Auditory naming is impaired in posterior cortical atrophy and early-onset Alzheimer's disease.

Introduction: Visual naming ability reflects semantic memory retrieval and is a hallmark deficit of Alzheimer's disease (AD). Naming impairment is most prominently observed in the late-onset amnestic and logopenic variant Primary Progressive Aphasia (lvPPA) syndromes. However, little is known about how other patients across the atypical AD syndromic spectrum perform on tests of auditory naming, particularly those with primary visuospatial deficits (Posterior Cortical Atrophy; PCA) and early onset (EOAD) syndromes. Auditory naming tests may be of particular relevance to more accurately measuring anomia in PCA syndrome and in others with visual perceptual deficits. Methods: Forty-six patients with biomarker-confirmed AD (16 PCA, 12 lvPPA, 18 multi-domain EOAD), at the stage of mild cognitive impairment or mild dementia, were administered the Auditory Naming Test (ANT). Performance differences between groups were evaluated using one-way ANOVA and post-hoc t-tests. Correlation analyses were used to examine ANT performance in relation to measures of working memory and word retrieval to elucidate cognitive mechanisms underlying word retrieval deficits. Whole-cortex general linear models were generated to determine the relationship between ANT performance and cortical atrophy. Results: Based on published cutoffs, out of a total possible score of 50 on the ANT, 56% of PCA patients (mean score = 45.3), 83% of EOAD patients (mean = 39.2), and 83% of lvPPA patients (mean = 29.8) were impaired. Total uncued ANT performance differed across groups, with lvPPA performing most poorly, followed by EOAD, and then PCA. ANT performance was still impaired in lvPPA and EOAD after cuing, while performance in PCA patients improved to the normal range with phonemic cues. ANT performance was also directly correlated with measures of verbal fluency and working memory, and was associated with cortical atrophy in a circumscribed semantic language network. Discussion: Auditory confrontation naming is impaired across the syndromic spectrum of AD including in PCA and EOAD, and is likely related to auditory-verbal working memory and verbal fluency which represent the nexus of language and executive functions. The left-lateralized semantic language network was implicated in ANT performance. Auditory naming, in the absence of a visual perceptual demand, may be particularly sensitive to measuring naming deficits in PCA.

Neural manifestation of L2 novel concept acquisition from multi-contexts via both episodic memory and semantic memory systems.

This study aims to examine the process of L2 novel word learning through the combination of episodic and semantic memory, and how the process differs between the formation of thematic and taxonomic relations. The major approach adopted was observing the neural effects of word learning, which is manifested in the N400 from event-related potentials (ERPs). Eighty-eight participants were recruited for the experiment. In the learning session, L2 contextual discourses related to novel words were learned by participants. In the testing session, discourses embedded with incongruous and congruous novel words in the final position were used for participants to judge the congruency which affected the N400 neural activity. The results showed that both recurrent and new-theme discourses elicited significant N400 effects, while taxonomic sentences did not. These results confirmed the formation of episodic and semantic memory during L2 new word learning, in which semantic memory was mainly supported by thematic relations.

The organization of the semantic network as reflected by the neural correlates of six semantic dimensions.

Multiple sensory-motor and non-sensory-motor dimensions have been proposed for semantic representation, but it remains unclear how the semantic system is organized along them in the human brain. Using naturalistic fMRI data and large-scale semantic ratings, we investigated the overlaps and dissociations between the neural correlates of six semantic dimensions: vision, motor, socialness, emotion, space, and time. Our findings revealed a more complex semantic atlas than what is predicted by current neurobiological models of semantic representation. Brain regions that are selectively sensitive to specific semantic dimensions were found both within and outside the brain networks assumed to represent multimodal general and/or abstract semantics. Overlaps between the neural correlates of different semantic dimensions were mainly found inside the default mode network, concentrated in the left anterior superior temporal gyrus and angular gyrus, which have been proposed as two connector hubs that bridge the multimodal experiential semantic system and the language-supported semantic system.

