Neuroscience Studies Research Articles

Transference, transformation, and transmission: some clinical and conceptual issues

ABSTRACT Transmission is a concept that has a place in the English translation of Freud’s work, and hence, as predicted, it figures in different contexts of later generations of analysts. Still, as a theoretical concept, it is ambiguous and elusive. In my opinion, the concept of ‘transmission’ is underdeveloped. In this article, I present some clinical and conceptual issues on transference and transformation in light of when, how, and what is transmitted. The article also discusses several psychoanalytic theories that elucidate transmission, such as transference, projective identification, the analytic third, and transformation. Some philosophical and neuroscientific studies are also discussed. I will argue that work in and with transference – by transformation – there is something that gets transmitted. The clinical implication of this is that the analyst must be willing to be receptive to the transmitted part. Thus, we can conclude that we may delineate transference as being either subjective or intersubjective in nature.

The Application Progress of Optogenetics in Neuroscience and Behavioral Studies

Optogenetics, as an emerging interdisciplinary technology, has enabled the precise manipulation of neuronal activity by combining optical and genetic approaches. This paper summarizes the application of optogenetics in neuroscience and behavioral research and introduces its research background and topics. The application of optogenetics in neuronal activity regulation, neural loop resolution, exploration of learning and memory mechanisms, and treating neurological diseases were focused on. By combining a literature review and case analysis, we analyze the core principles of optogenetics and the technical points of optogenetics and demonstrate their potential applications in different research scenarios. The types and properties of photosensitive proteins and their application strategies in experiments are detailed, demonstrating the unique advantages of optogenetics in revealing the mysteries of neurobiological processes. The results show that optogenetic technology provides a powerful tool to reveal the mysteries of neurobiological processes, but it still faces challenges such as light transmission efficiency and gene delivery safety in practical applications. This paper summarizes the advantages and limitations of optogenetics and prospects for its future direction, providing an important reference for the continuous development of neuroscience and behavioral research.

A simple statistical framework for small sample studies.

Most studies in psychology, neuroscience, and life science research make inferences about how strong an effect is on average in the population. Yet, many research questions could instead be answered by testing for the universality of the phenomenon under investigation. By using reliable experimental designs that maximize both sensitivity and specificity of individual experiments, each participant or subject can be treated as an independent replication. This approach is common in certain subfields. To date, there is however no formal approach for calculating the evidential value of such small sample studies and to define a priori evidence thresholds that must be met to draw meaningful conclusions. Here we present such a framework, based on the ratio of binomial probabilities between a model assuming the universality of the phenomenon versus the null hypothesis that any incidence of the effect is sporadic. We demonstrate the benefits of this approach, which permits strong conclusions from samples as small as two to five participants and the flexibility of sequential testing. This approach will enable researchers to preregister experimental designs based on small samples and thus enhance the utility and credibility of such studies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Mathematics teachers opinions after training on educational neuroscience

Educational neuroscience is a multidisciplinary field established to provide assistance to students by bringing together neuroscience, cognitive psychology and pedagogy. With the development of this field, it is likely that different approaches such as the creation of student-centered education models that appeal to different intelligence types will be applied in education. Educational neuroscience aims to understand the practices and methods in education by examining brain activity, learning processes and mental functions, and thus to create a more supportive learning environment in school environments. One way to adapt the findings obtained from neuroscience studies to education is to first inform the teacher about the basic information of the educational neuroscience approach. In this direction, the aim of the research conducted is to examine the views of mathematics teachers after the training application on educational neuroscience. In this study, a case study, which is one of the qualitative research methods, was preferred. The data of the study were obtained from mathematics teachers working in different schools, determined through easily accessible case sampling, which is one of the purposeful sampling methods. The teachers participating in the research consist of 10 people, 2 of whom have a master's degree, 1 of whom is doing a master's degree, and 7 of whom have a bachelor's degree. The findings reveal that teachers have a positive perception of the connection between neuroscience and education. In addition, the teachers particularly emphasized that educators, neuroscientists and other relevant experts should work together for the development and applicability of educational neuroscience. Teachers commented on the application of neuroscience data to educational practice in terms of mathematics lessons, such as making mathematical knowledge permanent and determining which students should use mathematical teaching materials more. It is recommended that teachers and researchers work collaboratively to advance the developing field of educational neuroscience.

