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Background: The insula is one of the most researched brain regions with many highly cited papers. However, unlike the literature of other fields, there is currently no study that has identified the 100 most cited papers within the literature of the insula. The aim of the current study was to fill in the knowledge gap by determining which publications concerning the insula have been cited most often, who contributed to them, and what topics they were dealing with.Methods: The Web of Science online database was searched to identify the 100 most cited publications mentioning the insular cortex in their titles, abstracts or keywords. To systematically exclude irrelevant publications, the search strategy was finalized as: TS = (insula OR insular OR “island of Reil”) NOT TS = (“insular biogeography” OR “insular mammal*” OR “*insular lymphatic*”) NOT WC = (“Geochemistry and Geophysics” OR “Ecology”). The identified publications were sorted in descending order of citation count. The 100 most cited publications concerning the insula of the brain were identified and their bibliometric data was extracted and assessed. The VOSviewer software was used with default parameters to generate a bubble map that analyzes and visualizes the words/phrases used in the titles and abstracts of the publications.Results: There were 67 articles on experiments/lab studies and 33 meta-analyses/reviews but no opinion or methods paper. They had an average of 943.4 citations (or 62.9 citations per year), 93.5 references and 13.4 pages. There were 35 papers published in open access. USA was the major contributing country. The most top-ranked publications were concerning emotion, salience and pain.Conclusion: Two-thirds of the publications concerned the normal brain function/mechanism (n = 67), whereas 20 publications concerned disease/therapeutic intervention and another 13 concerned normal anatomy. For the 67 original articles, 57 used human subjects whereas 10 used animal models. MRI was the commonest modality (n = 37), followed by PET (n = 16). Nine articles investigated by histology, two by multiple modalities and three by other modality.

Aging is associated with substantial physiological changes and constitutes a major risk factor for neurological disorders including dementia. Alterations in gene expression upon aging have been extensively studied; however, an in-depth characterization of post-transcriptional regulatory events remains elusive. Here, we profiled the age-related changes of the transcriptome and translatome in the female mouse hippocampus by RNA sequencing of total RNA and polysome preparations at four ages (3-, 6-, 12-, 20-month-old); and we implemented a variety of bioinformatics approaches to unravel alterations in transcript abundance, alternative splicing, and polyadenylation site selection. We observed mostly wellcoordinated transcriptome and translatome expression signatures across age including upregulation of transcripts related to immune system processes and neuroinflammation, though transcripts encoding ribonucleoproteins or associated with mitochondrial functions, calcium signaling and the cell-cycle displayed substantial discordant profiles, suggesting translational control associated with age-related deficits in hippocampal-dependent behavior. By contrast, alternative splicing was less preserved, increased with age and was associated with distinct functionally-related transcripts encoding proteins acting at synapses/dendrites, RNA-binding proteins; thereby predicting regulatory roles for RBM3 and CIRBP. Only minor changes in polyadenylation site selection were identified, indicating pivotal 3'-end selection in young adults compared to older groups. Overall, our study provides a comprehensive resource of age-associated post-transcriptional regulatory events in the mouse hippocampus, enabling further examination of the molecular features underlying age-associated neurological diseases. [ABSTRACT FROM AUTHOR]

Background: The insula is one of the most researched brain regions with many highly cited papers. However, unlike the literature of other fields, there is currently no study that has identified the 100 most cited papers within the literature of the insula. The aim of the current study was to fill in the knowledge gap by determining which publications concerning the insula have been cited most often, who contributed to them, and what topics they were dealing with. Methods: The Web of Science online database was searched to identify the 100 most cited publications mentioning the insular cortex in their titles, abstracts or keywords. To systematically exclude irrelevant publications, the search strategy was finalized as: TS = (insula OR insular OR “island of Reil”) NOT TS = (“insular biogeography” OR “insular mammal*” OR “*insular lymphatic*”) NOT WC = (“Geochemistry and Geophysics” OR “Ecology”). The identified publications were sorted in descending order of citation count. The 100 most cited publications concerning the insula of the brain were identified and their bibliometric data was extracted and assessed. The VOSviewer software was used with default parameters to generate a bubble map that analyzes and visualizes the words/phrases used in the titles and abstracts of the publications. Results: There were 67 articles on experiments/lab studies and 33 meta-analyses/reviews but no opinion or methods paper. They had an average of 943.4 citations (or 62.9 citations per year), 93.5 references and 13.4 pages. There were 35 papers published in open access. USA was the major contributing country. The most top-ranked publications were concerning emotion, salience and pain. Conclusion: Two-thirds of the publications concerned the normal brain function/mechanism (n = 67), whereas 20 publications concerned disease/therapeutic intervention and another 13 concerned normal anatomy. For the 67 original articles, 57 used human subjects whereas 10 used animal models. MRI was the commonest modality (n = 37), followed by PET (n = 16). Nine articles investigated by histology, two by multiple modalities and three by other modality. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a severe neurodegenerative disease for which there is currently no effective treatment. Mild cognitive impairment (MCI) is an early disease that may progress to AD. The effective diagnosis of AD and MCI in the early stage has important clinical significance. Methods: To this end, this paper proposed a hypergraph-based netNMF (HGnetNMF) algorithm for integrating structural magnetic resonance imaging (sMRI) of AD and MCI with corresponding gene expression profiles. Results: Hypergraph regularization assumes that regions of interest (ROIs) and genes were located on a non-linear low-dimensional manifold and can capture the inherent prevalence of two modalities of data and mined high-order correlation features of the two data. Further, this paper used the HG-netNMF algorithm to construct a brain structure connection network and a protein interaction network (PPI) with potential role relationships, mine the risk (ROI) and key genes of both, and conduct a series of bioinformatics analyses. Conclusion: Finally, this paper used the risk ROI and key genes of the AD and MCI groups to construct diagnostic models. The AUC of the AD group and MCI group were 0.8 and 0.797, respectively. [ABSTRACT FROM AUTHOR]

This paper describes the scientific figure of Roger Sperry as a maverick researcher, an original thinker who arrived at definitive notions about the working of the brain mostly by distancing himself from the prevalent views of his peers. After solving the riddle of the functions of the corpus callosum, he won a Nobel prize in physiology or medicine for identifying the different cognitive abilities of the disconnected right and left hemispheres of the human brain. He could have won another Nobel prize for his work on the prenatal formation of behavioral neuronal networks and their growth and development after birth. In the last part of his life, he fought a courageous but inconclusive battle for demonstrating that mental and spiritual factors can direct brain activity and behavior without violating the laws of orthodox neurophysiology. Some nodal points in his scientific career and some sources of inspirations for his thinking are identified and discussed within the historical background of the neurosciences of the twentieth century. [ABSTRACT FROM AUTHOR]

