze task‐specific brain activity by aggregating functional MRI (fMRI) data into 360 regions of interest (ROIs) based on the Glasser parcellation.MethodFunctional data from the Human Connectome Project (HCP), involving 100 participants, was utilized for this analysis. Participants performed seven cognitive tasks, including motor, working memory, emotion, language, social cognition, gambling, and relational reasoning, with each task collected over two acquisition runs (LR and RL) at a temporal resolution of 0.72 seconds. The data were preprocessed to extract ROI‐level information, and a custom‐built neural network was trained to predict brain activity across conditions for each task.ResultThe model achieved high predictive accuracy, successfully identifying task‐specific neural activation patterns across diverse cognitive paradigms. Key insights revealed robust hemispheric symmetry during motor tasks and distinct activation profiles for relational reasoning and social cognition tasks. The use of ROI‐level aggregation provided a balance between computational efficiency and spatial resolution, enhancing the interpretability of the results.ConclusionHis work demonstrates the potential of machine learning models in analyzing publicly available neuroimaging datasets to predict and characterize task‐specific brain activity. The findings underscore the importance of data‐driven approaches in uncovering neural dynamics and pave the way for applications in cognitive neuroscience and clinical interventions.

Purpose: This research project has a single purpose: the construction and evaluation of PKG-LLM, a knowledge graph framework whose application is primarily intended for cognitive neuroscience. It also aims to improve predictions of relationships among neurological entities and improve named entity recognition (NER) and relation extraction (RE) from large neurological datasets. Employing the GPT-4 and expert review, we aim to demonstrate how this framework may outperform traditional models by way of precision, recall, and F1 score, intending to provide key insights into possible future clinical and research applications in the field of neuroscience. Method: In the evaluation of PKG-LLM, there were two different tasks primarily: relation extraction (RE) and named entity recognition (NER). Both tasks processed data and obtained performance metrics, such as precision, recall, and F1-score, using GPT-4. Moreover, there was an integration of an expert review process comprising neurologists and domain experts reviewing those extracted relationships and entities and improving such final performance metrics. Model comparative performance was reported against StrokeKG and Heart Failure KG. On the other hand, PKG-LLM evinced itself to link prediction-in-cognition through metrics such as Mean Rank (MR), Mean Reciprocal Rank (MRR), and Precision at K (P@K). The model was evaluated against other link prediction models, including TransE, RotatE, DistMult, ComplEx, ConvE, and HolmE. Findings: PKG-LLM demonstrated competitive performance in both relation extraction and named entity recognition tasks. In its traditional form, PKG-LLM achieved a precision of 75.45\%, recall of 78.60\%, and F1-score of 76.89\% in relation extraction, which improved to 82.34\%, 85.40\%, and 83.85\% after expert review. In named entity recognition, the traditional model scored 73.42\% precision, 76.30\% recall, and 74.84\% F1-score, improving to 81.55\%, 84.60\%, and 82.99\% after expert review. For link prediction, PKG-LLM achieved an MRR of 0.396, P@1 of 0.385, and P@10 of 0.531, placing it in a competitive range compared to models like TransE, RotatE, and ConvE. Conclusion: This study showed that PKG-LLM mainly outperformed the existing models by adding expert reviews in its application in extraction and named entity recognition tasks. Further, the model's competitive edge in link prediction lends credence to its capability in knowledge graph construction and refinement in the field of cognitive neuroscience as well. PKG-LLM's superiority over existing models and its ability to generate more accurate results with clinical relevance indicates that it is a significant tool to augment neuroscience research and clinical applications. The evaluation process entailed using GPT-4 and expert review. This approach ensures that the resulting knowledge graph is scientifically compelling and practically beneficial in more advanced cognitive neuroscience research.

Mixed norms that promote structured sparsity have broad application in signal processing and machine learning problems. In this work we present a new algorithm for computing the projection onto the $\ell_{\infty,1}$ ball, which has found application in cognitive neuroscience and classification tasks. This algorithm is based on a Steffensen type root search technique, with a number of improvements over prior root search methods for the same problem. First, we theoretically derive an initial guess for the root search algorithm that helps to reduce the number of iterations to be performed. Second, we change the root search method, and through an analysis of the root search function, we construct a pruning strategy that significantly reduces the number of operations. Numerical simulations show that, compared to the state-of-the-art, our algorithm is between 4 and 5 times faster on average, and of up to 14 times faster for very sparse solutions.

Developing individualized spatial models that capture the complex dynamics of multi-electrode EEG data is essential for accurately decoding global neural activity. A widely used approach is network modeling, where electrodes are represented as nodes. A key challenge lies in defining the network edges and weights, as precise connectivity estimation is critical for enhancing neural characterization and extracting discriminative features, such as those needed for task decoding. In this work, we propose a method for inferring subject-specific brain graphs from EEG data, explicitly designed to exhibit small-world and scale-free network properties. Our approach begins by computing phase-locking values between EEG channel pairs to build a backbone graph, which is then refined into an individualized small-world and scale-free network. To reduce computational complexity while preserving subject-specific characteristics, we apply Kron reduction to the resulting graph. We evaluated the proposed method on motor imagery decoding and brain fingerprinting tasks using two EEG datasets. Results show that our model consistently outperforms other benchmark graph models. Furthermore, we show that integrating classical EEG features with those derived using graph signal processing principles significantly improves performance. Overall, our findings highlight the potential of the proposed graph construction framework to enhance EEG analysis, with promising implications for a wide range of applications in cognitive neuroscience and brain-computer interface research.

Summary Noninvasive brain stimulation with transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS) is valuable in research and has potential therapeutic applications in cognitive neuroscience, neurophysiology, psychiatry, neurology and neurorehabilitation. TMS and tDCS allow diagnostic and interventional neurophysiology applications, targeted neuropharmacology delivery and systematic exploration of local cortical plasticity and brain network dynamics. Repetitive TMS or tDCS can modulate cortical excitability of the directly targeted brain region beyond the duration of the brain stimulation train by the induction of phenomena similar to long-term potentiation (LTP) or long-term depression (LTD), which may increase or decrease cortical excitability respectively. The effects of TMS or tDCS do not remain limited to the targeted brain region, and thus disruption of brain activity by TMS or tDCS can result in behavioural facilitation via distant cortical or subcortical structures. In addition, state-dependent effects of noninvasive brain stimulation condition the impact of TMS and tDCS and may result in paradoxical behavioural effects of the stimulation. Greater understanding of the neurobiological mechanisms involved in such intances may allow us to systematically use TMS or tDCS to leverage paradoxical functional facilitation for therapeutic applications. Introduction In the past decades, neuroimaging techniques such as computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), magnetoencephalography (MEG) and electro-encephalography (EEG) have shaped the ways in which we model behaviour. Anatomical neuroimaging techniques produce ever more detailed descriptions of the extent of lesions produced by brain injury.

Predicting an individual's cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual's brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual's time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.