Dynamic brain lateralization patterns in Chinese naturalistic language comprehension and association with sex differences: a 7T functional magnetic resonance imaging study.

The level and volume of automatic computerized processing of linguistic information has become one of the most important criteria for measuring whether a country has entered the information society. The study begins with statistical linguistics and aims to process complicated Chinese information. In this paper, after establishing the word database of the Chinese language, the language model is smoothed and compressed, the Chinese character information and Chinese language information are extracted, and the processing of Chinese grammar and Chinese semantic information is emphasized. Among them, Chinese grammar processing includes Chinese word analysis and basic phrase analysis based on the maximum entropy model, and Chinese semantic processing includes Bayesian-based word sense disambiguation, semantic role labeling based on the conditional random field model, and thesaurus-based semantic similarity calculation method. In addition, SECTILE-based Chinese text categorization and statistical linguistics-based machine translation methods are explored and tested for their effectiveness in Chinese natural language processing. The results show that the total average check accuracy and check the completeness of Chinese text are 78.65% and 72.24%, respectively, and the BLEU values of the translation methods are improved by [1.62,3.73] and [0.93,5.01] compared with the Baseline method, which is able to process the Chinese information accurately. The research plays an important role in the process of information processing based on Chinese language processing.

To implement natural language user interface and an intelligent answer to questions, the knowledgebased semantic model for Chinese language processing is proposed. The article gives careful consideration to the existing methods and various knowledge bases for natural language processing. The analysis of these methods has led to the conclusion that in natural language processing, the knowledge base is the most fundamental and crucial part. The knowledge base makes it possible to ensure processing of a natural language based on initially described knowledge and to explain the processing operations. By virtue of the analysis of various methods for constructing knowledge bases about the English and Chinese languages, an ontological approach to the Chinese language processing was proposed. The Chinese language processing model has two main aspects: the design of knowledge base about the Chinese language and the development of ontology-based knowledge processing machine. The proposed approach is aimed at developing a semantic model of knowledge on the Chinese language. As a stage in the implementation of the approach, I designed the ontology of the Chinese language that can be applied for further processing of the language. This paper considers the preliminary version of the ontology and the principle of building a knowledge base about the Chinese language. There are no uniform standards and evaluation system for designing an ontology. Expansion, refinement and evaluation of the ontology require further research.

Current Awareness in NMR in Biomedicine

A service-oriented architecture called as HANS is proposed to facilitate Chinese natural language processing. This unified framework seamlessly integrates fundamental NLP tasks including word segmentation, part-of-speech tagging, named entity recognition, chunking, paring, and semantic role labeling to enhance Chinese language processing functionality. A basic Chinese word segmentation task is used to illustrate the function of the proposed architecture. to demonstrate the effects. Evaluated benchmarks are taken from the SIGHAN 2005 bakeoff and the NLPCC 2016 shared task. We implement publicly released toolkits including Stanford CoreNLP, FudanNLP and CKIP as services in our HANS framework for performance comparison. Experimental results confirm the feasibility of the proposed architecture. Findings are also discussed to point to potential future developments.

The Chinese language is not only spoken by the largest population in the world, but quite different from many western languages with a very special structure. It is not alphabetic: large number of Chinese characters are ideographic symbols and pronounced as monosyllables. The open vocabulary nature, the flexible wording structure and the tone behavior are also good examples within the special structure. It is believed that better results and performance will be obtainable in developing Chinese spoken language processing technologies, if this special structure can be taken into account. In this paper, a set of “feature units” for Chinese spoken language processing is identified, and the retrieval, segmentation and summarization of Chinese spoken documents are taken as examples in analyzing the use of such “feature units”. Experimental results indicate that by careful considerations of the special structure and proper choice of the “feature units”, significantly better performance can be achieved.

