Search Results (42)

The precise analysis of protein interactions in their native cellular context and the sensitive quantification of protein abundance in biological fluids are both fundamental to understanding health and disease. Traditional methods for cellular imaging and biochemical quantification often face limitations in specificity, sensitivity, or the preservation of spatial information. The proximity ligation assay (PLA) is a versatile technological platform developed to overcome these challenges by converting protein recognition events into amplifiable DNA signals, thereby achieving exceptional sensitivity. This foundational principle has given rise to two major formats: in situ PLA (isPLA) and solution-phase PLA. In basic research, isPLA provides high-resolution visualization of protein–protein interactions (PPIs), post-translational modifications (PTMs), and subcellular architecture directly within fixed cells and tissues. In translational and clinical applications, solution-phase PLA enables the highly sensitive quantification of low-abundance biomarkers in liquid samples, which is critical for diagnostics and prognostics in fields such as oncology, neuroscience, and infectious diseases. This review discusses the foundational principles, development, and diverse applications of PLA platforms. We also highlight significant technological advancements, including the development of high-throughput formats, integration with advanced readouts, and the use of alternative affinity reagents. These innovations continue to transform PLA from a targeted validation method into a powerful and multifaceted platform for both fundamental systems biology and clinical diagnostics. Full article

The pursuit of a comprehensive understanding of human consciousness, which includes the subjective experience of perception, is a long-standing endeavor. A multitude of disciplines have sought to elucidate and define consciousness, with a particular emphasis on its etiology. What is the cause of consciousness? One particularly eye-opening idea is that humans attempt to identify the source of consciousness by leveraging their own consciousness, as if something is attempting to elucidate itself. Strikingly, the results of brain-imaging experiments indicate that the brain processes a considerable amount of information outside conscious awareness of the organism in question. Perhaps, the vast majority of decision making, thinking, and planning processes originate from non-conscious brain processes. Nevertheless, consciousness is a fascinating phenomenon, and its intrinsic nature is both intriguing and challenging to ascertain. In the end, it is not necessarily given that consciousness, in particular the phenomenon of perception as the subjective experience it is, is a tangible function or process in the first place. This is why it must be acknowledged that this theoretical paper is not in a position to offer a definitive solution. However, it does present an interesting new concept that may at least assist future research and potential investigations in achieving a greater degree of elucidation. The concept is founded upon a physical (mathematical) phenomenon known as stochastic resonance. Without delving into the specifics, it is relatively straightforward to grasp one of its implications, which is employed here to introduce a novel direction regarding the potential for non-conscious information within the human brain to become conscious through the introduction of noise. It is noteworthy that this phenomenon can be visualized through a relatively simple approach that is provided in the frame of this paper. It is demonstrated that a completely white image is transformed into an image depicting clearly recognizable content by the introduction of noise. Similarly, information in the human brain that is processed below the threshold of consciousness could become conscious within a neural network by the introduction of noise. Thereby, the noise (neurophysiological energy) could originate from one or more of the well-known activating neural networks, with their nuclei being located in the brainstem and their axons connecting to various cortical regions. Even though stochastic resonance has already been introduced to neuroscience, the innovative nature of this paper is a formal introduction of this concept within the framework of consciousness, including higher-order perception phenomena. As such, it may assist in exploring novel avenues in the search for the origins of consciousness and perception in particular. Full article

The assessment of visual attention is important in visual and cognitive neuroscience, providing objective measures for researchers and clinicians. This study investigated the effects of chemotherapy on eye movements in women with breast cancer. Twelve women with breast cancer and twelve healthy controls aged between 33 and 59 years completed a visual search task, identifying an Arabic number among 79 alphabetic letters. Test duration, fixation duration, total fixation duration, and total visit duration were recorded. Compared to healthy controls, women with breast cancer exhibited significantly longer mean fixation duration [t = 4.54, p < 0.00]; mean total fixation duration [t = 2.41, p < 0.02]; mean total visitation duration [t = 2.05, p < 0.05]; and total test time [t = 2.32, p < 0.03]. Additionally, positive correlations were observed between the number of chemotherapy cycles and the eye tracking parameters. These results suggest the possibility of slower information processing in women experiencing acute effects of chemotherapy. However, further studies are needed to clarify this relationship. Full article

