Dataset Information

Distinct Functional Network Connectivity for Abstract and Concrete Mental Imagery.

ABSTRACT: In several behavioral psycholinguistic studies, it has been shown that concrete words are processed more efficiently. They can be remembered faster, recognized better, and can be learned easier than abstract words. This fact is called concreteness effect. There are fMRI studies which compared the neural representations of concrete and abstract concepts in terms of activated regions. In the present study, a comparison has been made between the condition-specific connectivity of functional networks (obtained by group ICA) during imagery of abstract and concrete words. The obtained results revealed that the functional network connectivity between three pairs of networks during concrete imagery is significantly different from that of abstract imagery (FDR correction at the significance level of 0.05). These results suggest that abstract and concrete concepts have different representations in terms of functional network connectivity pattern. Remarkably, in all of these network pairs, the connectivity during concrete imagery is significantly higher than that of abstract imagery. These more coherent networks include both linguistic and visual regions with a higher engagement of the right hemisphere, so the results are in line with dual coding theory. Additionally, these three pairs of networks include the contrasting regions which have shown stronger activation either in concrete or abstract word processing in former studies. The findings imply that the brain is more integrated and synchronized at the time of concrete imagery and it may explain the reason of faster concrete words processing. In order to validate the results, we used functional network connectivity distributions (FNCD). Wilcoxon rank-sum test was used to check if the abstract and concrete FNCDs extracted from whole subjects are the same. The result revealed that the corresponding distributions are different which indicates two different patterns of connectivity for abstract and concrete word processing. Also, the mean of FNCD is significantly higher at the time of concrete imagery than that of abstract imagery. Furthermore, FNCDs at the single-subject level are significantly more left-skewed or equally, include more strong connectivity for concrete imagery.

SUBMITTER: Hemati S

PROVIDER: S-EPMC6305479 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Distinct Functional Network Connectivity for Abstract and Concrete Mental Imagery.

Hemati Sobhan S Hossein-Zadeh Gholam-Ali GA

Frontiers in human neuroscience 20181218

In several behavioral psycholinguistic studies, it has been shown that concrete words are processed more efficiently. They can be remembered faster, recognized better, and can be learned easier than abstract words. This fact is called concreteness effect. There are fMRI studies which compared the neural representations of concrete and abstract concepts in terms of activated regions. In the present study, a comparison has been made between the condition-specific connectivity of functional network ...[more]

PMID: 30618689

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Project description:. Abstract, unlike concrete, nouns refer to notions beyond our perception. Even though there is no consensus among linguists as to what exactly constitutes a concrete or abstract word, neuroscientists found clear evidence of a "concreteness" effect. This can, for instance, be seen in patients with language impairments due to brain injury or developmental disorder who are capable of perceiving one category better than another. Even though the results are inconclusive, neuroimaging studies on healthy subjects also provide a spatial and temporal account of differences in the processing of abstract versus concrete words. A description of the neural pathways during abstract word reading, the manner in which the connectivity patterns develop over the different stages of lexical and semantic processing compared to that of concrete word processing are still debated. We conducted a high-density EEG study on 24 healthy young volunteers using an implicit categorization task. From this, we obtained high spatio-temporal resolution data and, by means of source reconstruction, reduced the effect of signal mixing observed on scalp level. A multivariate, time-varying and directional method of analyzing connectivity based on the concept of Granger Causality (Partial Directed Coherence) revealed a dynamic network that transfers information from the right superior occipital lobe along the ventral and dorsal streams towards the anterior temporal and orbitofrontal lobes of both hemispheres. Some regions along these pathways appear to be primarily involved in either receiving or sending information. A clear difference in information transfer of abstract and concrete words was observed during the time window of semantic processing, specifically for information transferred towards the left anterior temporal lobe. Further exploratory analysis confirmed a generally stronger connectivity pattern for processing concrete words. We believe our study could guide future research towards a more refined theory of abstract word processing in the brain.

Project description:BackgroundRecent studies of functional or effective connectivity in the brain have reported that motor-related brain regions were activated during motor execution and motor imagery, but the relationship between motor and cognitive areas has not yet been completely understood. The objectives of our study were to analyze the effective connectivity between motor and cognitive networks in order to define network dynamics during motor execution and motor imagery in healthy individuals. Second, we analyzed the differences in effective connectivity between correct and incorrect responses during motor execution and imagery using dynamic causal modeling (DCM) of electroencephalography (EEG) data.MethodTwenty healthy subjects performed a sequence of finger tapping trials using either motor execution or motor imagery, and the performances were recorded. Changes in effective connectivity between the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), and dorsolateral prefrontal cortex (DLPFC) were estimated using dynamic causal modeling. Bayesian model averaging with family-level inference and fixed-effects analysis was applied to determine the most likely connectivity model for these regions.ResultsMotor execution and imagery showed inputs to distinct brain regions, the premotor cortex and the supplementary motor area, respectively. During motor execution, the coupling strength of a feedforward network from the DLPFC to the PMC was greater than that during motor imagery. During motor imagery, the coupling strengths of a feedforward network from the PMC to the SMA and of a feedback network from M1 to the PMC were higher than that during motor execution. In imagined movement, although there were connectivity differences between correct and incorrect task responses, each motor imagery task that included correct and incorrect responses showed similar network connectivity characteristics. Correct motor imagery responses showed connectivity from the PMC to the DLPFC, while the incorrect responses had characteristic connectivity from the SMA to the DLPFC.ConclusionsThese findings provide an understanding of effective connectivity between motor and cognitive areas during motor execution and imagery as well as the basis for future connectivity studies for patients with stroke.

Dataset Information

Distinct Functional Network Connectivity for Abstract and Concrete Mental Imagery.

Publications

Distinct Functional Network Connectivity for Abstract and Concrete Mental Imagery.

Similar Datasets

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