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired controlmethods to tackle the intricacies associated with controlling complex robotic systems. [ABSTRACT FROM AUTHOR]

As the most common cause of dementia, Alzheimer's disease (AD) is characterized by neurodegeneration and synaptic loss with an increasing prevalence in the elderly. Increased inflammatory responses triggers brain cells to produce pro-inflammatory cytokines and accelerates the Aβ accumulation, tau protein hyper-phosphorylation leading to neurodegeneration. Therefore, in this paper, we discuss the current understanding of how inflammation affects brain activity to induce AD pathology, the inflammatory biomarkers and possible therapies that combat inflammation for AD. [ABSTRACT FROM AUTHOR]

For many animals, faces are a vitally important visual stimulus. Hence, it is not surprising that face perception has become a very popular research topic in neuroscience, with ca. 2000 papers published every year. As a result, significant progress has been made in understanding the intricate mechanisms underlying this phenomenon. However, the ontogeny of face perception, in particular the role of innate predispositions, remains largely unexplored at the neural level. Several influential studies in monkeys have suggested that seeing faces is necessary for the development of the face-selective brain domains. At the same time, behavioural experiments with newborn human babies and newlyhatched domestic chicks demonstrate that a spontaneous preference towards faces emerges early in life without pre-existing experience. Moreover, we were recently able to record face-selective neural responses in the brain of young, face-naïve chicks, thus demonstrating the existence of an innate face detection mechanism. In this review, we discuss these seemingly contradictory results and propose potential experimental approaches to resolve some of the open questions. [ABSTRACT FROM AUTHOR]

Background: Functional near infrared spectroscopy (fNIRS) has developed rapidly in recent years, and there are more and more studies on fNIRS. At present, there is no bibliometric analysis of the top 100 most cited articles on fNIRS research. Objective: To identify the top 100 most cited articles on fNIRS and analyze those most fundamental and popular articles through bibliometric research methods. Methods: The literature on fNIRS of web of science from 1990 to 2023 was searched and the top 100 most cited articles were identified by citations. Use the bibliometrix package in R studio and VOSviewer for data analysis and plotting to obtain the output characteristics and citation status of these 100 most cited articles, and analyze research trends in this field through keywords. Results: A total of 9,424 articles were retrieved from web of science since 1990. The average citation number of the 100 articles was 457.4 (range from 260 to 1,366). Neuroimage published the most articles (n = 31). Villringer, A. from Leipzig University had the largest number of top 100 papers. Harvard University (n = 22) conducted most cited articles. The United States, Germany, Japan, and the United Kingdom had most cited articles, respectively. The most common keywords were near-infrared spectroscopy, activation, cerebral-blood-flow, brain, newborn-infants, oxygenation, cortex, fMRI, spectroscopy. The fund sources mostly came from National Institutes of Health Unitd States (NIH) and United States Department of Health Human Services (n = 28). Conclusion: Neuroimage was the most popular journal. The top countries, institutions, and authors were the United States, Harvard University, and Villringer, A., respectively. Researchers and institutions from North America and Europe contributed the most. Near-infrared spectroscopy, activation, cerebral-blood-flow, brain, newborn-infants, oxygenation, cortex, fmri, spectroscopy, stimulation, blood-flow, light-propagation, infants, tissue comprise the future research directions and potential topic hotspots for fNIRS. [ABSTRACT FROM AUTHOR]

A neuromyth is a commonly accepted but unscientific claim about brain function. Many researchers have claimed Howard Gardner’s multiple intelligences (MI) theory is a neuromyth because they have seen no evidence supporting his proposal for independent brain-based intelligences for different types of cognitive abilities. Although Gardner has made claims that there are dedicated neural networks or modules for each of the intelligences, nonetheless Gardner has stated his theory could not be a neuromyth because he never claimed it was a neurological theory. This paper explains the lack of evidence to support MI theory. Most important, no researcher has directly looked for a brain basis for the intelligences. Moreover, factor studies have not shown the intelligences to be independent, and studies of MI teaching effects have not explored alternate causes for positive effects and have not been conducted by standard scientific methods. Gardner’s MI theory was not a neuromyth initially because it was based on theories of the 1980s of brain modularity for cognition, and few researchers then were concerned by the lack of validating brain studies. However, in the past 40 years neuroscience research has shown that the brain is not organized in separate modules dedicated to specific forms of cognition. Despite the lack of empirical support for Gardner’s theory, MI teaching strategies are widely used in classrooms all over the world. Crucially, belief in MI and use of MI in the classroom limit the effort to find evidence-based teaching methods. Studies of possible interventions to try to change student and teacher belief in neuromyths are currently being undertaken. Intervention results are variable: One research group found that teachers who knew more about the brain still believed education neuromyths. Teachers need to learn to detect and reject neuromyths. Widespread belief in a neuromyth does not make a theory legitimate. Theories must be based on sound empirical evidence. It is now time for MI theory to be rejected, once and for all, and for educators to turn to evidence-based teaching strategies. [ABSTRACT FROM AUTHOR]

Background: Recently, research on the microbiota-gut-brain axis (MGBA) has received increasing attention, and the number of studies related to Alzheimer’s disease (AD) has increased rapidly, but there is currently a lack of summary of MGBA in AD. Objective: To capture research hotspots, grasp the context of disciplinary research, and explore future research development directions. Methods: In the core dataset of Web of Science, documents are searched according to specific subject words. CiteSpace software is used to perform statistical analysis on measurement indicators such as the number of published papers, publishing countries, institutions, subject areas, authors, cocited journals, and keywords, and to visualize of a network of relevant content elements. Results: The research of MGBA in AD has shown an upward trend year by year, and the cooperation between countries is relatively close, and mainly involves the intersection of neuroscience, pharmacy, and microbiology. This research focuses on the relationship between MGBA and AD symptoms. Keyword hotspots are closely related to new technologies. Alzheimer’s disease, anterior cingulate cortex, inflammatory degeneration, dysbiosis, and other research are the focus of this field. Conclusion: The study revealed that the research and development of MGBA in AD rapidly progressed, but no breakthrough has been made in the past decade, it still needs to be closely combined with multidisciplinary technology to grasp the frontier hotspots. Countries should further strengthen cooperation, improve the disciplinary system, and increase the proportion of empirical research in all research. [ABSTRACT FROM AUTHOR]

