Field Of Neuroscience Research Articles

Estimation psychophysiological state via nonlinear dynamic integral models

The method of experimental research “input-output”of the human oculo-motor system was developed and implemented using innovative eye-tracking technology for recording oculo-motor systemresponses to test visual stimuli. Stimuli are displayed on the monitor screen at different distances from the starting position. This formally corresponds to the action of step signals with different amplitudes at the input of the oculo-motor system. According to the empirical data of the “input-output”studies of the respondent's oculo-motor systemobtained with the aid of the TobiiProTX300 eye tracker, the transient functions of the first and diagonal intersections of the transient functions of the second and third orders of the oculo-motor systemwere determined. Experimental studies of the respondent's oculo-motor systemto identify the state of fatigue were carried out before the beginning (in the morning) and after the working day (in the evening). The obtained multidimensional transient functions are used as a source of primary data in the implementation of intelligent information technology for diagnosis and monitoring of the psychophysiological state of a person. Instrumental algorithmic and software tools for determining diagnostic features based on the identification data of the oculo-motor systemin the form of multidimensional transient functionsin the Python language have been developed.Training samples of data fortwo states of the respondent (“Normal”and “Fatigue”) were formed on the basis of the proposed heuristic features, which are determined using integral and differential transformations of the obtained multidimensional transient functionsof the oculo-motor system. Training samples of data are used to build classifiers of psychophysiological states of an individual using machine learning tools. The informativeness of individual features and all their possible combinations in pairs according to the indicator of the probability of correct recognition was studied using the method of complete search. The research results were obtained by evaluating the quality of recognition of states built by Bayesian classifiers in different spaces of the proposed features. An analysis of the stability of the correct recognitioninformativeness indicator of different feature spaces under the influence of different levels of additive noise on the features was carried out. Two-dimensional feature spaces with the maximum and most stable value of the correct recognitionindicator were found when solving the scientific and practical task of assessing the psychophysiological state (fatigue) of a person (0.9375). Thus, it seems appropriate to use the multidimensional transient functionsobtained from eye-tracking data in diagnostic studies in the fields of neuroscience and experimental psychology

A Spatiotemporal Traffic Speed Prediction Method Combining Attention and Multivariate Graph Convolution Fusion

Spatiotemporal prediction is widely used in the fields of neuroscience, climate, and transportation, and traffic speed prediction is one of the typical research areas. Since traffic networks are irregular grid structures with complex nonlinear spatiotemporal dependencies between nodes, traditional single-feature prediction methods are difficult to adapt to complex road conditions. In order to improve the prediction accuracy of traffic speed, this paper proposes a spatiotemporal traffic speed prediction method combining attention and multivariate graph convolution fusion (CAMGCF). It constructs a spatiotemporal model, specifically, the network uses multivariate graph convolution to fuse external factor features and traffic speed features and uses an attention mechanism to adaptively fuse feature information from different time series to achieve stability in long-term prediction. Finally, the framework is evaluated on three real-world large-scale road network traffic datasets, and the experimental results show that the prediction accuracy of this paper has more accurate results compared with the benchmark method.

Editorial introductions.

Editorial introductions.

Neuropharmacological assessment of the methanolic stem back extract of Anopyxis klaineana (Pierre) Engl. (Rhizophoraceae) in mice