How the intrinsic functional connectivity patterns of the semantic network support semantic processing.

Semantic processing, a core of language comprehension, involves the activation of brain regions dispersed extensively across the frontal, temporal, and parietal cortices that compose the semantic network. To comprehend the functional structure of this semantic network and how it prepares for semantic processing, we investigated its intrinsic functional connectivity (FC) and the relation between this pattern and semantic processing ability in a large sample from the Human Connectome Project (HCP) dataset. We first defined a well-studied brain network for semantic processing, and then we characterized the within-network connectivity (WNC) and the between-network connectivity (BNC) within this network using a voxel-based global brain connectivity (GBC) method based on resting-state functional magnetic resonance imaging (fMRI). The results showed that 97.73% of the voxels in the semantic network displayed considerably greater WNC than BNC, demonstrating that the semantic network is a fairly encapsulated network. Moreover, multiple connector hubs in the semantic network were identified after applying the criterion of WNC > 1 SD above the mean WNC of the semantic network. More importantly, three of these connector hubs (i.e., the left anterior temporal lobe, angular gyrus, and orbital part of the inferior frontal gyrus) were reliably associated with semantic processing ability. Our findings suggest that the three identified regions use WNC as the central mechanism for supporting semantic processing and that task-independent spontaneous connectivity in the semantic network is essential for semantic processing.

Research trends in ergonomics, industrial safety and health: semantic network and topic analyses.

We evaluated the research trends in ergonomics, industrial safety and health from the 1980s to the present. In the ergonomics area, keywords and abstracts from five journals were analyzed. For industrial safety and health, six journal databases were evaluated. A frequency analysis, a semantic network of keywords and a topic network of abstracts were conducted. The results of ergonomics showed that 'macro-ergonomics' and 'manual material handling' were the most popular topics, and 'ergonomic' and 'electromyography' were the most cited keywords. 'Posture' and 'biomechanics' were the most frequently used with high centrality. The results of industrial safety and health showed that 'job stress' and 'organizational safety' were the most popular topics, and 'occupational exposure' and 'occupational health' were the most cited keywords. 'Dust' and 'exposure' were frequently used with high centrality. The results would be helpful in understanding the trends of research efforts and foreseeing trends of future research.

Analyzing Online Reviews to Uncover Customer Satisfaction Factors in Indian Cultural Tourism Destinations.

Tourism to Indian heritage destinations has been on the rise due to the increasing demand for heritage tourism. Increasing customer satisfaction and promoting Indian culture require tourism businesses to understand factors influencing tourists' experiences and behavior towards these destinations. Therefore, this study analyzes four popular heritage tourist destinations in India by using online reviews collected from Google Travel. Data are refined, processed, and visualized using the R programming language and UCINET 6.0. Furthermore, we explore the fundamental framework and interconnections among these characteristics through the utilization of exploratory factor analysis and linear regression analysis with the assistance of the SPSS software package. Based on customer reviews obtained from Google Reviews, an analysis was conducted on 6618 reviews of four heritage tourism destinations in India. From the top 60 words, four clusters of words were created, including "Physical characteristic", "Cultural and historical link", "atmosphere", and "area". Through explanatory factor analysis and linear regression analysis, we found that Physical characteristic, Cultural and historical link, atmosphere, and area all play a significant role in customer satisfaction. This study provides heritage destination managers and Indian government with insights into which attributes impact customer satisfaction the most and offers valuable marketing insights. As a result of this study, we are able to gain a greater understanding of the Indian heritage tourism market, and in doing so, we provide businesses with implications on how to enhance customer service.

Analyzing the dynamics of social media texts using coherency network analysis: a case study of the tweets with the co-hashtags of #BlackLivesMatter and #StopAsianHate.

