Language Network Research Articles

An Exploration of Online Abbreviations in Network Media Platforms

With the rapid development of network information technology and the increasing frequency of online communication, abbreviations in network media platforms have emerged as a significant linguistic phenomenon. Network abbreviations serve not only as convenient tools in online communication but also as a microcosm of network culture and language development. This paper aims to explore the origins, types, characteristics, and impacts of abbreviations in network media platforms. Through in-depth research on network abbreviations, this paper reveals the unique roles of network language in terms of communication efficiency and cultural expression, and puts forward reasonable suggestions for standardizing the use of abbreviations on network platforms, providing a new perspective for understanding network language phenomena.

The Pragmatic Value and Innovative Use of Puns in Chinese Network Context

With the rapid development of Internet technology and the widespread adoption of social media, the forms of language use in the network context have become increasingly diverse. Puns, as a unique rhetorical device, play a significant role in this environment. This paper delves into the pragmatic value and innovative use of puns in Chinese within the network context, analyzing their roles in enhancing humor, promoting implicitness, enriching diversity, and facilitating cultural exchange and integration. At the same time, this paper also addresses the challenges and issues faced by puns in the network context and proposes corresponding countermeasures. Through this research, we aim to provide new perspectives and directions for thinking in the study of network language, further promoting its development and application.

RADT-36. FUNCTION-ADAPTED RADIOTHERAPY PLANNING IN THE MANAGEMENT OF HIGH-GRADE GLIOMAS: A COMPARING STUDY FOR DOSE SPARING OF SPEECH-ELOQUENT BRAIN AREAS

Abstract BACKGROUND Radiotherapy (RT) for brain tumors can induce injury of functional white matter tracts. In neurosurgery navigated transcranial magnetic stimulation (nTMS)- and diffusion tensor imaging (DTI)-based fiber tracts (FT) is an established tool to protect functional FT during tumor resection. We investigated it´s applicability in RT planning with the aim of dose reduction of language-associated fiber tracts. METHODS Preoperative, nTMS-generated and DTI-based FT of the functional left hemispheric language network of patients with language-eloquent malignant gliomas (WHO III/IV) were imported into the planning software (ARIA®) according to the DICOM standard and contoured as a risk structure. Brain shift after tumor resection was compensated using elastic fusion (Brainlab®). The treatment plans were optimized or recalculated with the aim of reducing the dose to the fiber tracts outside the planning target volume (PTV). The dosimetric evaluation was performed according to dose volume histogram parameters. RESULTS RT plans of 27 patients were retrospectively evaluated in the study. The mean dose (Dmean) to the fiber tracts was 37.3+/-11.8 Gy, with plan optimization dose was 33.6 +/- 12.2 Gy (-10.9% p<0.01), within the 20 Gy isodose the dose reduction to be achieved was on average - 25.0% (range 0-48.5%). The V54Gy and V20Gy were on average 40.2 +/- 23.7% and 72.3 +/- 21.1% compared to 38.2 +/- 23.5% and 58.1 +/- 23.7% (both p<0.01) with optimization. The Dmean of the PTV did not change (59.8 Gy vs. 59.8 Gy (p=0.5)), nor did the dose homogeneity (HI, (D5-D95)/Dp)(p=0.63)). The dose exposure of the organs at risk was within the specified dose constraints. CONCLUSION This study showed that (nTMS)- and diffusion tensor imaging (DTI)-based fiber tractography could be used in radiotherapy planning to spare language-eloquent brain areas without having to accept dose reductions within the PTV. Whether the concept of tract-based sparing of language function brings clinical benefits to patients needs to be further investigated in prospective studies.