BackgroundMedicinal plants have recently attracted attention on a global scale for therapeutic interventions in the field of neuroscience. Anopyxis klaineana, a medicinal plant widely used in West Africa, has been studied extensively for its anti-inflammatory effects. However, studies on its neuropharmacological effects are quite limited. This study therefore assessed the neuropharmacological potential of the methanolic stem bark extract of Anopyxis klaineana (EAK) in murine models. Materials and methodsMice were treated orally with a methanolic stem back extract of Anopyxis klaineana (EAK) at 10, 30 or 100 mg/kg followed by testing in the elevated plus-maze (EPM) and open field (OF) tests for anxiolytic-like effects. EAK's antidepressant-like potential was also evaluated in the tail suspension test (TST), while anticonvulsant and central analgesic effects were evaluated with the pentylenetetrazol (PTZ)-induced convulsion threshold and tail withdrawal tests respectively at 30, 100 or 300 mg/kg. The extract was additionally subjected to chromatographic analyses using high resolution HPLC-MS. ResultsTotal ion current chromatogram of EAK revealed several peaks suggestive of secondary metabolites of the plant extract. EAK significantly (p<0.05) reduced depression-like behaviours in the TST although not as potent as fluoxetine (ED50: EAK=43.39±2.5; fluoxetine=1.16±1.6) (F3,20=9.663, p = 0.0004). It also showed significant anticonvulsive effect by reducing the frequency and duration of PTZ-induced convulsions (ED50=48.53±2.5 mg/kg) (F3,19=5.136, p = 0.0091). This anticonvulsant effect was seen in both clonic and tonic convulsions at 30 and 300 mg/kg. Additionally, EAK-treated mice showed higher latency to tail withdrawal in the analgesia test. We also observed significant (p<0.05) dose-dependent increase in anxiolytic-like effects in EAK-treated animals in the EPM (F3,20=10.77, p = 0.0002) and OF tests (F3,22=5.079, p = 0.008). ConclusionsThe methanolic Anopyxis klaineana stem back extract exhibits anxiolytic-like, antidepressant-like, analgesic and anticonvulsant effects in murine models for assessing neuropharmacological activity and is a good source for novel neuroactive compounds.

Implementasi Paradigma Integrasi Keilmuan dalam Pendidikan Islam

Islamic education is a study that is very relevant in every era. The reasons are, First, educators always involve humans as objects that are always dynamic. Second, there is a need for new innovations in education. Third, globalization demands that education must be open, follow the era, and be IT-based. Dynamic demands, innovation and creativity are challenges for the world of education. The Islamic Education System, which has been the guideline for the last period, actually still uses the Classical Education system. Educational reform by integrating Islamic education and neuroscience is the hope for the better. The effort is to reconstruct Islamic education according to its nature. The purpose of this study is to explain that Islamic education is starting to improve itself to restore the glory of previous educational developments, by reconstructing it by combining classical education and contemporary education by integrating neuroscience as a new foothold. This research is an interdisciplinary and qualitative research, namely library research. Sources of research data were referred to from literature in the field of Islamic education and neuroscience which were analyzed by content analysis. The results of the study show that hybridization in strategies and materials that are integrated into the use of neuroscience will produce a new learning that is easier for students to understand.

Brain works principle followed by neural information processing: a review of novel brain theory

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.

Habilitation beyond the Bionic Metaphor: Producing Deafnesses of the Future

In this article, we travel back to the early days of experimental use of cochlear implants (CIs) in the 1970s, when unsettled expectations of the device and broad investigations of its effects began to settle and center on speech outcomes. We describe how this attention to speech outcomes coalesced into specific understandings of what CIs do, and how implicit or explicit understandings of CIs as bionic devices that normalize hearing influenced research on and expectations of CIs into the present. We conclude that accumulated evidence about what is known and unknown about experiences and materialities with CIs calls for a decisive break from the metaphor of the bionic ear. This shift would create a space to reconsider the “deafness of history and the present,” as well as experiences of brain–computer interfaces that are inclusive of nonnormative life. This article is based on fieldwork in research and clinical facilities in Australia, Canada, and the United States. It included forty-three interviews with clinical experts and leading researchers in the fields of audiology, psychoacoustics, and neuroscience, among them scientists involved in the development and commercialization of one of the first CIs.

Big Brain Data Initiatives in Universiti Sains Malaysia: Data Stewardship to Data Repository and Data Sharing.

The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.

WormTensor: a clustering method for time-series whole-brain activity data from C. elegans