Although intracranial hypertension (ICH) has traditionally been framed as simply a numerical escalation of intracranial pressure (ICP) and usually dealt with in its clinical form and not in terms of its complex underlying pathophysiology, an emerging body of evidence indicates that ICH is not simply an elevated ICP process but a complex process of molecular dysregulation, glymphatic dysfunction, and neurovascular insufficiency. Our aim in this paper is to provide a complete synthesis of all the new thinking that is occurring in this space, primarily on the intersection of glymphatic dysfunction and cerebral vein physiology. The aspiration is to review how glymphatic dysfunction, largely secondary to aquaporin-4 (AQP4) dysfunction, can lead to delayed cerebrospinal fluid (CSF) clearance and thus the accumulation of extravascular fluid resulting in elevated ICP. A range of other factors such as oxidative stress, endothelin-1, and neuroinflammation seem to significantly impair cerebral autoregulation, making ICH challenging to manage. Combining recent studies, we intend to provide a revised conceptualization of ICH that recognizes the nuance and complexity of ICH that is understated by previous models. We wish to also address novel diagnostics aimed at better capturing the dynamic nature of ICH. Recent advances in non-invasive imaging (i.e., 4D flow MRI and dynamic contrast-enhanced MRI; DCE-MRI) allow for better visualization of dynamic changes to the glymphatic and cerebral blood flow (CBF) system. Finally, wearable ICP monitors and AI-assisted diagnostics will create opportunities for these continuous and real-time assessments, especially in limited resource settings. Our goal is to provide examples of opportunities that exist that might augment early recognition and improve personalized care while ensuring we realize practical challenges and limitations. We also consider what may be therapeutically possible now and in the future. Therapeutic opportunities discussed include CRISPR-based gene editing aimed at restoring AQP4 function, nano-robotics aimed at drug targeting, and bioelectronic devices purposed for ICP modulation. Certainly, these proposals are innovative in nature but will require ethically responsible confirmation of long-term safety and availability, particularly to low- and middle-income countries (LMICs), where the burdens of secondary ICH remain preeminent. Throughout the review, we will be restrained to a balanced pursuit of innovative ideas and ethical considerations to attain global health equity. It is not our intent to provide unequivocal answers, but instead to encourage informed discussions at the intersections of research, clinical practice, and the public health field. We hope this review may stimulate further discussion about ICH and highlight research opportunities to conduct translational research in modern neuroscience with real, approachable, and patient-centered care. Full article

Creating grayscale images from a color reality has been an inherent human practice since ancient times, but it became a technological challenge with the advent of the first black-and-white televisions and digital image processing. Decolorization is a process that projects visual information from a three-dimensional feature space to a one-dimensional space, thus reducing the dimensionality of the image while minimizing the loss of information. To achieve this, various strategies have been developed, including the application of color channel weights and the analysis of local and global image contrast, but there is no universal solution. In this paper, we propose a bio-inspired approach that combines findings from neuroscience on the architecture of the visual system and color coding with evidence from studies in the psychology of art. The goal is to simplify the decolorization process and facilitate its control through color-related concepts that are easily understandable to humans. This new method organizes colors in a scale that links activity on the retina with a system of opponent and complementary channels, thus allowing the adjustment of the perception of warmth and coolness in the image. The results show an improvement in chromatic contrast, especially in the warmth and coolness categories, as well as an enhanced ability to preserve subtle contrasts, outperforming other approaches in the Ishihara test used in color blindness detection. In addition, the method offers a computational advantage by reducing the process through direct pixel-level operation. Full article

Rural agricultural markets face unique challenges, yet neuromarketing applications in this sector are understudied. This systematic review investigates how neuroscience has been applied to shape marketing narratives for rural agricultural producers. The objectives were to catalog relevant studies, identify key themes using inductive thematic synthesis, and derive practical implications for rural marketing strategy and future research. We systematically searched English-language, peer-reviewed studies published between 2016 and 2024 across multiple academic databases, following PRISMA guidelines. Two independent reviewers screened the records, resulting in the inclusion of 20 studies. Key data from each study were extracted and synthesized using an inductive thematic analysis approach. The synthesis revealed several recurrent findings. First, in terms of social and community context, farmers showed greater trust and engagement with familiar local buyers than with distant corporations, indicating that local relationships strongly influence producer behavior. Second, regarding product and narrative attributes, marketing narratives that emphasized local provenance, organic or sustainable production, and ethical values such as animal welfare and environmental sustainability resonated strongly with rural consumers. Third, sensory and emotional cues particularly visual elements and storytelling techniques including color, imagery, and packaging design consistently enhanced consumer attention and engagement. Overall, these neuroscience-informed themes suggest that marketing narratives emphasizing authenticity, trust-building, and community values can effectively strengthen rural agricultural marketing. This review provides neuroscience-informed interpretations of key rural marketing challenges, drawing on dual-process theory and consumer decision models for applying neuromarketing insights in this context. Practically, rural producers can leverage these findings by designing marketing messages and packaging that highlight local identity and ethical values, thereby building consumer trust and loyalty. The review also highlights gaps such as the need for more field-based neuromarketing studies and suggests directions for future research, offering guidance for both scholars and practitioners working at the intersection of neuroscience and rural consumer behavior. Full article

