The impact of sampling density upon cortical network analysis: regions or points

Tohka, Jussi; He, Yong; Evans, Alan C.

doi:10.1016/j.mri.2012.02.029

Cited by 12 publications

(6 citation statements)

References 55 publications

(91 reference statements)

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“…Additionally, although both anatomical atlas-based and voxel-wise network analyses have consistently identified the PCC and the nearby precuneus as highly connected nodes, or hubs (Hagmann et al, 2008 ; van den Heuvel et al, 2008 ; Buckner et al, 2009 ), only voxel-wise networks allow for the precise localization of hub nodes within these anatomical areas (Hayasaka and Laurienti, 2010 ). It should be noted that (Tohka et al, 2012 ) compared a voxel-wise (40,962 nodes) and an anatomical atlas-based (54 nodes) network with results corroborating those of Hayasaka and Laurienti ( 2010 ). This ability to more accurately identify the spatial locations of hubs in functional brain networks allows researchers to more accurately quantify the assortativity of the network, efficiency in the flow of information in the network, resiliency of the network to targeted and random attack, and the nature of the degree distribution of the network (whether the degree distribution is truly scale-free or instead an exponentially truncated power law degree distribution).…”

Section: Node Definitions In the Literaturesupporting

confidence: 63%

Defining nodes in complex brain networks

Stanley

Moussa

Paolini

et al. 2013

Front. Comput. Neurosci.

166

149

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Network science holds great promise for expanding our understanding of the human brain in health, disease, development, and aging. Network analyses are quickly becoming the method of choice for analyzing functional MRI data. However, many technical issues have yet to be confronted in order to optimize results. One particular issue that remains controversial in functional brain network analyses is the definition of a network node. In functional brain networks a node represents some predefined collection of brain tissue, and an edge measures the functional connectivity between pairs of nodes. The characteristics of a node, chosen by the researcher, vary considerably in the literature. This manuscript reviews the current state of the art based on published manuscripts and highlights the strengths and weaknesses of three main methods for defining nodes. Voxel-wise networks are constructed by assigning a node to each, equally sized brain area (voxel). The fMRI time-series recorded from each voxel is then used to create the functional network. Anatomical methods utilize atlases to define the nodes based on brain structure. The fMRI time-series from all voxels within the anatomical area are averaged and subsequently used to generate the network. Functional activation methods rely on data from traditional fMRI activation studies, often from databases, to identify network nodes. Such methods identify the peaks or centers of mass from activation maps to determine the location of the nodes. Small (~10–20 millimeter diameter) spheres located at the coordinates of the activation foci are then applied to the data being used in the network analysis. The fMRI time-series from all voxels in the sphere are then averaged, and the resultant time series is used to generate the network. We attempt to clarify the discussion and move the study of complex brain networks forward. While the “correct” method to be used remains an open, possibly unsolvable question that deserves extensive debate and research, we argue that the best method available at the current time is the voxel-wise method.

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Section: Node Definitions In the Literaturesupporting

confidence: 63%

Defining nodes in complex brain networks

Stanley

Moussa

Paolini

et al. 2013

Front. Comput. Neurosci.

166

149

View full text Add to dashboard Cite

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“…The properties of brain structural co-variance networks diverge sharply from simulated networks in which edges are drawn at random between nodes 15,207 . There are important and unresolved questions about graph construction and analysis 208–210 .…”

Section: Figurementioning

confidence: 99%

Imaging structural co-variance between human brain regions

2013

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Brain structure varies between people in a markedly organized fashion. Communities of brain regions co-vary in their morphological properties. For example, cortical thickness in one region influences the thickness of structurally and functionally connected regions. Such networks of structural co-variance partially recapitulate the functional networks of healthy individuals and the foci of grey matter loss in neurodegenerative disease. This architecture is genetically heritable, is associated with behavioural and cognitive abilities and is changed systematically across the lifespan. The biological meaning of this structural co-variance remains controversial, but it appears to reflect developmental coordination or synchronized maturation between areas of the brain. This Review discusses the state of current research into brain structural co-variance, its underlying mechanisms and its potential value in the understanding of various neurological and psychiatric conditions.

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“…In addition, several studies have also used data-driven techniques such as independent brain components to define network nodes (Yu et al, 2011b, 2013). Nodal definitions have shown to have a large influence on graph-theoretical parameters (Tohka et al, 2012), and the definition of reliable, biological meaningful parcellations schemes continues to be an active area of current research (Geyer et al, 2011; Glasser and Van Essen, 2011; Van Essen et al, 2012). …”

Section: Graph Theory—modeling Network Topologymentioning

confidence: 99%

Imaging structural and functional brain networks in temporal lobe epilepsy

Bernhardt¹,

Hong²,

Bernasconi³

et al. 2013

Front. Hum. Neurosci.

188

145

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Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

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The impact of sampling density upon cortical network analysis: regions or points

Cited by 12 publications

References 55 publications

Defining nodes in complex brain networks

Defining nodes in complex brain networks

Imaging structural co-variance between human brain regions

Imaging structural and functional brain networks in temporal lobe epilepsy

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