Processing demands modulate the activities and functional connectivity patterns of the posterior (VWFA-1) and anterior (VWFA-2) VWFA

Previous studies have shown that the visual word form area (VWFA) has structural and intrinsic functional connectivity with both language and attention networks. Nevertheless, it is still unclear how the functional connectivity pattern of the VWFA is regulated by processing demands induced by experimental tasks, and whether processing demands differentially regulate the posterior (VWFA-1) and anterior (VWFA-2) subregions of the VWFA. To address these questions, the present study adopted two tasks varying in processing demands (i.e., verbal and non-verbal tasks), and used generalized psychophysiological interaction (gPPI) and dynamic causal modeling (DCM) analyses to explore the task-dependent functional connectivity patterns of the two subregions of the VWFA. Activation analysis revealed that the VWFA-2 showed higher activation for the verbal task than the non-verbal task, while there were no activation differences in the VWFA-1 after controlling for the stimulus driven effects. Functional and effective connectivity analyses revealed that, for both VWFA-1 and VWFA-2, the verbal task enhanced connections from VWFAs to the ventral language regions (e.g., the left orbital frontal cortex), while the non-verbal task enhanced connections from VWFAs to the dorsal visuospatial regions (e.g., the left intraparietal sulcus). Results of the present study indicate that processing demands induced by tasks modulate both the local activity and functional connectivity patterns of the VWFA, providing new insights for understanding its domain-general function.

Long-term neuroplasticity in language networks after anterior temporal lobe resection.

Anterior temporal lobe resection (ATLR) is an effective treatment for drug-resistant temporal lobe epilepsy (TLE), although language deficits may occur after both left and right ATLR. Functional reorganization of the language network has been observed in the ipsilateral and contralateral hemispheres within 12 months after ATLR, but little is known of longer-term plasticity effects. Our aim was to examine the plasticity of language functions up to a decade after ATLR, in relation to cognitive profiles. We examined 24 TLE patients (12 left [LTLE]) and 10 controls across four time points: pre-surgery, 4 months, 12 months, and ~9 years post-ATLR. Participants underwent standard neuropsychological assessments (naming, phonemic, and categorical fluency tests) and a verbal fluency functional magnetic resonance imaging (fMRI) task. Using a flexible factorial design, we analyzed longitudinal fMRI activations from 12 months to ~9 years post-ATLR, relative to controls, with separate analyses for people with hippocampal sclerosis (HS). Change in cognitive profiles was correlated with the long-term change in fMRI activations to determine the "efficiency" of reorganized networks. LTLE patients had increased long-term engagement of the left extra-temporal and contralateral temporal regions, with better language performance linked to bilateral activation. Those with HS exhibited more widespread bilateral activations. RTLE patients showed plasticity in the left extra-temporal regions, with better language outcomes associated with these areas. Both groups of patients achieved cognitive stability over 9 years, with more than 50% of LTLE patients improving. Older age, longer epilepsy duration, and lower pre-operative cognitive reserve negatively affected long-term language performance. Neuroplasticity continues for up to ~9 years post-epilepsy surgery in LTLE and RTLE, with effective language recovery linked to bilateral engagement of temporal and extra-temporal regions. This adaptive reorganization is associated with improved cognitive outcomes, challenging the traditional view of localized surgery effects. These findings emphasize the need for early intervention, tailored pre-operative counseling, and the potential for continued cognitive gains with extended post-ATLR rehabilitation.

Resting-state networks in chronic tinnitus: Increased connectivity between thalamus and visual areas

Tinnitus is thought to be associated with aberrant spontaneous activity in the central nervous system. Previous resting-state fMRI findings support this hypothesis and have shown a variety of alterations in neural activity in people with tinnitus compared to people without tinnitus. However, there is little replication of findings. Therefore, the current study aimed to extend on previous findings by investigating eight common resting-state networks (i.e. auditory, default mode, sensorimotor, visual, salience, dorsal attention, frontoparietal and language networks) using a a control group (n = 36) and a group of tinnitus patients (n = 46) matched for age, sex and years of education. Hearing profiles matched up to 2kHz and had a small but significant difference between groups in the high frequency range. Functional connectivity (FC) with dorsolateral prefrontal cortex (DLPFC) was also investigated separately for the first time, as this region is proposed to be core to tinnitus distress symptoms and most often used as a stimulation target in transcranial direct current stimulation (tDCS) research. The results showed that tinnitus patients had increased FC between bilateral thalamus and right visual association cortex compared to control participants. No differences were found with DLPFC, or with any of the resting-state networks (RSN), contrary to previous studies which have reported alterations in several RSNs.