The integration of in vitro biological neural networks (BNNs) with robotic systems to explore their information processing and adaptive learning in practical tasks has gained significant attention in the fields of neuroscience and robotics. However, existing BNN-based robotic systems cannot perceive the visual environment due to the inefficiency of sensory information encoding methods. In this study, we propose a biomimetic visual information spatiotemporal encoding method based on improved delayed phase encoding. This method transforms high-dimensional images into a series of pulse sequences through convolution, temporal delay, alignment, and compression for BNN stimuli. We conduct three stages of unsupervised training on in vitro BNNs using high-density microelectrode arrays (HD-MEAs) to validate the potential of the proposed encoding method for image recognition tasks. The neural activity is decoded via a logistic regression model. The experimental results show that the firing patterns of BNNs with different spatiotemporal stimuli are highly separable in the feature space. After the third training stage, the image recognition accuracy reaches 80.33% ± 7.94%, which is 13.64% higher than that of the first training stage. Meanwhile, the BNNs exhibit significant increases in the connection number, connection strength, and inter-module participation coefficient after unsupervised training. These results demonstrate that the proposed method significantly enhances the functional connectivity and cross-module information exchange in BNNs. Full article

Background and Objectives: Stroke units rely on interdisciplinary teams. Professionals with complementary alternative practices may join the team since such approaches are increasingly supporting the stroke recovery process. The aim of this study was to develop a better understanding of how a dance and somatic-informed movement intervention could be utilized in an inpatient setting as an adjunct to post-stroke therapy. We sought to identify (1) what knowledge we could draw on to develop the content and pedagogy for the intervention, (2) what helped/hindered the intervention aimed at functional recovery, as perceived by the practitioner-researchers, and (3) the relationships experienced with the various stakeholders. Materials and Methods: This exploratory qualitative study used the enhanced critical incident technique to collect retrospective self-report data from two practitioner-researchers engaged in delivering the intervention over two months. The data underwent thematic analysis. Patients (n = 6) in a stroke unit were selected within ≤72 h of hospital admission. The intervention was conducted four to six times a week until the vascular neurologist (co-researcher) authorized their transfer to a rehabilitation hospital. Results: The intervention evolved from crafting content and pedagogy at the intersection of different areas of knowledge (dance, somatics, neuroscience, and stroke). It was based on active, assisted, and passive movements. Verbal, tactile, visual, and imaginary inputs used to enhance body awareness were perceived as potentially helping patients recover some range of motion, quality of movements, and voluntary movement control, and fostering calmness and motivation. The intervention was well received by stakeholders. Conclusions: Dance and somatic-informed movement can be a complementary therapy in stroke units, although it requires a delicate juggling of time allocation within the interdisciplinary team. Further studies should be conducted with a larger number of patients and different practitioners. Collaboration between qualitative and quantitative researchers is needed to make a robust case for such interventions. Full article

The integration of multisensory inputs plays a crucial role in shaping perception and behavior, particularly in the visual system. The collicular pathway, encompassing the optic tectum in non-mammalian vertebrates and the superior colliculus (SC) in mammals, is a key hub for integrating sensory information and mediating adaptive motor responses. Comparative studies across species reveal evolutionary adaptations that enhance sensory processing and contribute to compensatory mechanisms following neuronal injury. The present review outlines the structure and function of the multisensory visual pathways, emphasizing the retinocollicular projections, and their multisensory integration, which depends on synaptic convergence of afferents conveying information from different sensory modalities. The cellular mechanisms underlying multimodal integration remain to be fully clarified, and further investigations are needed to clarify the link between neuronal activity in response to multisensory stimulation and behavioral response involving motor activity. By exploring the interplay between fundamental neuroscience and translational applications, we aim to address multisensory integration as a pivotal target for its potential role in visual rehabilitation strategies. Full article