Introduction: Neurocognitive symptoms and dysfunction of various severities have become increasingly recognized as potential consequences of SARS-CoV-2 infection. Although there are numerous observational and subjective survey- reporting studies of neurological symptoms, by contrast, those studies describing imaging abnormalities are fewer in number. Methods: This study conducted a metanalysis of 32 studies to determine the incidence of the common neurological abnormalities using magnetic resonance imaging (MRI) in patients with COVID-19. Results: We also present the common clinical findings associated with MRI abnormalities. We report the incidence of any MRI abnormality to be 55% in COVID-19 patients with perfusion abnormalities (53%) and SWI abnormalities (44%) being the most commonly reported injuries. Cognitive impairment, ICU admission and/or mechanical ventilation status, older age, and hospitalization or longer length of hospital stay were the most common clinical findings associated with brain injury in COVID-19 patients. Discussion: Overall, the presentation of brain injury in this study was diverse with no substantial pattern of injury emerging, yet most injuries appear to be of vascular origin. Moreover, analysis of the association between MRI abnormalities and clinical findings suggests that there are likely many mechanisms, both direct and indirect, by which brain injury occurs in COVID-19 patients. [ABSTRACT FROM AUTHOR]

This paper is focusing on a rather neglected issue that concerns both aspects of philosophy and neurobiology in relation to the concept of intentionality. Intentionality is concerned with the 'directedness' or 'aboutness' of mental phenomena towards an object. Despite the fact that in philosophy both concepts of aboutness and directedness are conceptually identical with intentionality, a careful neuroscientific approach can demonstrate that these two phenomena represent two distinct conceptual and neurobiological aspects of intentionality with complementary functions. We described the interaction between a series of intentionality and pathogenetic psychobiological factors, the corresponding brain topography, and the resulting clinical manifestation and psychopathology. A permanent failure of intentionality dominates in psychosis, which includes an inappropriateness of the intentional object or connection, from the outset, or even from the prodromal phase of the disorder. Affective disorders may result from imprecise interoceptive prediction error signals, due to a confused identification of the intentional object. In suicidal patients there is an emotional intentionality failure, characterized by an absence of intentional object or a loss of conscious access to normal intentional objects. We may model an 'intentional system' as a higher order system, with a monitoring and regulatory role attributed to the brain and behavior. Also, we may consider mental disorders as the result of a radical disruption of intentionality, due to an inappropriateness or lack of the intentional object or due to an inappropriate connection in some points of the suggested brain pathways of intentionality. [ABSTRACT FROM AUTHOR]

Social networking sites (SNS), with Facebook as a prominent example, have become an integral part of our daily lives and more than four billion people worldwide use SNS. However, the (over-)use of SNS also poses both psychological and physiological risks. In the present article, we review the scientific literature on the risk of Facebook (over-)use. Addressing this topic is critical because evidence indicates the development of problematic Facebook use ("Facebook addiction") due to excessive and uncontrolled use behavior with various psychological and physiological effects. We conducted a review to examine the scope, range, and nature of prior empirical research on the negative psychological and physiological effects of Facebook use. Our literature search process revealed a total of 232 papers showing that Facebook use is associated with eight major psychological effects (perceived anxiety, perceived depression, perceived loneliness, perceived eating disorders, perceived self-esteem, perceived life satisfaction, perceived insomnia, and perceived stress) and three physiological effects (physiological stress, human brain alteration, and affective experience state). The review also describes how Facebook use is associated with these effects and provides additional details on the reviewed literature, including research design, sample, age, and measures. Please note that the term "Facebook use" represents an umbrella term in the present work, and in the respective sections it will be made clear what kind of Facebook use is associated with a myriad of investigated psychological variables. Overall, findings indicate that certain kinds of Facebook use may come along with significant risks, both psychologically and physiologically. Based on our review, we also identify potential avenues for future research. [ABSTRACT FROM AUTHOR]

The dynamical properties of the brain and the dynamics of the body strongly influence one another. Their interaction generates complex adaptive behavior. While a wide variety of simulation tools exist for neural dynamics or biomechanics separately, there are few options for integrated brain-body modeling. Here, we provide a tutorial to demonstrate how the widely-used NEURON simulation platformcan support integrated neuromechanicalmodeling. As a first step toward incorporating biomechanics into a NEURON simulation, we provide a framework for integrating inputs from a "periphery" and outputs to that periphery. In other words, "body" dynamics are driven in part by "brain" variables, such as voltages or firing rates, and "brain" dynamics are influenced by "body" variables through sensory feedback. To couple the "brain" and "body" components, we use NEURON's pointer construct to share information between "brain" and "body" modules. This approach allows separate specification of brain and body dynamics and code reuse. Though simple in concept, the use of pointers can be challenging due to a complicated syntax and several different programming options. In this paper, we present five different computational models, with increasing levels of complexity, to demonstrate the concepts of code modularity using pointers and the integration of neural and biomechanical modeling within NEURON. The models include: (1) a neuromuscular model of calcium dynamics and muscle force, (2) a neuromechanical, closed-loop model of a half-center oscillator coupled to a rudimentary motor system, (3) a closed-loop model of neural control for respiration, (4) a pedagogical model of a non-smooth "brain/body" system, and (5) a closed-loop model of feeding behavior in the sea hare Aplysia californica that incorporates biologically-motivated non-smooth dynamics. This tutorial illustrates how NEURON can be integrated with a broad range of neuromechanical models. [ABSTRACT FROM AUTHOR]

Introduction: Apathy is a prevalent mood disturbance that occurs in a wide range of populations, including those with normal cognitive aging, mental disorders, neurodegenerative disorders and traumatic brain injuries. Recently, neuroimaging technologies have been employed to elucidate the neural substrates underlying brain disorders accompanying apathy. However, the consistent neural correlates of apathy across normal aging and brain disorders are still unclear. Methods: This paper first provides a brief review of the neural mechanism of apathy in healthy elderly individuals, those with mental disorders, neurodegenerative disorders, and traumatic brain injuries. Further, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, the structural and functional neuroimaging meta-analysis using activation likelihood estimation method is performed on the apathy group with brain disorders and the healthy elderly, aiming at exploring the neural correlates of apathy. Results: The structural neuroimaging meta-analysis showed that gray matter atrophy is associated with apathy in the bilateral precentral gyrus (BA 13/6), bilateral insula (BA 47), bilateral medial frontal gyrus (BA 11), bilateral inferior frontal gyrus, left caudate (putamen) and right anterior cingulate, while the functional neuroimaging meta-analysis suggested that the functional connectivity in putamen and lateral globus pallidus is correlated with apathy. Discussion: Through the neuroimaging meta-analysis, this study has identified the potential neural locations of apathy in terms of brain structure and function, which may offer valuable pathophysiological insights for developing more effective therapeutic interventions for affected patients. [ABSTRACT FROM AUTHOR]