The natural language user interface is a subclass of user interfaces that allows user and system to communicate using natural language. It is the development direction of the user interface of the intelligent system. The key technology for implementation of natural language user interface is the computer processing of natural language text. Due to the diversity and complexity of natural language, its understanding hasn’t completely achieved yet. By comparing Chinese language with other European languages, this article describes the characteristics of Chinese language and the difficulties in Chinese language processing. After an analysis of current mainstream natural language processing methods, it was shown that the knowledge base plays an important role in the natural language processing model. The knowledge base is the basis for natural language processing. This article proposes a method of computer processing of Chinese language text based on Chinese linguistic ontology and domain ontologies. The ontologies are used to build a unified semantic model of Chinese linguistic knowledge and domain knowledge for the processing of Chinese language text. In this way the Chinese linguistic knowledge is integrated in the Chinese language processing model, the application of Chinese linguistic knowledge makes the Chinese language processing model more interpretative.

This paper reports a study on the application of the theory of pivotal clause in chinese language processing. Three fundamental characteristics of Chinese grammar are briefly introduced: (i) grammatical simplification; (ii) grammatical compatibility; (iii) full exploitation of syntactic position. These characteristics may pose a great many difficulties for Chinese language processing. Based on these features, this paper argues that the theory of pivotal clause would be the best to study natural language processing taking into account the six influential theories in modern Chinese linguistics, for clause as the center of all grammatical units can both make up of sentence groups and discourse and closely relates to morphology as well as syntax. As an important theory to illustrate Chinese grammar, the theory of pivotal clause is not only in line with the characteristics of theme-oriented Chinese language but also in line with the international trend in linguistics, and it is of significance for the linguistic study of Chinese as well as for Chinese language processing. This paper also exhibits the specific application of the theory of pivotal clause in Chinese language processing based on two case studies, one of which is the segmentation and tagging of Chinese language, the other is automatic syntactic and semantic analysis of discourse coherence.

Depressive symptoms in Schizophrenia Spectrum Disorders (SSDs) negatively impact suicidality, prognosis, and quality of life. Despite this, efficacious treatments are limited, largely because the neural mechanisms underlying depressive symptoms in SSDs remain poorly understood. We conducted a systematic review to provide an overview of studies that investigated the neural correlates of depressive symptoms in SSDs using neuroimaging techniques. We searched MEDLINE, PsycINFO, EMBASE, Web of Science, and Cochrane Library databases from inception through June 19, 2023. Specifically, we focused on structural and functional magnetic resonance imaging (MRI), encompassing: (1) T1-weighted imaging measuring brain morphology; (2) diffusion-weighted imaging assessing white matter integrity; or (3) T2*-weighted imaging measures of brain function. Our search yielded 33 articles; 14 structural MRI studies, 18 functional (f)MRI studies, and 1 multimodal fMRI/MRI study. Reviewed studies indicate potential commonalities in the neurobiology of depressive symptoms between SSDs and major depressive disorders, particularly in subcortical and frontal brain regions, though confidence in this interpretation is limited. The review underscores a notable knowledge gap in our understanding of the neurobiology of depression in SSDs, marked by inconsistent approaches and few studies examining imaging metrics of depressive symptoms. Inconsistencies across studies’ findings emphasize the necessity for more direct and comprehensive research focusing on the neurobiology of depression in SSDs. Future studies should go beyond “total score” depression metrics and adopt more nuanced assessment approaches considering distinct subdomains. This could reveal unique neurobiological profiles and inform investigations of targeted treatments for depression in SSDs.