Biologically inspired retinal preprocessing improves visual perception by efficiently encoding and reducing entropy in images. In this study, we introduce a new saliency prediction framework that combines a retinal model with deep neural networks (DNNs) using information theory ideas. By mimicking the human retina, our method creates clearer saliency maps with lower entropy and supports efficient computation with DNNs by optimizing information flow and reducing redundancy. We treat saliency prediction as an information maximization problem, where important regions have high information and low local entropy. Tests on several benchmark datasets show that adding the retinal model boosts the performance of various bottom-up saliency prediction methods by better managing information and reducing uncertainty. We use metrics like mutual information and entropy to measure improvements in accuracy and efficiency. Our framework outperforms state-of-the-art models, producing saliency maps that closely match where people actually look. By combining neurobiological insights with information theory—using measures like Kullback–Leibler divergence and information gain—our method not only improves prediction accuracy but also offers a clear, quantitative understanding of saliency. This approach shows promise for future research that brings together neuroscience, entropy, and deep learning to enhance visual saliency prediction. Full article

Hypergraph analysis extends traditional graph theory by enabling the study of complex, many-to-many relationships in networks, offering powerful tools for understanding brain connectivity. This case study introduces a novel methodology for constructing both graphs and hypergraphs of functional brain connectivity during figurative attention tasks, where subjects interpret the ambiguous Necker cube illusion. Using a frequency-tagging approach, we simultaneously modulated two cube faces at distinct frequencies while recording electroencephalography (EEG) responses. Brain connectivity networks were constructed using multiple measures—coherence, cross-correlation, and mutual information—providing complementary insights into functional relationships between regions. Our hypergraph analysis revealed distinct connectivity patterns associated with attending to different cube orientations, including previously unobserved higher-order relationships between brain regions. The results demonstrate bilateral cortico–cortical interactions and suggest integrated processing hubs that may coordinate visual attention networks. This methodological framework not only advances our understanding of the neural basis of visual attention but also offers potential applications in attention monitoring and clinical assessment of attention disorders. While based on a single subject, this proof-of-concept study establishes a foundation for larger-scale investigations of brain network dynamics during ambiguous visual processing. Full article

Despite the widespread use of information measures in analyzing probabilistic systems, effective visualization tools for understanding complex dependencies in sequential data are scarce. In this work, we introduce the information matrix (InfoMat), a novel and intuitive matrix representation of information transfer in sequential systems. InfoMat provides a structured visual perspective on mutual information decompositions, enabling the discovery of new relationships between sequential information measures and enhancing interpretability in time series data analytics. We demonstrate how InfoMat captures key sequential information measures, such as directed information and transfer entropy. To facilitate its application in real-world datasets, we propose both an efficient Gaussian mutual information estimator and a neural InfoMat estimator based on masked autoregressive flows to model more complex dependencies. These estimators make InfoMat a valuable tool for uncovering hidden patterns in data analytics applications, encompassing neuroscience, finance, communication systems, and machine learning. We further illustrate the utility of InfoMat in visualizing information flow in real-world sequential physiological data analysis and in visualizing information flow in communication channels under various coding schemes. By mapping visual patterns in InfoMat to various modes of dependence structures, we provide a data-driven framework for analyzing causal relationships and temporal interactions. InfoMat thus serves as both a theoretical and empirical tool for data-driven decision making, bridging the gap between information theory and applied data analytics. Full article