The connecting brain in context: How adolescent plasticity supports learning and development

Puberty initiates significant neurobiological changes that amplify adolescents’ responsiveness to their environment, facilitating neural adaptation through processes like synaptic pruning, myelination, and neuronal reorganization. This heightened neuroplasticity, combined with their burgeoning social curiosity and appetite for risk, propels adolescents to explore diverse new environments and forge social bonds. Such exploration can accelerate experiential learning and the formation of social networks as adolescents prepare for adult independence. This review examines the complex interplay between adolescent neuroplasticity, environmental influences, and learning processes, synthesizing findings from recent studies that illustrate how factors such as social interactions, school environments, and neighborhood contexts influence both the transient activation and enduring organization of the developing brain. We advocate for incorporating social interaction into adolescent-tailored interventions, leveraging their social plasticity to optimize learning and development during this critical phase. Going forward, we discuss the importance of longitudinal studies that employ multimodal approaches to characterize the dynamic interactions between development and environment, highlighting recent advancements in quantifying environmental impacts in studies of developmental neuroscience. Ultimately, this paper provides an updated synopsis of adolescent neuroplasticity and the environment, underscoring the potential for environmental enrichment programs to support healthy brain development and resilience at this critical development stage.

Leveraging research into sex differences and steroid hormones to improve brain health.

Sex differences, driven in part by steroid hormones, shape the structure and function of the brain throughout the lifespan and manifest across brain health and disease. The influence of steroid hormones on neuroplasticity, particularly in the adult hippocampus, differs between the sexes, which has important implications for disorders and diseases that compromise hippocampus integrity, such as depression and Alzheimer disease. This Review outlines the intricate relationship between steroid hormones and hippocampal neuroplasticity across the adult lifespan and explores how the unique physiology of male and femaleindividuals can affect health and disease. Despite calls to include sex and gender in research, only 5% of neuroscience studies published in 2019 directly investigated the influence of sex. Drawing on insights from depression, Alzheimer disease and relevant hippocampal plasticity, this Review underscores the importance of considering sex and steroid hormones to achieve a comprehensive understanding of disease susceptibility and mechanisms. Such consideration will enable the discovery of personalized treatments, ultimately leading to improved health outcomes for all.

Surfing beta burst waveforms to improve motor imagery-based BCI

Abstract Our understanding of motor-related, macroscale brain processes has been significantly shaped by the description of the event-related desynchronization (ERD) and synchronization (ERS) phenomena in the mu and beta frequency bands prior to, during and following movement. The demonstration of reproducible, spatially- and band-limited signal power changes has, consequently, attracted the interest of non invasive brain-computer interface (BCI) research for a long time. BCIs often rely on motor imagery (MI) experimental paradigms that are expected to generate brain signal modulations analogous to movement-related ERD and ERS. However, a number of recent neuroscience studies has questioned the nature of these phenomena. Beta band activity has been shown to occur, on a single-trial level, in short, transient and heterogeneous events termed bursts rather than sustained oscillations. In a previous study, we established that an analysis of hand MI binary classification tasks based on beta bursts can be superior to beta power in terms of classification score. In this article we elaborate on this idea, proposing a signal processing algorithm that is comparable to- and compatible with state-of-the-art techniques. Our pipeline filters brain recordings by convolving them with kernels extracted from beta bursts and then applies spatial filtering before classification. This data-driven filtering allowed for a simple and efficient analysis of signals from multiple sensors thus being suitable for online applications. By adopting a time-resolved decoding approach we explored MI dynamics and showed the specificity of the new classification features. In accordance with previous results, beta bursts improved classification performance compared to beta band power, while often increasing information transfer rate compared to state-of-the-art approaches.

Worry and rumination elicit similar neural representations: neuroimaging evidence for repetitive negative thinking.

Repetitive negative thinking (RNT) captures shared cognitive and emotional features of content-specific cognition, including future-focused worry and past-focused rumination. The degree to which these distinct but related processes recruit overlapping neural structures is undetermined, because most neuroscientific studies only examine worry or rumination in isolation. To address this, we developed a paradigm to elicit idiographic worries and ruminations during an fMRI scan in 39 young adults with a range of trait RNT scores. We measured concurrent emotion ratings and heart rate as a physiological metric of arousal. Multivariate representational similarity analysis revealed that regions distributed across default mode, salience, and frontoparietal control networks encode worry and rumination similarly. Moreover, heart rate did not differ between worry and rumination. Capturing the shared neural features between worry and rumination throughout networks supporting self-referential processing, memory, salience detection, and cognitive control provides novel empirical evidence to bolster cognitive and clinical models of RNT.