Introduction: This study examines the associations between time series, termed "coherency," using spectral analysis. Coherence squared, analogous to the squared correlation coefficient, serves as a metric to quantify the degree of interdependence and co-evolution of individual nodes. Methods: We utilized spectral analysis to compute coherence squared, unveiling relationships and co-evolution patterns among individual nodes. The resultant matrix of these relationships was subjected to network analysis. Results: By conducting a case study analyzing tweets associated with the co-hashtags #StopAsianHate and #BlackLivesMatter, we present a novel approach utilizing coherency network analysis to investigate the dynamics of social media text. Frequency domain analysis aided in calculating coherence squared, effectively illustrating the relationships and co-evolution of individual nodes. Furthermore, an analysis of the phase spectrum's slope facilitated the determination of time lag and potential causality direction between highly co-evolved node pairs. Discussion: Our findings underline the potential of coherency network analysis in comprehending the intricate dynamics of social media text. This approach offers valuable insights into how topics, sentiments, or movements manifest and evolve within the digital realm. Future research should explore diverse datasets and domains to broaden our understanding of this novel analytical technique.

Stable organization of the early lexical-semantic network in 18- and 24-month-old preterm and full-term infants: an eye-tracker study.

Introduction: An organized mental lexicon determines new information acquisition by orienting attention during language processing. Adult-like lexical-semantic knowledge organization has already been demonstrated in 24-month-olds. However, the outcomes of earlier studies have been contradictory in terms of the organizational capacities of 18-month-olds, thus our aim was to examine lexical-semantic organization in this younger age group. In prematurely born infants, audiovisual integration deficits have been found alongside disruptions in language perception. By including late preterm infants with corrected ages in our study, we aimed to test whether maturational differences influence lexical-semantic organization when vocabulary is growing rapidly. Methods: We tested 47 late preterm and full-term 18- and 24-month-old infants by means of an infant-adapted target-absent task using a slightly modified version of the original visual world paradigm for eye tracker. Results: We found a longer fixation duration for the lexical and semantic distractors compared to the neutral pictures. Neither language proficiency nor age affected the looking time results. We found a dissociation by age between taxonomic and associative semantic relations. Maturational differences were detectable in the initial processing of taxonomic relations, as processing in the preterm group was slightly delayed and qualitatively different in the first half of the looking time. The size and composition of the expressive vocabulary differed only by age. Discussion: In general, our study demonstrated a stable lexical-semantic organization between 18 and 24 months of age, regardless of maturational differences.

Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning.

OBJECTIVE: Use heuristic, deep learning (DL), and hybrid AI methods to predict semantic group (SG) assignments for new UMLS Metathesaurus atoms, with target accuracy ≥95%. MATERIALS AND METHODS: We used train-test datasets from successive 2020AA-2022AB UMLS Metathesaurus releases. Our heuristic "waterfall" approach employed a sequence of 7 different SG prediction methods. Atoms not qualifying for a method were passed on to the next method. The DL approach generated BioWordVec and SapBERT embeddings for atom names, BioWordVec embeddings for source vocabulary names, and BioWordVec embeddings for atom names of the second-to-top nodes of an atom's source hierarchy. We fed a concatenation of the 4 embeddings into a fully connected multilayer neural network with an output layer of 15 nodes (one for each SG). For both approaches, we developed methods to estimate the probability that their predicted SG for an atom would be correct. Based on these estimations, we developed 2 hybrid SG prediction methods combining the strengths of heuristic and DL methods. RESULTS: The heuristic waterfall approach accurately predicted 94.3% of SGs for 1 563 692 new unseen atoms. The DL accuracy on the same dataset was also 94.3%. The hybrid approaches achieved an average accuracy of 96.5%. CONCLUSION: Our study demonstrated that AI methods can predict SG assignments for new UMLS atoms with sufficient accuracy to be potentially useful as an intermediate step in the time-consuming task of assigning new atoms to UMLS concepts. We showed that for SG prediction, combining heuristic methods and DL methods can produce better results than either alone.