Theories of consciousness grounded in neuroscience must explain the phenomenal binding problem, e.g., how micro-units of information are combined to create the macro-scale conscious experience common to human phenomenology. An example is how single ‘pixels’ of a visual scene are experienced as a single holistic image in the ‘mind’s eye’, rather than as individual, separate, and massively parallel experiences, corresponding perhaps to individual neuron activations, neural ensembles, or foveal saccades, any of which could conceivably deliver identical functionality from an information processing point of view. There are multiple contested candidate solutions to the phenomenal binding problem. This paper explores how the metaphysical infrastructure of Integrated Information Theory (IIT) v4.0 can provide a distinctive solution. The solution—that particular entities aggregable from multiple units (‘complexes’) define existence—might work in a static picture, but introduces issues in a dynamic system. We ask what happens to our phenomenal self as the main complex moves around a biological neural network. Our account of conscious entities developing through time leads to an apparent dilemma for IIT theorists between non-local entity transitions and contiguous selves: the ‘dynamic entity evolution problem’. As well as specifying the dilemma, we describe three ways IIT might dissolve the dilemma before it gains traction. Clarifying IIT’s position on the phenomenal binding problem, potentially underpinned with novel empirical or theoretical research, helps researchers understand IIT and assess its plausibility. We see our paper as contributing to IIT’s current research emphasis on the shift from static to dynamic analysis. Full article

Recent advancements in ocular physiology suggest that the eyes may function similarly to radar antennae or ultrasound probes, with the occipital cortex acting as a detector, challenging the traditional view of binocular vision as the primary mechanism for depth and distance perception. Methods: We conducted a comprehensive analysis of the neuroanatomical and histological architecture of the neuro-optico-cortical systems in a male wild rabbit model. The objective was to identify potential structural and functional similarities between the retino-optical system and radar/ultrasound effector-detector systems. Results: Histological examination revealed significant similarities between retinal morphology and radar/ultrasound systems. The outermost retinal layer resembled an acoustic lens, with underlying layers functioning as acoustic matching layers. The ganglion cell layer exhibited characteristics akin to the piezoelectric elements of transducers. Conclusions: Our findings support the hypothesis that the retinal apparatus functions similarly to radar antennae or ultrasound probes. Light-stimulated retinal-occipital cortex cells perceive objects and emit electromagnetic waves through the retina, which are reflected by objects and processed in the occipital cortex to provide information on their distance, shape, and depth. This mechanism may complement binocular vision and enhance depth and distance perception in the visual system. These results open new avenues for research in visual neuroscience and could have implications for understanding various visual phenomena and disorders. Full article

This study aims to provide a comprehensive knowledge mapping and extensive analysis of NeuroIS research, elucidating global trends and directions within this field from January 2007 to January 2024. A visual analysis of 256 research articles sourced from the Scopus database is conducted. The knowledge mapping, utilizing CiteSpace (CiteSpace 3.6 R1) and VOSviewer (VOSviewer 1.6.19), illustrates the current research landscape, encompassing collaboration networks, co-citation networks, references exhibiting citation bursts, and keyword analysis. The findings highlight the United States and Germany as leading nations in the exploration of NeuroIS, with the Karlsruher Institut für Technologie in Germany identified as a prominent institution in this domain. René Riedl, Pierre-Majorique Léger, Marc T. P. Adam, and Christof Weinhardt emerge as the most prolific authors in the field. Noteworthy themes that have garnered attention in recent years include customer experience, information systems, and information processing. Document analysis reveals that the study by Dimoka et al. in 2012 is the most cited work, providing a comprehensive overview of global NeuroIS research. Analysis of the document co-citation network identifies electroencephalography (EEG) in the context of technostress, the social impact of information in security alerts, and user experience in human–computer interaction as key areas of focus. René Riedl is recognized as the most cited researcher, while MIS Quarterly is distinguished as the leading journal in this field. Twelve NeuroIS papers exhibit high citation counts, with significant activity noted in 2021 and 2022. The timeline delineates the evolution of topics such as neuroscience, fMRI, cognitive neuroscience, social media, trust, eye tracking, and human–computer interaction. This study pioneers the examination of the current research status of NeuroIS through bibliometric analysis and the latest available data. It advocates for enhanced collaborations among scholars and institutions to improve information systems management and foster the development of NeuroIS. The study underscores the importance of ongoing research and cooperation in NeuroIS to deepen our understanding of how neuroscience can inform information systems design and management, thereby enhancing human–technology interaction. By identifying key trends, influential authors, and prominent themes, this analysis lays the groundwork for further exploration and innovation in this interdisciplinary domain. As technology continues to advance and our reliance on information systems intensifies, the insights derived from NeuroIS research can provide valuable perspectives on enhancing user experiences, optimizing information processing, and applying neuroscientific principles to develop more effective IT artifacts. Through sustained collaboration and knowledge sharing, the NeuroIS community can drive progress and shape the future of information systems management in an increasingly dynamic digital landscape. Full article