To evaluate sex differences in mild traumatic brain injury (MTBI) with working memory functional magnetic resonance (MR) imaging. Research ethics committee approval and patient written informed consent were obtained. Working memory brain activation patterns were assessed with functional MR imaging in 30 patients (15 consecutive men and 15 consecutive women) with MTBI and 30 control subjects (15 consecutive men and 15 consecutive women). Two imaging studies were performed in patients: the initial study, which was performed within 1 month after the injury, and a follow-up study, which was performed 6 weeks after the first study. For each participant, digit span and continuous performance testing were performed before functional MR imaging. Clinical data were analyzed by using Kruskal-Wallis, Mann-Whitney U, Wilcoxon signed rank, and Fisher exact tests. Within- and between-group differences of functional MR imaging data were analyzed with one- and two-sample t tests, respectively. Among female participants, the total digit span score was lower in the MTBI group than in the control group (P = .044). In initial working memory functional MR imaging studies, hyperactivation was found in the male MTBI group and hypoactivation was found in the female MTBI group compared with control male and female groups, respectively. At the 6-week follow-up study, the female MTBI group showed persistent hypoactivation, whereas the male MTBI group showed a regression of hyperactivation at visual comparison of activation maps. The male MTBI group was also found to have a higher initial ß value than the male control group (P = .040), and there was no significant difference between the male MTBI group and the male control group (P = .221) at follow-up evaluation, which was comparable to findings on activation maps. In the female MTBI group, average ß values at both initial and follow-up studies were lower compared with those in the female control group but were not statistically significant (P = .663 and P = .191, respectively). Female patients with MTBI had lower digit span scores than did female control subjects, and functional MR imaging depicted sex differences in working memory functional activation; hypoactivation with nonrecovery of activation change at follow-up studies may suggest a worse working memory outcome in female patients with MTBI.

This narrative review discusses how peripheral and central inflammation processes affect brain function and structure in depression, and reports on recent peripheral inflammatory marker-based functional and structural magnetic resonance imaging (MRI) studies from the perspective of neural-circuit dysfunction in depression. Chronic stress stimulates the activity of microglial cells, which increases the production of pro-inflammatory cytokines in the brain. In addition, microglial activation promotes a shift from the synthesis of serotonin to the synthesis of neurotoxic metabolites of the kynurenine pathway, which induces glutamate-mediated excitotoxicity in neurons. Furthermore, the region specificity of microglial activation is hypothesized to contribute to the vulnerability of specific brain regions in the depression-related neural circuits to inflammation-mediated brain injury. MRI studies are increasingly investigating how the blood levels of inflammatory markers such as C-reactive protein, interleukin (IL)-1β, IL-6, and tumor necrosis factor-α are associated with functional and structural neuroimaging markers in depression. Functional MRI studies have found that peripheral inflammatory markers are associated with aberrant activation patterns and altered functional connectivity in neural circuits involved in emotion regulation, reward processing, and cognitive control in depression. Structural MRI studies have suggested that peripheral inflammatory markers are related to reduced cortical gray matter and subcortical volumes, cortical thinning, and decreased integrity of white matter tracts within depression-related neural circuits. These neuroimaging findings may improve our understanding of the relationships between neuroinflammatory processes at the molecular level and macroscale in vivo neuralcircuit dysfunction in depression.

Chinese word segmentation refers to the process of dividing a sequence of Chinese characters into individual words. It constitutes a fundamental component of Chinese natural language processing. Due to the intricacies of the Chinese language, Chinese word segmentation has garnered significant attention from researchers. Based on a review of historical literature, segmentation methods can be broadly categorized into rule-based, statistical, semantic-based, and comprehension-based approaches. With the advancement of machine learning, neural networks have emerged as the mainstream algorithm for word segmentation. However, Chinese presents several unique challenges, leading to segmentation results that are less effective compared to morphological analysis in languages like English. Moreover, word segmentation faces new challenges such as dependency on the quality and scale of corpora, as well as domain-specific segmentation in diverse fields. Addressing these emerging challenges will undoubtedly become a focal point in future research endeavors in this field. This review provides a comprehensive summary of existing methods, discusses the current state of Chinese word segmentation, and outlines directions for addressing the evolving complexities in the field. As Chinese language processing continues to advance, finding robust solutions for accurate word segmentation remains a critical area of research.