Heritability and genetic contribution analysis of structural-functional coupling in human brain

Abstract The flow of functional connectivity (FC) is thought to be supported by white matter structural connectivity (SC). While research on the correlations between SC and FC (SC-FC coupling) has progressed, the genetic implications of SC-FC coupling have not been thoroughly examined. Traditionally, SC-FC coupling investigations utilize predefined atlases. Here, we adopted an atlas-free SC-FC coupling built on the high-resolution white surface (the interface of white matter and gray matter) to uncover common genetic variations. Leveraging data from the Human Connectome Project, we demonstrated considerable heritability in areas within the early and intermediate visual cortex and across dorsal-attention, language, and somatomotor functional networks. We detected 334 genetic loci (spanning 234 cytogenetic bands) linked to SC-FC coupling (P < 1.26 × 10−11), notably in cingulo-opercular, somatomotor, and default mode networks. Using an external dataset from the Adolescent Brain Cognitive Development study, we confirmed 187 cytogenetic bands associated with SC-FC coupling across 22 brain regions (P < 1 × 10-5). Genetic correlation analyses revealed high genetic interrelatedness for SC-FC coupling in neighboring regions. Furthermore, it showed genetic correlations with a spectrum of complex traits, encompassing various neurological and psychiatric conditions. In essence, our study paves the way towards deciphering the genetic interplay between structural and functional connectivity of the brain.

Generative technology for human emotion recognition: A scoping review

Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue machines with the ability to comprehend and respond to human emotions. Central to this field is emotion recognition, which endeavors to identify and interpret human emotional states from different modalities, such as speech, facial images, text, and physiological signals. In recent years, important progress has been made in generative models, including Autoencoder, Generative Adversarial Network, Diffusion Model, and Large Language Model. These models, with their powerful data generation capabilities, emerge as pivotal tools in advancing emotion recognition. However, up to now, there remains a paucity of systematic efforts that review generative technology for emotion recognition. This survey aims to bridge the gaps in the existing literature by conducting a comprehensive analysis of over 330 research papers until June 2024. Specifically, this survey will firstly introduce the mathematical principles of different generative models and the commonly used datasets. Subsequently, through a taxonomy, it will provide an in-depth analysis of how generative techniques address emotion recognition based on different modalities in several aspects, including data augmentation, feature extraction, semi-supervised learning, cross-domain, etc. Finally, the review will outline future research directions, emphasizing the potential of generative models to advance the field of emotion recognition and enhance the emotional intelligence of AI systems.

The language network ages well: Preserved selectivity, lateralization, and within-network functional synchronization in older brains.

All organs, including brains, change as we age. However, the brain is not a uniform structure: it comprises multiple distinct networks, each supporting a different aspect of perception, motor control, and cognition. We examine two cognitive brain networks using fMRI and-across two independent cohorts-find a clear dissociation: the so-called Multiple Demand network, which supports executive functions (e.g., working memory), shows clear age-related decline; however, the language-selective network, which supports comprehension and production, remains young-like on all measures of network function and connectivity, in line with the preservation of linguistic skills in older adults. These findings challenge the notion of generalized brain aging and highlight the importance of dissociable components in the brain and mind.

Toden-E: Topology-Based and Density-Based Ensembled Clustering for the Development of Super-PAG in Functional Genomics using PAG Network and LLM.