The use of medicinal plants has grown in popularity in recent decades because, as natural ingredients, they have fewer adverse effects and are more effective than synthetic alternatives. As a small perennial herb, Glycyrrhiza glabra L. (Licorice) has been investigated for its therapeutic efficacy against neural disorders mainly ischemic stroke as well as the neurodegenerative diseases such as dementia and Alzheimer’s disease, and Parkinson’s disease which has been attributed to its HMGB inhibitory function, reactive oxygen scavenging and anti-inflammatory activity. The objective of current review is to review the evidence for the pharmacological effects of licorice and its vital active components on neurological disorders and the underlying signaling networks. We reviewed Papers published from 2000.1.1 up to 2 January 2023 in web of science, Google Scholar and PubMed data bases using key words including “Licorice,” “Glycyrrhiza glabra L.,” “Glycyrrhizic acid,” “brain,” “neurodegenerative disease,” “Alzheimer’s,” and “Parkinson” were used to search in title/abstracts. Licorice extract and/or its active components can be used safely in therapeutic doses for optimizing the management of a multiple neurodegenerative disorders, and hampering the extent of neural tissue injury and neurologic deficits subsequent to cerebrovascular accidents. [ABSTRACT FROM AUTHOR]

The Multiple-Network Poroelastic Theory (MPET) is a numerical model to characterize the transport of multiple fluid networks in the brain, which overcomes the problem of conducting separate analyses on individual fluid compartments and losing the interactions between tissue and fluids, in addition to the interaction between the different fluids themselves. In this paper, the blood perfusion results from MPET modeling are partially validated using cerebral blood flow (CBF) data obtained from arterial spin labeling (ASL) magnetic resonance imaging (MRI), which uses arterial blood water as an endogenous tracer to measure CBF. Two subjects—one healthy control and one patient with unilateral middle cerebral artery (MCA) stenosis are included in the validation test. The comparison shows several similarities between CBF data from ASL and blood perfusion results from MPET modeling, such as higher blood perfusion in the gray matter than in the white matter, higher perfusion in the periventricular region for both the healthy control and the patient, and asymmetric distribution of blood perfusion for the patient. Although the partial validation is mainly conducted in a qualitative way, it is one important step toward the full validation of the MPET model, which has the potential to be used as a testing bed for hypotheses and new theories in neuroscience research. [ABSTRACT FROM AUTHOR]

The paper by Guedes et al. extends TBI-related studies beyond proteomics in reporting the use of extracellular vesicles and microRNAs as TBI biomarkers. Keywords: brain; injury; biomarker; proteomics; diagnostics EN brain injury biomarker proteomics diagnostics N.PAG N.PAG 2 03/23/21 20210318 NES 210318 The brain has remained as the only organ where blood- or other fluid-based biomarkers have been practically lacking from the commonly used clinical diagnostic tools. Second, to what extent the biomarker reflects brain tissue damage, and to what extent the biomarker level depends on the integrity of the BBB and/or the glymphatic function, is a challenging question to clarify. [Extracted from the article]

Background: Ferroptosis is a newly proposed concept of programmed cell death and has been widely studied in many diseases during the past decade. However, a bibliometric study that concentrates on publication outputs and research trends of ferroptosis related to the brain is lacking. Methods: We retrieved publication data in the field of ferroptosis in the brain from the Web of Science Core Collection on 31 December 2021. A bibliometric analysis was performed using VOSviewer and CiteSpace software. Results: Six hundred fifty-six documents focusing on ferroptosis in the brain were published from 2012 to 2021. The number of publications in this field has shown a steady increase in recent years. Most publications were from China (338) and the United States (166), while the most productive organizations were at the University of Melbourne (34) and University of Pittsburgh (23). Ashley I. Bush was the most productive author, while Scott J Dixon was the most cocited author. The journal Free Radical Biology and Medicine published the most articles in this field, while Cell was the most cited journal. Among 656 publications, top 10 cited documents were cited at least 300 times. Among the top 20 references with the strongest citation bursts, half of the papers had a burst until 2021. The keywords analysis suggests that the top 20 keywords appeared at least 40 times. Additionally, “amyloid precursor protein” was the keyword with strongest bursts. Conclusion: Research on ferroptosis in the brain will continue to be highly regarded. This study analyzed the research landscape of ferroptosis in the brain and offers a new reference for researchers in this field. [ABSTRACT FROM AUTHOR]

The assessment of disease activity using serial brain MRI scans is one of the most valuable strategies for monitoring treatment response in patients with multiple sclerosis (MS) receiving disease-modifying treatments. Recently, several deep learning approaches have been proposed to improve this analysis, obtaining a good trade-off between sensitivity and specificity, especially when using T1-w and T2-FLAIR images as inputs. However, the need to acquire two different types of images is time-consuming, costly and not always available in clinical practice. In this paper, we investigate an approach to generate synthetic T1-w images from T2-FLAIR images and subsequently analyse the impact of using original and synthetic T1-w images on the performance of a state-of-the-art approach for longitudinal MS lesion detection. We evaluate our approach on a dataset containing 136 images from MS patients, and 73 images with lesion activity (the appearance of new T2 lesions in follow-up scans). To evaluate the synthesis of the images, we analyse the structural similarity index metric and the median absolute error and obtain consistent results. To study the impact of synthetic T1-w images, we evaluate the performance of the new lesion detection approach when using (1) both T2-FLAIR and T1-w original images, (2) only T2-FLAIR images, and (3) both T2-FLAIR and synthetic T1-w images. Sensitivities of 0.75, 0.63, and 0.81, respectively, were obtained at the same false-positive rate (0.14) for all experiments. In addition, we also present the results obtained when using the data fromthe international MSSEG-2 challenge, showing also an improvement when including synthetic T1-w images. In conclusion, we show that the use of synthetic images can support the lack of data or even be used instead of the original image to homogenize the contrast of the different acquisitions in new T2 lesions detection algorithms. [ABSTRACT FROM AUTHOR]

Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the test and train sets data distributions. These domain shifts are expected due to several factors, such as scanner hardware and software differences, technology updates, and differences in MRI acquisition parameters. Domain adaptation (DA) methods can make machine learning models more resilient to these domain shifts. This paper proposes a benchmark for investigating DA techniques for brain MR image segmentation using data collected across sites with scanners from different vendors (Philips, Siemens, and General Electric). Our work provides labeled data, publicly available source code for a set of baseline and DA models, and a benchmark for assessing different brain MR image segmentation techniques. We applied the proposed benchmark to evaluate two segmentation tasks: skull-stripping; and white-matter, gray-matter, and cerebrospinal fluid segmentation, but the benchmark can be extended to other brain structures. Our main findings during the development of this benchmark are that there is not a single DA technique that consistently outperforms others, and hyperparameter tuning and computational times for these methods still pose a challenge before broader adoption of these methods in the clinical practice. [ABSTRACT FROM AUTHOR]

Detection of early morphological changes in the brain and early diagnosis are important for Alzheimer's disease (AD), and high-resolution magnetic resonance imaging (MRI) can be used to help diagnose and predict the disease. In this paper, we proposed two improved ResNet algorithms that introduced the Contextual Transformer (CoT) module, group convolution, and Channel Shuffle mechanism into the traditional ResNet residual blocks. The CoT module is used to replace the 3 x 3 convolution in the residual block to enhance the feature extraction capability of the residual block, while the Channel Shuffle mechanism is used to reorganize the feature maps of different groups in the input layer to improve the communication between the feature maps from different groups. Images of 503 subjects, including 116 healthy controls (HC), 187 subjects with mild cognitive impairment (MCI), and 200 subjects with AD, were selected and collated from the ADNI database, and then, the data were pre-processed and sliced. After that, 10,060 slices were obtained and the three groups of AD, MCI and HC were classified using the improved algorithms. The experiments showed that the refined ResNet-18-based algorithm improved the top-1 accuracy by 2.06%, 0.33%, 1.82%, and 1.52% over the traditional ResNet-18 algorithm for four medical image classification tasks, namely AD: MCI, AD: HC, MCI: HC, and AD: MCI: HC, respectively. The enhanced ResNet-50-based algorithm improved the top-1 accuracy by 1.02%, 2.92%, 3.30%, and 1.31%, respectively, over the traditional ResNet-50 algorithm in four medical image classification tasks, demonstrating the effectiveness of the CoT module replacement and the inclusion of the channel shuffling mechanism, as well as the competitiveness of the improved algorithms. [ABSTRACT FROM AUTHOR]

Spoken language is a distinctive trace of our species and it is naturally acquired during infancy. Written language, in contrast, is artificial, and the correspondences between arbitrary visual symbols and the spoken language for reading and writing should be explicitly learned with external help. In this paper, I present several examples of how written language acquisition is both shaped by and has an impact on brain function and cognition. They show in one hand how our phylogenetic legacy influences education and on the other hand how ontogenetic needs for education can rapidly subdue deeply rooted neurocognitive mechanisms. The understanding of this bidirectional influences provides a more dynamic view of how plasticity interfaces phylogeny and ontogeny in human learning, with implications for both neurosciences and education. [ABSTRACT FROM AUTHOR]

The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer's disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some features that have no positive impact on the diagnosis, while others have a significant impact on the diagnosis. In this paper, a novel Key Features Screening Method based on Extreme Learning Machine (KFS-ELM) is proposed. It can screen for key features that are relevant to the classification (diagnosis). It can also assign weights to key features based on their importance. We designed an experiment to screen for key features of AD. A total of 920 key functional connections screened from 4005 functional connections. Their weights were also obtained. The results of the experiment showed that: (1) Using all (4,005) features to diagnose AD, the accuracy is 95.33%. Using 920 key features to diagnose AD, the accuracy is 99.20%. The 3,085 (4,005 - 920) features that were screened out had a negative effect on the diagnosis of AD. This indicates the KFS-ELM is effective in screening key features. (2) The higher the weight of the key features and the smaller their number, the greater their impact on AD diagnosis. This indicates that the KFS-ELM is rational in assigning weights to the key features for their importance. Therefore, KFS-ELM can be used as a tool for studying features and also for improving classification accuracy. [ABSTRACT FROM AUTHOR]

Today, as research into the contemplative sciences is being widely referenced, the research community would benefit from an understanding of the Heartfulness method of meditation. Heartfulness offers an in-depth experiential practice focused on the evolution of human consciousness using the ancient technique of Pranahuti (yogic Transmission) during Meditation, in combination with the more active mental practice of "Cleaning." Both are enabled by initiation into the Heartfulness practices. These unique features distinguish Heartfulness from other paths that have been described in the scientific literature thus far. In this introductory paper, we present the Heartfulness practices, the philosophy upon which the practices are based, and we reflect on the putative mechanisms through which Heartfulness could exert its effects on the human body and mind in the light of scientific research that has been done in other meditation systems. We conclude with suggestions for future research on the Heartfulness way of meditation. [ABSTRACT FROM AUTHOR]

Current brain network studies based on persistent homology mainly focus on the spatial evolution over multiple spatial scales, and there is little research on the evolution of a spatiotemporal brain network of Alzheimer's disease (AD). This paper proposed a persistent homology-based method by combining multiple temporal windows and spatial scales to study the spatiotemporal evolution of brain functional networks. Specifically, a time-sliding window method was performed to establish a spatiotemporal network, and the persistent homology-based features of such a network were obtained. We evaluated our proposed method using the resting-state functional MRI (rs-fMRI) data set from Alzheimer's Disease Neuroimaging Initiative (ADNI) with 31 patients with AD and 37 normal controls (NCs). In the statistical analysis experiment, most network properties showed a better statistical power in spatiotemporal networks than in spatial networks. Moreover, compared to the standard graph theory properties in spatiotemporal networks, the persistent homology-based features detected more significant differences between the groups. In the clustering experiment, the brain networks on the sliding windows of all subjects were clustered into two highly structured connection states. Compared to the NC group, the AD group showed a longer residence time and a higher window ratio in a weak connection state, which may be because patients with AD have not established a firm connection. In summary, we constructed a spatiotemporal brain network containing more detailed information, and the dynamic spatiotemporal brain network analysis method based on persistent homology provides stronger adaptability and robustness in revealing the abnormalities of the functional organization of patients with AD. [ABSTRACT FROM AUTHOR]