Neuroscience Studies on ADHD and Autism: Exploring Neurodevelopmental Mechanisms and Clinical Implications

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are two prevalent neurodevelopmental conditions characterized by distinct yet overlapping cognitive, behavioral, and neurological profiles. Neuroscience research has provided profound insights into the underlying neurodevelopmental mechanisms that contribute to these conditions, offering pathways for improved diagnosis, treatment, and support. This study explores the neural correlates of ADHD and ASD, focusing on brain structure, functional connectivity, and neurotransmitter systems. It also investigates genetic and environmental influences that contribute to these disorders. By integrating cutting-edge neuroimaging techniques such as functional MRI (fMRI) and electroencephalography (EEG) with behavioral assessments, this research aims to identify biomarkers that enhance diagnostic precision and therapeutic interventions. Additionally, it examines the implications of neuroplasticity and early intervention strategies in mitigating symptom severity. The study emphasizes the importance of multidisciplinary approaches that bridge neuroscience, psychology, and clinical practice to improve outcomes for individuals with ADHD and ASD. By providing a deeper understanding of the neurodevelopmental underpinnings of these conditions, this research contributes to a growing body of knowledge aimed at fostering inclusive and evidence-based care.

Characterizing Behavioral Dynamics in Bipolar Disorder with Computational Ethology.

New technologies for the quantification of behavior have revolutionized animal studies in social, cognitive, and pharmacological neurosciences. However, comparable studies in understanding human behavior, especially in psychiatry, are lacking. In this study, we utilized data-driven machine learning to analyze natural, spontaneous open-field human behaviors from people with euthymic bipolar disorder (BD) and non-BD participants. Our computational paradigm identified representations of distinct sets of actions ( motifs ) that capture the physical activities of both groups of participants. We propose novel measures for quantifying dynamics, variability, and stereotypy in BD behaviors. These fine-grained behavioral features reflect patterns of cognitive functions of BD and better predict BD compared with traditional ethological and psychiatric measures and action recognition approaches. This research represents a significant computational advancement in human ethology, enabling the quantification of complex behaviors in real-world conditions and opening new avenues for characterizing neuropsychiatric conditions from behavior.

Combined LinkNet-MBi-LSTM for brain activity recognition with new Stockwell transform features.

Recognizing brain activity from EEG waves is an important field of study in biomedical engineering and neuroscience. Conventional approaches usually begin with signal processing techniques to extract features from the EEG data, and then machine learning algorithms are applied to classify the data. However, the spatial resolution of these EEG signals is low, which makes it difficult to pinpoint the exact location of the neural activity source in the brain. There are ongoing initiatives to use DL-based brain activity recognition algorithms to overcome these constraints. Therefore, this work presents a novel hybrid framework for brain activity detection using the enhanced Stockwell transform and an EEG signal that is called LinkNet and modified bidirectional-long short-term memory (LN-MBi-LSTM) model. This framework follows a methodical approach that includes stages for feature extraction, brain activity recognition and preprocessing. Firstly, the improved Weiner filtering (IWF) approach is used to preprocess the EEG input signal. The relevant features are then extracted using a feature extraction technique from the preprocessed EEG signal. To identify the brain activity, these recovered feature sets are subsequently processed separately using LinkNet and modified bidirectional-long short-term memory (MBi-LSTM). A thorough analysis that takes into account both simulation and experimental calculations is part of the validation process for the LN-MBi-LSTM model. Finally, this study demonstrates the therapeutic potential of the LN-MBi-LSTM framework by presenting a strong and verified model for brain activity recognition. With the highest precision of 0.997, the LinkNet-MBi-LSTM model distinguishes itself from other models and confirms its exceptional capacity to produce accurate positive predictions.

Synchronization-Inspired Interpretable Neural Networks.