The integrative analysis of gene sets, networks, and pathways is pivotal for deciphering omics data in translational biomedical research. To significantly increase gene coverage and enhance the utility of pathways, annotated gene lists, and gene signatures from diverse sources, we introduced pathways, annotated gene lists, and gene signatures (PAGs) enriched with metadata to represent biological functions. Furthermore, we established PAG-PAG networks by leveraging gene member similarity and gene regulations. However, in practice, high similarity in functional descriptions or gene membership often leads to redundant PAGs, hindering the interpretation from a fuzzy enriched PAG list. In this study, we developed todenE (topology-based and density-based ensemble) clustering, pioneering in integrating topology-based and density-based clustering methods to detect PAG communities leveraging the PAG network and Large Language Models (LLM). In computational genomics annotation, the genes can be grouped/clustered through the gene relationships and gene functions via guilt by association. Similarly, PAGs can be grouped into higher-level clusters, forming concise functional representations called Super-PAGs. TodenE captures PAG-PAG similarity and encapsulates functional information through LLM, in characterizing network-based functional Super-PAGs. In synthetic data, we introduced a metric called the Disparity Index (DI), measuring the connectivity of gene neighbors to gauge clusterability. We compared multiple clustering algorithms to identify the best method for generating performance-driven clusters. In non-simulated data (Gene Ontology), by leveraging transfer learning and LLM, we formed a language-based similarity embedding. TodenE utilizes this embedding together with the topology-based embedding to generate putative Super-PAGs with superior performance in semantic and gene member inclusiveness.

Evidence of community structure in phonological networks of multiple languages.

Thousands of phonological word forms known to a speaker can be organised as a lexical network using the tools of network science. In these networks, nodes represent words and edges are placed between phonological neighbours. Previous work has shown that phonological networks of various languages have similar macrolevel network properties. The present study aimed to investigate if phonological networks of different languages also have similar mesolevel properties, specifically, the presence of robust community structure. Prior community detection analyses revealed robust community structure for English. Community detection analyses conducted on French, German, Dutch, and Spanish networks indicate that all networks showed strong evidence of community structure-mesolevel clustering of word forms whereby larger communities tended to contain shorter, frequent words with many phonological neighbours. Words of the same community tended to share similar phonotactic structures. Results suggest that the organisation of phonological word forms in language are governed by similar principles that could have important implications for lexical processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Modelling the bilingual lexicon as a multiplex phonological network.

Phonological word form networks of the mental lexicon are of psycholinguistic relevance, offering insights into the efficiency of lexical access. While much research has concentrated on first languages, there is growing evidence suggesting that phonological networks of second languages are equally significant for lexical processes. Bilingual language processing is proposed to involve the integration of first and second languages, with lexical activation spreading between similar word forms in both languages. Multiplex networks provide a framework to combine different phonological networks, allowing for the analysis of the integrated lexical system's behaviour during lexical processing. In the context of the present study, which focusses on German learners of English as a second language, a multiplex network analysis was constructed to model the interactive complexity of the bilingual mental lexicon. The study tested cross-linguistic effects in a word recognition task using English stimuli. Results revealed that during lexical processing in their second language English, German speakers also activate phonological neighbours from German. In addition, the bilinguals are attuned to the interconnectedness (i.e., clustering) of the German and English neighbours with one another in the phonological neighbourhood of the English target words. These findings can contribute to the ongoing debate on the degree of integration in the bilingual mental lexicon and shed light on the role that phonological networks can play in modelling bilingual lexical processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Does Mobility Drive Language Use? A Dual‐Spatialization Perspective

ABSTRACTMultilingualism refers to a phenomenon where individuals routinely use three or more languages. Spatial processes, such as mobility, may shape the outcome of multilingual linguistic behaviors but are considerably under‐explored. We evaluate the effect of mobility on language use in the framework of dual spatialization in a small‐scale multilingual society. We use a footpath network to characterize mobility in absolute space, and a language network to characterize language use in relational space; we then assess the correspondence between the two networks. Redundancy analysis and the k‐means method are used to support the research goal. We found a high correspondence between mobility and language use. The results identify the absence of regional “centers of gravity” as a distinctive feature in language use, as mobility has fostered local clusters of language use. Conceptually, this study showcases the power of dual spatialization in understanding the mechanisms underlying the space–language connection.