BackgroundIn the field of neuroscience, neural modules and circuits that control biological functions have been found throughout entire neural networks. Correlations in neural activity can be used to identify such neural modules. Recent technological advances enable us to measure whole-brain neural activity with single-cell resolution in several species including Caenorhabditis elegans. Because current neural activity data in C. elegans contain many missing data points, it is necessary to merge results from as many animals as possible to obtain more reliable functional modules.ResultsIn this work, we developed a new time-series clustering method, WormTensor, to identify functional modules using whole-brain activity data from C. elegans. WormTensor uses a distance measure, modified shape-based distance to account for the lags and the mutual inhibition of cell–cell interactions and applies the tensor decomposition algorithm multi-view clustering based on matrix integration using the higher orthogonal iteration of tensors (HOOI) algorithm (MC-MI-HOOI), which can estimate both the weight to account for the reliability of data from each animal and the clusters that are common across animals.ConclusionWe applied the method to 24 individual C. elegans and successfully found some known functional modules. Compared with a widely used consensus clustering method to aggregate multiple clustering results, WormTensor showed higher silhouette coefficients. Our simulation also showed that WormTensor is robust to contamination from noisy data. WormTensor is freely available as an R/CRAN package https://cran.r-project.org/web/packages/WormTensor.

Comprehensive bibliometric research in neuroscience: focusing on ophthalmology.

This study aimed to comprehensively summarize the knowledge structure and research hotspots of ophthalmology in the field of neuroscience through bibliometric and visual analysis. We searched the Web of Science Core Collection database for articles from 2002 to 2021 related to ophthalmology in the field of neuroscience. Using VOSviewer and CiteSpace, bibliometric analysis was conducted on the number of annual ophthalmology publications, authors, organizations, countries, journals, cited references, keywords, and burst keywords. A total of 9,179 articles were published from 34,073 authors, 4,987 organizations, and 87 countries. The cited references in these articles were published in 23,054 journals. Moreover, there were 30,864 keywords among the 9,179 articles. Notably, scholars have increasingly begun paying attention to ophthalmology in the field of neuroscience in the past 20 years. Claudio Babiloni published the most articles. The University of Washington had the greatest number of articles. The United States, Germany, and England led in the number of articles published. The Journal of Neuroscience was the most cited. The article with the highest outbreak intensity was an article published by Maurizio Corbetta in Nature Reviews Neuroscience in 2002 entitled "Control of goal-directed and stimulus-driven attention in the brain." The most important keyword was the brain, and the top burst keyword was functional connectivity. This study visualized ophthalmology research in the field of neuroscience through bibliometric analysis and predicted potential research trends in future to help clinicians and basic researchers provide diversified perspectives and further carry out in-depth research on ophthalmology.

MEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops.

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

Neural entrainment underpins sensorimotor synchronization to dynamic rhythmic stimuli

Neural entrainment, defined as unidirectional synchronization of neural oscillations to an external rhythmic stimulus, is a topic of major interest in the field of neuroscience. Despite broad scientific consensus on its existence, on its pivotal role in sensory and motor processes, and on its fundamental definition, empirical research struggles in quantifying it with non-invasive electrophysiology. To this date, broadly adopted state-of-the-art methods still fail to capture the dynamic underlying the phenomenon.Here, we present event-related frequency adjustment (ERFA) as a methodological framework to induce and to measure neural entrainment in human participants, optimized for multivariate EEG datasets. By applying dynamic phase and tempo perturbations to isochronous auditory metronomes during a finger-tapping task, we analyzed adaptive changes in instantaneous frequency of entrained oscillatory components during error correction. Spatial filter design allowed us to untangle, from the multivariate EEG signal, perceptual and sensorimotor oscillatory components attuned to the stimulation frequency.Both components dynamically adjusted their frequency in response to perturbations, tracking the stimulus dynamics by slowing down and speeding up the oscillation over time. Source separation revealed that sensorimotor processing enhanced the entrained response, supporting the notion that the active engagement of the motor system plays a critical role in processing rhythmic stimuli. In the case of phase shift, motor engagement was a necessary condition to observe any response, whereas sustained tempo changes induced frequency adjustment even in the perceptual oscillatory component. Although the magnitude of the perturbations was controlled across positive and negative direction, we observed a general bias in the frequency adjustments towards positive changes, which points at the effect of intrinsic dynamics constraining neural entrainment.We conclude that our findings provide compelling evidence for neural entrainment as mechanism underlying overt sensorimotor synchronization, and highlight that our methodology offers a paradigm and a measure for quantifying its oscillatory dynamics by means of non-invasive electrophysiology, rigorously informed by the fundamental definition of entrainment.

The neuroscience of cancer.