Synchronization is a ubiquitous phenomenon in nature that enables the orderly presentation of information. In the human brain, for instance, functional modules such as the visual, motor, and language cortices form through neuronal synchronization. Inspired by biological brains and previous neuroscience studies, we propose an interpretable neural network incorporating a synchronization mechanism. The basic idea is to constrain each neuron, such as a convolution filter, to capture a single semantic pattern while synchronizing similar neurons to facilitate the formation of interpretable functional modules. Specifically, we regularize the activation map of a neuron to surround its focus position of the activated pattern in a sample. Moreover, neurons locally interact with each other, and similar ones are synchronized together during the training phase adaptively. Such local aggregation preserves the globally distributed representation nature of the neural network model, enabling a reasonably interpretable representation. To analyze the neuron interpretability comprehensively, we introduce a series of novel evaluation metrics from multiple aspects. Qualitative and quantitative experiments demonstrate that the proposed method outperforms many state-of-the-art algorithms in terms of interpretability. The resulting synchronized functional modules show module consistency across data and semantic specificity within modules.

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.

Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical image analysis and neuroscience research, as it streamlines broad downstream tasks without the need for numerous costly annotations. However, there has been limited investigation into brain network foundation models, limiting their adaptability and generalizability for broad neuroscience studies. In this study, we aim to bridge this gap. In particular, 1) we curated a comprehensive dataset by collating images from 30 datasets, which comprises 70,781 samples of 46,686 participants. Moreover, we introduce pseudo-functional connectivity (pFC) to further generates millions of augmented brain networks by randomly dropping certain timepoints of the BOLD signal; 2) we propose the BrainMass framework for brain network self-supervised learning via mask modeling and feature alignment. BrainMass employs Mask-ROI Modeling (MRM) to bolster intra-network dependencies and regional specificity. Furthermore, Latent Representation Alignment (LRA) module is utilized to regularize augmented brain networks of the same participant with similar topological properties to yield similar latent representations by aligning their latent embeddings. Extensive experiments on eight internal tasks and seven external brain disorder diagnosis tasks show BrainMass's superior performance, highlighting its significant generalizability and adaptability. Nonetheless, BrainMass demonstrates powerful few/zero-shot learning abilities and exhibits meaningful interpretation to various diseases, showcasing its potential use for clinical applications.

Contrasting topologies of synchronous and asynchronous functional brain networks

ABSTRACT We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy and compared aFN topology with the correlation-based synchronous functional networks (sFNs), which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and Neurodevelopment in Adolescence study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the default mode network in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity, which the other type of network does not.

Multilingualism and Cognitive Flexibility: Insights from Neuroscience and Linguistics

This paper explores the relationship between multilingualism and cognitive flexibility, examining the cognitive benefits associated with managing multiple languages. Drawing on both neuroscientific and linguistic studies, the paper argues that multilingual individuals exhibit enhanced cognitive flexibility, superior executive control functions, and improved task-switching abilities. These advantages, often referred to as the "bilingual advantage," extend beyond language use and positively impact problem-solving, decision-making, and mental adaptability. The paper also highlights the long-term benefits of multilingualism, such as delayed cognitive decline and greater neuroplasticity, particularly in older adults. Furthermore, it addresses the practical implications of promoting multilingual education in early childhood to enhance cognitive development and foster global citizenship. The paper concludes with suggestions for future research, emphasizing the need for interdisciplinary collaboration to further understand the cognitive and societal impacts of multilingualism.

The Connection Between Literary Images and Visual Perception in Meditative Practice

The study of practice based on visualization and the role of visualization in shaping the outcomes of contemplative practice is an overlooked research niche. The aim of this article is to contribute to a deeper understanding of the dynamics of the process that connects literary images and visual perception in relevant meditative practices. The key question is which elements contribute to forming this connection in contemplative practices like meditation. To gain a deeper understanding of the dynamics involved, the study employed a mix of primary and secondary data sources, along with analytical, descriptive, and phenomenological methods, drawing from an interdisciplinary approach that includes psychology, neuroscience, and literature. The following meditation practices were selected: contemplative meditation, creative visualization, koan meditation, imaginative meditation, creative workshops with haiku poetry as an outcome, and meditative storytelling. The identified elements in the connection between literary depictions and visual perception include mental imagery and visualization, cognitive processes, sensory processes, and emotional processes. The importance of literary techniques is particularly highlighted – using descriptive language with metaphors, similes, allegories, and other stylistic figures that create strong visual images. This connection has a neurological basis – neuroscience studies show that reading/listening to texts describing visual experiences activates brain areas involved in actual visual perception. This overlap suggests that the brain processes literary descriptions similarly to how it processes real visual stimuli. The connection between literary depictions and visual perception significantly enhances the quality of meditative practice and promotes deeper understanding and emotional-volitional engagement with the text and personal development of the meditators. Both secondary and primary data sources were used in the paper.

