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Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

Akama H, Miyake M, Jung J, Murphy B - PLoS ONE (2015)

Bottom Line: We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain.Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created.This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Decision Science and Technology, Tokyo Institute of Technology, Tokyo, Japan.

ABSTRACT
In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

No MeSH data available.


EAT subgraph around the lexical stimuli used in by Mitchell et al.This graph is composed of the 60 fMRI nouns used by Mitchell et al. [26] (red circles) and the 2708 in-between words (blue circles) linking them on the shortest path routes in the EAT semantic network. The magnitude of the radius for each vertex corresponds to the degree value. This visualisation was made with R using the igraph package.
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pone.0125725.g002: EAT subgraph around the lexical stimuli used in by Mitchell et al.This graph is composed of the 60 fMRI nouns used by Mitchell et al. [26] (red circles) and the 2708 in-between words (blue circles) linking them on the shortest path routes in the EAT semantic network. The magnitude of the radius for each vertex corresponds to the degree value. This visualisation was made with R using the igraph package.

Mentions: In this research, we extract a subgraph from the EAT that connects all 60 nouns used as stimulus items in the fMRI experiments of Mitchell et al. [26] (Fig 2). These fMRI nouns are classified into 12 semantic categories (animals, body parts, buildings, building parts, clothing, furniture, insects, kitchen items, tools, vegetables, vehicles, and other man-made items), each including five nouns. EAT contains all of these fMRI nouns, except ‘CELERY’ and ‘REFRIGERATOR’, so ‘CABBAGE’ and ‘FRIDGE’ are instead selected as synonyms for these absent nouns. This non-directed and non-weighted subgraph (see S1 and S2 Datasets) has 2768 vertices (60 fMRI nouns plus 2708 in-between words), a connection rate of 0.005, mean degree of 7.23, and clustering coefficient of 0.042. The maximum and mean shortest path lengths between the fMRI nouns and the in-between words are 6 (so we set γ = 6) and 4.09, respectively. The degree distribution follows a clear power law (or, more specifically, Zipf’s law) [20,17].


Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

Akama H, Miyake M, Jung J, Murphy B - PLoS ONE (2015)

EAT subgraph around the lexical stimuli used in by Mitchell et al.This graph is composed of the 60 fMRI nouns used by Mitchell et al. [26] (red circles) and the 2708 in-between words (blue circles) linking them on the shortest path routes in the EAT semantic network. The magnitude of the radius for each vertex corresponds to the degree value. This visualisation was made with R using the igraph package.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482269&req=5

pone.0125725.g002: EAT subgraph around the lexical stimuli used in by Mitchell et al.This graph is composed of the 60 fMRI nouns used by Mitchell et al. [26] (red circles) and the 2708 in-between words (blue circles) linking them on the shortest path routes in the EAT semantic network. The magnitude of the radius for each vertex corresponds to the degree value. This visualisation was made with R using the igraph package.
Mentions: In this research, we extract a subgraph from the EAT that connects all 60 nouns used as stimulus items in the fMRI experiments of Mitchell et al. [26] (Fig 2). These fMRI nouns are classified into 12 semantic categories (animals, body parts, buildings, building parts, clothing, furniture, insects, kitchen items, tools, vegetables, vehicles, and other man-made items), each including five nouns. EAT contains all of these fMRI nouns, except ‘CELERY’ and ‘REFRIGERATOR’, so ‘CABBAGE’ and ‘FRIDGE’ are instead selected as synonyms for these absent nouns. This non-directed and non-weighted subgraph (see S1 and S2 Datasets) has 2768 vertices (60 fMRI nouns plus 2708 in-between words), a connection rate of 0.005, mean degree of 7.23, and clustering coefficient of 0.042. The maximum and mean shortest path lengths between the fMRI nouns and the in-between words are 6 (so we set γ = 6) and 4.09, respectively. The degree distribution follows a clear power law (or, more specifically, Zipf’s law) [20,17].

Bottom Line: We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain.Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created.This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Decision Science and Technology, Tokyo Institute of Technology, Tokyo, Japan.

ABSTRACT
In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

No MeSH data available.