The nervous system regulates tissue stem and precursor populations throughout life. Parallel to roles in development, the nervous system is emerging as a critical regulator of cancer, from oncogenesis to malignant growth and metastatic spread. Various preclinical models in a range of malignancies have demonstrated that nervous system activity can control cancer initiation and powerfully influence cancer progression and metastasis. Just as the nervous system can regulate cancer progression, cancer also remodels and hijacks nervous system structure and function. Interactions between the nervous system and cancer occur both in the local tumour microenvironment and systemically. Neurons and glial cells communicate directly with malignant cells in the tumour microenvironment through paracrine factors and, in some cases, through neuron-to-cancer cell synapses. Additionally, indirect interactions occur at a distance through circulating signals and through influences on immune cell trafficking and function. Such cross-talk among the nervous system, immune system and cancer-both systemically and in the local tumour microenvironment-regulates pro-tumour inflammation and anti-cancer immunity. Elucidating the neuroscience of cancer, which calls for interdisciplinary collaboration among the fields of neuroscience, developmental biology, immunology and cancer biology, may advance effective therapies for many of the most difficult to treat malignancies.

Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering.

Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.

Neural probe integrated with low-impedance electrodes implemented using vertically aligned carbon nanotubes for three-dimensional mapping of neural signals

High-quality recordings of neural interfaces are crucial in advancing neuroscience research, but conventional electrodes with highly conductive materials have limitations due to their limited effective surface area. Here, we develop a neural probe integrated with an electrode implemented using vertically aligned carbon nanotubes (VACNTs) that remarkably improves recording capability. By directly synthesizing VACNT bundles on the electrode array of the neural probe, we successfully fabricate an electrode with the lowest area-specific impedance. Owing to the dramatically lowered impedance of the VACNT electrode, its signal-to-noise ratio increases by more than 3 times compared with that of a black Pt electrode with low impedance. Moreover, the number of spikes observed in the measured signals increases by more than 4 times. Notably, our VACNT electrode enables accurate mapping of the spatial distribution of neurons, demonstrating its potential to significantly advance neuroscience research through massive neural recordings with improved mapping capability. This powerful technology that maximizes electrode performance could pave the way for new discoveries and insights in the field of neuroscience.

Embracing deepfakes and AI-generated images in neuroscience research.

The rise of deepfakes and AI-generated images has raised concerns regarding their potential misuse. However, this commentary highlights the valuable opportunities these technologies offer for neuroscience research. Deepfakes deliver accessible, realistic and customisable dynamic face stimuli, while generative adversarial networks (GANs) can generate and modify diverse and high-quality static content. These advancements can enhance the variability and ecological validity of research methods and enable the creation of previously unattainable stimuli. When AI-generated images are informed by brain responses, they provide unique insights into the structure and function of visual systems. The authors argue that experimental psychologists and cognitive neuroscientists stay informed about these emerging tools and embrace their potential to advance the field of visual neuroscience.

Beneath, Beyond Burnout: Solving for Causes.

This article offers a different perspective of the current crisis in health care-burnout that is causing medical errors, disengagement, and economic chaos and forcing talented, experienced health care professionals to leave their institutions or their chosen professions altogether. The lack of meaningful impact lies in the focus on treating problems observed rather than on system issues underlying the more overt symptoms of burnout and attrition. The system within which health care workers perform impacts their capacity to consistently deliver high-quality care. Existing systems and structures often yield undesirable results, and harm individual workers. The authors explore strategies that focus on understanding and responding to the causes impacting staff and organizational performance. Lack of application of continually evolving evidence from numerous intersecting fields of neuroscience leads to the design of work systems that cause trauma and moral injury or that exacerbate original early life trauma, reducing the capability to operate successfully in the complex environments in which we work and live. It also leads to incomplete, insufficient, and, at times, outmoded systems of support for the well-being of all within the system. Too often, burnout results. In contrast to problem-solving, cause-solving requires holistic approaches to understanding interactions of system components. The authors will put forth a road map for creating components of a healing ecosystem that support trauma-informed and system-wide transformation. Recognition leads to commitment to systemic transformation toward a more healing system for all. Long-term, system performance cannot be sustained, nor organizational needs met, when people in the system are distressed.

