Neurological evaluation of the selection stage of metaphor comprehension in individuals with and without autism spectrum disorder

Brain functional connectivity alterations of Wernicke's area in individuals with autism spectrum conditions in multi-frequency bands: A mega-analysis

Characterized by severe deficits in communication, most individuals with autism spectrum conditions (ASC) experience significant language dysfunctions, thereby impacting their overall quality of life. Wernicke's area, a classical and traditional brain region associated with language processing, plays a substantial role in the manifestation of language impairments. The current study carried out a mega-analysis to attain a comprehensive understanding of the neural mechanisms underpinning ASC, particularly in the context of language processing. The study employed the Autism Brain Image Data Exchange (ABIDE) dataset, which encompasses data from 443 typically developing (TD) individuals and 362 individuals with ASC. The objective was to detect abnormal functional connectivity (FC) between Wernicke's area and other language-related functional regions, and identify frequency-specific altered FC using Wernicke's area as the seed region in ASC. The findings revealed that increased FC in individuals with ASC has frequency-specific characteristics. Further, in the conventional frequency band (0.01-0.08 Hz), individuals with ASC exhibited increased FC between Wernicke's area and the right thalamus compared with TD individuals. In the slow-5 frequency band (0.01-0.027 Hz), increased FC values were observed in the left cerebellum Crus II and the right lenticular nucleus, pallidum. These results provide novel insights into the potential neural mechanisms underlying communication deficits in ASC from the perspective of language impairments.

Neural network of metaphor comprehension: an ALE meta-analysis and MACM analysis

The comprehension of metaphor, a vivid and figurative language, is a complex endeavor requiring cooperation among multiple cognitive systems. There are still many important questions regarding neural mechanisms implicated in specific types of metaphor. To address these questions, we conducted activation likelihood estimation meta-analyses on 30 studies (containing data of 480 participants) and meta-analytic connectivity modeling analyses. First, the results showed that metaphor comprehension engaged the inferior frontal gyrus, middle temporal gyrus, fusiform gyrus, lingual gyrus, and middle occipital gyrus-all in the left hemisphere. In addition to the commonly reported networks of language and attention, metaphor comprehension engaged networks of visual. Second, sub-analysis showed that the contextual complexity can modulate figurativeness, with the convergence on the left fusiform gyrus during metaphor comprehension at discourse-level. Especially, right hemisphere only showed convergence in studies of novel metaphors, suggesting that the right hemisphere is more associated with difficulty than metaphorical. The work here extends knowledge of the neural mechanisms underlying metaphor comprehension in individual brain regions and neural networks.

The neurobiological map of theory of mind and pragmatic communication in autism

Children with autism often have difficulty with Theory of Mind (ToM), the ability to infer mental states, and pragmatic skills, the contextual use of language. Neuroimaging research suggests ToM and pragmatic skills overlap, as the ability to understand another's mental state is a prerequisite to interpersonal communication. To our knowledge, no study in the last decade has examined this overlap further. To assess the emerging consensus across neuroimaging studies of ToM and pragmatic skills in autism, we used coordinate-based activation likelihood estimation (ALE) analysis of 35 functional magnetic resonance imaging (MRI) studies (13 pragmatic skills, 22 ToM), resulting in a meta-analysis of 1,295 participants (647 autistic, 648 non-autistic) aged 7 to 49 years. Group difference analysis revealed decreased left inferior frontal gyrus (LIFG) activation in autistic participants during pragmatic skills tasks. For ToM tasks, we found reduced anterior cingulate cortex (ACC), medial prefrontal cortex (MPFC), and temporoparietal junction (TPJ) activation in autistic participants. Collectively, both ToM and pragmatic tasks showed activation in IFG and superior temporal gyrus (STG) and a reduction in left hemispheric activation in autistic participants. Overall, the findings underscore the cognitive and neural processing similarities between ToM and pragmatic skills, and their underlying neurobiological differences in autism.

Functional Connectivity Underlying Symptoms in Preschool Boys With Autism: A Resting-State Functional Magnetic Resonance Imaging Study

Background

Single-sex children have been regarded as one of the best subjects to understand the abnormal development patterns of autism spectrum disorders (ASDs). However, the functional connectivity (FC) behind their symptoms is still unknown.

Methods

Based on FC analysis, the acquired resting-state functional magnetic resonance imaging (rs-fMRI) data sets, including 86 boys with ASD and 54 normal controls (NC), were used to detect the neural synchronous activity between brain regions. Pearson correlation analysis was used to evaluate the relationship between the abnormal FC value and clinical features.

Results

Individuals with ASD showed enhanced FC between the right calcarine and the right lingual gyrus (LG). The right medial orbital frontal cortex also showed increased FC with bilateral inferior temporal gyrus (ITG) [two-tailed, voxel-level p < 0.001, gaussian random field (GRF) correction, cluster-level p < 0.05]. We did not find a correlation between the abnormal FC value and clinical scales.

Conclusion

Our study reveals a possible relationship between atypical visual attention and poor learning ability in subjects with ASD, and delayed social language development may be a secondary symptom to ASD.

