The associative-semantic network for words and pictures: Effective connectivity and graph analysis

Vandenberghe, Rik; Wang, Yu; Nelissen, Natalie; Vandenbulcke, Mathieu; Dhollander, Thijs; Sunaert, Stefan; Dupont, Patrick

doi:10.1016/j.bandl.2012.09.005

Cited by 39 publications

(38 citation statements)

References 76 publications

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“…Our DTI analysis revealed that the temporo-parietal area that was significantly activated in comprehension participates in a number of white matter pathways that have been associated with language processing including the SLF/AF, the IFOF, the ILF and the MdLF. These connections could provide the anatomical basis for the hub status attributed to this area (Vandenberghe et al, 2013). Of those that were observed in at least 15/19 subjects (MdLF, ILF, IFOF), the pathway density of the MdLF was correlated with subjects behavioural imageability scores.…”

Section: Heteromodal Representationsmentioning

confidence: 98%

“…One could imagine a similar expansion in the representation of meaning as image stimuli increase in complexity from simple objects, to richer spatial scenes (Harel et al, 2013). From Vandenberghe's early work on words and images (Vandenberghe et al, 1996) to recent studies and meta-analyses of the semantic system (Binder and Desai, 2011;Binder et al, 2009;Graves et al, 2010;Vandenberghe et al, 2013) the cortex in the temporalparietal junction including the angular gyrus appears to play the role of an integrating convergence zone in a distributed semantic network.…”

Section: Introductionmentioning

confidence: 95%

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Beyond the word and image: characteristics of a common meaning system for language and vision revealed by functional and structural imaging

et al. 2015

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Section: Heteromodal Representationsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 95%

Beyond the word and image: characteristics of a common meaning system for language and vision revealed by functional and structural imaging

et al. 2015

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“…The choice of the nodes definition can also vary from the use of pre-segmented atlas in a standard space [26,27], the use of segmentation tools to individually parcellate each subject's brain [23,28], brain activation clusters extracted from fMRI analysis [26,29] or simply to the use of all voxels in the image [25]. Such diversity of possibilities demands a thorough knowledge on how these might affect the characteristics of the network in all its range of possibilities.…”

Section: Brain Connectivitymentioning

confidence: 99%

The Impact of Normalization and Segmentation on Resting-State Brain Networks

Magalhães

Marques²,

Soares³

et al. 2015

Brain Connectivity

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Graph theory has recently received a lot of attention from the neuroscience community as a method to represent and characterize brain networks. Still, there is a lack of a gold standard for the methods that should be employed for the preprocessing of the data and the construction of the networks, as well as a lack of knowledge on how different methodologies can affect the metrics reported. The authors used graph theory analysis applied to resting-state functional magnetic resonance imaging to investigate the influence of different node-defining strategies and the effect of normalizing the functional acquisition on several commonly reported metrics used to characterize brain networks. The nodes of the network were defined using either the individual FreeSurfer segmentation of each subject or the FreeSurfer segmented Montreal National Institute (MNI) 152 template, using the Destrieux and subcortical atlas. The functional acquisition was either kept on the functional native space or normalized into MNI standard space. The comparisons were done at three levels: on the connections, on the edge properties, and on the network properties levels. The results reveal that different registration and brain parcellation strategies have a strong impact on all the levels of analysis, possibly favoring the use of individual segmentation strategies and conservative registration approaches. In conclusion, several technical aspects must be considered so that graph theoretical analysis of connectivity MRI data can provide a framework to understand brain pathologies.

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“…Building on this theory, Simmons and Barsalou (2003) proposed a hierarchical distribution model in which posterior regions are more modality specific, and anterior structures more modality general (further reviewed in Meyer & Damasio, 2009). Recent studies on the semantic system also suggest a role for posterior higher-order associative areas in the temporo-parietal junction including the angular gyrus, which acts as a convergence zone in a distributed semantic network (Binder, Desai, Graves, & Conant, 2009;Graves, Binder, Desai, Conant, & Seidenberg, 2010;Vandenberghe et al, 2013), and the posterior middle temporal gyrus, which acts as a lexical hub (Gow, 2012;Hickok & Poeppel, 2004, 2007. Concepts of novel words can be sensory-specific if the novel word is associated with input in a specific modality, such as visual or auditory information.…”

Section: Introductionmentioning

confidence: 95%

Interaction between episodic and semantic memory networks in the acquisition and consolidation of novel spoken words

Takashima

Bakker²,

Hell

et al. 2017

Brain and Language

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When a novel word is learned, its memory representation is thought to undergo a process of consolidation and integration. In this study, we tested whether the neural representations of novel words change as a function of consolidation by observing brain activation patterns just after learning and again after a delay of one week. Words learned with meanings were remembered better than those learned without meanings. Both episodic (hippocampus-dependent) and semantic (dependent on distributed neocortical areas) memory systems were utilised during recognition of the novel words. The extent to which the two systems were involved changed as a function of time and the amount of associated information, with more involvement of both systems for the meaningful words than for the form-only words after the one-week delay. These results suggest that the reason the meaningful words were remembered better is that their retrieval can benefit more from these two complementary memory systems.

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The associative-semantic network for words and pictures: Effective connectivity and graph analysis

Cited by 39 publications

References 76 publications

Beyond the word and image: characteristics of a common meaning system for language and vision revealed by functional and structural imaging

Beyond the word and image: characteristics of a common meaning system for language and vision revealed by functional and structural imaging

The Impact of Normalization and Segmentation on Resting-State Brain Networks

Interaction between episodic and semantic memory networks in the acquisition and consolidation of novel spoken words

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