Advances in computer and communications technology have deeply affected the way we communicate. Social media have emerged as a major means of human communication. However, a major limitation in such media is the lack of non-verbal stimuli, which sometimes hinders the understanding of the message, and in particular the associated emotional content. In an effort to compensate for this, people started to use emoticons, which are combinations of keyboard characters that resemble facial expressions, and more recently their evolution: emojis, namely, small colorful images that resemble faces, actions and daily life objects. This paper presents evidence of the effect of emojis on memory retrieval through a functional Magnetic Resonance Imaging (fMRI) study. A total number of fifteen healthy volunteers were recruited for the experiment, during which successive stimuli were presented, containing words with intense emotional content combined with emojis, either with congruent or incongruent emotional content. Volunteers were asked to recall a memory related to the stimulus. The study of the reaction times showed that emotional incongruity among word+emoji combinations led to longer reaction times in memory retrieval compared to congruent combinations. General Linear Model (GLM) and Blind Source Separation (BSS) methods have been tested in assessing the influence of the emojis on the process of memory retrieval. The analysis of the fMRI data showed that emotional incongruity among word+emoji combinations activated the Broca’s area (BA44 and BA45) in both hemispheres, the Supplementary Motor Area (SMA) and the inferior prefrontal cortex (BA47), compared to congruent combinations. Furthermore, compared to pseudowords, word+emoji combinations activated the left Broca’s area (BA44 and BA45), the amygdala, the right temporal pole (BA48) and several frontal regions including the SMA and the inferior prefrontal cortex.

Advances in computer and communications technology have deeply affected the way we communicate. Social media have emerged as a major means of human communication. However, a major limitation in such media is the lack of non-verbal stimuli, which sometimes hinders the understanding of the message, and in particular the associated emotional content. In an effort to compensate for this, people started to use emoticons, which are combinations of keyboard characters that resemble facial expressions, and more recently their evolution: emojis, namely, small colorful images that resemble faces, actions and daily life objects. This paper presents evidence of the effect of emojis on memory retrieval through a functional Magnetic Resonance Imaging (fMRI) study. A total number of fifteen healthy volunteers were recruited for the experiment, during which successive stimuli were presented, containing words with intense emotional content combined with emojis, either with congruent or incongruent emotional content. Volunteers were asked to recall a memory related to the stimulus. The study of the reaction times showed that emotional incongruity among word+emoji combinations led to longer reaction times in memory retrieval compared to congruent combinations. General Linear Model (GLM) and Blind Source Separation (BSS) methods have been tested in assessing the influence of the emojis on the process of memory retrieval. The analysis of the fMRI data showed that emotional incongruity among word+emoji combinations activated the Broca's area (BA44 and BA45) in both hemispheres, the Supplementary Motor Area (SMA) and the inferior prefrontal cortex (BA47), compared to congruent combinations. Furthermore, compared to pseudowords, word+emoji combinations activated the left Broca's area (BA44 and BA45), the amygdala, the right temporal pole (BA48) and several frontal regions including the SMA and the inferior prefrontal cortex.

Brain Activity Mapping with Functional MR Imaging

Schizophrenia is a chronic psychiatric disorder with characteristic symptoms of delusions, hallucinations, lack of motivation, and paucity of thought. Recent evidence suggests that the symptoms of schizophrenia, negative symptoms in particular, vary widely between the sexes and that symptom onset is earlier in males. A better understanding of sex-based differences in functional magnetic resonance imaging (fMRI) studies of schizophrenia may provide a key to understanding sex-based symptom differences. This study aimed to summarize sex-based functional magnetic resonance imaging (fMRI) differences in brain activity of patients with schizophrenia. We searched PubMed and Scopus to find fMRI studies that assessed sex-based differences in the brain activity of patients with schizophrenia. We excluded studies that did not evaluate brain activity using fMRI, did not evaluate sex differences, and were nonhuman or in vitro studies. We found 12 studies that met the inclusion criteria for the current systematic review. Compared to females with schizophrenia, males with schizophrenia showed more blood oxygen level-dependent (BOLD) activation in the cerebellum, the temporal gyrus, and the right precuneus cortex. Male patients also had greater occurrence of low-frequency fluctuations in cerebral blood flow in frontal and parietal lobes and the insular cortex, while female patients had greater occurrence of low-frequency fluctuations in the hippocampus, parahippocampus, and lentiform nucleus. The current study summarizes fMRI studies that evaluated sex-based fMRI brain differences in schizophrenia that may help to shed light on the underlying pathophysiology and further understanding of sex-based differences in the clinical presentation and course of the disorder.