High-definition transcranial direct current stimulation (HD-tDCS) of the left middle temporal gyrus (LMTG) improves mathematical reasoning.

The role of the visuospatial network in mathematical processing has been established, but the role of the semantic neural network in mathematical processing is still poorly understood. The current study used high-definition transcranial direct current stimulation (HD-tDCS) to examine whether the semantic network supports mathematical processing. Using a single-blind, randomized, sham-controlled experimental design, 48 participants were randomly assigned to receive either anodal or sham HD-tDCS on the left middle temporal gyrus (LMTG), a core region of the semantic network. A number series completion task was used to measure mathematical reasoning and an arithmetical computation task was used as a control condition. Both tasks were administered before and after the 20 min HD-tDCS. The results showed that anodal HD-tDCS on the LMTG enhanced performance on the number series completion task, but not on the arithmetical computation task. Trial-level analysis further showed greater improvement at the more difficult problems of the number series completion task. These results demonstrate that the semantic network plays an important role in mathematical processing.

Comparation of differences in the performance of corporate social responsibility between Chinese and American pharmaceutical enterprises-based on corporate social responsibility report.

Objective: We compared Chinese and American pharmaceutical companies' corporate social responsibility (CSR) reports to determine their differences and to analyze the possible reasons for them. Methods: We took as a model the top 500 pharmaceutical companies from Torreya's (a global investment bank) list of the 1,000 most valuable pharmaceutical companies in the world. We then collected the 2020 corporate social responsibility reports of 97 Chinese and 94 American pharmaceutical companies. These reports were analyzed using software such as ROST Content Mining 6.0 and Gephi 0.92. Results: We formed a high-frequency word list, a semantic network diagram, and a high-frequency word centrality scale for the Chinese and American pharmaceutical corporate social responsibility reports. The Chinese pharmaceutical companies' corporate social responsibility reports formed a layout of "double centers and double themes," and the text paid more attention to the disclosure of environmental protection information. The American pharmaceutical companies formed a report presentation form of "three centers and two themes," focusing on corporate social responsibility information disclosures from the perspective of humanistic care. Discussion: The differences in between Chinese and American pharmaceutical companies' corporate social responsibility reports may be due to different corporate development strategies, regulatory requirements, social demands, and the concept of "corporate citizenship." This study makes recommendations for Chinese pharmaceutical companies to better fulfill their CSR at three levels: policy-making, company management, and society.

The involvement of the semantic neural network in rule identification of mathematical processing.

The role of the visuospatial network in mathematical processing has been established, but the involvement of the semantic network in mathematical processing is still poorly understood. The current study utilized a number series completion paradigm with the event-related potential (ERP) technique to examine whether the semantic network supports mathematical processing and to find the corresponding spatiotemporal neural marker. In total, 32 right-handed undergraduate students were recruited and asked to complete the number series completion as well as the arithmetical computation task in which numbers were presented in sequence. The event-related potential and multi-voxel pattern analysis showed that the rule identification process involves more semantic processing when compared with the arithmetical computation processes, and it elicited higher amplitudes for the late negative component (LNC) in left frontal and temporal lobes. These results demonstrated that the semantic network supports the rule identification in mathematical processing, with the LNC acting as the neural marker.

Lack of source memory as a potential marker of early assimilation of novel items into current knowledge.

In daily life, humans process a plethora of new information that can be either consistent (familiar) or inconsistent (novel) with prior knowledge. Over time, both types of information can integrate into our accumulated knowledge base via distinct pathways. However, the mnemonic processes supporting the integration of information that is inconsistent with prior knowledge remain under-characterized. In the current study, we used functional magnetic resonance imaging (fMRI) to examine the initial assimilation of novel items into the semantic network. Participants saw three repetitions of adjective-noun word pairs that were either consistent or inconsistent with prior knowledge. Twenty-four hours later, they were presented with the same stimuli again while undergoing fMRI scans. Outside the scanner, participants completed a surprise recognition test. We found that when the episodic context associated with initially inconsistent items was irretrievable, the neural signature of these items was indistinguishable from that of consistent items. In contrast, initially inconsistent items with accessible episodic contexts showed neural signatures that differed from those associated with consistent items. We suggest that, at least one day post encoding, items inconsistent with prior knowledge can show early assimilation into the semantic network only when their episodic contexts become inaccessible during retrieval, thus evoking a sense of familiarity.