Supplemental Material for Evidence of Community Structure in Phonological Networks of Multiple Languages

Supplemental Material for Evidence of Community Structure in Phonological Networks of Multiple Languages

뉴스 기사 빅데이터 분석을 통한 북한이탈주민의 사회적 이슈와 언어 문제 탐색: 2019-2024

Objectives The purpose of this study is to explore social issues and language problems that appear in news articles related to North Korean defectors and to suggest their implications. Methods 561 news articles related to North Korean defectors covered by major domestic newspapers and media companies from 2019 to June 2024 were collected. For analysis tools, language network analysis and topic modeling techniques were used to analyze major keywords and topics and visualize them. In addition, based on the analysis results, social issues and language problems of North Korean defectors were explored, and Korean language education implications were presented. Results he research results are as follows. First, the amount of news coverage has been increasing significantly over the past three years, and political articles have shown an overwhelmingly high proportion. Second, the main keywords are ‘government, support, human rights, protection, and settlement’. The main topics were protection of human rights of North Korean defectors, increasing number of North Korean defectors among elites and the 2030 generation, and complaints of difficulties due to mental and economic discrimination and prejudice in the process of settling in Korea. The content of the government's integrated support policy accounted for a high proportion. Third, it was found that North Korean defectors were struggling with the language difference between North and South Korea, and Korean language education focusing on pronunciation, accent, and vocabulary in each area was necessary for them. Conclusions The main topic of the news article was the integrated support for the protection of human rights and stable settlement of North Korean defectors. However, although North Korean defectors complain of ‘difficulty’ due to economic, psychological, emotional, and linguistic differences, these related topics are hardly covered in the media. Therefore, this paper suggested implications for Korean language education research for North Korean defectors' language adaptation.

Mapping the basal temporal language network: a SEEG functional connectivity study

The Basal Temporal Language Area (BTLA) is recognized in epilepsy surgery setting when cortical electrical stimulation (CES) of the ventral temporal cortex (VTC) trigger anomia or paraphasia during naming tasks. Despite acknowledging a ventral language stream, current cognitive language models fail to properly integrate this entity. In this SEEG study we used cortico-cortical evoked potentials in nine epileptic patients to assess and compare the effective connectivity of 73 sites in the left VTC of which 26 were deemed eloquent for naming after CES (BTLA). Eloquent sites connectivity supports the existence of a basal temporal language network (BTLN) structured around posterior projectors while the fusiform gyrus behaved as an integrator. BTLN was strongly connected to the amygdala and hippocampus unlike the non-eloquent sites, except for the anterior fusiform gyrus (FG). These observations support the FG as a multimodal functional hub and add to our understanding of ventral temporal language processing.

Task-State Functional MRI of Brain Regions with Reduced Activation in Aphasia Patients: A Meta-analysis.

Language is one of the most celebrated hallmarks of human cognition. With the continuous improvement of medical technology, functional MRI (fMRI) has been used in aphasia. Although many related studies have been carried out, most studies have not extensively focused on brain regions with reduced activation in aphasic patients. The aim of this study was to identify brain regions normally activated in healthy controls but with reduced activation in aphasic patients during fMRI language tasks. We collected all previous task-state fMRI studies of secondary aphasia. The brain regions showed normal activation in healthy controls and reduced activation in aphasic patients were conducted activation likelihood estimation (ALE) meta-analysis to obtain the brain regions with consistently reduced activation in aphasic patients. The ALE meta-analysis revealed that the left inferior frontal gyrus, left middle temporal gyrus, left superior temporal gyrus, left fusiform gyrus, left lentiform nucleus and the culmen of the cerebellum were the brain regions with reduced activation in aphasic patients. These findings from the ALE meta-analysis have significant implications for understanding the language network and the potential for recovery of language functions in individuals with aphasia.

Functional connectivity compensation in Parkinson's disease with freezing of gait.