Creating an ignorance-base: Exploring known unknowns in the scientific literature

Background:Scientific discovery progresses by exploring new and uncharted territory. More specifically, it advances by a process of transforming unknown unknowns first into known unknowns, and then into knowns. Over the last few decades, researchers have developed many knowledge bases to capture and connect the knowns, which has enabled topic exploration and contextualization of experimental results. But recognizing the unknowns is also critical for finding the most pertinent questions and their answers. Prior work on known unknowns has sought to understand them, annotate them, and automate their identification. However, no knowledge-bases yet exist to capture these unknowns, and little work has focused on how scientists might use them to trace a given topic or experimental result in search of open questions and new avenues for exploration. We show here that a knowledge base of unknowns can be connected to ontologically grounded biomedical knowledge to accelerate research in the field of prenatal nutrition. Results:We present the first ignorance-base, a knowledge-base created by combining classifiers to recognize ignorance statements (statements of missing or incomplete knowledge that imply a goal for knowledge) and biomedical concepts over the prenatal nutrition literature. This knowledge-base places biomedical concepts mentioned in the literature in context with the ignorance statements authors have made about them. Using our system, researchers interested in the topic of vitamin D and prenatal health were able to uncover three new avenues for exploration (immune system, respiratory system, and brain development) by searching for concepts enriched in ignorance statements. These were buried among the many standard enriched concepts. Additionally, we used the ignorance-base to enrich concepts connected to a gene list associated with vitamin D and spontaneous preterm birth and found an emerging topic of study (brain development) in an implied field (neuroscience). The researchers could look to the field of neuroscience for potential answers to the ignorance statements. Conclusion:Our goal is to help students, researchers, funders, and publishers better understand the state of our collective scientific ignorance (known unknowns) in order to help accelerate research through the continued illumination of and focus on the known unknowns and their respective goals for scientific knowledge.

Community-Derived Core Concepts for Neuroscience Higher Education.

Core concepts provide a framework for organizing facts and understanding in neuroscience higher education curricula. Core concepts are overarching principles that identify patterns in neuroscience processes and phenomena and can be used as a foundational scaffold for neuroscience knowledge.The need for community-derived core concepts is pressing, because both the pace of research and number of neuroscience programs are rapidly expanding. While general biology and many subdisciplines within biology have identified core concepts, neuroscience has yet to establish a community-derived set of core concepts for neuroscience higher education. We used an empirical approach involving more than 100 neuroscience educators to identify a list of core concepts.The process of identifying neuroscience core concepts was modeled after the process used to develop physiology core concepts and involved a nationwide survey and a working session of 103 neuroscience educators.The iterative process identified eight core concepts and accompanying explanatory paragraphs.The eight core concepts are abbreviated as communication modalities, emergence, evolution, gene-environment interactions, information processing, nervous system functions, plasticity, and structure-function. Here, we describe the pedagogical research process used to establish core concepts for the neuroscience field and provide examples on how the core concepts can be embedded in neuroscience education.

Social cognitive neuroscience in the digital age.

Human interactions are increasingly taking place from a distance through methods of remote interpersonal communication like video chatting and social media. While remote interpersonal communication has existed for millennia-with the first postal system arising in ∼2400 B.C.-accelerated advances in technology and the recent global COVID-19 pandemic have led to a dramatic increase in remote interpersonal communication use in daily life. Remote interpersonal communication presents a challenge to the field of social-cognitive neuroscience, as researchers seek to understand the implications of various types of remote interpersonal communication for the "social brain." The present paper reviews our current understanding of the social-cognitive neural network and summarizes critical differences between the neural correlates of social cognition in remote vs. face-to-face interactions. In particular, empirical and theoretical work is reviewed that highlight disparities in the neural mechanisms of social perception, evaluation of social stimuli, human motivation, evaluation of social reward, and theory of mind. Potential impacts of remote interpersonal communication on the development of the brain's social-cognitive network are also discussed. Finally, this review closes with future directions for research on social-cognitive neuroscience in our digital technology-connected world and outlines a neural model for social cognition in the context of remote interpersonal communication. For the field of social-cognitive neuroscience to advance alongside of the ever-evolving society, it is crucial for researchers to acknowledge the implications and concepts suggested for future research in this review.