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

Aim: This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown. Subject and Methods: After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results. Results: First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government's anti-epidemic policy. Conclusion: Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.

Using spreading activation to understand repetitive negative thinking.

Repetitive negative thinking (RNT) describes a recursive, unproductive pattern of thought that is commonly observed in individuals who experience anxiety and depression. Past research on RNT has primarily relied on self-report, which fails to capture the potential mechanisms that underlie the persistence of maladaptive thought. We investigated whether RNT may be maintained by a negatively biased semantic network. The present study used a modified free association task to assess state RNT. Following the presentation of a valenced (positive, neutral, negative) cue word, participants generated a series of free associates, which allowed for the dynamic progression of responses. State RNT was conceptualised as the length of consecutive, negatively valenced free associates (i.e. chains). Participants also completed two self-report measures that assessed trait RNT and trait negative affect. Within a structural equation model, negative (but not positive or neutral) response chain length positively predicted trait RNT and negative affect, and this was only the case for positive (but not negative or neutral) cue words. These results suggest that RNT tendencies may be reflected in semantic retrieval and can be assessed without self-report.

What kind of impacts can artwork have on viewers? Establishing a taxonomy for aesthetic impacts.

What kinds of impacts can visual art have on a viewer? To identify potential art impacts, we recruited five aesthetics experts from different academic disciplines: art history, neuroscience, philosophy, psychology and theology. Together, the group curated a set of terms that corresponded to descriptive features (124 terms) and cognitive-affective impacts (69 terms) of artworks. Using these terms as prompts, participants (n = 899) were given one minute to generate words for each term related to how an artwork looked (descriptive features) or made them think or feel (cognitive-affective impacts). Using network psychometric approaches, we identified terms that were semantically similar based on participants' responses and applied hierarchical exploratory graph analysis to map the relationships between the terms. Our analyses identified 17 descriptive dimensions, which could be further reduced to 5, and 11 impact dimensions, which could be further reduced to 4. The resulting taxonomy demonstrated overlap between the descriptive and impact networks as well as consistency with empirical evidence. This taxonomy could serve as the foundation to empirically evaluate art's impacts on viewers.

Disorganization of Semantic Brain Networks in Schizophrenia Revealed by fMRI.

OBJECTIVES: Schizophrenia is a mental illness that presents with thought disorders including delusions and disorganized speech. Thought disorders have been regarded as a consequence of the loosening of associations between semantic concepts since the term "schizophrenia" was first coined by Bleuler. However, a mechanistic account of this cardinal disturbance in terms of functional dysconnection has been lacking. To evaluate how aberrant semantic connections are expressed through brain activity, we characterized large-scale network structures of concept representations using functional magnetic resonance imaging (fMRI). STUDY DESIGN: We quantified various concept representations in patients' brains from fMRI activity evoked by movie scenes using encoding modeling. We then constructed semantic brain networks by evaluating the similarity of these semantic representations and conducted graph theory-based network analyses. STUDY RESULTS: Neurotypical networks had small-world properties similar to those of natural languages, suggesting small-worldness as a universal property in semantic knowledge networks. Conversely, small-worldness was significantly reduced in networks of schizophrenia patients and was correlated with psychological measures of delusions. Patients' semantic networks were partitioned into more distinct categories and had more random within-category structures than those of controls. CONCLUSIONS: The differences in conceptual representations manifest altered semantic clustering and associative intrusions that underlie thought disorders. This is the first study to provide pathophysiological evidence for the loosening of associations as reflected in randomization of semantic networks in schizophrenia. Our method provides a promising approach for understanding the neural basis of altered or creative inner experiences of individuals with mental illness or exceptional abilities, respectively.