The Elephant in the Room: A Systematic Review of Stimulus Control in Neuro-Measurement Studies on Figurative Language Processing

The processing of metaphors and idioms has been the subject of neuroscientific research for several decades. However, results are often contradictory, which can be traced back to inconsistent terminology and stimulus control. In this systematic review of research methods, we analyse linguistic aspects of 116 research papers which used EEG, fMRI, PET, MEG, or NIRS to investigate the neural processing of the two figurative subtypes metaphor and idiom. We critically examine the theoretical foundations as well as stimulus control by performing a systematic literature synthesis according to the PRISMA guidelines. We explicitly do not analyse the findings of the studies but instead focus on four primary aspects: definitions of figurative language and its subtypes, linguistic theory behind the studies, control for factors influencing figurative language processing, and the relationship between theoretical and operational definitions. We found both a lack and a broad variety in existing definitions and operationalisation, especially in regard to familiarity and conventionality. We identify severe obstacles in the comparability and validation potential of the results of the papers in our review corpus. We propose the development of a consensus in fundamental terminology and more transparency in the reporting of stimulus design in the research on figurative language processing.

The Role of the Dorsolateral Prefrontal Cortex for Speech and Language Processing

This review article summarizes various functions of the dorsolateral prefrontal cortex (DLPFC) that are related to language processing. To this end, its connectivity with the left-dominant perisylvian language network was considered, as well as its interaction with other functional networks that, directly or indirectly, contribute to language processing. Language-related functions of the DLPFC comprise various aspects of pragmatic processing such as discourse management, integration of prosody, interpretation of nonliteral meanings, inference making, ambiguity resolution, and error repair. Neurophysiologically, the DLPFC seems to be a key region for implementing functional connectivity between the language network and other functional networks, including cortico-cortical as well as subcortical circuits. Considering clinical aspects, damage to the DLPFC causes psychiatric communication deficits rather than typical aphasic language syndromes. Although the number of well-controlled studies on DLPFC language functions is still limited, the DLPFC might be an important target region for the treatment of pragmatic language disorders.

He said, she said: Autism spectrum diagnosis and gender differentially affect relationships between executive functions and social communication

Autism spectrum disorder is characterized by difficulties with social communication, with a preponderance in males. Evidence supports a relationship between metacognitive executive functions (e.g. planning, working memory) and social communication in autism spectrum disorder, yet relationships with specific metacognitive executive functions and how gender alters the expression of these relationships require further study. We used multiple regression to examine relationships between informant-based measures of metacognitive executive function and social communication in intellectually able (IQ ⩾ 85) female ( n = 111; mean age = 10.2 ± 2.8; 31 autism spectrum disorder) and male youth ( n = 310; mean age = 10.5 ± 1.9; 146 autism spectrum disorder) with and without autism spectrum disorder from the Autism Brain Imaging Data Exchange-II database. Executive function–social communication relationships were different in females and males with autism spectrum disorder. Relationships between the entire metacognitive index and social communication were stronger in males with autism spectrum disorder than without; this pattern was also observed for metacognitive sub-indices ‘monitor’ and ‘working memory’. These patterns were not observed in females. Relationships between executive function and social communication appear different for female and male youth with an autism spectrum disorder diagnosis. To better understand the nature of metacognitive contributions to social communication in autism spectrum disorder, future work should investigate the co-development of monitoring, working memory and social communication, while taking gender into account.

Sparse Graphical Models for Functional Connectivity Networks: Best Methods and the Autocorrelation Issue

Sparse graphical models are frequently used to explore both static and dynamic functional brain networks from neuroimaging data. However, the practical performance of the models has not been studied in detail for brain networks. In this work, we have two objectives. First, we compare several sparse graphical model estimation procedures and several selection criteria under various experimental settings, such as different dimensions, sample sizes, types of data, and sparsity levels of the true model structures. We discuss in detail the superiority and deficiency of each combination. Second, in the same simulation study, we show the impact of autocorrelation and whitening on the estimation of functional brain networks. We apply the methods to a resting-state functional magnetic resonance imaging (fMRI) data set. Our results show that the best sparse graphical model, in terms of detection of true connections and having few false-positive connections, is the smoothly clipped absolute deviation (SCAD) estimating method in combination with the Bayesian information criterion (BIC) and cross-validation (CV) selection method. In addition, the presence of autocorrelation in the data adversely affects the estimation of networks but can be helped by using the CV selection method. These results question the validity of a number of fMRI studies where inferior graphical model techniques have been used to estimate brain networks.

Sparse Graphical Models for Functional Connectivity Networks: Best Methods and the Autocorrelation Issue

Sparse graphical models are frequently used to explore both static and dynamic functional brain networks from neuroimaging data. However, the practical performance of the models has not been studied in detail for brain networks. In this work, we have two objectives. First, we compare several sparse graphical model estimation procedures and several selection criteria under various experimental settings, such as different dimensions, sample sizes, types of data, and sparsity levels of the true model structures. We discuss in detail the superiority and deficiency of each combination. Second, in the same simulation study, we show the impact of autocorrelation and whitening on the estimation of functional brain networks. We apply the methods to a resting-state functional magnetic resonance imaging (fMRI) data set. Our results show that the best sparse graphical model, in terms of detection of true connections and having few false-positive connections, is the smoothly clipped absolute deviation (SCAD) estimating method in combination with the Bayesian information criterion (BIC) and cross-validation (CV) selection method. In addition, the presence of autocorrelation in the data adversely affects the estimation of networks but can be helped by using the CV selection method. These results question the validity of a number of fMRI studies where inferior graphical model techniques have been used to estimate brain networks.