Functional Clusters, Hubs, and Communities in the Cortical Microconnectome

Shimono, Masanori; Beggs, John M.

doi:10.1093/cercor/bhu252

Cited by 120 publications

(188 citation statements)

References 98 publications

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“…The availability of large-scale cellular-resolution recording methods will soon provide data sets that are amenable to the detection of functional modules in dynamic brain activity. For example, clusters of highly correlated brain regions can be derived from optical recordings of neural activity (Ahrens et al 2013) or from spike time series on multielectrode arrays (Shimono & Beggs 2015). In general, network-based module detection methods are important tools for dimension reduction and compact descriptions of functional neuronal assemblies in animal recordings.…”

Section: Evidence For Modules In Brain Networkmentioning

confidence: 99%

Modular Brain Networks

Sporns

Betzel

2016

Annu. Rev. Psychol.

1,179

1,016

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The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization and complex dynamics.

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Section: Evidence For Modules In Brain Networkmentioning

confidence: 99%

Modular Brain Networks

Sporns

Betzel

2016

Annu. Rev. Psychol.

1,179

1,016

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“…Node degree of IPL.L and CAU.L node is less than normal, so the degree of functional effect is less than normal. However, the statistics value of node IPL.L is [31][32][33] greater than 0, which indicate that the function connection strength of this region is increased in whole brain, but the experimental results show that in the local area of decline. Similarly, the same is true for the four modules selected, such as in patient module 7, node degree of ORBinf.L is greater than normal, we can believe that the correlation is relatively weak between the region and other regions, while normal human is enhanced.…”

mentioning

confidence: 83%

Modularization Analysis of Brain Functional Network Using Fuzzy C-means Algorithm and Correlation in Resting State

Zhuqing¹,

Huan²,

Zhao³

2017

J Health Med Inform

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Aiming to study the local functional structure of brain function network in resting state, the Fuzzy C-means (FCM) algorithm is adapted to modular the brain functional network. Then the nodes in module are connected into a network using correlation between the time series extracted from Functional Magnetic Resonance Imaging (fMRI) data. Afterwards node degree, clustering coefficient and shortest path length are used to analyse the functional characteristics of networks. Finally, the differences in activation between patients and normal controls' brain regions are compared through Amplitude of Low Frequency Fluctuation (ALFF). Experimental results demonstrate that, the shortest path length of the patient is smaller than that of the normal human, so the information transmission rate increases. Clustering coefficient is higher than the normal, and the degree of grouping of network is enhanced. The correlation between the patient nodes is generally greater than the normal, and there is a weakened situation in the local area. It also found that the proportion of region for the activation level higher than the average of the whole brain in normal with more than the patient. In particular, the activation level of the Precentral gyrus (PreCG) and other regions in patient has a large degree decline. And the activation level in left Caudate nucleus (CAU.L), the lenticular nucleus, Putamen (PUT) and the lenticular nucleus, Palladium (PAL) and other regions is increased for patient. The research results verify the feasibility of modularization analysis of brain functional network using algorithm and correlation in resting state. The main idea of the k-means is the minimization of an objective function, which is normally chosen to be the total distance between all patterns from their respective cluster centres. Its solution relies on an iterative scheme, which starts with arbitrarily chosen initial cluster memberships or centres. The distribution of objects among clusters and the updating of cluster centres are the two main steps of the k-means algorithm [15]. The algorithm alternates between these two steps until the value of the objective function cannot be reduced anymore. Modularization Analysis of Brain Functional Network Using Fuzzy C-means Algorithm and Correlation in Resting StateZhuqingFCM Clustering is a soft version of k-means, where each data point has a fuzzy degree of belonging to each cluster [16]. The FCM algorithm for vector set is a clustering technique that aims to partitioning a set of measured vector xi (i=1,2,...,n) into Gi (i=1,2,...,c) clusters, the main result is the minimization of an objective function J(U,G) with respect to a fuzzy partition matrix U and a set of prototypes G through cluster centre of each cluster.Where d (xk-Gi) represents a universal distance function. Corresponding to the fuzzy partition, elements value of U= [uij]c×n is allowed 0 to 1. However, with the normalization rule, the membership of cluster is equal to oneWhen the Euclidean distance is chosen as the non-similar...

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“…In this calculation, a neuron pair is identified as connected when the spike history of neuron 1 improves the ability to predict the spiking activity of neuron 2 beyond the prediction based on neuron 2’s spike history alone. That is, when two neurons spike in a temporally synchronous pattern, the transfer entropy calculation will be more positive than a neuron pair that does not spike synchronously (Shimono & Beggs, 2014). …”

Section: Methodsmentioning

confidence: 99%

Domoic acid disrupts the activity and connectivity of neuronal networks in organotypic brain slice cultures

et al. 2016

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Domoic acid is a neurotoxin produced by algae and is found in seafood during harmful algal blooms. As a glutamate agonist, domoic acid inappropriately stimulates excitatory activity in neurons.At high doses, this leads to seizures and brain lesions, but it is unclear how lower, asymptomatic exposures disrupt neuronal activity. Domoic acid has been detected in an increasing variety of species across a greater geographical range than ever before, making it critical to understand the potential health impacts of low-level exposure on vulnerable marine mammal and human populations. To determine whether prolonged domoic acid exposure altered neuronal activity in hippocampal networks, we used a custom-made 512 multi-electrode array with high spatial and temporal resolution to record extracellular potentials (spikes) in mouse organotypic brain slice cultures. We identified individual neurons based on spike waveform and location, and measured the activity and functional connectivity within the neuronal networks of brain slice cultures. Domoic acid exposure significantly altered neuronal spiking activity patterns, and increased functional connectivity within exposed cultures, in the absence of overt cellular or neuronal toxicity. While the overall spiking activity of neurons in domoic acid-exposed cultures was comparable to controls, exposed neurons spiked significantly more often in bursts. We also identified a subset of neurons that were electrophysiologically silenced in exposed cultures, and putatively identified those neurons as fast-spiking inhibitory neurons. These results provide evidence that domoic acid affects neuronal activity in the absence of cytotoxicity, and suggest that neurodevelopmental exposure to domoic acid may alter neurological function in the absence of clinical symptoms.

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Functional Clusters, Hubs, and Communities in the Cortical Microconnectome

Cited by 120 publications

References 98 publications

Modular Brain Networks

Modular Brain Networks

Modularization Analysis of Brain Functional Network Using Fuzzy C-means Algorithm and Correlation in Resting State

Domoic acid disrupts the activity and connectivity of neuronal networks in organotypic brain slice cultures

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