The purpose of this study is to clarify whether there is a learning effect on brain activity after writing with an ink pen vs. a digital pen. Previous studies have reported the superiority of handwriting to typing in terms of learning performance, but differences between the use of an ink pen vs. a digital pen remain unclear. In the present study, the participants learned to read difficult words by writing with an ink pen vs. a digital pen. After the learning period, electroencephalography (EEG) signals were measured, while the participants underwent a repetition priming paradigm with the use of the learned words. The repetition priming effect of the N400 event-related potential (ERP) was quantified as an index of the learning effect and the effects between pen types were compared. The groups were also subdivided according to whether a digital pen is frequently used (familiar vs. unfamiliar group). The number of writing repetitions for each word within 10 min during the learning activity and the post-learning test scores were not affected by the pen-type or familiarity with a digital pen. However, the repetition priming effect of the N400 was greater for words written with a digital pen in the learning session, as compared with an ink pen, in the familiar group, but not the unfamiliar group. These results suggest that for those familiar with its use, writing with a digital pen may improve learning relative to the use of an ink pen. [ABSTRACT FROM AUTHOR]

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders. [ABSTRACT FROM AUTHOR]

Stroke is a multiphasic process, and the initial ischemic phase of neuronal damage is followed by secondary innate and adaptive responses that unfold over days after stroke, offer a longer time frame of intervention, and represent a novel therapeutic target. Therefore, revealing the distinct functions of immune cells in both brain and periphery is important for identification of immunotherapeutic targets for stroke to extend the treatment time window. In this paper an examination of the cellular dynamics of the immune response in the central nervous system (CNS) and periphery provoked by cerebral ischemia is provided. New data is presented for the number of immune cells in brain and spleen of mice during the 7 days following middle cerebral artery occlusion (MCAO). A novel analysis of the correlation among various cell types in the brain and spleen following stroke is presented. It is found that the infiltrated macrophages in the ischemic hemisphere positively correlate with neutrophils which implies their synergic effect in migrating into the brain after stroke onset. It is noted that during infiltration of adaptive immune cells, the number of neutrophils correlate positively with T cells, which suggests neutrophils contribute to T cell infiltration in the stroked brain. Furthermore, the correlation among neurological deficit and various immune cells suggests that microglia and splenic adaptive immune cells (T and B cells) are protective while infiltrating peripheral myeloid cells (macrophage and neutrophils) worsen stroke outcome. Comprehension of such immune responses post cerebral ischemia is crucial for differentiating the drivers of outcomes and also predicting the stroke outcome. [ABSTRACT FROM AUTHOR]

Segmentation of brain images from Magnetic Resonance Images (MRI) is an indispensable step in clinical practice. Morphological changes of sub-cortical brain structures and quantification of brain lesions are considered biomarkers of neurological and neurodegenerative disorders and used for diagnosis, treatment planning, and monitoring disease progression. In recent years, deep learning methods showed an outstanding performance in medical image segmentation. However, these methods suffer from generalisability problem due to inter-centre and inter-scanner variabilities of the MRI images. The main objective of the study is to develop an automated deep learning segmentation approach that is accurate and robust to the variabilities in scanner and acquisition protocols. In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are sets of images from two different domains: (1) source—images with manually annotated labels; and (2) target—images without expert annotations. Then, the network is jointly optimised integrating both source and target images into the transductive training process to segment the regions of interest and to minimise the domain-shift effect. We proposed to use a histogram loss in the feature level to carry out the latter optimisation problem. In order to demonstrate the benefit of the proposed approach, the method has been tested in two different brain MRI image segmentation problems using multi-centre and multi-scanner databases for: (1) sub-cortical brain structure segmentation; and (2) white matter hyperintensities segmentation. The experiments showed that the segmentation performance of a pre-trained model could be significantly improved by up to 10%. For the first segmentation problem it was possible to achieve a maximum improvement from 0.680 to 0.799 in average Dice Similarity Coefficient (DSC) metric and for the second problem the average DSC improved from 0.504 to 0.602. Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster execution time. Taking this into account, this work presents one more step toward the practical implementation of deep learning algorithms into the clinical routine. [ABSTRACT FROM AUTHOR]

Introduction: Screen-based and mobile technology has grown at an unprecedented rate. However, little is understood about whether increased screen-use affects executive functioning (EF), the range of mental processes that aid goal attainment and facilitate the selection of appropriate behaviors. To examine this, a systematic review was conducted. Method: This systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A comprehensive literature search was conducted using Web of Science, MEDLINE, PsycINFO and Scopus databases to identify articles published between 2007 and March 2020, examining the use of mobile technologies on aspects of EF in healthy adults aged 18–35 years. In total 6079 articles were screened by title, and 39 screened by full text. Eight eligible papers were identified for inclusion. Our methods were pre-registered on the PROSPERO international prospective register of systematic reviews. Results: A total of 438 participants were included across the eight studies. Five of the eight studies examined more than one EF. Five studies measured inhibition, and four studies measured decision-making. Smartphone use was negatively associated with inhibition and decision-making. Working memory performance was found to be improved by increased time engaging in video games and by refraining from smartphone use prior to bedtime. Quality assessments indicated high risk of methodological biases across the studies and a low quality of evidence for determining the relationship between technology use and executive functioning. Conclusions: This review highlights the scarcity of the literature in this area. It presents a call for rigorous and objective research to further our understanding of the impact of mobile technology on different aspects of executive function. [ABSTRACT FROM AUTHOR]

Gene co-expression networks are a powerful type of analysis to construct gene groupings based on transcriptomic profiling. Co-expression networks make it possible to discover modules of genes whose mRNA levels are highly correlated across samples. Subsequent annotation of modules often reveals biological functions and/or evidence of cellular specificity for cell types implicated in the tissue being studied. There are multiple ways to perform such analyses with weighted gene co-expression network analysis (WGCNA) amongst one of the most widely used R packages. While managing a few network models can be done manually, it is often more advantageous to study a wider set of models derived from multiple independently generated transcriptomic data sets (e.g., multiple networks built from many transcriptomic sources). However, there is no software tool available that allows this to be easily achieved. Furthermore, the visual nature of co-expression networks in combination with the coding skills required to explore networks, makes the construction of a web-based platform for their management highly desirable. Here, we present the CoExp Web application, a user-friendly online tool that allows the exploitation of the full collection of 109 co-expression networks provided by the CoExpNets suite of R packages. We describe the usage of CoExp, including its contents and the functionality available through the family of CoExpNets packages. All the tools presented, including the web front- and back-ends are available for the research community so any research group can build its own suite of networks and make them accessible through their own CoExp Web application. Therefore, this paper is of interest to both researchers wishing to annotate their genes of interest across different brain network models and specialists interested in the creation of GCNs looking for a tool to appropriately manage, use, publish, and share their networks in a consistent and productive manner. [ABSTRACT FROM AUTHOR]