The Effects of Transcutaneous Vagus Nerve Stimulation on Functional Connectivity Within Semantic and Hippocampal Networks in Mild Cognitive Impairment.

Better treatments are needed to improve cognition and brain health in people with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Transcutaneous vagus nerve stimulation (tVNS) may impact brain networks relevant to AD through multiple mechanisms including, but not limited to, projection to the locus coeruleus, the brain's primary source of norepinephrine, and reduction in inflammation. Neuropathological data suggest that the locus coeruleus may be an early site of tau pathology in AD. Thus, tVNS may modify the activity of networks that are impaired and progressively deteriorate in patients with MCI and AD. Fifty patients with MCI (28 women) confirmed via diagnostic consensus conference prior to MRI (sources of info: Montreal Cognitive Assessment Test (MOCA), Clinical Dementia Rating scale (CDR), Functional Activities Questionnaire (FAQ), Hopkins Verbal Learning Test - Revised (HVLT-R) and medical record review) underwent resting state functional magnetic resonance imaging (fMRI) on a Siemens 3 T scanner during tVNS (left tragus, n = 25) or sham control conditions (left ear lobe, n = 25). During unilateral left tVNS, compared with ear lobe stimulation, patients with MCI showed alterations in functional connectivity between regions of the brain that are important in semantic and salience functions including regions of the temporal and parietal lobes. Furthermore, connectivity from hippocampi to several cortical and subcortical clusters of ROIs also demonstrated change with tVNS compared with ear lobe stimulation. In conclusion, tVNS modified the activity of brain networks in which disruption correlates with deterioration in AD. These findings suggest afferent target engagement of tVNS, which carries implications for the development of noninvasive therapeutic intervention in the MCI population.

Bipartite functional fractionation within the neural system for social cognition supports the psychological continuity of self versus other.

Research of social neuroscience establishes that regions in the brain's default-mode network (DN) and semantic network (SN) are engaged by socio-cognitive tasks. Research of the human connectome shows that DN and SN regions are both situated at the transmodal end of a cortical gradient but differ in their loci along this gradient. Here we integrated these 2 bodies of research, used the psychological continuity of self versus other as a "test-case," and used functional magnetic resonance imaging to investigate whether these 2 networks would encode social concepts differently. We found a robust dissociation between the DN and SN-while both networks contained sufficient information for decoding broad-stroke distinction of social categories, the DN carried more generalizable information for cross-classifying across social distance and emotive valence than did the SN. We also found that the overarching distinction of self versus other was a principal divider of the representational space while social distance was an auxiliary factor (subdivision, nested within the principal dimension), and this representational landscape was more manifested in the DN than in the SN. Taken together, our findings demonstrate how insights from connectome research can benefit social neuroscience and have implications for clarifying the 2 networks' differential contributions to social cognition.

Meditation as panacea: A longitudinal semantic network analysis of meditation coverage in campus newspapers from 1997-2018.

OBJECTIVE: Media framing of health issues reflects public opinion and impacts readers' perceptions and behavior. This study examines how meditation - a recommended stress coping strategy for college students - is framed in campus newspapers from 1997-2018. PARTICIPANTS: A total of 494 articles were analyzed. METHODS: Semantic network analysis was used to automatically detect frames and the longitudinal trend. RESULTS: Five major frames emerged: (1) building a meditation community within a campus community, (2) meditation benefits, (3) yoga for enhancing mind and body awareness, (4) meditation techniques, and (5) secularizing meditation on campus. There is a shift in coverage from interest in religion to secular views of health benefits throughout the years. Discussions of adverse effects that have emerged from the literature were entirely absent. CONCLUSIONS: The trend of secularizing meditation practices on college campuses is evident. Emphasizing the techniques and benefits could encourage participation and build a learning community.