Prediction of the main cortical areas and connections involved in the tactile function of the visual cortex by network analysis

Négyessy, László; Nepusz, Tamás; Kocsis, László; Bazsó, Fülöp

doi:10.1111/j.1460-9568.2006.04678.x

Cited by 62 publications

(58 citation statements)

References 57 publications

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“…Another possibility is the involvement of direct, long-range cortico-cortical pathways connecting primary sensory cortex with other sensory or multimodal cortices. This latter proposition has received support from both anatomical and connectivity studies demonstrating the existence of direct connections to V1 from both unimodal cortex and conventional multimodal areas (Cappe and Barone 2005;Clavagnier et al 2004;Falchier et al 2002;Negyessy et al 2006;Rockland and Ojima 2003).…”

Section: Introductionmentioning

confidence: 95%

Combined Activation and Deactivation of Visual Cortex During Tactile Sensory Processing

Merabet

Swisher²,

McMains³

et al. 2007

Journal of Neurophysiology

136

119

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The involvement of occipital cortex in sensory processing is not restricted solely to the visual modality. Tactile processing has been shown to modulate higher-order visual and multisensory integration areas in sighted as well as visually deprived subjects; however, the extent of involvement of early visual cortical areas remains unclear. To investigate this issue, we employed functional magnetic resonance imaging in normally sighted, briefly blindfolded subjects with well-defined visuotopic borders as they tactually explored and rated raised-dot patterns. Tactile task performance resulted in significant activation in primary visual cortex (V1) and deactivation of extrastriate cortical regions V2, V3, V3A, and hV4 with greater deactivation in dorsal subregions and higher visual areas. These results suggest that tactile processing affects occipital cortex via two distinct pathways: a suppressive top-down pathway descending through the visual cortical hierarchy and an excitatory pathway arising from outside the visual cortical hierarchy that drives area V1 directly.

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Section: Introductionmentioning

confidence: 95%

Combined Activation and Deactivation of Visual Cortex During Tactile Sensory Processing

Merabet

Swisher²,

McMains³

et al. 2007

Journal of Neurophysiology

136

119

View full text Add to dashboard Cite

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“…(a) Connectivity data We analysed the cortical networks that were subjects of previous studies including the macaque visuotactile cortex consisting of 45 nodes and 463 edges ( Négyessy et al 2006), the whole macaque cortex consisting of 71 nodes and 746 edges (Sporns 2002), as well as the cat cerebral cortex containing 55 nodes and 892 edges (Hilgetag et al 2000a). Adjacency matrices contained unweighted connectivity data published originally in tract-tracing studies.…”

Section: Methodsmentioning

confidence: 99%

“…For the macaque cerebral cortex, most of the data were derived from the COCOMAC database (www.cocomac.org) and additional searches were made on PubMed (http://www.ncbi.nlm.nih. gov/entrez/query.fcgi?dbZPubMed; Sporns 2002; Négyessy et al 2006). Connectional data of the whole macaque cerebral cortex were downloaded from http://www.indiana.edu/w cortex/connectivity_toolbox.html (for further information, see Sporns 2002 and references therein).…”

Section: Methodsmentioning

confidence: 99%

“…Our proposition is that additional information on edge properties helps to overcome the drawbacks of applying node properties alone in studying the topological characteristics underlying large-scale cortical integration. Notably, available information suggests that link properties, edge betweenness in particular, are sensitive measures in exploring biologically relevant characteristics of the cortical networks Négyessy et al 2006).…”

Section: Introductionmentioning

confidence: 99%

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Convergence and divergence are mostly reciprocated properties of the connections in the network of cortical areas

et al. 2008

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Cognition is based on the integrated functioning of hierarchically organized cortical processing streams in a manner yet to be clarified. Because integration fundamentally depends on convergence and the complementary notion of divergence of the neuronal connections, we analysed integration by measuring the degree of convergence/divergence through the connections in the network of cortical areas. By introducing a new index, we explored the complementary convergent and divergent nature of connectional reciprocity and delineated the backward and forward cortical sub-networks for the first time. Integrative properties of the areas defined by the degree of convergence/divergence through their afferents and efferents exhibited distinctive characteristics at different levels of the cortical hierarchy. Areas previously identified as hubs exhibit information bottleneck properties. Cortical networks largely deviate from random graphs where convergence and divergence are balanced at low reciprocity level. In the cortex, which is dominated by reciprocal connections, balance appears only by further increasing the number of reciprocal connections. The results point to the decisive role of the optimal number and placement of reciprocal connections in large-scale cortical integration. Our findings also facilitate understanding of the functional interactions between the cortical areas and the information flow or its equivalents in highly recurrent natural and artificial networks.

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“…Given the expected difference between topological and functional clusters, as an application of our framework we analyze an empirical network providing anatomical connectivity within and between visual cortical and sen-sorimotor areas in Macaque brain [34]. Our analysis (see Suppl.…”

Section: Synchronization Dynamics Let Us Indicate With a Ijmentioning

confidence: 99%

Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena

Domenico

2017

Phys. Rev. Lett.

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Collective phenomena emerge from the interaction of natural or artificial units with a complex organization. The interplay between structural patterns and dynamics might induce functional clusters that, in general, are different from topological ones. In biological systems, like the human brain, the overall functionality is often favored by the interplay between connectivity and synchronization dynamics, with functional clusters that do not coincide with anatomical modules in most cases. In social, socio-technical and engineering systems, the quest for consensus favors the emergence of clusters.Despite the unquestionable evidence for mesoscale organization of many complex systems and the heterogeneity of their inter-connectivity, a way to predict and identify the emergence of functional modules in collective phenomena continues to elude us. Here, we propose an approach based on random walk dynamics to define the diffusion distance between any pair of units in a networked system. Such a metric allows to exploit the underlying diffusion geometry to provide a unifying framework for the intimate relationship between metastable synchronization, consensus and random search dynamics in complex networks, pinpointing the functional mesoscale organization of synthetic and biological systems.The absence of a central authority coordinating the interactions among units of a complex system might lead to interesting collective phenomena, such as synchronization [1] in biological systems or consensus [2] in social and technological networks. This type of self-organization is affected by the underlying structure, which for a wide variety of real systems is highly heterogenous [3] and modular [4,5]. Understanding the interplay between structure and dynamics of such systems has been, and still is, a major challenge in the study of complex systems. Empirical observations, confirmed by numerical simulations and theoretical predictions, suggest that complex systems with hierarchical and/or modular mesoscale organization of their units [6] are characterized by topological scales [7] and the emergence of functional clusters that might be, in general, different from topological ones.In this letter, we show that such functional clusters might be predicted and identified for a wide variety of complex networks. More specifically, for biological systems which can be modeled as networks of oscillators, and for systems of individuals or sensors attempting to reach consensus. The unifying picture is provided by diffusion geometry [8], developed one decade ago for nonlinear dimensionality reduction of complex data. This approach uses Markov processes to integrate local similarities at different scales, allowing to approximate the manifold which better describes the data while preserving their topological features. From a physical perspective, this approach relies on topological information gathered by random searches across time, a principle that has been used successfully in network science to unravel the topological mesoscale organization of ...

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Prediction of the main cortical areas and connections involved in the tactile function of the visual cortex by network analysis

Cited by 62 publications

References 57 publications

Combined Activation and Deactivation of Visual Cortex During Tactile Sensory Processing

Combined Activation and Deactivation of Visual Cortex During Tactile Sensory Processing

Convergence and divergence are mostly reciprocated properties of the connections in the network of cortical areas

Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena

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