Studying brain function is a challenging task. In the past, we could only study brain anatomical structures post-mortem, or infer brain functions from clinical data of patients with a brain injury. Nowadays technology, such as functional magnetic resonance imaging (fMRI), enable non-invasive brain activity observation. Several approaches have been proposed to interpret brain activity data. The brain connectivity model is a graphical tool that represents the interaction between brain regions, during certain states. It depicts how a brain region cause changes to other parts of the brain, which can be implied as information flow. This model can be used to help interpret how the brain works. There are several mathematical frameworks that can be used to infer the connectivity model from brain activity signals. Granger causality is one such approach and is one of the first that has been applied to brain activity data. However, due to the concept of the framework, such as the use of pairwise correlation, combined with the limitation of brain activity data such as low temporal resolution in case of fMRI signal, makes the interpretation of the connectivity difficult. We therefore propose the application of the Tigramite causal discovery framework on fMRI data. The Tigramite framework uses measures such as causal effect to analyze causal relations in the system. This enables the framework to identify both direct and indirect pathways or connectivities. In this paper, we applied the framework to the Human Connectome Project motor task-fMRI dataset. We then present the results and discuss how the framework improves interpretability of the connectivity model. We hope that this framework will help us understand more complex brain functions such as memory, consciousness, or the resting-state of the brain, in the future. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses the regulation of endurance performance with topics including fatigue and recovery, the training physiological aspects of endurance performance, and the physiological and personality traits limits of human endurance performance.

The SP theory of intelligence, with its realization in the SP computer model, aims to simplify and integrate observations and concepts across artificial intelligence, mainstream computing, mathematics, and human perception and cognition, with information compression as a unifying theme. This paper describes how abstract structures and processes in the theory may be realized in terms of neurons, their interconnections, and the transmission of signals between neurons. This part of the SP theory—SP-neural—is a tentative and partial model for the representation and processing of knowledge in the brain. Empirical support for the SP theory—outlined in the paper—provides indirect support for SP-neural. In the abstract part of the SP theory (SP-abstract), all kinds of knowledge are represented with patterns, where a pattern is an array of atomic symbols in one or two dimensions. In SP-neural, the concept of a “pattern” is realized as an array of neurons called a pattern assembly, similar to Hebb’s concept of a “cell assembly” but with important differences. Central to the processing of information in SP-abstract is information compression via the matching and unification of patterns (ICMUP) and, more specifically, information compression via the powerful concept of multiple alignment, borrowed and adapted from bioinformatics. Processes such as pattern recognition, reasoning and problem solving are achieved via the building of multiple alignments, while unsupervised learning is achieved by creating patterns from sensory information and also by creating patterns from multiple alignments in which there is a partial match between one pattern and another. It is envisaged that, in SP-neural, short-lived neural structures equivalent to multiple alignments will be created via an inter-play of excitatory and inhibitory neural signals. It is also envisaged that unsupervised learning will be achieved by the creation of pattern assemblies from sensory information and from the neural equivalents of multiple alignments, much as in the non-neural SP theory—and significantly different from the “Hebbian” kinds of learning which are widely used in the kinds of artificial neural network that are popular in computer science. The paper discusses several associated issues, with relevant empirical evidence. [ABSTRACT FROM AUTHOR]

This paper surveys two different mechanisms by which a presynaptic cell can modulate the structure and function of the postsynaptic cell. We first present the evidence that this occurs, and then discuss two mechanisms that could bring this about. The first hypothesis relates to the long lasting effects that the spike patterns of presynaptic axons may exert by modulating activity-inducible programs in postsynaptic cells. The second hypothesis is based on recently obtained evidence that, the afferent neuron at the neuromuscular junction buds off exosomes at its synapse and carries a cargo of Wg and Evi, which are large molecular transsynaptic signaling agents (LMTSAs). Further evidence indicates that many types of neurons bud off exosomes containing payloads of various lipids, proteins, and types of RNA. The evidence suggests that they are transmitted across the synapse and are taken up by the postsynaptic structure either by perisynaptic or exosynaptic mechanisms, thus mediating the transfer of information between neurons. To date, the molecular hypothesis has been limited to local interactions within the synapse of concern. In this paper, we explore the possibility that this represents a mechanism for information transfer involving the postsynaptic neuron as a whole. This entails a review of the known functions of these molecules in neuronal physiology, together with an estimate of the possible types of information they could carry and how they might affect neurocomputations. [ABSTRACT FROM AUTHOR]

The advent of preclinical research scanners for in vivo imaging of small animals has added confidence into the multi-step decision-making process of radiotracer discovery and development. Furthermore, it has expanded the utility of imaging techniques available to dissect clinical questions, fostering a cyclic interaction between the clinical and the preclinical worlds. Significant efforts from medicinal chemistry have also made available several high-affinity and selective compounds amenable for radiolabeling, that target different receptors, transporters and enzymes in vivo. This substantially increased the range of applications of molecular imaging using positron emission tomography (PET) or single photon emission computed tomography (SPECT). However, the process of developing novel radiotracers for in vivo imaging of the human brain is a multi-step process that has several inherent pitfalls and technical difficulties, which often hampers the successful translation of novel imaging agents from preclinical research into clinical use. In this paper, the process of radiotracer development and its relevance in brain research is discussed; as well as, its pitfalls, technical challenges and future promises. Examples of successful and unsuccessful translation of brain radiotracers will be presented. [ABSTRACT FROM AUTHOR]

The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience. The potential of criticality to explain various brain properties, including optimal information processing, has made it an increasingly exciting area of investigation for neuroscientists. Recent reviews on this topic, sometimes termed brain criticality, make brief mention of clinical applications of these findings to several neurological disorders such as epilepsy, neurodegenerative disease, and neonatal hypoxia. Other clinicallyrelevant domains – including anesthesia, sleep medicine, developmental-behavioral pediatrics, and psychiatry – are seldom discussed in review papers of brain criticality. Thorough assessments of these application areas and their relevance for clinicians have also yet to be published. In this scoping review, studies of brain criticality involving human data of all ages are evaluated for their current and future clinical relevance. To make the results of these studies understandable to a more clinical audience, a review of the key concepts behind criticality (e.g., phase transitions, long-range temporal correlation, self-organized criticality, power laws, branching processes) precedes the discussion of human clinical studies. Open questions and forthcoming areas of investigation are also considered. [ABSTRACT FROM AUTHOR]

