Frequency-specific neuromodulation of local and distant connectivity in aging and episodic memory function

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

A growing literature has focused on the brain's ability to augment processing in local regions by recruiting distant communities of neurons in response to neural decline or insult. In particular, both younger and older adult populations recruit bilateral prefrontal cortex (PFC) as a means of compensating for increasing neural effort to maintain successful cognitive function. However, it remains unclear how local changes in neural activity affect the recruitment of this adaptive mechanism. To address this problem, we combined graph theoretical measures from functional MRI with diffusion weighted imaging and repetitive transcranial magnetic stimulation (rTMS) to resolve a central hypothesis: how do aged brains flexibly adapt to local changes in cortical activity? Specifically, we applied neuromodulation to increase or decrease local activity in a cortical region supporting successful memory encoding (left dorsolateral PFC or DLPFC) using 5 or 1 Hz rTMS, respectively. We then assessed a region's local within‐module degree, or the distributed between‐module degree (BMD) between distant cortical communities. We predicted that (1) local stimulation‐related deficits may be counteracted by boosting BMD between bilateral PFC, and that this effect should be (2) positively correlated with structural connectivity. Both predictions were confirmed; 5 Hz rTMS increased local success‐related activity and local increases in PFC connectivity, while 1 Hz rTMS decreases local activity and triggered a more distributed pattern of bilateral PFC connectivity to compensate for this local inhibitory effect. These results provide an integrated, causal explanation for the network interactions associated with successful memory encoding in older adults. Hum Brain Mapp 38:5987–6004, 2017. © 2017 Wiley Periodicals, Inc.

Related collections

Most cited references 60

Record: found
Abstract: found
Article: found

Is Open Access

Modularity and community structure in networks

M. Newman (2006)

Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.

0 comments Cited 1190 times – based on 0 reviews

Preprint

     Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Finding and evaluating community structure in networks

M. Newman, M Girvan (2003)

We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.

0 comments Cited 813 times – based on 0 reviews

Preprint

     Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Functional cartography of complex metabolic networks

, (2005)

High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that one can (i) find functional modules in complex networks, and (ii) classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a ``cartographic representation'' of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with more potential for immediate applicability. We use our method to analyze the metabolic networks of twelve organisms from three different super-kingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that low-degree metabolites that connect different modules are more conserved than hubs whose links are mostly within a single module.