Freezing of gait (FOG) is a disabling motor symptom prevalent in patients with Parkinson's disease (PD); however, its pathophysiological mechanisms are poorly understood. This study aimed to investigate whole-brain functional connectivity (FC) pattern alterations in PD patients with FOG. A total of 18 PD patients, 10 with FOG (PD-FOG) and 8 without FOG (PD-nFOG), and 10 healthy controls were enrolled. High-resolution 3D T1-weighted and resting-state functional MRI (rs-fMRI) data were obtained from all participants. The groups' internetwork connectivity differences were explored with rs-fMRI FC using seed-based analysis and graph theory. Multiple linear regression analysis estimated the relationship between FC changes and clinical measurements. Rs-fMRI analysis demonstrated alterations in FC in various brain regions between the three groups. Freezing of Gait Questionnaire severity was correlated with decreased brain functional connection between Vermis12 and the left temporal occipital fusiform cortex (r = -0.82, P < .001). Graph theory topological metrics indicated a decreased clustering coefficient in the right superior temporal gyrus in the PD-nFOG group. PD-FOG patients exhibited a compensatory increase in connectivity between the left inferior frontal gyrus language network and the postcentral gyrus compared to PD-nFOG patients. Further, the decreased connection between Vermis 12 and the left temporal occipital fusiform cortex may serve as a potential neuroimaging biomarker for tracking PD-FOG and distinguishing between PD subtypes.

언어 네트워크를 활용한 가정과 교육과정 비교 분석: 2015 및 2022 개정 교육과정을 중심으로

This study aims to compare the 2015 and 2022 home economics education curricula using a language network analysis, examining changes in core keywords and major topics between these two periods. Keywords from middle and high school home economics curriculum documents of both periods were used to construct language networks, analyzed using centrality measures and topic modeling. The results follow. First, the core keywords of the middle school 2015 revised home economics curriculum were ‘family’, ‘health’, ‘problem’, and ‘solution’, and the core keywords of the 2022 revised home economics curriculum were ‘life’, ‘choice’, ‘environment’, ‘routine’, and ‘change’. In addition, a comparison of six topics extracted by both periods revealed that the 2015 revised curriculum dealt with the contents related to ‘family’ and subject competency (practical problem-solving ability) as core elements, while the 2022 revised curriculum emphasized changes in living environment, daily life, and sustainable choices. Secondly, the core keywords of the high school 2015 revised home economics curriculum were ‘family’, ‘life’, ‘safety’, and ‘problem’, and the core keywords of the 2022 revised home economics curriculum were ‘living’, ‘family’, ‘culture’, ‘life’, and ‘welfare’. An examination of the five topics extracted by both periods confirmed that the emphasis on ‘family’ and ‘safety’ decreased and the influence of content related to life culture, digital environment, and life welfare increased in the 2022 revised curriculum. This study is essential in providing foundational data for developing the next curriculum and offers insights into subject competency, teaching and learning methods, and evaluation.

Extraction of logical networks during decompiling transistor-level CMOS circuit descriptions

Objectives. The problem of restoring the functional description of digital VLSI devices presented at the transistor level is considered. The objective of the work is to develop means for extraction of blocks representing logical networks from two-level descriptions of CMOS circuits at the transistor level, which were obtained as a result of recognition (extraction) of subcircuits that implement logic elements.Methods. Graph based methods and software tools are proposed for extracting a connected blocks representing a logical network from two-level descriptions of a transistor circuits in SPICE format. In the graph interpretation, the task is reduced to constructing a labeled directed graph of a logical network based on a labeled undirected bipartite graph specifying a two-level description of the transistor circuit.Results. The proposed method makes it possible to identify lexicographically ranked logical networks, from which a transition is made to logical equations that specify the functions implemented at the outputs of the resulting networks. Software tools have been developed that provide the generation of a hierarchical description in SPICE format that implements the original circuit at the transistor level, as well as descriptions of found logical networks in the SF language of hierarchical structural and functional descriptions of discrete devices and in high-level languages (VHDL and Verilog).Conclusion. The developed methods are implemented in C++, included in the program for decompiling transistor CMOS circuits and tested within it on practical examples of transistor-level circuits. The paper provides examples of reverse engineering of some practical transistor circuits.