From an embodied and enactive point of view, the mind–body problem has been reformulated as the relation between the lived or subject body on the one hand and the physiological or object body on the other ("body–body problem"). The aim of the paper is to explore the concept of circularity as a means of explaining the relation between the phenomenology of lived experience and the dynamics of organism–environment interactions. This concept of circularity also seems suitable for connecting enactive accounts with ecological psychology. It will be developed in a threefold way: (1) As the circular structure of embodiment , which manifests itself (a) in the homeostatic cycles between the brain and body and (b) in the sensorimotor cycles between the brain, body, and environment. This includes the interdependence of an organism's dispositions of sense-making and the affordances of the environment. (2) As the circular causality , which characterizes the relation between parts and whole within the living organism as well as within the organism–environment system. (3) As the circularity of process and structure in development and learning. Here, it will be argued that subjective experience constitutes a process of sense-making that implies (neuro-)physiological processes so as to form modified neuronal structures, which in turn enable altered future interactions. On this basis, embodied experience may ultimately be conceived as the integration of brain–body and body–environment interactions, which has a top-down, formative, or ordering effect on physiological processes. This will serve as an approach to a solution of the body–body problem. [ABSTRACT FROM AUTHOR]

Mindfulness Based Cognitive Therapy (MBCT) was developed to combine methods from cognitive behavioral therapy and meditative techniques, with the specific goal of preventing relapse in recurrent depression. While supported by empirical evidence from multiple clinical trials, the cognitive mechanisms behind the effectiveness of MBCT are not well understood in computational (information processing) or biological terms. This article introduces a testable theory about the computational mechanisms behind MBCT that is grounded in "Bayesian brain" concepts of perception from cognitive neuroscience, such as predictive coding. These concepts regard the brain as embodying a model of its environment (including the external world and the body) which predicts future sensory inputs and is updated by prediction errors, depending on how precise these error signals are. This article offers a concrete proposal how core concepts of MBCT—(i) the being mode (accepting whatever sensations arise, without judging or changing them), (ii) decentering (experiencing thoughts and percepts simply as events in the mind that arise and pass), and (iii) cognitive reactivity (changes in mood reactivate negative beliefs)—could be understood in terms of perceptual and metacognitive processes that draw on specific computational mechanisms of the "Bayesian brain." Importantly, the proposed theory can be tested experimentally, using a combination of behavioral paradigms, computational modelling, and neuroimaging. The novel theoretical perspective on MBCT described in this paper may offer opportunities for finessing the conceptual and practical aspects of MBCT. [ABSTRACT FROM AUTHOR]

Recent success stories in automated object or face recognition, partly fuelled by deep learning artificial neural network (ANN) architectures, have led to the advancement of biometric research platforms and, to some extent, the resurrection of Artificial Intelligence (AI). In line with this general trend, inter-disciplinary approaches have been taken to automate the recognition of emotions in adults or children for the benefit of various applications, such as identification of children's emotions prior to a clinical investigation. Within this context, it turns out that automating emotion recognition is far from being straightforward, with several challenges arising for both science (e.g., methodology underpinned by psychology) and technology (e.g., the iMotions biometric research platform). In this paper, we present a methodology and experiment and some interesting findings, which raise the following research questions for the recognition of emotions and attention in humans: (a) the adequacy of well-established techniques such as the International Affective Picture System (IAPS), (b) the adequacy of state-of-the-art biometric research platforms, (c) the extent to which emotional responses may be different in children and adults. Our findings and first attempts to answer some of these research questions are based on a mixed sample of adults and children who took part in the experiment, resulting in a statistical analysis of numerous variables. These are related to both automatically and interactively captured responses of participants to a sample of IAPS pictures. [ABSTRACT FROM AUTHOR]

Introduction: Ocular manifestations in several neurological pathologies accentuate the strong relationship between the eye and the brain. Retinal alterations in particular can serve as surrogates for cerebral changes. Offering a "window to the brain," the transparent eye enables non-invasive imaging of these changes in retinal structure and vasculature. Fractal dimension (FD) reflects the overall complexity of the retinal vasculature. Changes in FD could reflect subtle changes in the cerebral vasculature that correspond to preclinical stages of neurodegenerative diseases. In this review, the potential of this retinal vessel metric to serve as a biomarker in neurodegeneration and stroke will be explored. Methods: A literature search was conducted, following the PRISMA Statement 2009 criteria, in four large bibliographic databases (Pubmed, Embase, Web Of Science and Cochrane Library) up to 12 October 2019. Articles have been included based upon their relevance. Wherever possible, level of evidence (LOE) has been assessed by means of the Oxford Centre for Evidence-based Medicine Level of Evidence classification. Results: Twenty-one studies were included for qualitative synthesis. We performed a narrative synthesis and produced summary tables of findings of included papers because methodological heterogeneity precluded a meta-analysis. A significant association was found between decreased FD and neurodegenerative disease, mainly addressing cognitive impairment (CI) and dementia. In acute, subacute as well as chronic settings, decreased FD seems to be associated with stroke. Differences in FD between subtypes of ischemic stroke remain unclear. Conclusions: This review provides a summary of the scientific literature regarding the association between retinal FD and neurodegenerative disease and stroke. Central pathology is associated with a decreased FD, as a measure of microvascular network complexity. As retinal FD reflects the global integrity of the cerebral microvasculature, it is an attractive parameter to explore. Despite obvious concerns, mainly due to a lack of methodological standardization, retinal FD remains a promising non-invasive and low-cost diagnostic biomarker for neurodegenerative and cerebrovascular disease. Before FD can be implemented in clinic as a diagnostic biomarker, the research community should strive for uniformization and standardization. [ABSTRACT FROM AUTHOR]

The evidence for the benefits of physical activity on cognitive functioning has increased in recent years. Although the relationship between these variables has been analyzed for decades, the development of evaluation techniques has resolved several issues and advanced this area of knowledge. Moreover, several authors have pointed out the association between the cognitive functioning of athletes and their performance in competition. These recent studies suggest that some specific cognitive abilities of athletes could help them become more effective and improve their chances of success. The objective of this paper was to identify the most relevant advances in these areas of study and to highlight more promising lines of research for the next few years. We have discussed findings from the application of different physical activity programs as well as the most significant cognitive performance variables for sports practice. The limitations of the findings were also discussed. [ABSTRACT FROM AUTHOR]