Impact of obesity on allergic respiratory diseases and on mental and cognitive performance

Summary Background Obesity and allergies are among the most common diseases of our civilization. Given the simultaneous rise in the prevalence of these diseases in recent years, a potential causal link between the two has been proposed. In particular, obese patients are at an increased risk of developing bronchial asthma, likely due to mechanical restrictions but also to metabolic changes that adversely affect immune function. Neuroscience studies have also shown that obesity can lead to impaired brain function and mental health. In the following review, we will take a closer look at our studies that focus on the influence of obesity on allergic diseases and cognitive performance. Results Both human studies and animal models (mice) have shown that obesity leads to increased allergic responses in the airways. Our studies in a mouse model of obesity confirm that an obese phenotype is associated with increased allergic sensitization and manifestation. These changes are associated with significant shifts in the composition of the gut microbial flora. The microbiome changes are further associated with allergic airway inflammation and an increased incidence of T helper 1 (Th1) type pulmonary macrophages. Interestingly, despite the changes in the microbiome, it is possible to effectively prevent allergy development by inducing oral tolerance. Furthermore, it was observed that obese mice show increased signs of anxiety and depression, as well as reduced cognitive performance. Conclusion Obesity is a complex metabolic disease that significantly impacts our body’s gut microbiome and immune system, resulting in an increased incidence of allergic asthma and neurological/psychological changes. Attention should be given to both the prophylactic and therapeutic measures to mitigate the impact of obesity, including oral tolerance for managing existing allergic diseases.

Magnetoelectric nanodiscs enable wireless transgene-free neuromodulation.

Deep brain stimulation with implanted electrodes has transformed neuroscience studies and treatment of neurological and psychiatric conditions. Discovering less invasive alternatives to deep brain stimulation could expand its clinical and research applications. Nanomaterial-mediated transduction of magnetic fields into electric potentials has been explored as a means for remote neuromodulation. Here we synthesize magnetoelectric nanodiscs (MENDs) with a core-double-shell Fe3O4-CoFe2O4-BaTiO3 architecture (250 nm diameter and 50 nm thickness) with efficient magnetoelectric coupling. We find robust responses to magnetic field stimulation in neurons decorated with MENDs at a density of 1 µg mm-2 despite individual-particle potentials below the neuronal excitation threshold. We propose a model for repetitive subthreshold depolarization that, combined with cable theory, supports our observations in vitro and informs magnetoelectric stimulation in vivo. Injected into the ventral tegmental area or the subthalamic nucleus of genetically intact mice at concentrations of 1 mg ml-1, MENDs enable remote control of reward or motor behaviours, respectively. These findings set the stage for mechanistic optimization of magnetoelectric neuromodulation towards applications in neuroscience research.

Beyond the lab: Feasibility of cognitive neuroscience data collection during a speleological expedition

In human cognitive neuroscience and neuropsychology studies, laboratory-based research tasks have been important to establish principles of brain function and its relationship to behaviour; however, they differ greatly from real-life experiences. Several elements of real-life situations that impact human performance, such as stressors, are difficult or impossible to replicate in the laboratory. Expeditions offer unique possibilities for studying human cognition in complex environments that can transfer to other situations with similar features. For example, as caves share several of the physical and psychological challenges of safety-critical environments such as spaceflight, underground expeditions have been developed as an analogue for astronaut training purposes, suggesting that they might also be suitable for studying aspects of behaviour and cognition that cannot be fully examined under laboratory conditions. While a large range of topics and tools have been proposed for application in such environments, few have been used in the field. Methods and procedures for maximizing the robustness and scientific value of expedition research designs must first be developed and validated. We tested the feasibility of collecting human physiological, cognitive, and subjective experience data concerning brain state, sleep, cognitive workload, and fatigue, during a speleological expedition in a remote region. We document our approaches and challenges experienced, and provide recommendations and suggestions to aid future work. The data support the idea that cave expeditions are relevant naturalistic paradigms that offer unique possibilities for cognitive neuroscience to complement laboratory work and help improve human performance and safety in operational environments.

In search of problems

With early training in physics, my career was marked by continuous learning and searching for interesting problems in biology. Here, I recount some key events that influenced my choices of research topics. The diversity of topics could be attributed to my own lack of a particular focus and the interests of students and postdocs who happened to join my laboratory. My scientific and educational ventures led to my extensive involvement in Chinese neuroscience and various studies in non-human primates.